DataCamp Data Manipulation with Pandas.ipynb

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   "source": [
    "## Data Manipulation with pandas\n",
    "\n",
    "\n"
   ]
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   "source": [
    "# Course Description\n",
    "\n",
    "pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. In this course, you'll learn how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis. Using pandas you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python!"
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    "##  Transforming DataFrames\n",
    "Free\n",
    "0%\n",
    "\n",
    "Let’s master the pandas basics. Learn how to inspect DataFrames and perform fundamental manipulations, including sorting rows, subsetting, and adding new columns.\n",
    "\n",
    "    Introducing DataFrames    50 xp\n",
    "    Inspecting a DataFrame    100 xp\n",
    "    Parts of a DataFrame    100 xp\n",
    "    Sorting and subsetting    50 xp\n",
    "    Sorting rows    100 xp\n",
    "    Subsetting columns    100 xp\n",
    "    Subsetting rows    100 xp\n",
    "    Subsetting rows by categorical variables    100 xp\n",
    "    New columns    50 xp\n",
    "    Adding new columns    100 xp\n",
    "    Combo-attack!    100 xp\n"
   ]
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    "##  Aggregating DataFrames\n",
    "0%\n",
    "\n",
    "In this chapter, you’ll calculate summary statistics on DataFrame columns, and master grouped summary statistics and pivot tables.\n",
    "\n",
    "    Summary statistics    50 xp\n",
    "    Mean and median    100 xp\n",
    "    Summarizing dates    100 xp\n",
    "    Efficient summaries    100 xp\n",
    "    Cumulative statistics    100 xp\n",
    "    Counting    50 xp\n",
    "    Dropping duplicates    100 xp\n",
    "    Counting categorical variables    100 xp\n",
    "    Grouped summary statistics    50 xp\n",
    "    What percent of sales occurred at each store type?    100 xp\n",
    "    Calculations with .groupby()    100 xp\n",
    "    Multiple grouped summaries    100 xp\n",
    "    Pivot tables    50 xp\n",
    "    Pivoting on one variable    100 xp\n",
    "    Fill in missing values and sum values with pivot tables    100 xp\n"
   ]
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    "##  Slicing and Indexing DataFrames\n",
    "0%\n",
    "\n",
    "Indexes are supercharged row and column names. Learn how they can be combined with slicing for powerful DataFrame subsetting.\n",
    "\n",
    "    Explicit indexes    50 xp\n",
    "    Setting and removing indexes    100 xp\n",
    "    Subsetting with .loc[]    100 xp\n",
    "    Setting multi-level indexes    100 xp\n",
    "    Sorting by index values    100 xp\n",
    "    Slicing and subsetting with .loc and .iloc    50 xp\n",
    "    Slicing index values    100 xp\n",
    "    Slicing in both directions    100 xp\n",
    "    Slicing time series    100 xp\n",
    "    Subsetting by row/column number    100 xp\n",
    "    Working with pivot tables    50 xp\n",
    "    Pivot temperature by city and year    100 xp\n",
    "    Subsetting pivot tables    100 xp\n",
    "    Calculating on a pivot table    100 xp\n"
   ]
  },
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    "##  Creating and Visualizing DataFrames\n",
    "0%\n",
    "\n",
    "Learn to visualize the contents of your DataFrames, handle missing data values, and import data from and export data to CSV files.\n",
    "\n",
    "    Visualizing your data    50 xp\n",
    "    Which avocado size is most popular?    100 xp\n",
    "    Changes in sales over time    100 xp\n",
    "    Avocado supply and demand    100 xp\n",
    "    Price of conventional vs. organic avocados    100 xp\n",
    "    Missing values    50 xp\n",
    "    Finding missing values    100 xp\n",
    "    Removing missing values    100 xp\n",
    "    Replacing missing values    100 xp\n",
    "    Creating DataFrames    50 xp\n",
    "    List of dictionaries    100 xp\n",
    "    Dictionary of lists    100 xp\n",
    "    Reading and writing CSVs    50 xp\n",
    "    CSV to DataFrame    100 xp\n",
    "    DataFrame to CSV    100 xp\n",
    "    Wrap-up    50 xp \n",
    "    "
   ]
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    "## Introducing DataFrames\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**Pandas is a Python package for data manipulation.  It can also be used for data visulation, which will be introduced in chapter4.  We'll start be talking about DataFrames, which form the core of Pandas.  In chapter2, we'll discuss aggregating data to gather insights.  In chapter3, we'll learn all about slicing and indexing to subset DataFrames.  Finally, we'll visualize our data, deal with missing data, and read data into a DataFrame.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Pandas is built on top of two essential Python packages (I thought just based on NumPy): NumPy and Matplotlib.  NumPy provides multidimensional array objects for easy data manipulation that Pandas uses to store data, and Matplotlib has powerful data visualization capabilities that Pandas takes advantage of.  \n",
    "\n",
    "# Almost the entire Python data science community using Pandas.  There are several ways to store data for analysis, but rectangular data, sometimes called tabular data is the most cmmon form.  \n",
    "\n",
    "In below example with dogs, each observation or each dog is a row, and each variable or each dog property is a column.  Pandas is designed to work with rectangular data like this.  In Pandas, rectangular data is represented as a DataFrame object.  Each programming language used for data analysis has something similar to this.  R also has DataFrames, while SQL has database tables.  Every value within a column has the same data type, either text or numeric, but different columns can contain different data types.  \n",
    "\n",
    "\n",
    "**When you first receive a new dataset, you want to quickly explore it and get a sense of its contents. \n",
    "# *******************************************************************************************************************\n",
    "Pandas provides several methods for this, the first is .head(), which returns the first few rows of the DataFrame.  With this method, we only had several rows to begain with, not super exciting, but it bocomes very useful if you have many rows.  \n",
    "The .info() method displays the names of columns, the data types they contain, and whether they have any missing values.  \n",
    "A DataFrame's .shape attribute contains a tuple that holds the number of rows followed by the number of columns.  Since this is an attribute instead of a method, you write it without parentheses.  \n",
    "The .describe() method computes some summary statistics for numerical columns, like mean and median.  the count is the number of non-missing values in each column.  Describe is good for a quick overview of numeric variables, but if you want more control, you'll see how to perform more specific calculations later in the course.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# DataFrame consist of 3 different components, accessible using attributes.  .values, .columns, .index\n",
    "The .values attribute as you might expect, contains the data values in a 2-dimensional NumPy array.  The other two components of a DataFrame are labels for columns and rows.  The .columns attributes contains column names, and the .index attribute contains row numbers or row names.  Be careful, since row labels are stored in .index not in .rows.  Notice that these are index objects, which we'll cover in chapter3.  This allows for flexibility in labels.  For example, the dogs data uses row numbers, but row names are also possible.  \n",
    "\n",
    "\n",
    "Python has a semi-official philosophy on how to write good code called The Zen of Python.  One suggestion is that given aprogramming problem, there should be one obvious solution.  As you go through this course, bear in mind that Pandas deliberately doesnt follow this philosopyh.  Instead there are often multiple ways to solve a problem, leaving you to choose the best.  \n",
    "\n",
    "In this respect, Pandas is like a Swiss Army Knife, giving you a variety of tools, making it increibly powerful, but more difficult to learn.  \n",
    "\n",
    "In this course, we aim for a more streamlined approach to Pandas, only covering the most important ways of doing things.  \n",
    "\n",
    "\n",
    "---------------------------------------------------------------------------------\n",
    "Name      | Breed       | Color   | Height(cm)  | Weight(kg)  | Dataof Birth\n",
    "Bella     | Labrador    | Brown   | 56          | 25          | 2013-07-01\n",
    "Charlie   | Poodle      | Black   | 43          | 23          | 2016-09-16\n",
    "Lucy      | Chow Chow   | Brown   | 46          | 22          | 2014-08-25\n",
    "Cooper    | Schnauzer   | Gray    | 49          | 17          | 2011-12-11\n",
    "Max       | Labrador    | Black   | 59          | 29          | 2017-01-20\n",
    "Stella    | Chihuahua   | Tan     | 18          | 2           | 2015-04-20\n",
    "Bernie    | St.Bernard  | White   | 77          | 74          | 2018-02-27\n",
    "\n",
    "\n"
   ]
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      "      Name       Breed  Color  Height(cm)  Weight(kg) Dataof Birth\n",
      "0    Bella    Labrador  Brown          56          25   2013-07-01\n",
      "1  Charlie      Poodle  Black          43          23   2016-09-16\n",
      "2     Lucy   Chow Chow  Brown          46          22   2014-08-25\n",
      "3   Cooper   Schnauzer   Gray          49          17   2011-12-11\n",
      "4      Max    Labrador  Black          59          29   2017-01-20\n",
      "5   Stella   Chihuahua    Tan          18           2   2015-04-20\n",
      "6   Bernie  St.Bernard  White          77          74   2018-02-27\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7 entries, 0 to 6\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Name          7 non-null      object\n",
      " 1   Breed         7 non-null      object\n",
      " 2   Color         7 non-null      object\n",
      " 3   Height(cm)    7 non-null      int64 \n",
      " 4   Weight(kg)    7 non-null      int64 \n",
      " 5   Dataof Birth  7 non-null      object\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 464.0+ bytes\n",
      "None\n",
      "\n",
      "       Height(cm)  Weight(kg)\n",
      "count    7.000000    7.000000\n",
      "mean    49.714286   27.428571\n",
      "std     17.960274   22.292429\n",
      "min     18.000000    2.000000\n",
      "25%     44.500000   19.500000\n",
      "50%     49.000000   23.000000\n",
      "75%     57.500000   27.000000\n",
      "max     77.000000   74.000000\n",
      "\n",
      "Index(['Name', 'Breed', 'Color', 'Height(cm)', 'Weight(kg)', 'Dataof Birth'], dtype='object')\n",
      "RangeIndex(start=0, stop=7, step=1)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.read_csv('dogs.csv')\n",
    "\n",
    "print(df)\n",
    "print()\n",
    "\n",
    "\n",
    "print(df.info())\n",
    "print()\n",
    "\n",
    "\n",
    "print(df.describe())\n",
    "print()\n",
    "\n",
    "\n",
    "print(df.columns)\n",
    "print(df.index)"
   ]
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    "## Inspecting a DataFrame\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# When you get a new DataFrame to work with, the first thing you need to do is explore it and see what it contains. There are several useful methods and attributes for this.\n",
    "\n",
    "    .head() returns the first few rows (the “head” of the DataFrame).\n",
    "    .info() shows information on each of the columns, such as the data type and number of missing values.\n",
    "    .shape returns the number of rows and columns of the DataFrame.\n",
    "    .describe() calculates a few summary statistics for each column.\n",
    "\n",
    "homelessness is a DataFrame containing estimates of homelessness in each U.S. state in 2018. The individual column is the number of homeless individuals not part of a family with children. The family_members column is the number of homeless individuals part of a family with children. The state_pop column is the state's total population.\n",
    "\n",
    "pandas is imported for you.\n",
    "Instructions 1/4\n",
    "25 XP\n",
    "\n",
    "    Question 1\n",
    "    Print the head of the homelessness DataFrame.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    Print information about the column types and missing values in homelessness.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 3\n",
    "    Print the number of rows and columns in homelessness.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 4\n",
    "    \n",
    "    Print some summary statistics that describe the homelessness DataFrame.\n"
   ]
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     "text": [
      "             region       state  individuals  family_members  state_pop\n",
      "0  EastSouthCentral     Alabama       2570.0           864.0    4887681\n",
      "1           Pacific      Alaska       1434.0           582.0     735139\n",
      "2          Mountain     Arizona       7259.0          2606.0    7158024\n",
      "3  WestSouthCentral    Arkansas       2280.0           432.0    3009733\n",
      "4           Pacific  California     109008.0         20964.0   39461588\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51 entries, 0 to 50\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   region          51 non-null     object \n",
      " 1   state           51 non-null     object \n",
      " 2   individuals     51 non-null     float64\n",
      " 3   family_members  51 non-null     float64\n",
      " 4   state_pop       51 non-null     int64  \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 2.1+ KB\n",
      "None\n",
      "(51, 5)\n",
      "         individuals  family_members     state_pop\n",
      "count      51.000000       51.000000  5.100000e+01\n",
      "mean     7225.784314     3504.882353  6.405637e+06\n",
      "std     15991.025083     7805.411811  7.327258e+06\n",
      "min       434.000000       75.000000  5.776010e+05\n",
      "25%      1446.500000      592.000000  1.777414e+06\n",
      "50%      3082.000000     1482.000000  4.461153e+06\n",
      "75%      6781.500000     3196.000000  7.340946e+06\n",
      "max    109008.000000    52070.000000  3.946159e+07\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.read_csv('homelessness.csv')\n",
    "\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.shape)\n",
    "print(df.describe())"
   ]
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    "## Parts of a DataFrame\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# To better understand DataFrame objects, it's useful to know that they consist of three components, stored as attributes:\n",
    "\n",
    "    .values: A two-dimensional NumPy array of values.\n",
    "    .columns: An index of columns: the column names.\n",
    "    .index: An index for the rows: either row numbers or row names.\n",
    "\n",
    "You can usually think of indexes as a list of strings or numbers, though the pandas Index data type allows for more sophisticated options. (These will be covered later in the course.)\n",
    "\n",
    "homelessness is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Import pandas using the alias pd.\n",
    "    Print a 2D NumPy array of the values in homelessness.\n",
    "    Print the column names of homelessness.\n",
    "    Print the index of homelessness.\n"
   ]
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     "text": [
      "[['EastSouthCentral' 'Alabama' 2570.0 864.0 4887681]\n",
      " ['Pacific' 'Alaska' 1434.0 582.0 735139]\n",
      " ['Mountain' 'Arizona' 7259.0 2606.0 7158024]\n",
      " ['WestSouthCentral' 'Arkansas' 2280.0 432.0 3009733]\n",
      " ['Pacific' 'California' 109008.0 20964.0 39461588]\n",
      " ['Mountain' 'Colorado' 7607.0 3250.0 5691287]\n",
      " ['NewEngland' 'Connecticut' 2280.0 1696.0 3571520]\n",
      " ['SouthAtlantic' 'Delaware' 708.0 374.0 965479]\n",
      " ['SouthAtlantic' 'DistrictofColumbia' 3770.0 3134.0 701547]\n",
      " ['SouthAtlantic' 'Florida' 21443.0 9587.0 21244317]\n",
      " ['SouthAtlantic' 'Georgia' 6943.0 2556.0 10511131]\n",
      " ['Pacific' 'Hawaii' 4131.0 2399.0 1420593]\n",
      " ['Mountain' 'Idaho' 1297.0 715.0 1750536]\n",
      " ['EastNorthCentral' 'Illinois' 6752.0 3891.0 12723071]\n",
      " ['EastNorthCentral' 'Indiana' 3776.0 1482.0 6695497]\n",
      " ['WestNorthCentral' 'Iowa' 1711.0 1038.0 3148618]\n",
      " ['WestNorthCentral' 'Kansas' 1443.0 773.0 2911359]\n",
      " ['EastSouthCentral' 'Kentucky' 2735.0 953.0 4461153]\n",
      " ['WestSouthCentral' 'Louisiana' 2540.0 519.0 4659690]\n",
      " ['NewEngland' 'Maine' 1450.0 1066.0 1339057]\n",
      " ['SouthAtlantic' 'Maryland' 4914.0 2230.0 6035802]\n",
      " ['NewEngland' 'Massachusetts' 6811.0 13257.0 6882635]\n",
      " ['EastNorthCentral' 'Michigan' 5209.0 3142.0 9984072]\n",
      " ['WestNorthCentral' 'Minnesota' 3993.0 3250.0 5606249]\n",
      " ['EastSouthCentral' 'Mississippi' 1024.0 328.0 2981020]\n",
      " ['WestNorthCentral' 'Missouri' 3776.0 2107.0 6121623]\n",
      " ['Mountain' 'Montana' 983.0 422.0 1060665]\n",
      " ['WestNorthCentral' 'Nebraska' 1745.0 676.0 1925614]\n",
      " ['Mountain' 'Nevada' 7058.0 486.0 3027341]\n",
      " ['NewEngland' 'NewHampshire' 835.0 615.0 1353465]\n",
      " ['Mid-Atlantic' 'NewJersey' 6048.0 3350.0 8886025]\n",
      " ['Mountain' 'NewMexico' 1949.0 602.0 2092741]\n",
      " ['Mid-Atlantic' 'NewYork' 39827.0 52070.0 19530351]\n",
      " ['SouthAtlantic' 'NorthCarolina' 6451.0 2817.0 10381615]\n",
      " ['WestNorthCentral' 'NorthDakota' 467.0 75.0 758080]\n",
      " ['EastNorthCentral' 'Ohio' 6929.0 3320.0 11676341]\n",
      " ['WestSouthCentral' 'Oklahoma' 2823.0 1048.0 3940235]\n",
      " ['Pacific' 'Oregon' 11139.0 3337.0 4181886]\n",
      " ['Mid-Atlantic' 'Pennsylvania' 8163.0 5349.0 12800922]\n",
      " ['NewEngland' 'RhodeIsland' 747.0 354.0 1058287]\n",
      " ['SouthAtlantic' 'SouthCarolina' 3082.0 851.0 5084156]\n",
      " ['WestNorthCentral' 'SouthDakota' 836.0 323.0 878698]\n",
      " ['EastSouthCentral' 'Tennessee' 6139.0 1744.0 6771631]\n",
      " ['WestSouthCentral' 'Texas' 19199.0 6111.0 28628666]\n",
      " ['Mountain' 'Utah' 1904.0 972.0 3153550]\n",
      " ['NewEngland' 'Vermont' 780.0 511.0 624358]\n",
      " ['SouthAtlantic' 'Virginia' 3928.0 2047.0 8501286]\n",
      " ['Pacific' 'Washington' 16424.0 5880.0 7523869]\n",
      " ['SouthAtlantic' 'WestVirginia' 1021.0 222.0 1804291]\n",
      " ['EastNorthCentral' 'Wisconsin' 2740.0 2167.0 5807406]\n",
      " ['Mountain' 'Wyoming' 434.0 205.0 577601]]\n",
      "<class 'numpy.ndarray'>\n",
      "Index(['region', 'state', 'individuals', 'family_members', 'state_pop'], dtype='object')\n",
      "RangeIndex(start=0, stop=51, step=1)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('homelessness.csv')\n",
    "\n",
    "\n",
    "print(df.values)\n",
    "print(type(df.values))\n",
    "\n",
    "print(df.columns)\n",
    "print(df.index)"
   ]
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    "## Sorting and subsetting\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**In this video, we'll cover 2 simple stand possibly most important ways to find interesting parts of your DataFrame.  The first thing you can do is change the order of rows by sorting them so that the most interesting data is at the top of the DataFrame.  \n",
    "\n",
    "\n",
    "\n",
    "**You can sort rows using the .sort_values() method, passing in a column name that you want to sort by.  Setting the ascending=False argument to False will sort the data the other way around.\n",
    "# *******************************************************************************************************************\n",
    "We can also sort by multiple variables by passing a list of column names to .sort_values() method.  To change the direction values are sorted in, pass a list to the ascending argument to specify which direction sorting should be done for each variable.  \n"
   ]
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      "              region               state  individuals  family_members  \\\n",
      "50          Mountain             Wyoming        434.0           205.0   \n",
      "45        NewEngland             Vermont        780.0           511.0   \n",
      "8      SouthAtlantic  DistrictofColumbia       3770.0          3134.0   \n",
      "1            Pacific              Alaska       1434.0           582.0   \n",
      "34  WestNorthCentral         NorthDakota        467.0            75.0   \n",
      "\n",
      "    state_pop  \n",
      "50     577601  \n",
      "45     624358  \n",
      "8      701547  \n",
      "1      735139  \n",
      "34     758080  \n",
      "              region          state  individuals  family_members  state_pop\n",
      "32      Mid-Atlantic        NewYork      39827.0         52070.0   19530351\n",
      "4            Pacific     California     109008.0         20964.0   39461588\n",
      "21        NewEngland  Massachusetts       6811.0         13257.0    6882635\n",
      "9      SouthAtlantic        Florida      21443.0          9587.0   21244317\n",
      "43  WestSouthCentral          Texas      19199.0          6111.0   28628666\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('homelessness.csv')\n",
    "\n",
    "\n",
    "print(df.sort_values('state_pop').head())\n",
    "\n",
    "print(df.sort_values('family_members', ascending=False).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "bfa9d5bd-285c-4f83-94b6-2be07aebec4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name      breed  color  height_cn  weight_km data_of_birth\n",
      "0    Bella   Labrador  Brown         56         24    2013-07-01\n",
      "1  Charlie     Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy  Chow Chow  Brown         46         24    2014-08-25\n",
      "3   Cooper  Schnauzer   Gray         49         17    2011-12-11\n",
      "4      Max   Labrador  Black         59         29    2017-01-20\n",
      "      name       breed  color  height_cn  weight_km data_of_birth\n",
      "6   Bernie  St.Bernard  White         77         74    2018-02-27\n",
      "4      Max    Labrador  Black         59         29    2017-01-20\n",
      "1  Charlie      Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy   Chow Chow  Brown         46         24    2014-08-25\n",
      "0    Bella    Labrador  Brown         56         24    2013-07-01\n",
      "3   Cooper   Schnauzer   Gray         49         17    2011-12-11\n",
      "5   Stella   Chihuahua    Tan         18          2    2015-04-20\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('dogs2.csv')\n",
    "\n",
    "\n",
    "print(df.head())\n",
    "\n",
    "#####################################################################################################################\n",
    "print(df.sort_values(['weight_km', 'height_cn'], ascending=[False, True]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3428d8ac-d95b-44e3-8448-aff7e27be465",
   "metadata": {},
   "source": [
    "\n",
    "# We may want to zoom in on just one column.  We can do this using the name of the DataFrame, followed by square brackets with a column name inside.  \n",
    "\n",
    "\n",
    "To select multiple columns you need 2 pairs of square brackets.  In this condition, the inner and the outer square brackets are performing different tasks.  \n",
    "\n",
    "* The outer square brackets are responsible for subsetting the DataFrame, and the inner square brackets are creating a list of column names to subset.  This means you could provide a separate list of column names as a variable and then use that list to perform the same subsetting.  Usually, its easier to do in one line.  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aa6c79ec-3d6c-47af-8e15-dae3a7fb7c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      Bella\n",
      "1    Charlie\n",
      "2       Lucy\n",
      "3     Cooper\n",
      "4        Max\n",
      "5     Stella\n",
      "6     Bernie\n",
      "Name: name, dtype: object\n",
      "      name       breed  color\n",
      "0    Bella    Labrador  Brown\n",
      "1  Charlie      Poodle  Black\n",
      "2     Lucy   Chow Chow  Brown\n",
      "3   Cooper   Schnauzer   Gray\n",
      "4      Max    Labrador  Black\n",
      "5   Stella   Chihuahua    Tan\n",
      "6   Bernie  St.Bernard  White\n",
      "      name       breed  color  height_cn\n",
      "0    Bella    Labrador  Brown         56\n",
      "1  Charlie      Poodle  Black         43\n",
      "2     Lucy   Chow Chow  Brown         46\n",
      "3   Cooper   Schnauzer   Gray         49\n",
      "4      Max    Labrador  Black         59\n",
      "5   Stella   Chihuahua    Tan         18\n",
      "6   Bernie  St.Bernard  White         77\n"
     ]
    }
   ],
   "source": [
    "print(df['name'])\n",
    "\n",
    "\n",
    "print(df[['name', 'breed', 'color']])\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "what_i_need = ['name', 'breed', 'color', 'height_cn']\n",
    "\n",
    "print(df[what_i_need])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699df515-5e3e-482d-a92a-a7d552984b59",
   "metadata": {},
   "source": [
    "# *******************************************************************************************************************\n",
    "# There are lots of different ways to subset rows, the most common way to do this is by creating a logical condition to filter against.  \n",
    "For example, lets find all the dogs whose height is greater than 50 centimeters.  Now we have a True or False value for every row.  We can use logical condition inside of square brackets to subset the rows we are interested in to get all of the dogs taller than 50 centimeters.  \n",
    "\n",
    "**We can also subset rows based on text data.  Here we use double equal sign in the logical condition to filter the dogs that are Labradors.  \n",
    "\n",
    "**We can also subset based on dates.  Here we filter all the dogs born before 2015.  Notice thdates are in quotes and are written as year-month-day, this is international standard date format.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**To subset the rows that meet multiple conditions, you can combine contitions using logical operators, such as and operator seen here.  This means that only rows that meet both of these conditions will be subsetted.  You can also do this in one line of code, but you'll also need to add parentheses around each condition.  \n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# *******************************************************************************************************************\n",
    "**If you want to filter on multiple values of a categorical variable, the easiest way is to use .isin() method.  This method takes in a list of values to filter for.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7e3bb8f4-1683-483d-a643-fe22290d770c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     True\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "4     True\n",
      "5    False\n",
      "6     True\n",
      "Name: height_cn, dtype: bool\n",
      "     name       breed  color  height_cn  weight_km data_of_birth\n",
      "0   Bella    Labrador  Brown         56         24    2013-07-01\n",
      "4     Max    Labrador  Black         59         29    2017-01-20\n",
      "6  Bernie  St.Bernard  White         77         74    2018-02-27\n"
     ]
    }
   ],
   "source": [
    "above_50 = df['height_cn']>50\n",
    "\n",
    "print(above_50)\n",
    "\n",
    "\n",
    "print(df[above_50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1075cad6-35b3-43ed-a72d-a683ea483152",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    name     breed  color  height_cn  weight_km data_of_birth\n",
      "0  Bella  Labrador  Brown         56         24    2013-07-01\n",
      "4    Max  Labrador  Black         59         29    2017-01-20\n"
     ]
    }
   ],
   "source": [
    "labrador = df['breed'] == 'Labrador'\n",
    "\n",
    "\n",
    "print(df[labrador])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6c24bcdc-a161-4a8f-aa6f-08c520ad2ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df['data_of_birth'][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1ccd5c-ce61-4744-8879-f3b603bd683c",
   "metadata": {},
   "source": [
    "# *******************************************************************************************************************\n",
    "Important question, can we change the column data types? like here from string to data? or datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bd638ccb-9561-4464-842f-e56a278075ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name       breed  color  height_cn  weight_km data_of_birth\n",
      "1  Charlie      Poodle  Black         43         24    2016-09-16\n",
      "4      Max    Labrador  Black         59         29    2017-01-20\n",
      "5   Stella   Chihuahua    Tan         18          2    2015-04-20\n",
      "6   Bernie  St.Bernard  White         77         74    2018-02-27\n"
     ]
    }
   ],
   "source": [
    "after_2015 = df['data_of_birth'] > '2015'\n",
    "\n",
    "\n",
    "print(df[before_2015])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "652d8d33-13e3-450e-bf83-36c15b1dcda5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name       breed  color  height_cn  weight_km data_of_birth\n",
      "1  Charlie      Poodle  Black         43         24    2016-09-16\n",
      "4      Max    Labrador  Black         59         29    2017-01-20\n",
      "5   Stella   Chihuahua  Black         18          2    2015-04-20\n",
      "6   Bernie  St.Bernard  Black         77         74    2018-02-27\n",
      "\n",
      "      name       breed  color  height_cn  weight_km data_of_birth\n",
      "0    Bella    Labrador  Black         56         24    2013-07-01\n",
      "1  Charlie      Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy   Chow Chow  Black         46         24    2014-08-25\n",
      "3   Cooper   Schnauzer  Black         49         17    2011-12-11\n",
      "4      Max    Labrador  Black         59         29    2017-01-20\n",
      "5   Stella   Chihuahua  Black         18          2    2015-04-20\n",
      "6   Bernie  St.Bernard  Black         77         74    2018-02-27\n",
      "\n",
      "      name       breed  color  height_cn  weight_km data_of_birth\n",
      "0    Bella    Labrador  Black         56         24    2013-07-01\n",
      "1  Charlie      Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy   Chow Chow  Black         46         24    2014-08-25\n",
      "3   Cooper   Schnauzer  Black         49         17    2011-12-11\n",
      "4      Max    Labrador  Black         59         29    2017-01-20\n",
      "5   Stella   Chihuahua  Black         18          2    2015-04-20\n",
      "6   Bernie  St.Bernard  Black         77         74    2018-02-27\n",
      "\n",
      "      name       breed  color  height_cn  weight_km data_of_birth\n",
      "1  Charlie      Poodle  Black         43         24    2016-09-16\n",
      "4      Max    Labrador  Black         59         29    2017-01-20\n",
      "5   Stella   Chihuahua  Black         18          2    2015-04-20\n",
      "6   Bernie  St.Bernard  Black         77         74    2018-02-27\n"
     ]
    }
   ],
   "source": [
    "after_2015 = df['data_of_birth'] > '2015'\n",
    "black = df['color'] == 'Black'\n",
    "\n",
    "\n",
    "                                  ###################################################################################\n",
    "print(df[after_2015 & black])     # Why we cant replace & with and ???\n",
    "# This is because ‘and’ tests whether both expressions are logically True \n",
    "# while ‘&’ performs bitwise AND operation on the result of both statements.\n",
    "\n",
    "print()\n",
    "print(df[after_2015 + black])\n",
    "\n",
    "\n",
    "print()\n",
    "print(df[after_2015 | black])\n",
    "\n",
    "print()\n",
    "#####################################################################################################################\n",
    "print(df[(df['data_of_birth']>'2015') & (df['color']=='Black')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "15fa4a3c-a2b4-4287-ac45-09400e768214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name      breed  color  height_cn  weight_km data_of_birth\n",
      "0    Bella   Labrador  Brown         56         24    2013-07-01\n",
      "1  Charlie     Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy  Chow Chow  Brown         46         24    2014-08-25\n",
      "4      Max   Labrador  Black         59         29    2017-01-20\n",
      "      name      breed  color  height_cn  weight_km data_of_birth\n",
      "0    Bella   Labrador  Brown         56         24    2013-07-01\n",
      "1  Charlie     Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy  Chow Chow  Brown         46         24    2014-08-25\n",
      "4      Max   Labrador  Black         59         29    2017-01-20\n"
     ]
    }
   ],
   "source": [
    "black_brown = df['color'].isin(['Black', 'Brown'])\n",
    "#####################################################################################################################\n",
    "\n",
    "\n",
    "print(df[black_brown])\n",
    "\n",
    "\n",
    "black_brown2 = df[(df['color']=='Black') | (df['color']=='Brown')]   # Using or operator\n",
    "print(black_brown2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c3a515-22fb-44a5-a9a5-57604ce6ee2b",
   "metadata": {},
   "source": [
    "## Sorting rows\n",
    "\n",
    "# Finding interesting bits of data in a DataFrame is often easier if you change the order of the rows. You can sort the rows by passing a column name to .sort_values().\n",
    "\n",
    "In cases where rows have the same value (this is common if you sort on a categorical variable), you may wish to break the ties by sorting on another column. You can sort on multiple columns in this way by passing a list of column names.\n",
    "Sort on … \tSyntax\n",
    "one column \tdf.sort_values(\"breed\")\n",
    "multiple columns \tdf.sort_values([\"breed\", \"weight_kg\"])\n",
    "\n",
    "By combining .sort_values() with .head(), you can answer questions in the form, \"What are the top cases where…?\".\n",
    "\n",
    "homelessness is available and pandas is loaded as pd.\n",
    "Instructions 1/3\n",
    "35 XP\n",
    "\n",
    "    Question 1\n",
    "    Sort homelessness by the number of homeless individuals, from smallest to largest, and save this as homelessness_ind.\n",
    "    Print the head of the sorted DataFrame.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    Sort homelessness by the number of homeless family_members in descending order, and save this as homelessness_fam.\n",
    "    Print the head of the sorted DataFrame.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 3\n",
    "#    Sort homelessness first by region (ascending), and then by number of family members (descending). Save this as homelessness_reg_fam.\n",
    "    Print the head of the sorted DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c6ba4b47-268f-4622-8fb4-d368c6544b07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              region        state  individuals  family_members  state_pop\n",
      "50          Mountain      Wyoming        434.0           205.0     577601\n",
      "34  WestNorthCentral  NorthDakota        467.0            75.0     758080\n",
      "7      SouthAtlantic     Delaware        708.0           374.0     965479\n",
      "39        NewEngland  RhodeIsland        747.0           354.0    1058287\n",
      "45        NewEngland      Vermont        780.0           511.0     624358\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "homelessness = pd.read_csv('homelessness.csv')\n",
    "\n",
    "# Sort homelessness by individual\n",
    "homelessness_ind = homelessness.sort_values('individuals')  #.ascending=False\n",
    "\n",
    "# Print the top few rows\n",
    "print(homelessness_ind.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "07428abe-6782-4f74-827a-d1a247d2763e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              region          state  individuals  family_members  state_pop\n",
      "32      Mid-Atlantic        NewYork      39827.0         52070.0   19530351\n",
      "4            Pacific     California     109008.0         20964.0   39461588\n",
      "21        NewEngland  Massachusetts       6811.0         13257.0    6882635\n",
      "9      SouthAtlantic        Florida      21443.0          9587.0   21244317\n",
      "43  WestSouthCentral          Texas      19199.0          6111.0   28628666\n"
     ]
    }
   ],
   "source": [
    "# Sort homelessness by descending family members\n",
    "homelessness_fam = homelessness.sort_values('family_members', ascending=False)\n",
    "\n",
    "# Print the top few rows\n",
    "print(homelessness_fam.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9b6a027f-9222-4b8a-942d-579c26f93d93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              region      state  individuals  family_members  state_pop\n",
      "13  EastNorthCentral   Illinois       6752.0          3891.0   12723071\n",
      "35  EastNorthCentral       Ohio       6929.0          3320.0   11676341\n",
      "22  EastNorthCentral   Michigan       5209.0          3142.0    9984072\n",
      "49  EastNorthCentral  Wisconsin       2740.0          2167.0    5807406\n",
      "14  EastNorthCentral    Indiana       3776.0          1482.0    6695497\n"
     ]
    }
   ],
   "source": [
    "# Sort homelessness by region, then descending family members\n",
    "homelessness_reg_fam = homelessness.sort_values(['region', 'family_members'], ascending=[True, False])\n",
    "#####################################################################################################################\n",
    "\n",
    "\n",
    "# Print the top few rows\n",
    "print(homelessness_reg_fam.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "067db118-db3b-4e5d-aee5-7b75b8e91b22",
   "metadata": {},
   "source": [
    "## Subsetting columns\n",
    "\n",
    "When working with data, you may not need all of the variables in your dataset. Square brackets ([]) can be used to select only the columns that matter to you in an order that makes sense to you. To select only \"col_a\" of the DataFrame df, use\n",
    "\n",
    "df[\"col_a\"]\n",
    "\n",
    "To select \"col_a\" and \"col_b\" of df, use\n",
    "\n",
    "df[[\"col_a\", \"col_b\"]]\n",
    "\n",
    "homelessness is available and pandas is loaded as pd.\n",
    "Instructions 1/3\n",
    "35 XP\n",
    "\n",
    "    Question 1\n",
    "    Create a DataFrame called individuals that contains only the individuals column of homelessness.\n",
    "    Print the head of the result.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    Create a DataFrame called state_fam that contains only the state and family_members columns of homelessness, in that order.\n",
    "    Print the head of the result.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 3\n",
    "    Create a DataFrame called ind_state that contains the individuals and state columns of homelessness, in that order.\n",
    "    Print the head of the result.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "89e74345-cde8-420a-bb19-47a5ce21fb2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      2570.0\n",
      "1      1434.0\n",
      "2      7259.0\n",
      "3      2280.0\n",
      "4    109008.0\n",
      "Name: individuals, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Select the individuals column\n",
    "individuals = homelessness['individuals']\n",
    "\n",
    "# Print the head of the result\n",
    "print(individuals.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0935625e-63bb-4ff0-a740-7d94b6a55c36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        state  family_members\n",
      "0     Alabama           864.0\n",
      "1      Alaska           582.0\n",
      "2     Arizona          2606.0\n",
      "3    Arkansas           432.0\n",
      "4  California         20964.0\n"
     ]
    }
   ],
   "source": [
    "# Select the state and family_members columns\n",
    "state_fam = homelessness[['state', 'family_members']]\n",
    "\n",
    "# Print the head of the result\n",
    "print(state_fam.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "83f2307e-c4a4-4371-87ad-9d650f449062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   individuals       state\n",
      "0       2570.0     Alabama\n",
      "1       1434.0      Alaska\n",
      "2       7259.0     Arizona\n",
      "3       2280.0    Arkansas\n",
      "4     109008.0  California\n"
     ]
    }
   ],
   "source": [
    "# Select only the individuals and state columns, in that order\n",
    "ind_state = homelessness[['individuals', 'state']]\n",
    "\n",
    "# Print the head of the result\n",
    "print(ind_state.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a50aaa3-70ff-4cb1-9f97-710ed2b9b9ed",
   "metadata": {},
   "source": [
    "## Subsetting rows\n",
    "\n",
    "A large part of data science is about finding which bits of your dataset are interesting. One of the simplest techniques for this is to find a subset of rows that match some criteria. This is sometimes known as filtering rows or selecting rows.\n",
    "\n",
    "There are many ways to subset a DataFrame, perhaps the most common is to use relational operators to return True or False for each row, then pass that inside square brackets.\n",
    "\n",
    "dogs[dogs[\"height_cm\"] > 60]\n",
    "dogs[dogs[\"color\"] == \"tan\"]\n",
    "\n",
    "You can filter for multiple conditions at once by using the \"bitwise and\" operator, &.\n",
    "\n",
    "dogs[(dogs[\"height_cm\"] > 60) & (dogs[\"color\"] == \"tan\")]\n",
    "\n",
    "homelessness is available and pandas is loaded as pd.\n",
    "Instructions 1/3\n",
    "35 XP\n",
    "\n",
    "    Question 1\n",
    "    Filter homelessness for cases where the number of individuals is greater than ten thousand, assigning to ind_gt_10k. View the printed result.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    Filter homelessness for cases where the USA Census region is \"Mountain\", assigning to mountain_reg. View the printed result.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 3\n",
    "    Filter homelessness for cases where the number of family_members is less than one thousand and the region is \"Pacific\", assigning to fam_lt_1k_pac. View the printed result.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d9b28467-7ade-4ea3-a86b-dca8113995c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              region       state  individuals  family_members  state_pop\n",
      "4            Pacific  California     109008.0         20964.0   39461588\n",
      "9      SouthAtlantic     Florida      21443.0          9587.0   21244317\n",
      "32      Mid-Atlantic     NewYork      39827.0         52070.0   19530351\n",
      "37           Pacific      Oregon      11139.0          3337.0    4181886\n",
      "43  WestSouthCentral       Texas      19199.0          6111.0   28628666\n",
      "47           Pacific  Washington      16424.0          5880.0    7523869\n"
     ]
    }
   ],
   "source": [
    "# Filter for rows where individuals is greater than 10000\n",
    "ind_gt_10k = homelessness[homelessness['individuals']>10000]\n",
    "\n",
    "# See the result\n",
    "print(ind_gt_10k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "b2e9e60e-1538-4bdf-8707-6b80df7479b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      region     state  individuals  family_members  state_pop\n",
      "2   Mountain   Arizona       7259.0          2606.0    7158024\n",
      "5   Mountain  Colorado       7607.0          3250.0    5691287\n",
      "12  Mountain     Idaho       1297.0           715.0    1750536\n",
      "26  Mountain   Montana        983.0           422.0    1060665\n",
      "28  Mountain    Nevada       7058.0           486.0    3027341\n"
     ]
    }
   ],
   "source": [
    "# Filter for rows where region is Mountain\n",
    "mountain_reg = homelessness[homelessness['region']=='Mountain']\n",
    "\n",
    "# See the result\n",
    "print(mountain_reg.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "9f86f88e-6b1c-400f-bb57-65675e6d533c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    region   state  individuals  family_members  state_pop\n",
      "1  Pacific  Alaska       1434.0           582.0     735139\n"
     ]
    }
   ],
   "source": [
    "# Filter for rows where family_members is less than 1000 \n",
    "# and region is Pacific\n",
    "fam_lt_1k_pac = homelessness[(homelessness['family_members']<1000) & (homelessness['region']=='Pacific')]\n",
    "\n",
    "# See the result\n",
    "print(fam_lt_1k_pac)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee347bb8-3053-4aa4-84b0-c2a5e6718afe",
   "metadata": {},
   "source": [
    "## Subsetting rows by categorical variables\n",
    "\n",
    "Subsetting data based on a categorical variable often involves using the \"or\" operator (|) to select rows from multiple categories. This can get tedious when you want all states in one of three different regions, for example. Instead, use the .isin() method, which will allow you to tackle this problem by writing one condition instead of three separate ones.\n",
    "\n",
    "colors = [\"brown\", \"black\", \"tan\"]\n",
    "condition = dogs[\"color\"].isin(colors)\n",
    "dogs[condition]\n",
    "\n",
    "homelessness is available and pandas is loaded as pd.\n",
    "Instructions 1/2\n",
    "50 XP\n",
    "\n",
    "    Question 1\n",
    "    Filter homelessness for cases where the USA census region is \"South Atlantic\" or it is \"Mid-Atlantic\", assigning to south_mid_atlantic. View the printed result.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    Filter homelessness for cases where the USA census state is in the list of Mojave states, canu, assigning to mojave_homelessness. View the printed result.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "7da61d2b-1fe7-427e-bd7b-1748429b0094",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           region               state  individuals  family_members  state_pop\n",
      "7   SouthAtlantic            Delaware        708.0           374.0     965479\n",
      "8   SouthAtlantic  DistrictofColumbia       3770.0          3134.0     701547\n",
      "9   SouthAtlantic             Florida      21443.0          9587.0   21244317\n",
      "10  SouthAtlantic             Georgia       6943.0          2556.0   10511131\n",
      "20  SouthAtlantic            Maryland       4914.0          2230.0    6035802\n",
      "30   Mid-Atlantic           NewJersey       6048.0          3350.0    8886025\n",
      "32   Mid-Atlantic             NewYork      39827.0         52070.0   19530351\n",
      "33  SouthAtlantic       NorthCarolina       6451.0          2817.0   10381615\n",
      "38   Mid-Atlantic        Pennsylvania       8163.0          5349.0   12800922\n",
      "40  SouthAtlantic       SouthCarolina       3082.0           851.0    5084156\n",
      "46  SouthAtlantic            Virginia       3928.0          2047.0    8501286\n",
      "48  SouthAtlantic        WestVirginia       1021.0           222.0    1804291\n"
     ]
    }
   ],
   "source": [
    "#print(homelessness)\n",
    "\n",
    "\n",
    "# Subset for rows in South Atlantic or Mid-Atlantic regions\n",
    "south_mid_atlantic = homelessness[homelessness['region'].isin(['SouthAtlantic', 'Mid-Atlantic'])]\n",
    "\n",
    "# See the result\n",
    "print(south_mid_atlantic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0089217d-d1ef-4df0-8409-4ce6e0572df1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      region       state  individuals  family_members  state_pop\n",
      "2   Mountain     Arizona       7259.0          2606.0    7158024\n",
      "4    Pacific  California     109008.0         20964.0   39461588\n",
      "28  Mountain      Nevada       7058.0           486.0    3027341\n",
      "44  Mountain        Utah       1904.0           972.0    3153550\n"
     ]
    }
   ],
   "source": [
    "# The Mojave Desert states\n",
    "canu = [\"California\", \"Arizona\", \"Nevada\", \"Utah\"]\n",
    "\n",
    "# Filter for rows in the Mojave Desert states\n",
    "mojave_homelessness = homelessness[homelessness['state'].isin(canu)]\n",
    "\n",
    "# See the result\n",
    "print(mojave_homelessness)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72bdf0a6-d91b-46e1-83a1-393ccf02ce3f",
   "metadata": {},
   "source": [
    "## New columns\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**In the last lesson, you saw how to subset and sort a DataFrame to extract interesting bits.  However, often when you first receive a DataFrame, the contents arent exactly what you want.  You may have to add new columns derived from existing columns.  Creating and adding new columns can go by many names, including mutating a DataFrame, transforming a DataFrame, and frature engineering.  \n",
    "\n",
    "Lets say we want to add a new column to our DataFrame that has each dog's height in meters instead of centimeters.  On the left-hand side of the equal sign, we use square brackets with the name of the new column we want to create.  On the right-hand side, we have the calculation.  Notice that both the existing column and the new column we just created are in the DataFrame.  \n",
    "Lets see what the results are if we calculate the body mass index (BMI), of those dogs.  BMI = Wieght in kg/(Height in m)^2.  \n",
    "\n",
    "\n",
    "**The real power of Pandas comes in when you combine all the skills you've learned so far.  \n",
    "\n",
    "Lets figure out the names of skinny, tall dogs.  First to define the skinny dogs, we take the subset of the dogs who have BMI under 100.  Next we sort the results in descending order of heightto get the tallest skinny dogs at the top.  Finally we keep only the columns we'vre interested in.  Here you can see that Max is the tallest dog with a BMI of under 100.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "4b27e6ab-a709-456e-933a-fa9bdfcb813b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name       breed  color  height_cn  weight_km data_of_birth  height_m  \\\n",
      "0    Bella    Labrador  Brown         56         24    2013-07-01      0.56   \n",
      "1  Charlie      Poodle  Black         43         24    2016-09-16      0.43   \n",
      "2     Lucy   Chow Chow  Brown         46         24    2014-08-25      0.46   \n",
      "3   Cooper   Schnauzer   Gray         49         17    2011-12-11      0.49   \n",
      "4      Max    Labrador  Black         59         29    2017-01-20      0.59   \n",
      "5   Stella   Chihuahua    Tan         18          2    2015-04-20      0.18   \n",
      "6   Bernie  St.Bernard  White         77         74    2018-02-27      0.77   \n",
      "\n",
      "          BMI  \n",
      "0   76.530612  \n",
      "1  129.799892  \n",
      "2  113.421550  \n",
      "3   70.803832  \n",
      "4   83.309394  \n",
      "5   61.728395  \n",
      "6  124.810255  \n",
      "     name      breed  color  height_m        BMI\n",
      "4     Max   Labrador  Black      0.59  83.309394\n",
      "0   Bella   Labrador  Brown      0.56  76.530612\n",
      "3  Cooper  Schnauzer   Gray      0.49  70.803832\n",
      "5  Stella  Chihuahua    Tan      0.18  61.728395\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('dogs2.csv')\n",
    "#print(df)\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "df['height_m'] = df['height_cn']/100\n",
    "df['BMI'] = df['weight_km']/df['height_m']**2\n",
    "\n",
    "\n",
    "print(df)\n",
    "\n",
    "\n",
    "bmi_under_100 = df[df['BMI']<100]\n",
    "#####################################################################################################################\n",
    "print(bmi_under_100.sort_values('height_m', ascending=False)[['name', 'breed', 'color', 'height_m', 'BMI']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34cb93b9-b6c6-4461-ab5c-feef82396d40",
   "metadata": {},
   "source": [
    "# DataCamp Object-Oriented Programming in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8479b86e-6e1a-4b83-b4b2-f7c24d9bf8c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     col1    col2    col3    col4\n",
      "0  data11  data12  data13  data14\n",
      "1  data21  data22  data23  data24\n",
      "2  data31  data32  data33  data34\n",
      "3  data41  data42  data43  data44\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "class LoggedDF(pd.DataFrame):\n",
    "    \n",
    "    def __init__(self, *args, **kwargs):\n",
    "        pd.DataFrame.__init__(self, *args, **kwargs)\n",
    "        self.created_at = datetime.today()\n",
    "    #################################################################################################################\n",
    "    # We've added .created_at attribute, but what to do if we want to add a .read_csv() method ????   Think further\n",
    "    #################################################################################################################\n",
    "\n",
    "\n",
    "\n",
    "ldf = LoggedDF([{'col1': 'data11', 'col2': 'data12', 'col3': 'data13', 'col4': 'data14'}, \n",
    "                {'col1': 'data21', 'col2': 'data22', 'col3': 'data23', 'col4': 'data24'}, \n",
    "                {'col1': 'data31', 'col2': 'data32', 'col3': 'data33', 'col4': 'data34'}, \n",
    "                {'col1': 'data41', 'col2': 'data42', 'col3': 'data43', 'col4': 'data44'}])\n",
    "\n",
    "print(ldf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f39e12b-5955-4069-9471-fa039a51df15",
   "metadata": {},
   "source": [
    "## Adding new columns\n",
    "\n",
    "You aren't stuck with just the data you are given. Instead, you can add new columns to a DataFrame. \n",
    "# This has many names, such as transforming, mutating, and feature engineering.\n",
    "\n",
    "You can create new columns from scratch, but it is also common to derive them from other columns, for example, by adding columns together or by changing their units.\n",
    "\n",
    "homelessness is available and pandas is loaded as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Add a new column to homelessness, named total, containing the sum of the individuals and family_members columns.\n",
    "    Add another column to homelessness, named p_individuals, containing the proportion of homeless people in each state who are individuals.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "8c4c9853-1c70-4e4c-af18-d2bfa0c662b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             region       state  individuals  family_members  state_pop  \\\n",
      "0  EastSouthCentral     Alabama       2570.0           864.0    4887681   \n",
      "1           Pacific      Alaska       1434.0           582.0     735139   \n",
      "2          Mountain     Arizona       7259.0          2606.0    7158024   \n",
      "3  WestSouthCentral    Arkansas       2280.0           432.0    3009733   \n",
      "4           Pacific  California     109008.0         20964.0   39461588   \n",
      "\n",
      "      total  p_invdividuals  \n",
      "0    3434.0        0.748398  \n",
      "1    2016.0        0.711310  \n",
      "2    9865.0        0.735834  \n",
      "3    2712.0        0.840708  \n",
      "4  129972.0        0.838704  \n"
     ]
    }
   ],
   "source": [
    "# Add total col as sum of individuals and family_members\n",
    "homelessness['total'] = homelessness['individuals'] + homelessness['family_members']\n",
    "\n",
    "# Add p_individuals col as proportion of individuals\n",
    "homelessness['p_invdividuals'] = homelessness['individuals']/homelessness['total']\n",
    "\n",
    "# See the result\n",
    "print(homelessness.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f32ee78-7715-4147-aa3f-b9c5737c0cd9",
   "metadata": {},
   "source": [
    "## Combo-attack!\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# You've seen the four most common types of data manipulation: sorting rows, subsetting columns, subsetting rows, and adding new columns. In a real-life data analysis, you can mix and match these four manipulations to answer a multitude of questions.\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "In this exercise, you'll answer the question, \"Which state has the highest number of homeless individuals per 10,000 people in the state?\" Combine your new pandas skills to find out.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Add a column to homelessness, indiv_per_10k, containing the number of homeless individuals per ten thousand people in each state.\n",
    "    Subset rows where indiv_per_10k is higher than 20, assigning to high_homelessness.\n",
    "    Sort high_homelessness by descending indiv_per_10k, assigning to high_homelessness_srt.\n",
    "    Select only the state and indiv_per_10k columns of high_homelessness_srt and save as result. Look at the result.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "9f852d7f-e80f-48d9-9018-0bc7a3126132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 state  indiv_per_10k\n",
      "8   DistrictofColumbia      53.738381\n",
      "11              Hawaii      29.079406\n",
      "4           California      27.623825\n",
      "37              Oregon      26.636307\n",
      "28              Nevada      23.314189\n",
      "47          Washington      21.829195\n",
      "32             NewYork      20.392363\n"
     ]
    }
   ],
   "source": [
    "# Create indiv_per_10k col as homeless individuals per 10k state pop\n",
    "homelessness[\"indiv_per_10k\"] = 10000 * homelessness['individuals'] / homelessness['state_pop'] \n",
    "\n",
    "# Subset rows for indiv_per_10k greater than 20\n",
    "high_homelessness = homelessness[homelessness['indiv_per_10k']>20]\n",
    "\n",
    "# Sort high_homelessness by descending indiv_per_10k\n",
    "high_homelessness_srt = high_homelessness.sort_values('indiv_per_10k', ascending=False)\n",
    "\n",
    "# From high_homelessness_srt, select the state and indiv_per_10k cols\n",
    "result = high_homelessness_srt[['state', 'indiv_per_10k']]\n",
    "\n",
    "# See the result\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc48a48a-7516-4852-aff4-2685582fb744",
   "metadata": {},
   "source": [
    "## Summary statistics\n",
    "\n",
    "\n",
    "\n",
    "**In the first chapter, you learned about DataFrames, how to sort and subset them, and how to add new columns to them.  In this chapter, we'll talk about aggregating data, starting with summary statistics.  \n",
    "\n",
    "Summary statistics as follows from their name, are numbers that summarize and tell you about your dataset.  One of the most common summary statistics for numeric data is the .mean(), which is one way of telling you where the center of your data is.  You can calculate the mean of a column by selecting the column with square brackets and calling .mean().  \n",
    "# *******************************************************************************************************************\n",
    "There are lots of other summary statistics that you can compute on columns, like .median(), .mode(), .min(), .max(), .var(), and .std().  You can also take .sum() and calculate the .quantile().  You can also get summary statistics for date columns.  For example we can find the oldest dog by taking the .min() on the date of birth column, dogs['date_of_birth'].min()\n",
    "\n",
    "To aggregate or .agg() method allows you to compute custom summary statistics.  Here we def a function called pct30 that computes the 30th percentile of a DataFrame column.  Now we can subset the weight column and call .agg(pct30) passing the name of our defined function.  The .agg() can also be used on more thann one column, by selecting the weight and height columns before calling agg, we get the answer of both required columns.  We can also use .agg() to get multiple summary statistics at once.  We can actually pass a list of functions into .agg() method. \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Pandas also has methods for computing cumulative statistics, for example the cumulative sum.  Calling .cumsum() on a column returns not just one number, but a number for each row of the DataFrame.  The first number is returned, or the number in the zeroth index, is the first dog's weight.  The next number is the sum of the first and second dog's weights.  The third number is the sum of the first, second, and the third dog's weights, and soon.  The last number is the sum of all the dog's weights.  \n",
    "Pandas also has methods for other cumulative statistics, such as the cumulative maximum - .cummax(), cumulative minimum - .cummin(), and the cumulative product - .cumprod().  These methods all returns an entire column of DF.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "In this chapter, you'll be working with data on Walmart stores, which is a chain of department store in the US.  The dataset contains weekly sales in US dollars in various stores.  Each store has an ID number and a specific store type.  The sales are also separated by department ID.  Along with weekly sales, there is information about whether it was a holiday week or not, the average temperature during the week in that location, the average fuel price in dullars per liter that week, and the national unemployment rate that week.  \n",
    "\n",
    "\n",
    "def pct30(column):\n",
    "    return column.quantile(0.3)\n",
    "    \n",
    "def pct40(column):\n",
    "    return column.quantile(0.4)\n",
    "\n",
    "\n",
    "---------------------------------------------------------------------------------------------------------------\n",
    "store  | type   | dept    | date        | weekly_sales  | is_holiday   | temp_c    | fuel_price  | unemp\n",
    "1      | A      | 1       | 2010-02-05  | 24924.50      | False        | 5.73      | 0.679       | 8.106\n",
    "1      | A      | 2       | 2010-02-05  | 50605.27      | False        | 5.73      | 0.679       | 8.106\n",
    "1      | A      | 3       | 2010-02-05  | 13740.12      | False        | 5.73      | 0.679       | 8.106\n",
    "1      | A      | 4       | 2010-02-05  | 39954.04      | False        | 5.73      | 0.679       | 8.106\n",
    "1      | A      | 5       | 2010-02-05  | 32229.38      | False        | 5.73      | 0.679       | 8.106\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "0f33df67-157c-4045-96a4-f5f4ad93a1ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.430003339266477\n",
      "7.122409297196753\n",
      "0    6.0\n",
      "dtype: float64\n",
      "3.435065849943979\n",
      "53.73838103505538\n",
      "77.96469732888109\n",
      "8.829762019946012\n",
      "531.9301703025903\n",
      "7.122409297196753\n",
      "6.04980494709957\n",
      "9.99472105823659\n",
      "\n",
      "Help on function quantile in module pandas.core.series:\n",
      "\n",
      "quantile(self, q=0.5, interpolation='linear')\n",
      "    Return value at the given quantile.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    q : float or array-like, default 0.5 (50% quantile)\n",
      "        The quantile(s) to compute, which can lie in range: 0 <= q <= 1.\n",
      "    interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}\n",
      "        This optional parameter specifies the interpolation method to use,\n",
      "        when the desired quantile lies between two data points `i` and `j`:\n",
      "    \n",
      "            * linear: `i + (j - i) * fraction`, where `fraction` is the\n",
      "              fractional part of the index surrounded by `i` and `j`.\n",
      "            * lower: `i`.\n",
      "            * higher: `j`.\n",
      "            * nearest: `i` or `j` whichever is nearest.\n",
      "            * midpoint: (`i` + `j`) / 2.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    float or Series\n",
      "        If ``q`` is an array, a Series will be returned where the\n",
      "        index is ``q`` and the values are the quantiles, otherwise\n",
      "        a float will be returned.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    core.window.Rolling.quantile : Calculate the rolling quantile.\n",
      "    numpy.percentile : Returns the q-th percentile(s) of the array elements.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> s = pd.Series([1, 2, 3, 4])\n",
      "    >>> s.quantile(.5)\n",
      "    2.5\n",
      "    >>> s.quantile([.25, .5, .75])\n",
      "    0.25    1.75\n",
      "    0.50    2.50\n",
      "    0.75    3.25\n",
      "    dtype: float64\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(homelessness['indiv_per_10k'].mean())\n",
    "\n",
    "print(homelessness['indiv_per_10k'].median())\n",
    "\n",
    "print(round(homelessness['indiv_per_10k']).mode())\n",
    "\n",
    "print(homelessness['indiv_per_10k'].min())\n",
    "\n",
    "print(homelessness['indiv_per_10k'].max())\n",
    "\n",
    "print(homelessness['indiv_per_10k'].var())\n",
    "\n",
    "print(homelessness['indiv_per_10k'].std())\n",
    "\n",
    "print(homelessness['indiv_per_10k'].sum())\n",
    "\n",
    "print(homelessness['indiv_per_10k'].quantile())\n",
    "print(homelessness['indiv_per_10k'].quantile(0.25))\n",
    "print(homelessness['indiv_per_10k'].quantile(0.75))\n",
    "\n",
    "\n",
    "print()\n",
    "print(help(pd.Series.quantile))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "5fcac18a-1677-4a5a-aa09-e9496ba31948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 3, 5, 6, 9, 11, 12, 13, 19, 21, 22, 32, 35, 36, 45, 44, 55, 68, 79, 80, 81, 88, 90, 91, 92, 100, 112, 113, 114, 120, 121, 132, 145, 146, 149, 150, 155, 180, 189, 190]\n",
      "78.85\n"
     ]
    }
   ],
   "source": [
    "values = '1 3 5 6 9 11 12 13 19 21 22 32 35 36 45 44 55 68 79 80 81 88 90 91 92 100 112 113 \\\n",
    "114 120 121 132 145 146 149 150 155 180 189 190'\n",
    "\n",
    "vals = values.split(' ')\n",
    "vals = [int(i) for i in vals]\n",
    "print(vals)\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "np_vals = np.array(vals)\n",
    "\n",
    "print(np_vals.mean())\n",
    "#print(np_vals.quantile())   # This is a Pandas Series method, not NumPy Array method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "f696d536-e2ae-47e0-b50a-fc652263278d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name       breed  color  height_cn  weight_km data_of_birth\n",
      "0    Bella    Labrador  Brown         56         24    2013-07-01\n",
      "1  Charlie      Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy   Chow Chow  Brown         46         24    2014-08-25\n",
      "3   Cooper   Schnauzer   Gray         49         17    2011-12-11\n",
      "4      Max    Labrador  Black         59         29    2017-01-20\n",
      "5   Stella   Chihuahua    Tan         18          2    2015-04-20\n",
      "6   Bernie  St.Bernard  White         77         74    2018-02-27\n",
      "\n",
      "pct30    45.4\n",
      "pct40    47.2\n",
      "Name: height_cn, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.read_csv('dogs2.csv')\n",
    "print(df)\n",
    "print()\n",
    "\n",
    "#print(dir(df['height_cn']))   # agg is there, \n",
    "\n",
    "\n",
    "def pct30(column):\n",
    "    return column.quantile(0.3)\n",
    "\n",
    "def pct40(column):\n",
    "    return column.quantile(0.4)\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "print(df['height_cn'].agg([pct30, pct40]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192fc1bd-3b65-4a76-b12c-a10ab21b5a9b",
   "metadata": {},
   "source": [
    "## Mean and median\n",
    "\n",
    "Summary statistics are exactly what they sound like - they summarize many numbers in one statistic. For example, mean, median, minimum, maximum, and standard deviation are summary statistics. Calculating summary statistics allows you to get a better sense of your data, even if there's a lot of it.\n",
    "\n",
    "sales is available and pandas is loaded as pd.\n",
    "Instructions\n",
    "70 XP\n",
    "\n",
    "    Explore your new DataFrame first by printing the first few rows of the sales DataFrame.\n",
    "    Print information about the columns in sales.\n",
    "    Print the mean of the weekly_sales column.\n",
    "    Print the median of the weekly_sales column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "760d77ac-c847-4cd9-a013-ffa1a8adb689",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   store type  department        date  weekly_sales  is_holiday  \\\n",
      "0      1    A           1  2010-02-05      24924.50       False   \n",
      "1      1    A           1  2010-03-05      21827.90       False   \n",
      "2      1    A           1  2010-04-02      57258.43       False   \n",
      "3      1    A           1  2010-05-07      17413.94       False   \n",
      "4      1    A           1  2010-06-04      17558.09       False   \n",
      "\n",
      "   temperature_c  fuel_price_usd_per_l  unemployment  \n",
      "0       5.727778              0.679451         8.106  \n",
      "1       8.055556              0.693452         8.106  \n",
      "2      16.816667              0.718284         7.808  \n",
      "3      22.527778              0.748928         7.808  \n",
      "4      27.050000              0.714586         7.808  \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10774 entries, 0 to 10773\n",
      "Data columns (total 9 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   store                 10774 non-null  int64  \n",
      " 1   type                  10774 non-null  object \n",
      " 2   department            10774 non-null  int64  \n",
      " 3   date                  10774 non-null  object \n",
      " 4   weekly_sales          10774 non-null  float64\n",
      " 5   is_holiday            10774 non-null  bool   \n",
      " 6   temperature_c         10774 non-null  float64\n",
      " 7   fuel_price_usd_per_l  10774 non-null  float64\n",
      " 8   unemployment          10774 non-null  float64\n",
      "dtypes: bool(1), float64(4), int64(2), object(2)\n",
      "memory usage: 768.1+ KB\n",
      "None\n",
      "\n",
      "23843.95014850566\n",
      "12049.064999999999\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "sales = pd.read_csv('sales_subset.csv', index_col=0)\n",
    "#print(sales.head())\n",
    "\n",
    "\n",
    "# Print the head of the sales DataFrame\n",
    "print(sales.head())\n",
    "print()\n",
    "\n",
    "# Print the info about the sales DataFrame\n",
    "print(sales.info())\n",
    "print()\n",
    "\n",
    "# Print the mean of weekly_sales\n",
    "print(sales['weekly_sales'].mean())\n",
    "\n",
    "# Print the median of weekly_sales\n",
    "print(sales['weekly_sales'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3c24ce-1093-404d-9559-bd85c242bc45",
   "metadata": {},
   "source": [
    "## Summarizing dates\n",
    "\n",
    "Summary statistics can also be calculated on date columns that have values with the data type datetime64. Some summary statistics — like mean — don't make a ton of sense on dates, but others are super helpful, for example, minimum and maximum, which allow you to see what time range your data covers.\n",
    "\n",
    "sales is available and pandas is loaded as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Print the maximum of the date column.\n",
    "    Print the minimum of the date column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7883e86f-1746-4cf8-b48e-08fee653b9bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-10-26\n",
      "2010-02-05\n"
     ]
    }
   ],
   "source": [
    "# Print the maximum of the date column\n",
    "print(sales['date'].max())\n",
    "\n",
    "# Print the minimum of the date column\n",
    "print(sales['date'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b650b3f5-2ca0-452b-8734-b2acb33d1755",
   "metadata": {},
   "source": [
    "## Efficient summaries\n",
    "\n",
    "While pandas and NumPy have tons of functions, sometimes, you may need a different function to summarize your data.\n",
    "\n",
    "The .agg() method allows you to apply your own custom functions to a DataFrame, as well as apply functions to more than one column of a DataFrame at once, making your aggregations super-efficient. For example,\n",
    "\n",
    "df['column'].agg(function)\n",
    "\n",
    "In the custom function for this exercise, \"IQR\" is short for inter-quartile range, which is the 75th percentile minus the 25th percentile. It's an alternative to standard deviation that is helpful if your data contains outliers.\n",
    "\n",
    "sales is available and pandas is loaded as pd.\n",
    "Instructions 1/3\n",
    "35 XP\n",
    "\n",
    "    Question 1\n",
    "    Use the custom iqr function defined for you along with .agg() to print the IQR of the temperature_c column of sales.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    Update the column selection to use the custom iqr function with .agg() to print the IQR of temperature_c, fuel_price_usd_per_l, and unemployment, in that order.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 3\n",
    "    Update the aggregation functions called by .agg(): include iqr and np.median in that order.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "de86d794-ea49-4de9-9798-bad292aee150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.583333333333336\n"
     ]
    }
   ],
   "source": [
    "# A custom IQR function\n",
    "def iqr(column):\n",
    "    return column.quantile(0.75) - column.quantile(0.25)\n",
    "    \n",
    "# Print IQR of the temperature_c column\n",
    "print(sales['temperature_c'].agg(iqr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a451cc41-4bc5-4633-abe9-7b1f7f220854",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temperature_c           16.583333\n",
      "fuel_price_usd_per_l     0.073176\n",
      "unemployment             0.565000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# A custom IQR function\n",
    "def iqr(column):\n",
    "    return column.quantile(0.75) - column.quantile(0.25)\n",
    "\n",
    "\n",
    "# Print IQR of the temperature_c column\n",
    "print(sales[['temperature_c', 'fuel_price_usd_per_l', 'unemployment']].agg(iqr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "016c288f-f792-44af-89ef-e75bfcdd9c6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        temperature_c  fuel_price_usd_per_l  unemployment\n",
      "iqr         16.583333              0.073176         0.565\n",
      "median      16.966667              0.743381         8.099\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "# A custom IQR function\n",
    "def iqr(column):\n",
    "    return column.quantile(0.75) - column.quantile(0.25)\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "# Print IQR of the temperature_c column\n",
    "print(sales[['temperature_c', 'fuel_price_usd_per_l', 'unemployment']].agg([iqr, np.median]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65983e2-30f0-4652-8701-524853ab3735",
   "metadata": {},
   "source": [
    "## Cumulative statistics\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Cumulative statistics can also be helpful in tracking summary statistics over time. In this exercise, you'll calculate the cumulative sum and cumulative max of a department's weekly sales, which will allow you to identify what the total sales were so far as well as what the highest weekly sales were so far.\n",
    "\n",
    "A DataFrame called sales_1_1 has been created for you, which contains the sales data for department 1 of store 1. pandas is loaded as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Sort the rows of sales_1_1 by the date column in ascending order.\n",
    "    Get the cumulative sum of weekly_sales and add it as a new column of sales_1_1 called cum_weekly_sales.\n",
    "    Get the cumulative maximum of weekly_sales, and add it as a column called cum_max_sales.\n",
    "    Print the date, weekly_sales, cum_weekly_sales, and cum_max_sales columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "86b18a22-b6d7-4ea7-8e77-506824b6d37e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    store type  department        date  weekly_sales  is_holiday  \\\n",
      "0       1    A           1  2010-02-05      24924.50       False   \n",
      "1       1    A           1  2010-03-05      21827.90       False   \n",
      "2       1    A           1  2010-04-02      57258.43       False   \n",
      "3       1    A           1  2010-05-07      17413.94       False   \n",
      "4       1    A           1  2010-06-04      17558.09       False   \n",
      "5       1    A           1  2010-07-02      16333.14       False   \n",
      "6       1    A           1  2010-08-06      17508.41       False   \n",
      "7       1    A           1  2010-09-03      16241.78       False   \n",
      "8       1    A           1  2010-10-01      20094.19       False   \n",
      "9       1    A           1  2010-11-05      34238.88       False   \n",
      "10      1    A           1  2010-12-03      22517.56       False   \n",
      "11      1    A           1  2011-01-07      15984.24       False   \n",
      "\n",
      "    temperature_c  fuel_price_usd_per_l  unemployment  \n",
      "0        5.727778              0.679451         8.106  \n",
      "1        8.055556              0.693452         8.106  \n",
      "2       16.816667              0.718284         7.808  \n",
      "3       22.527778              0.748928         7.808  \n",
      "4       27.050000              0.714586         7.808  \n",
      "5       27.172222              0.705076         7.787  \n",
      "6       30.644444              0.693980         7.787  \n",
      "7       27.338889              0.680772         7.787  \n",
      "8       22.161111              0.687640         7.838  \n",
      "9       14.855556              0.710359         7.838  \n",
      "10       9.594444              0.715378         7.838  \n",
      "11       9.038889              0.786176         7.742  \n",
      "\n",
      "          date  weekly_sales  cum_weekly_sales  cum_max_sales\n",
      "0   2010-02-05      24924.50          24924.50       24924.50\n",
      "1   2010-03-05      21827.90          46752.40       24924.50\n",
      "2   2010-04-02      57258.43         104010.83       57258.43\n",
      "3   2010-05-07      17413.94         121424.77       57258.43\n",
      "4   2010-06-04      17558.09         138982.86       57258.43\n",
      "5   2010-07-02      16333.14         155316.00       57258.43\n",
      "6   2010-08-06      17508.41         172824.41       57258.43\n",
      "7   2010-09-03      16241.78         189066.19       57258.43\n",
      "8   2010-10-01      20094.19         209160.38       57258.43\n",
      "9   2010-11-05      34238.88         243399.26       57258.43\n",
      "10  2010-12-03      22517.56         265916.82       57258.43\n",
      "11  2011-01-07      15984.24         281901.06       57258.43\n"
     ]
    }
   ],
   "source": [
    "#####################################################################################################################\n",
    "#####################################################################################################################\n",
    "sales_1_1 = sales[(sales['store']==1) & (sales['department']==1)]\n",
    "\n",
    "print(sales_1_1)\n",
    "print()\n",
    "\n",
    "\n",
    "# Sort sales_1_1 by date\n",
    "sales_1_1 = sales_1_1.sort_values('date', ascending=True)\n",
    "\n",
    "# Get the cumulative sum of weekly_sales, add as cum_weekly_sales col\n",
    "sales_1_1['cum_weekly_sales'] = sales_1_1['weekly_sales'].cumsum()\n",
    "\n",
    "# Get the cumulative max of weekly_sales, add as cum_max_sales col\n",
    "sales_1_1['cum_max_sales'] = sales_1_1['weekly_sales'].cummax()\n",
    "\n",
    "# See the columns you calculated\n",
    "print(sales_1_1[[\"date\", \"weekly_sales\", \"cum_weekly_sales\", \"cum_max_sales\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ffef7b1-6b2b-4e56-8f1f-38d0101efaa7",
   "metadata": {},
   "source": [
    "## Counting\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "So far in this chapter, you've learned how to summarize numeric variables.  In this video you'll learn how to summarize categorical data using counting.  Counting dogs is no easy task when they're running around the park, its hard to keep track of who you have and havent counted.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# Here is a DataFrame that contains vet visits, the vet's office want to know how many dogs of each breed have visited their office.  However some dogs have been to the vet more than once, like Max and Stella, so we can't count the number of each breed in the breed column.  \n",
    "\n",
    "**Lets try to fix this by removing rows that contain a dog name already listed earlier in the dataset, or in other words, we'll extract a dog with each name from the dataset once.  We can do this using .drop_duplicates() method, it takes an argument - subset=, which is the column we want to find our duplicates based on.  Now we have a list of dogs where each one apperas once.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**We have Max the Chow Chow, but where did Max the Labrador go?  Because we have two different dogs with the same name, we'll need to consider more than just name when droping duplicates.  Since Max the Chow Chow and Max the Labrador are different breed, we can drop the rows with pairs of name and breed we listed earlier in the dataset.  To base our duplicate dropping on multiple columns, we can pass a list of column names to the subset= argument, now both Maxes have been included and we can start counting.  \n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**To count the dogs of each breed, we'll subset the breed column and use the .value_counts() method.  We can also use the sort= argument to get the breeds with biggest counts on top.  Also the normalize= argument can be used to turn the counts into proportions of the total.\n",
    "\n",
    "\n",
    "\n",
    "print(vet_visits)\n",
    "-----------------------------------------------------\n",
    "date          | name      | breed       | weight_kg\n",
    "2018-09-02    | Bella     | Labrador    | 24.87\n",
    "2019-06-07    | Max       | Labrador    | 28.35\n",
    "2018-01-17    | Stella    | Chihuahua   | 1.51\n",
    "2019-10-19    | Lucy      | Chow Chow   | 24.07\n",
    "......\n",
    "2018-01-20    | Stella    | Chihuahua   | 2.83\n",
    "2019-06-07    | Max       | Chow Chow   | 24.01\n",
    "2018-08-20    | Lucy      | Chow Chow   | 24.40\n",
    "2019-04-22    | Max       | Labrador    | 28.54\n",
    "\n",
    "\n",
    "vet_visits.drop_duplicates(subset='name')\n",
    "-----------------------------------------------------\n",
    "date          | name      | breed       | weight_kg\n",
    "2018-09-02    | Bella     | Labrador    | 24.87\n",
    "2019-06-07    | Max       | Chow Chow   | 24.01\n",
    "2019-03-19    | Charlie   | Poodle      | 24.95\n",
    "2018-01-17    | Stella    | Chihuahua   | 1.51\n",
    "2019-10-19    | Lucy      | Chow Chow   | 24.07\n",
    "2019-03-30    | Cooper    | Schnauzer   | 16.91\n",
    "2019-01-04    | Bernie    | St. Bernard | 74.98\n",
    "\n",
    "\n",
    "vet_visits.drop_duplicates(['name', 'breed'])\n",
    "-----------------------------------------------------\n",
    "date          | name      | breed       | weight_kg\n",
    "2018-09-02    | Bella     | Labrador    | 24.87\n",
    "2019-03-13    | Max       | Chow Chow   | 24.13\n",
    "2019-03-19    | Charlie   | Poodle      | 24.95\n",
    "2018-01-17    | Stella    | Chihuahua   | 1.51\n",
    "2019-10-19    | Lucy      | Chow Chow   | 24.07\n",
    "2019-06-07    | Max       | Chow Chow   | 24.01\n",
    "2019-03-30    | Cooper    | Schnauzer   | 16.91\n",
    "2019-01-04    | Bernie    | St. Bernard | 74.98\n",
    "\n",
    "\n",
    "unique_dogs['breed'].value_counts(sort=True)\n",
    "----------------------------------------------------\n",
    "Labrador      2\n",
    "Chow Chow     2\n",
    "Schnauzer     1\n",
    "St. Bernard   1\n",
    "Poodle        1\n",
    "Chihuahua     1\n",
    "Name: breed, dtype: int64\n",
    "\n",
    "\n",
    "unique_dogs['breed'].value_counts(sort=True, normalize=True)\n",
    "----------------------------------------------------\n",
    "Labrador      0.250\n",
    "Chow Chow     0.250\n",
    "Schnauzer     0.125\n",
    "St. Bernard   0.125\n",
    "Poodle        0.125\n",
    "Chihuahua     0.125\n",
    "Name: breed, dtype: int64\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ff945b-7d2a-4d89-9a41-aa16df665557",
   "metadata": {},
   "source": [
    "## Dropping duplicates\n",
    "\n",
    "Removing duplicates is an essential skill to get accurate counts because often, you don't want to count the same thing multiple times. In this exercise, you'll create some new DataFrames using unique values from sales.\n",
    "\n",
    "sales is available and pandas is imported as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Remove rows of sales with duplicate pairs of store and type and save as store_types and print the head.\n",
    "    Remove rows of sales with duplicate pairs of store and department and save as store_depts and print the head.\n",
    "    Subset the rows that are holiday weeks using the is_holiday column, and drop the duplicate dates, saving as holiday_dates.\n",
    "    Select the date column of holiday_dates, and print.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5d6698e-c4e9-4cf2-b630-c1958f2c1107",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      store type  department        date  weekly_sales  is_holiday  \\\n",
      "0         1    A           1  2010-02-05      24924.50       False   \n",
      "901       2    A           1  2010-02-05      35034.06       False   \n",
      "1798      4    A           1  2010-02-05      38724.42       False   \n",
      "2699      6    A           1  2010-02-05      25619.00       False   \n",
      "3593     10    B           1  2010-02-05      40212.84       False   \n",
      "4495     13    A           1  2010-02-05      46761.90       False   \n",
      "5408     14    A           1  2010-02-05      32842.31       False   \n",
      "6293     19    A           1  2010-02-05      21500.58       False   \n",
      "7199     20    A           1  2010-02-05      46021.21       False   \n",
      "8109     27    A           1  2010-02-05      32313.79       False   \n",
      "9009     31    A           1  2010-02-05      18187.71       False   \n",
      "9899     39    A           1  2010-02-05      21244.50       False   \n",
      "\n",
      "      temperature_c  fuel_price_usd_per_l  unemployment  \n",
      "0          5.727778              0.679451         8.106  \n",
      "901        4.550000              0.679451         8.324  \n",
      "1798       6.533333              0.686319         8.623  \n",
      "2699       4.683333              0.679451         7.259  \n",
      "3593      12.411111              0.782478         9.765  \n",
      "4495      -0.261111              0.704283         8.316  \n",
      "5408      -2.605556              0.735455         8.992  \n",
      "6293      -6.133333              0.780365         8.350  \n",
      "7199      -3.377778              0.735455         8.187  \n",
      "8109      -2.672222              0.780365         8.237  \n",
      "9009       3.916667              0.679451         8.324  \n",
      "9899       6.833333              0.679451         8.554  \n",
      "       store type  department        date  weekly_sales  is_holiday  \\\n",
      "0          1    A           1  2010-02-05      24924.50       False   \n",
      "12         1    A           2  2010-02-05      50605.27       False   \n",
      "24         1    A           3  2010-02-05      13740.12       False   \n",
      "36         1    A           4  2010-02-05      39954.04       False   \n",
      "48         1    A           5  2010-02-05      32229.38       False   \n",
      "...      ...  ...         ...         ...           ...         ...   \n",
      "10715     39    A          95  2010-02-05      88385.24       False   \n",
      "10727     39    A          96  2010-02-05      21450.05       False   \n",
      "10739     39    A          97  2010-02-05      21162.05       False   \n",
      "10751     39    A          98  2010-02-05       9023.09       False   \n",
      "10763     39    A          99  2010-03-05          0.01       False   \n",
      "\n",
      "       temperature_c  fuel_price_usd_per_l  unemployment  \n",
      "0           5.727778              0.679451         8.106  \n",
      "12          5.727778              0.679451         8.106  \n",
      "24          5.727778              0.679451         8.106  \n",
      "36          5.727778              0.679451         8.106  \n",
      "48          5.727778              0.679451         8.106  \n",
      "...              ...                   ...           ...  \n",
      "10715       6.833333              0.679451         8.554  \n",
      "10727       6.833333              0.679451         8.554  \n",
      "10739       6.833333              0.679451         8.554  \n",
      "10751       6.833333              0.679451         8.554  \n",
      "10763      10.516667              0.693452         8.554  \n",
      "\n",
      "[929 rows x 9 columns]\n",
      "\n",
      "<class 'numpy.bool_'>\n",
      "\n",
      "      store type  department        date  weekly_sales  is_holiday  \\\n",
      "498       1    A          45  2010-09-10         11.47        True   \n",
      "691       1    A          77  2011-11-25       1431.00        True   \n",
      "2315      4    A          47  2010-02-12        498.00        True   \n",
      "6735     19    A          39  2012-09-07         13.41        True   \n",
      "6810     19    A          47  2010-12-31       -449.00        True   \n",
      "\n",
      "      temperature_c  fuel_price_usd_per_l  unemployment  \n",
      "498       25.938889              0.677602         7.787  \n",
      "691       15.633333              0.854861         7.866  \n",
      "2315      -1.755556              0.679715         8.623  \n",
      "6735      22.333333              1.076766         8.193  \n",
      "6810      -1.861111              0.881278         8.067  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "sales = pd.read_csv('sales_subset.csv', index_col=0)\n",
    "\n",
    "\n",
    "# Drop duplicate store/type combinations\n",
    "store_types = sales.drop_duplicates(subset=['store', 'type'])\n",
    "print(store_types)\n",
    "\n",
    "# Drop duplicate store/department combinations\n",
    "store_depts = sales.drop_duplicates(subset=['store', 'department'])\n",
    "print(store_depts)\n",
    "print()\n",
    "\n",
    "# Subset the rows where is_holiday is True and drop duplicate dates\n",
    "holiday_dates = sales[sales['is_holiday']==True].drop_duplicates(subset='date')\n",
    "#####################################################################################################################\n",
    "\n",
    "print(type(sales['is_holiday'][0]))\n",
    "print()\n",
    "\n",
    "# Print date col of holiday_dates\n",
    "print(holiday_dates.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58e7bea-7252-4a20-b560-833a6de7ae50",
   "metadata": {},
   "source": [
    "## Counting categorical variables\n",
    "\n",
    "Counting is a great way to get an overview of your data and to spot curiosities that you might not notice otherwise. In this exercise, you'll count the number of each type of store and the number of each department number using the DataFrames you created in the previous exercise:\n",
    "\n",
    "# Drop duplicate store/type combinations\n",
    "store_types = sales.drop_duplicates(subset=[\"store\", \"type\"])\n",
    "\n",
    "# Drop duplicate store/department combinations\n",
    "store_depts = sales.drop_duplicates(subset=[\"store\", \"department\"])\n",
    "\n",
    "The store_types and store_depts DataFrames you created in the last exercise are available, and pandas is imported as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "#    Count the number of stores of each store type in store_types.\n",
    "    Count the proportion of stores of each store type in store_types.\n",
    "#    Count the number of different departments in store_depts, sorting the counts in descending order.\n",
    "    Count the proportion of different departments in store_depts, sorting the proportions in descending order.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6d937569-2bf7-4306-86c9-0397e733e7b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    11\n",
      "B     1\n",
      "Name: type, dtype: int64\n",
      "A    0.916667\n",
      "B    0.083333\n",
      "Name: type, dtype: float64\n",
      "1     12\n",
      "55    12\n",
      "72    12\n",
      "71    12\n",
      "67    12\n",
      "      ..\n",
      "37    10\n",
      "48     8\n",
      "50     6\n",
      "39     4\n",
      "43     2\n",
      "Name: department, Length: 80, dtype: int64\n",
      "1     0.012917\n",
      "55    0.012917\n",
      "72    0.012917\n",
      "71    0.012917\n",
      "67    0.012917\n",
      "        ...   \n",
      "37    0.010764\n",
      "48    0.008611\n",
      "50    0.006459\n",
      "39    0.004306\n",
      "43    0.002153\n",
      "Name: department, Length: 80, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "sales = pd.read_csv('sales_subset.csv', index_col=0)\n",
    "\n",
    "\n",
    "store_types = sales.drop_duplicates(subset=['store', 'type'])\n",
    "#####################################################################################################################\n",
    "store_depts = sales.drop_duplicates(subset=['store', 'department'])\n",
    "\n",
    "\n",
    "store_counts = store_types['type'].value_counts()\n",
    "print(store_counts)\n",
    "\n",
    "store_props = store_types['type'].value_counts(normalize=True)\n",
    "print(store_props)\n",
    "\n",
    "dept_counts_sorted = store_depts['department'].value_counts(sort=True)\n",
    "print(dept_counts_sorted)\n",
    "\n",
    "dept_props_sorted = store_depts['department'].value_counts(sort=True, normalize=True)\n",
    "print(dept_props_sorted)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5773bc5-7433-4a09-bad3-b0f1dab886f7",
   "metadata": {},
   "source": [
    "## Grouped summary statistics\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**So far, you've been calculating summary statistics fro all rows of a dataset, but summary statistics can be useful to compare different groups.  While computing summary statistics of entire columns may be useful, you can gain many insights from summaries of individual groups.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**For example, does one color of dog weight more than another on average?  Are female dogs taller than males?  You can already answer these questions with what you've learned so far.  \n",
    "\n",
    "**We can subset the dogs into groups based on their color, and take the mean of each.  But thats a lot of work, and the duplicated code means you can easily introduce copy and paste bugs.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**Thats where the .groupby() method comes in.  \n",
    "\n",
    "We can group by the color variable, select the weight column and take the mean.  This will give us the mean weight for each dog color.  This was just one line of code compared to the five we had to write before to get the same results.  Just like with ungrouped summary statistics, we can use the .agg() method to get multiple statistics.  \n",
    "**You can also group by multiple columns and calculate summary statistics.  Here we group by color and breed, select the weight column and take the mean.  \n",
    "**You can also group by multiple columns and aggregate by multiple columns.  \n",
    "\n",
    "\n",
    "dogs[dogs['color']=='black']['weight_kg'].mean()\n",
    "dogs[dogs['color']=='brown']['weight_kg'].mean()\n",
    "dogs[dogs['color']=='Tan']['weight_kg'].mean()\n",
    ".........\n",
    "\n",
    "\n",
    "dogs.groupby('color')['weight_kg'].mean()\n",
    "\n",
    "\n",
    "dogs.groupby('color')['weight_kg'].agg([np.mean, np.median, pd.Series.quantile(0.7)])\n",
    "\n",
    "\n",
    "dogs.groupby(['color', 'breed'])['weight_kg'].mean()\n",
    "\n",
    "\n",
    "dogs.groupby(['color', 'breed'])[['weight_kg', 'height_cm']].mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8205e6f6-abfa-4922-ae26-c8edfa8ac4e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name      breed  color  height_cn  weight_km data_of_birth\n",
      "0    Bella   Labrador  Brown         56         24    2013-07-01\n",
      "1  Charlie     Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy  Chow Chow  Brown         46         24    2014-08-25\n",
      "3   Cooper  Schnauzer   Gray         49         17    2011-12-11\n",
      "4      Max   Labrador  Black         59         29    2017-01-20\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>var</th>\n",
       "      <th>quantile75</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>26.5</td>\n",
       "      <td>26.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>27.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brown</th>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gray</th>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tan</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>74.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean  median   var  quantile75\n",
       "color                                \n",
       "Black  26.5    26.5  12.5       27.75\n",
       "Brown  24.0    24.0   0.0       24.00\n",
       "Gray   17.0    17.0   NaN       17.00\n",
       "Tan     2.0     2.0   NaN        2.00\n",
       "White  74.0    74.0   NaN       74.00"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "dogs = pd.read_csv('dogs2.csv')\n",
    "print(dogs.head())\n",
    "\n",
    "\n",
    "def quantile75(column):\n",
    "    return column.quantile(0.75)\n",
    "\n",
    "                                                                 ## pd.Series.quantile(0.75) approach failed\n",
    "dogs.groupby('color')['weight_km'].agg([np.mean, np.median, np.var, quantile75])\n",
    "#dogs.groupby('color')['weight_km'].agg([np.mean, np.median, np.var, np.quantile(self, 0.75)])   ### Is a NP function\n",
    "#####################################################################################################################\n",
    "# The purpoise of quantiles?  So we can imagine the destribution curve??\n",
    "\n",
    "\n",
    "#print(dir(pd.Series))   # quantile is included\n",
    "\n",
    "#print(help(pd.Series.quantile))\n",
    "\n",
    "#print(help(np.quantile))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "164e88ba-0726-4220-b6cd-e89e0da0e0e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.5\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "our_data = np.array([1, 2, 3, 4, 5, 6, 7])\n",
    "\n",
    "#print(our_data.quantile(0.75))  # AttributeError: 'numpy.ndarray' object has no attribute 'quantile'\n",
    "#####################################################################################################################\n",
    "print(np.quantile(our_data, 0.75))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24e6a96-0bde-45a8-a39a-9eb3e67999e1",
   "metadata": {},
   "source": [
    "## What percent of sales occurred at each store type?\n",
    "\n",
    "While .groupby() is useful, you can calculate grouped summary statistics without it.\n",
    "\n",
    "Walmart distinguishes three types of stores: \"supercenters,\" \"discount stores,\" and \"neighborhood markets,\" encoded in this dataset as type \"A,\" \"B,\" and \"C.\" In this exercise, you'll calculate the total sales made at each store type, without using .groupby(). You can then use these numbers to see what proportion of Walmart's total sales were made at each type.\n",
    "\n",
    "sales is available and pandas is imported as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Calculate the total weekly_sales over the whole dataset.\n",
    "    Subset for type \"A\" stores, and calculate their total weekly sales.\n",
    "    Do the same for type \"B\" and type \"C\" stores.\n",
    "    Combine the A/B/C results into a list, and divide by sales_all to get the proportion of sales by type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3d81865f-2ad1-45f2-ac71-bc401a703e3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256894718.89999998\n",
      "<class 'list'>\n",
      "<class 'numpy.ndarray'>\n",
      "[0.9097747 0.0902253 0.       ]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "sales = pd.read_csv('sales_subset.csv')\n",
    "#print(sales.head())\n",
    "\n",
    "\n",
    "# Calc total weekly sales\n",
    "sales_all = sales['weekly_sales'].sum()\n",
    "print(sales_all)\n",
    "\n",
    "# Subset for type A stores, calc total weekly sales\n",
    "sales_A = sales[sales['type']=='A']['weekly_sales'].sum()\n",
    "\n",
    "# Subset for type B stores, calc total weekly sales\n",
    "sales_B = sales[sales['type']=='B']['weekly_sales'].sum()\n",
    "\n",
    "# Subset for type C stores, calc total weekly sales\n",
    "sales_C = sales[sales['type']=='C']['weekly_sales'].sum()\n",
    "\n",
    "# Get proportion for each type\n",
    "sales_propn_by_type = [sales_A, sales_B, sales_C]/sales_all\n",
    "#####################################################################################################################\n",
    "# I thought you put sales_A, sales_B sales_C into a list, it becomes a list object, broadcasting not working anymore\n",
    "\n",
    "question = [sales_A, sales_B, sales_C]\n",
    "print(type(question))  # <class 'list'>\n",
    "#####################################################################################################################\n",
    "print(type(sales_propn_by_type))    # So NumPy Array infected the list into working broadcasting?  Very Interesting\n",
    "\n",
    "\n",
    "print(sales_propn_by_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0902b712-ff6e-4df8-9691-043596b38c51",
   "metadata": {},
   "source": [
    "## Calculations with .groupby()\n",
    "\n",
    "The .groupby() method makes life much easier. In this exercise, you'll perform the same calculations as last time, except you'll use the .groupby() method. You'll also perform calculations on data grouped by two variables to see if sales differ by store type depending on if it's a holiday week or not.\n",
    "\n",
    "sales is available and pandas is loaded as pd.\n",
    "Instructions 1/2\n",
    "50 XP\n",
    "\n",
    "    Question 1\n",
    "    Group sales by \"type\", take the sum of \"weekly_sales\", and store as sales_by_type.\n",
    "    Calculate the proportion of sales at each store type by dividing by the sum of sales_by_type. Assign to sales_propn_by_type.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "#    Group sales by \"type\" and \"is_holiday\", take the sum of weekly_sales, and store as sales_by_type_is_holiday\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "48534ca5-c702-43ac-b3d6-9562915d5f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type\n",
      "A    0.909775\n",
      "B    0.090225\n",
      "Name: weekly_sales, dtype: float64\n",
      "type  is_holiday\n",
      "A     False         2.336927e+08\n",
      "      True          2.360181e+04\n",
      "B     False         2.317678e+07\n",
      "      True          1.621410e+03\n",
      "Name: weekly_sales, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "sales = pd.read_csv('sales_subset.csv')\n",
    "#print(sales.head())\n",
    "\n",
    "# Group by type; calc total weekly sales\n",
    "sales_by_type = sales.groupby('type')['weekly_sales'].sum()\n",
    "\n",
    "\n",
    "# Get proportion for each type\n",
    "sales_propn_by_type = sales_by_type/sum(sales_by_type)\n",
    "print(sales_propn_by_type)\n",
    "\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "#####################################################################################################################\n",
    "sales_by_type_is_holiday = sales.groupby(['type', 'is_holiday'])['weekly_sales'].sum()\n",
    "print(sales_by_type_is_holiday)\n",
    "\n",
    "\n",
    "# Think why we want this?  wha's the purpose of it?  \n",
    "#####################################################################################################################\n",
    "# So based on the prospective of Walmart, 1) weekly_sales means a lot for each store, 2) is_holiday had the\n",
    "# huge influence on weekly_sales (like unemployment_rate and fuel_price, those influence people's mind on shoping)\n",
    "# And we also want to know the performance of each type, 'supercenters', 'discount stores', 'neighborhood markets',"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a069ead-21e3-418d-b4cd-e828ec670852",
   "metadata": {},
   "source": [
    "## Multiple grouped summaries\n",
    "\n",
    "Earlier in this chapter, you saw that the .agg() method is useful to compute multiple statistics on multiple variables. It also works with grouped data. NumPy, which is imported as np, has many different summary statistics functions, including: np.min, np.max, np.mean, and np.median.\n",
    "\n",
    "sales is available and pandas is imported as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Import numpy with the alias np.\n",
    "    Get the min, max, mean, and median of weekly_sales for each store type using .groupby() and .agg(). Store this as sales_stats. Make sure to use numpy functions!\n",
    "#    Get the min, max, mean, and median of unemployment and fuel_price_usd_per_l for each store type. Store this as unemp_fuel_stats.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d3485706-d412-4336-bd6a-1f0dbb4f96bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        amin       amax          mean    median\n",
      "type                                           \n",
      "A    -1098.0  293966.05  23674.667242  11943.92\n",
      "B     -798.0  232558.51  25696.678370  13336.08\n",
      "     unemployment                         fuel_price_usd_per_l            \\\n",
      "             amin   amax      mean median                 amin      amax   \n",
      "type                                                                       \n",
      "A           3.879  8.992  7.972611  8.067             0.664129  1.107410   \n",
      "B           7.170  9.765  9.279323  9.199             0.760023  1.107674   \n",
      "\n",
      "                          \n",
      "          mean    median  \n",
      "type                      \n",
      "A     0.744619  0.735455  \n",
      "B     0.805858  0.803348  \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "sales = pd.read_csv('sales_subset.csv')\n",
    "#print(sales.head())\n",
    "\n",
    "\n",
    "sales_stats = sales.groupby('type')['weekly_sales'].agg([np.min, np.max, np.mean, np.median])\n",
    "print(sales_stats)\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "unemp_fuel_stats=sales.groupby('type')[['unemployment','fuel_price_usd_per_l']].agg([np.min,np.max,np.mean,np.median])\n",
    "print(unemp_fuel_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89be9c47-425a-4213-bcfa-c20283c1faee",
   "metadata": {},
   "source": [
    "## Pivot tables\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**Pivot tables are another way of calculating grouped summary statistics.  Lets see how to create pivot tables in Pandas.  In the last lesson, we grouped the dogs by color and calculated their mean weights.  \n",
    "\n",
    "# We can do the same thing using the .pivot_table() method.  \n",
    "# *******************************************************************************************************************\n",
    "The values= argument is the column that you want to summarize, and the \n",
    "index= argument is the column that you want to groupby.  \n",
    "By default, .pivot_table() takes the mean value for each group.  If we want a different summary statistics, we can use the aggfunc= argument and pass it a function.  Here we take the median for each dog color using NumPy's .median() function.  \n",
    "\n",
    "To get multiple summary statistics at a time, we can pass a list of functions into the aggfunc= argument, belwo we get the median and max weight_kg for each colors group.  \n",
    "\n",
    "\n",
    "**We also previously computed the mean weight_kg grouped by two variables, dog color and dog breed.  We can also do this using the .pivot_table() method.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "To group by two variables, we can pass a second variable name into the columns= argument.  While the result looks a little different than what we had before, it contains the same numbers.  There are NaNs, or missing values, because there are no black Chihuahuas or gray Labradors in our dataset, for example.  \n",
    "\n",
    "Instead of having lots of missing values in our pivot table, we can have them filled in using the fill_value=0 argument.  If we set the margins= argument to True, the last row and last column of the pivot_table contain the mean of all the values in the column or row, not including the missing values that were filled with 0s.  The value in the bottom right, in the last row and last column, is the mean weight_kg of all the dogs in our dataset.  \n",
    "\n",
    "Using margins=True allows us to see a summary statistics for multiple levels of dataset: the entire dataset, the grouped by one variable, by another variable, or by two variables. \n",
    "\n",
    "\n",
    "\n",
    "dogs.groupby('color')['weight_kg'].median()\n",
    "\n",
    "dogs.pivot_table(values='weight_kg', index='colors')\n",
    "\n",
    "dogs.pivot_table(values='weight_kg', index='colors', aggfunc=np.median)\n",
    "\n",
    "dogs.pivot_table(values='weight_kg', index='colors', aggfunc=[np.median, np.max])\n",
    "\n",
    "\n",
    "dog.groupby(['color', 'breed'])['weight_kg'].mean()\n",
    "\n",
    "dog.pivot_table(values=['weight_kg', 'height_cm'], index='color', columns='breed', aggfunc=[np.median, np.max])\n",
    "\n",
    "dog.pivot_table(values=['weight_kg', 'height_cm'], index='color', columns='breed', fill_value=0, aggfunc=[np.median, np.max])\n",
    "\n",
    "dog.pivot_table(values=['weight_kg', 'height_cm'], index='color', columns='breed', fill_value=0, margins=True)\n",
    "\n",
    "\n",
    "\n",
    "dog.pivot_table(values=['weight_kg', 'height_cm'], index=['color', 'breed'], aggfunc=[np.median, np.max])\n",
    "## Do Not Use This One, cause two level of indexing makes it very hard to read on the scream\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ee5e7cf0-a97a-48dc-8a04-faecb6f9e281",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name      breed  color  height_cn  weight_km data_of_birth\n",
      "0    Bella   Labrador  Brown         56         24    2013-07-01\n",
      "1  Charlie     Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy  Chow Chow  Brown         46         24    2014-08-25\n",
      "3   Cooper  Schnauzer   Gray         49         17    2011-12-11\n",
      "4      Max   Labrador  Black         59         29    2017-01-20\n",
      "                    median                amax          \n",
      "                 height_cn weight_km height_cn weight_km\n",
      "color breed                                             \n",
      "Black Labrador          59        29        59        29\n",
      "      Poodle            43        24        43        24\n",
      "Brown Chow Chow         46        24        46        24\n",
      "      Labrador          56        24        56        24\n",
      "Gray  Schnauzer         49        17        49        17\n",
      "Tan   Chihuahua         18         2        18         2\n",
      "White St.Bernard        77        74        77        74\n",
      "         median                                                           \\\n",
      "      height_cn                                                weight_km   \n",
      "breed Chihuahua Chow Chow Labrador Poodle Schnauzer St.Bernard Chihuahua   \n",
      "color                                                                      \n",
      "Black       NaN       NaN     59.0   43.0       NaN        NaN       NaN   \n",
      "Brown       NaN      46.0     56.0    NaN       NaN        NaN       NaN   \n",
      "Gray        NaN       NaN      NaN    NaN      49.0        NaN       NaN   \n",
      "Tan        18.0       NaN      NaN    NaN       NaN        NaN       2.0   \n",
      "White       NaN       NaN      NaN    NaN       NaN       77.0       NaN   \n",
      "\n",
      "                                 ...      amax                              \\\n",
      "                                 ... height_cn                               \n",
      "breed Chow Chow Labrador Poodle  ...  Labrador Poodle Schnauzer St.Bernard   \n",
      "color                            ...                                         \n",
      "Black       NaN     29.0   24.0  ...      59.0   43.0       NaN        NaN   \n",
      "Brown      24.0     24.0    NaN  ...      56.0    NaN       NaN        NaN   \n",
      "Gray        NaN      NaN    NaN  ...       NaN    NaN      49.0        NaN   \n",
      "Tan         NaN      NaN    NaN  ...       NaN    NaN       NaN        NaN   \n",
      "White       NaN      NaN    NaN  ...       NaN    NaN       NaN       77.0   \n",
      "\n",
      "                                                                \n",
      "      weight_km                                                 \n",
      "breed Chihuahua Chow Chow Labrador Poodle Schnauzer St.Bernard  \n",
      "color                                                           \n",
      "Black       NaN       NaN     29.0   24.0       NaN        NaN  \n",
      "Brown       NaN      24.0     24.0    NaN       NaN        NaN  \n",
      "Gray        NaN       NaN      NaN    NaN      17.0        NaN  \n",
      "Tan         2.0       NaN      NaN    NaN       NaN        NaN  \n",
      "White       NaN       NaN      NaN    NaN       NaN       74.0  \n",
      "\n",
      "[5 rows x 24 columns]\n",
      "      height_cn                                                weight_km  \\\n",
      "breed Chihuahua Chow Chow Labrador Poodle Schnauzer St.Bernard Chihuahua   \n",
      "color                                                                      \n",
      "Black         0         0       59     43         0          0         0   \n",
      "Brown         0        46       56      0         0          0         0   \n",
      "Gray          0         0        0      0        49          0         0   \n",
      "Tan          18         0        0      0         0          0         2   \n",
      "White         0         0        0      0         0         77         0   \n",
      "\n",
      "                                                      \n",
      "breed Chow Chow Labrador Poodle Schnauzer St.Bernard  \n",
      "color                                                 \n",
      "Black         0       29     24         0          0  \n",
      "Brown        24       24      0         0          0  \n",
      "Gray          0        0      0        17          0  \n",
      "Tan           0        0      0         0          0  \n",
      "White         0        0      0         0         74  \n",
      "      height_cn                                                            \\\n",
      "breed Chihuahua Chow Chow Labrador Poodle Schnauzer St.Bernard        All   \n",
      "color                                                                       \n",
      "Black         0         0     59.0     43         0          0  51.000000   \n",
      "Brown         0        46     56.0      0         0          0  51.000000   \n",
      "Gray          0         0      0.0      0        49          0  49.000000   \n",
      "Tan          18         0      0.0      0         0          0  18.000000   \n",
      "White         0         0      0.0      0         0         77  77.000000   \n",
      "All          18        46     57.5     43        49         77  49.714286   \n",
      "\n",
      "      weight_km                                                            \n",
      "breed Chihuahua Chow Chow Labrador Poodle Schnauzer St.Bernard        All  \n",
      "color                                                                      \n",
      "Black         0         0     29.0     24         0          0  26.500000  \n",
      "Brown         0        24     24.0      0         0          0  24.000000  \n",
      "Gray          0         0      0.0      0        17          0  17.000000  \n",
      "Tan           2         0      0.0      0         0          0   2.000000  \n",
      "White         0         0      0.0      0         0         74  74.000000  \n",
      "All           2        24     26.5     24        17         74  27.714286  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "dogs = pd.read_csv('dogs2.csv')\n",
    "print(dogs.head())\n",
    "\n",
    "\n",
    "# One line of code Yes, elegant No\n",
    "#####################################################################################################################\n",
    "print(dogs.pivot_table(values=['weight_km', 'height_cn'], index=['color', 'breed'], aggfunc=[np.median, np.max]))\n",
    "                                                          ####### DO Not Take This Approach\n",
    "                                                          ####### Cause its hard to read infos\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "print(dogs.pivot_table(values=['weight_km','height_cn'], index='color', columns='breed', aggfunc=[np.median,np.max]))\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "print(dogs.pivot_table(values=['weight_km','height_cn'], index='color', columns='breed', fill_value=0))\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "print(dogs.pivot_table(values=['weight_km','height_cn'], index='color', columns='breed', fill_value=0, margins=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c37109f1-d232-4068-b2a2-e76df7c8e46e",
   "metadata": {},
   "source": [
    "## Pivoting on one variable\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# *******************************************************************************************************************\n",
    "# Pivot tables are the standard way of aggregating data in spreadsheets. In pandas, pivot tables are essentially just another way of performing grouped calculations. That is, the .pivot_table() method is just an alternative to .groupby().\n",
    "\n",
    "In this exercise, you'll perform calculations using .pivot_table() to replicate the calculations you performed in the last lesson using .groupby().\n",
    "\n",
    "sales is available and pandas is imported as pd.\n",
    "Instructions 1/3\n",
    "35 XP\n",
    "\n",
    "    Question 1\n",
    "    Get the mean weekly_sales by type using .pivot_table() and store as mean_sales_by_type.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "#    Get the mean and median (using NumPy functions) of weekly_sales by type using .pivot_table() and store as mean_med_sales_by_type.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 3\n",
    "#    Get the mean of weekly_sales by type and is_holiday using .pivot_table() and store as mean_sales_by_type_holiday.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7d187a2a-ba36-4cc0-9593-f8922fc2e901",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  store type  department        date  weekly_sales  is_holiday  \\\n",
      "0           0      1    A           1  2010-02-05      24924.50       False   \n",
      "1           1      1    A           1  2010-03-05      21827.90       False   \n",
      "2           2      1    A           1  2010-04-02      57258.43       False   \n",
      "3           3      1    A           1  2010-05-07      17413.94       False   \n",
      "4           4      1    A           1  2010-06-04      17558.09       False   \n",
      "\n",
      "   temperature_c  fuel_price_usd_per_l  unemployment  \n",
      "0       5.727778              0.679451         8.106  \n",
      "1       8.055556              0.693452         8.106  \n",
      "2      16.816667              0.718284         7.808  \n",
      "3      22.527778              0.748928         7.808  \n",
      "4      27.050000              0.714586         7.808  \n",
      "      weekly_sales\n",
      "type              \n",
      "A     23674.667242\n",
      "B     25696.678370\n",
      "              mean       median\n",
      "      weekly_sales weekly_sales\n",
      "type                           \n",
      "A     23674.667242     11943.92\n",
      "B     25696.678370     13336.08\n",
      "is_holiday         False       True\n",
      "type                               \n",
      "A           23768.583523  590.04525\n",
      "B           25751.980533  810.70500\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "sales = pd.read_csv('sales_subset.csv')\n",
    "print(sales.head())\n",
    "\n",
    "# Pivot for mean weekly_sales for each store type\n",
    "mean_sales_by_type = sales.pivot_table(values='weekly_sales', index='type')\n",
    "                                                                         # .mean() Wrong, Which is correct?  aggfunc\n",
    "#####################################################################################################################\n",
    "\n",
    "\n",
    "# Print mean_sales_by_type\n",
    "print(mean_sales_by_type)\n",
    "\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Pivot for mean and median weekly_sales for each store type\n",
    "mean_med_sales_by_type = sales.pivot_table(values='weekly_sales', \n",
    "                                           index='type', \n",
    "                                           aggfunc=[np.mean, np.median])\n",
    "\n",
    "print(mean_med_sales_by_type )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "mean_sales_by_type_holiday = sales.pivot_table(values='weekly_sales', index='type', columns='is_holiday')\n",
    "print(mean_sales_by_type_holiday)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c25944f-7f61-430d-b3b5-e43ddb2e0c5b",
   "metadata": {},
   "source": [
    "## Fill in missing values and sum values with pivot tables\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# *******************************************************************************************************************\n",
    "# The .pivot_table() method has several useful arguments, including fill_value and margins.\n",
    "\n",
    "    fill_value replaces missing values with a real value (known as imputation). What to replace missing values with is a topic big enough to have its own course (Dealing with Missing Data in Python), but the simplest thing to do is to substitute a dummy value.\n",
    "#    margins is a shortcut for when you pivoted by two variables, but also wanted to pivot by each of those variables separately: it gives the row and column totals of the pivot table contents.\n",
    "\n",
    "In this exercise, you'll practice using these arguments to up your pivot table skills, which will help you crunch numbers more efficiently!\n",
    "\n",
    "sales is available and pandas is imported as pd.\n",
    "Instructions 1/2\n",
    "50 XP\n",
    "\n",
    "    Question 1\n",
    "    Print the mean weekly_sales by department and type, filling in any missing values with 0.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    Print the mean weekly_sales by department and type, filling in any missing values with 0 and summing all rows and columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b10c0a98-cc1f-46d2-af8c-82410ec543ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type                    A              B\n",
      "department                              \n",
      "1            30961.725379   44050.626667\n",
      "2            67600.158788  112958.526667\n",
      "3            17160.002955   30580.655000\n",
      "4            44285.399091   51219.654167\n",
      "5            34821.011364   63236.875000\n",
      "...                   ...            ...\n",
      "95          123933.787121   77082.102500\n",
      "96           21367.042857    9528.538333\n",
      "97           28471.266970    5828.873333\n",
      "98           12875.423182     217.428333\n",
      "99             379.123659       0.000000\n",
      "\n",
      "[80 rows x 2 columns]\n",
      "type                   A              B           All\n",
      "department                                           \n",
      "1           30961.725379   44050.626667  32052.467153\n",
      "2           67600.158788  112958.526667  71380.022778\n",
      "3           17160.002955   30580.655000  18278.390625\n",
      "4           44285.399091   51219.654167  44863.253681\n",
      "5           34821.011364   63236.875000  37189.000000\n",
      "...                  ...            ...           ...\n",
      "96          21367.042857    9528.538333  20337.607681\n",
      "97          28471.266970    5828.873333  26584.400833\n",
      "98          12875.423182     217.428333  11820.590278\n",
      "99            379.123659       0.000000    379.123659\n",
      "All         23674.667242   25696.678370  23843.950149\n",
      "\n",
      "[81 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Print mean weekly_sales by department and type; fill missing values with 0\n",
    "print(sales.pivot_table(values='weekly_sales', index='department', columns='type', fill_value=0))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Print mean weekly_sales by department and type; fill missing values with 0, and summing all rows and columns\n",
    "print(sales.pivot_table(values='weekly_sales', \n",
    "                        index='department', \n",
    "                        columns='type', \n",
    "                        fill_value=0, \n",
    "                        margins=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ade0881-e3fd-43f5-9184-3490a2a6d619",
   "metadata": {},
   "source": [
    "## Explicit indexes\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "In chapter one, we saw that DataFrames are composed of 3 parts: a NumPy array for the data, and two indexes to store the row and column details.  \n",
    "\n",
    "Here is the dogs dataset again, racall that dogs.columns attribute contains an index object of column names, and dogs.index attribute contains an index object of row numbers.  \n",
    "\n",
    "**You can move a column from the body of the DataFrame to be index.  This is called setting an index, and it uses the .set_index() method.  Notice that the output has changed slightly, in particular, a quick visual clue that name is now in the index is that the index values are left-aligned rather than right-aligned.  \n",
    "\n",
    "**To undo what we just did, we can apply the .reset_index() method to the DataFrame.  The reset_index() has a drop= argument that allows you to discard an index.  Here setting drop=True removes the dog names column.  \n",
    "\n",
    "\n",
    "\n",
    "You may be wondering why you should bother with indexes, the answer is that it makes subsetting code cleaner.  \n",
    "# *******************************************************************************************************************\n",
    "# Consider this example of subsetting for the rows where the dogs is called Bella or Stella.  Its a fairly tricky line of code for such a simple task.  Now look at the equivalent when the name column is the index.  \n",
    "\n",
    "DataFrame have a subsetting method called .loc[], which filters on the index values.  Much easier. \n",
    "\n",
    "\n",
    "dogs[dogs['name'].isin(['Bella', 'Stella'])]\n",
    "---------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "new_dogs.loc['Bella', 'Stella']]\n",
    "---------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "The values in the index don't need to be unique, you can set breed as index with two Labrador in it.  Then if we subset on Labrador using .loc[] method, all the Labrador data is retuined.  \n",
    "\n",
    "You can include multiple columns in the index by passing a list of column names to .set_index() method.  These are called multi-level indexes, or hierarchical indexes.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "There is an implication here that the inner level of index, in this case: color, is nested inside the outer level: breed.  To take a subset of rows at the outer level index, you pass a list of index values to .loc[] method.  Here the list contains Labrador and Chihuahua, and the resulting subset contains all dogs from both breeds.  \n",
    "\n",
    "To subset on inner level index, you need to pass a list of tuples.  Here the first tuple specifies Labrador at the outer level of index, and Brown at the inner level.  The resulting rows have to match all conditions from a tuple.  For example the black Labrador wasn't returned because the Brown condition wasn't matched.  \n",
    "\n",
    "\n",
    "\n",
    "new1_dogs = dogs.set_index(['breed', 'color'])\n",
    "\n",
    "\n",
    "new1_dogs.loc(['Labrador', 'Chihuahua'])\n",
    "\n",
    "\n",
    "\n",
    "new1_dogs.loc[('Labrador', 'Brown')]\n",
    "\n",
    "new1_dogs.loc[[('Labrador', 'Brown'), ('Chihuahua', 'Tan')]]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "\n",
    "**In chapter 1, you saw how to sort the rows of a DataFrame using .sort_values() method.  You can also sort by index values using .sort_index() method.  By default, it sorts all index levels from outer to inner, in ascending order.  You can control the sorting by passing lists to the level= and ascending= arguments.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Indexes are controversal.  Although they simplify subsetting sode, there are also some downsides.  Index values are just data, and storing data in multiple forms makes it harder to think aboyt.  \n",
    "\n",
    "There is a concept called tidy data, where data is stored in tabular form - like a DataFrame.  Each row contains a single observation, and each variable is stored in its own column.  Indexes violate the last rule since index values don't get they own column.  \n",
    "\n",
    "In Pandas, the syntax for working with indexes is different from the syntax for working with columns.  By using two syntaxes, your code is more complicated, which can result in more bugs.  \n",
    "\n",
    "If you decide you don't want to use indexes, that's perfectly reasonable.  However, its useful to know how they work for cases when you need to read other people's code.  In this chapter, you'll work with a monthly time series of air temperatures in cities around the world.  \n",
    "\n",
    "\n",
    "\n",
    "new1_dogs.sort_index()\n",
    "\n",
    "\n",
    "new1_dogs.sort_index(level=['color', 'breed'], ascending=[True, False])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "--------------------------------------------------------------\n",
    "  date        | city       | country        | avg_temp_c\n",
    "  2000-01-01  | Abidjan    | Cote D'lvoire  | 27.293\n",
    "  2000-02-01  | Abidjan    | Cote D'lvoire  | 27.685\n",
    "  2000-03-01  | Abidjan    | Cote D'lvoire  | 29.061\n",
    "  2000-04-01  | Abidjan    | Cote D'lvoire  | 28.162\n",
    "  2000-05-01  | Abidjan    | Cote D'lvoire  | 27.547\n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c15b8637-9994-4e0b-bfce-ed5b0c5e36e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             breed  color  height_cn  weight_km data_of_birth\n",
      "name                                                         \n",
      "Bella     Labrador  Brown         56         24    2013-07-01\n",
      "Charlie     Poodle  Black         43         24    2016-09-16\n",
      "Lucy     Chow Chow  Brown         46         24    2014-08-25\n",
      "Cooper   Schnauzer   Gray         49         17    2011-12-11\n",
      "Max       Labrador  Black         59         29    2017-01-20\n",
      "Index(['Bella', 'Charlie', 'Lucy', 'Cooper', 'Max', 'Stella', 'Bernie'], dtype='object', name='name')\n",
      "       breed  color  height_cn  weight_km data_of_birth\n",
      "0   Labrador  Brown         56         24    2013-07-01\n",
      "1     Poodle  Black         43         24    2016-09-16\n",
      "2  Chow Chow  Brown         46         24    2014-08-25\n",
      "3  Schnauzer   Gray         49         17    2011-12-11\n",
      "4   Labrador  Black         59         29    2017-01-20\n",
      "\n",
      "     name      breed  color  height_cn  weight_km data_of_birth\n",
      "0   Bella   Labrador  Brown         56         24    2013-07-01\n",
      "5  Stella  Chihuahua    Tan         18          2    2015-04-20\n",
      "            breed  color  height_cn  weight_km data_of_birth\n",
      "name                                                        \n",
      "Bella    Labrador  Brown         56         24    2013-07-01\n",
      "Stella  Chihuahua    Tan         18          2    2015-04-20\n",
      "\n",
      "                     name  height_cn  weight_km data_of_birth\n",
      "color breed                                                  \n",
      "Brown Labrador      Bella         56         24    2013-07-01\n",
      "Black Poodle      Charlie         43         24    2016-09-16\n",
      "Brown Chow Chow      Lucy         46         24    2014-08-25\n",
      "Gray  Schnauzer    Cooper         49         17    2011-12-11\n",
      "Black Labrador        Max         59         29    2017-01-20\n",
      "Tan   Chihuahua    Stella         18          2    2015-04-20\n",
      "White St.Bernard   Bernie         77         74    2018-02-27\n",
      "\n",
      "name                  Bella\n",
      "height_cn                56\n",
      "weight_km                24\n",
      "data_of_birth    2013-07-01\n",
      "Name: (Brown, Labrador), dtype: object\n",
      "                   name  height_cn  weight_km data_of_birth\n",
      "color breed                                                \n",
      "Brown Labrador    Bella         56         24    2013-07-01\n",
      "Tan   Chihuahua  Stella         18          2    2015-04-20\n",
      "                 name  height_cn  weight_km data_of_birth\n",
      "color breed                                              \n",
      "Brown Labrador  Bella         56         24    2013-07-01\n",
      "Black Labrador    Max         59         29    2017-01-20\n",
      "                     name  height_cn  weight_km data_of_birth\n",
      "color breed                                                  \n",
      "Black Poodle      Charlie         43         24    2016-09-16\n",
      "      Labrador        Max         59         29    2017-01-20\n",
      "Brown Labrador      Bella         56         24    2013-07-01\n",
      "      Chow Chow      Lucy         46         24    2014-08-25\n",
      "Gray  Schnauzer    Cooper         49         17    2011-12-11\n",
      "Tan   Chihuahua    Stella         18          2    2015-04-20\n",
      "White St.Bernard   Bernie         77         74    2018-02-27\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "dogs = pd.read_csv('dogs2.csv')\n",
    "\n",
    "new_dogs = dogs.set_index('name')\n",
    "print(new_dogs.head())\n",
    "print(new_dogs.index)\n",
    "#print(help(pd.DataFrame.set_index))\n",
    "#print(help(dogs.set_index))\n",
    "#####################################################################################################################\n",
    "# I want to remove the first row after the header, the empty value row\n",
    "\n",
    "\n",
    "nn_dogs = new_dogs.reset_index(drop=True)\n",
    "print(nn_dogs.head())\n",
    "print()\n",
    "\n",
    "print(dogs[dogs['name'].isin(['Bella', 'Stella'])])\n",
    "print(new_dogs.loc[['Bella', 'Stella']])\n",
    "\n",
    "\n",
    "\n",
    "print()\n",
    "#####################################################################################################################\n",
    "co_breed = dogs.set_index(['color', 'breed'])\n",
    "print(co_breed)\n",
    "\n",
    "print()\n",
    "print(co_breed.loc['Brown', 'Labrador'])\n",
    "#####################################################################################################################\n",
    "print(co_breed.loc[[('Brown', 'Labrador'), ('Tan', 'Chihuahua')]])    ## Double square brackets\n",
    "print(co_breed.loc[[('Brown', 'Labrador'), ('Black', 'Labrador')]])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(co_breed.sort_index(level=['color', 'breed'], ascending=[True, False]))\n",
    "#####################################################################################################################\n",
    "# Did you niticed? When put two indexes level nested here, it becomes harder for human eyes to read information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9b63333-6210-4407-b229-00dc433f497a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "273a1579-640d-4bd1-bc69-4824547633de",
   "metadata": {},
   "source": [
    "## Setting and removing indexes\n",
    "\n",
    "pandas allows you to designate columns as an index. This enables cleaner code when taking subsets (as well as providing more efficient lookup under some circumstances).\n",
    "\n",
    "In this chapter, you'll be exploring temperatures, a DataFrame of average temperatures in cities around the world. pandas is loaded as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Look at temperatures.\n",
    "    Set the index of temperatures to \"city\", assigning to temperatures_ind.\n",
    "    Look at temperatures_ind. How is it different from temperatures?\n",
    "    Reset the index of temperatures_ind, keeping its contents.\n",
    "    Reset the index of temperatures_ind, dropping its contents.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "675f0f5e-e681-4688-a792-7b98db6cc070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date     city        country  avg_temp_c\n",
      "0  2000-01-01  Abidjan  Côte D'Ivoire      27.293\n",
      "1  2000-02-01  Abidjan  Côte D'Ivoire      27.685\n",
      "2  2000-03-01  Abidjan  Côte D'Ivoire      29.061\n",
      "3  2000-04-01  Abidjan  Côte D'Ivoire      28.162\n",
      "4  2000-05-01  Abidjan  Côte D'Ivoire      27.547\n",
      "               date        country  avg_temp_c\n",
      "city                                          \n",
      "Abidjan  2000-01-01  Côte D'Ivoire      27.293\n",
      "Abidjan  2000-02-01  Côte D'Ivoire      27.685\n",
      "Abidjan  2000-03-01  Côte D'Ivoire      29.061\n",
      "Abidjan  2000-04-01  Côte D'Ivoire      28.162\n",
      "Abidjan  2000-05-01  Côte D'Ivoire      27.547\n",
      "      city        date        country  avg_temp_c\n",
      "0  Abidjan  2000-01-01  Côte D'Ivoire      27.293\n",
      "1  Abidjan  2000-02-01  Côte D'Ivoire      27.685\n",
      "2  Abidjan  2000-03-01  Côte D'Ivoire      29.061\n",
      "3  Abidjan  2000-04-01  Côte D'Ivoire      28.162\n",
      "4  Abidjan  2000-05-01  Côte D'Ivoire      27.547\n",
      "         date        country  avg_temp_c\n",
      "0  2000-01-01  Côte D'Ivoire      27.293\n",
      "1  2000-02-01  Côte D'Ivoire      27.685\n",
      "2  2000-03-01  Côte D'Ivoire      29.061\n",
      "3  2000-04-01  Côte D'Ivoire      28.162\n",
      "4  2000-05-01  Côte D'Ivoire      27.547\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv', index_col=0)\n",
    "#print(temperatures.head())\n",
    "\n",
    "\n",
    "# Look at temperatures\n",
    "print(temperatures.head())\n",
    "\n",
    "# Index temperatures by city\n",
    "temperatures_ind = temperatures.set_index('city')\n",
    "\n",
    "# Look at temperatures_ind\n",
    "print(temperatures_ind.head())\n",
    "\n",
    "# Reset the index, keeping its contents\n",
    "print(temperatures_ind.reset_index().head())\n",
    "\n",
    "# Reset the index, dropping its contents\n",
    "print(temperatures_ind.reset_index(drop=True).head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd2a4ac7-cd27-4cf3-8357-b4b706f8c6e1",
   "metadata": {},
   "source": [
    "## Subsetting with .loc[]\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# The killer feature for indexes is .loc[]: a subsetting method that accepts index values. When you pass it a single argument, it will take a subset of rows.\n",
    "\n",
    "The code for subsetting using .loc[] can be easier to read than standard square bracket subsetting, which can make your code less burdensome to maintain.\n",
    "\n",
    "pandas is loaded as pd. temperatures and temperatures_ind are available; the latter is indexed by city.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Create a list called cities that contains \"Moscow\" and \"Saint Petersburg\".\n",
    "    Use [] subsetting to filter temperatures for rows where the city column takes a value in the cities list.\n",
    "    Use .loc[] subsetting to filter temperatures_ind for rows where the city is in the cities list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b77c3d01-a20e-414e-b1fe-fe4a69b5a35f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             date              city country  avg_temp_c\n",
      "10725  2000-01-01            Moscow  Russia      -7.313\n",
      "10726  2000-02-01            Moscow  Russia      -3.551\n",
      "10727  2000-03-01            Moscow  Russia      -1.661\n",
      "10728  2000-04-01            Moscow  Russia      10.096\n",
      "10729  2000-05-01            Moscow  Russia      10.357\n",
      "...           ...               ...     ...         ...\n",
      "13360  2013-05-01  Saint Petersburg  Russia      12.355\n",
      "13361  2013-06-01  Saint Petersburg  Russia      17.185\n",
      "13362  2013-07-01  Saint Petersburg  Russia      17.234\n",
      "13363  2013-08-01  Saint Petersburg  Russia      17.153\n",
      "13364  2013-09-01  Saint Petersburg  Russia         NaN\n",
      "\n",
      "[330 rows x 4 columns]\n",
      "                        date country  avg_temp_c\n",
      "city                                            \n",
      "Moscow            2000-01-01  Russia      -7.313\n",
      "Moscow            2000-02-01  Russia      -3.551\n",
      "Moscow            2000-03-01  Russia      -1.661\n",
      "Moscow            2000-04-01  Russia      10.096\n",
      "Moscow            2000-05-01  Russia      10.357\n",
      "...                      ...     ...         ...\n",
      "Saint Petersburg  2013-05-01  Russia      12.355\n",
      "Saint Petersburg  2013-06-01  Russia      17.185\n",
      "Saint Petersburg  2013-07-01  Russia      17.234\n",
      "Saint Petersburg  2013-08-01  Russia      17.153\n",
      "Saint Petersburg  2013-09-01  Russia         NaN\n",
      "\n",
      "[330 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv', index_col=0)\n",
    "\n",
    "temperatures_ind = temperatures.set_index('city')\n",
    "\n",
    "# Make a list of cities to subset on\n",
    "cities = [\"Moscow\", \"Saint Petersburg\"]\n",
    "\n",
    "# Subset temperatures using square brackets\n",
    "print(temperatures[temperatures['city'].isin(cities)])\n",
    "\n",
    "# Subset temperatures_ind using .loc[]\n",
    "#print(temperatures.loc[cities])\n",
    "#####################################################################################################################\n",
    "print(temperatures_ind.loc[cities])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecc74f0-2955-41aa-b76c-6b37c472ab5a",
   "metadata": {},
   "source": [
    "## Setting multi-level indexes\n",
    "\n",
    "# Indexes can also be made out of multiple columns, forming a multi-level index (sometimes called a hierarchical index). There is a trade-off to using these.\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "The benefit is that multi-level indexes make it more natural to reason about nested categorical variables. For example, in a clinical trial, you might have control and treatment groups. Then each test subject belongs to one or another group, and we can say that a test subject is nested inside the treatment group. Similarly, in the temperature dataset, the city is located in the country, so we can say a city is nested inside the country.\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "The main downside is that the code for manipulating indexes is different from the code for manipulating columns, so you have to learn two syntaxes and keep track of how your data is represented.\n",
    "\n",
    "pandas is loaded as pd. temperatures is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Set the index of temperatures to the \"country\" and \"city\" columns, and assign this to temperatures_ind.\n",
    "    Specify two country/city pairs to keep: \"Brazil\"/\"Rio De Janeiro\" and \"Pakistan\"/\"Lahore\", assigning to rows_to_keep.\n",
    "    Print and subset temperatures_ind for rows_to_keep using .loc[].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bc19ce0e-271b-43ac-8521-f49979a21baf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             date  avg_temp_c\n",
      "country       city                           \n",
      "Côte D'Ivoire Abidjan  2000-01-01      27.293\n",
      "              Abidjan  2000-02-01      27.685\n",
      "              Abidjan  2000-03-01      29.061\n",
      "              Abidjan  2000-04-01      28.162\n",
      "              Abidjan  2000-05-01      27.547\n",
      "                               date  avg_temp_c\n",
      "country  city                                  \n",
      "Brazil   Rio De Janeiro  2000-01-01      25.974\n",
      "         Rio De Janeiro  2000-02-01      26.699\n",
      "         Rio De Janeiro  2000-03-01      26.270\n",
      "         Rio De Janeiro  2000-04-01      25.750\n",
      "         Rio De Janeiro  2000-05-01      24.356\n",
      "...                             ...         ...\n",
      "Pakistan Lahore          2013-05-01      33.457\n",
      "         Lahore          2013-06-01      34.456\n",
      "         Lahore          2013-07-01      33.279\n",
      "         Lahore          2013-08-01      31.511\n",
      "         Lahore          2013-09-01         NaN\n",
      "\n",
      "[330 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv', index_col=0)\n",
    "\n",
    "\n",
    "# Index temperatures by country & city\n",
    "temperatures_ind = temperatures.set_index(['country', 'city'])\n",
    "print(temperatures_ind.head())\n",
    "\n",
    "# List of tuples: Brazil, Rio De Janeiro & Pakistan, Lahore\n",
    "rows_to_keep = [('Brazil', 'Rio De Janeiro'), ('Pakistan', 'Lahore')]\n",
    "#####################################################################################################################\n",
    "print(temperatures_ind.loc[rows_to_keep])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "248b1ed4-4c48-431a-b8a7-e8edf6d026cc",
   "metadata": {},
   "source": [
    "## Sorting by index values\n",
    "\n",
    "Previously, you changed the order of the rows in a DataFrame by calling .sort_values(). It's also useful to be able to sort by elements in the index. For this, you need to use .sort_index().\n",
    "\n",
    "pandas is loaded as pd. temperatures_ind has a multi-level index of country and city, and is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Sort temperatures_ind by the index values.\n",
    "    Sort temperatures_ind by the index values at the \"city\" level.\n",
    "    Sort temperatures_ind by ascending country then descending city.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "57d554b9-0153-4e7d-8598-24085fb83687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0        date     city        country  avg_temp_c\n",
      "0           0  2000-01-01  Abidjan  Côte D'Ivoire      27.293\n",
      "1           1  2000-02-01  Abidjan  Côte D'Ivoire      27.685\n",
      "2           2  2000-03-01  Abidjan  Côte D'Ivoire      29.061\n",
      "3           3  2000-04-01  Abidjan  Côte D'Ivoire      28.162\n",
      "4           4  2000-05-01  Abidjan  Côte D'Ivoire      27.547\n",
      "                             date  avg_temp_c\n",
      "country       city                           \n",
      "Côte D'Ivoire Abidjan  2000-01-01      27.293\n",
      "              Abidjan  2000-02-01      27.685\n",
      "              Abidjan  2000-03-01      29.061\n",
      "              Abidjan  2000-04-01      28.162\n",
      "              Abidjan  2000-05-01      27.547\n",
      "                          date  avg_temp_c\n",
      "country     city                          \n",
      "Afghanistan Kabul   2000-01-01       3.326\n",
      "            Kabul   2000-02-01       3.454\n",
      "            Kabul   2000-03-01       9.612\n",
      "            Kabul   2000-04-01      17.925\n",
      "            Kabul   2000-05-01      24.658\n",
      "...                        ...         ...\n",
      "Zimbabwe    Harare  2013-05-01      18.298\n",
      "            Harare  2013-06-01      17.020\n",
      "            Harare  2013-07-01      16.299\n",
      "            Harare  2013-08-01      19.232\n",
      "            Harare  2013-09-01         NaN\n",
      "\n",
      "[16500 rows x 2 columns]\n",
      "                             date  avg_temp_c\n",
      "country       city                           \n",
      "Côte D'Ivoire Abidjan  2000-01-01      27.293\n",
      "              Abidjan  2000-02-01      27.685\n",
      "              Abidjan  2000-03-01      29.061\n",
      "              Abidjan  2000-04-01      28.162\n",
      "              Abidjan  2000-05-01      27.547\n",
      "...                           ...         ...\n",
      "China         Xian     2013-05-01      18.979\n",
      "              Xian     2013-06-01      23.522\n",
      "              Xian     2013-07-01      25.251\n",
      "              Xian     2013-08-01      24.528\n",
      "              Xian     2013-09-01         NaN\n",
      "\n",
      "[16500 rows x 2 columns]\n",
      "                          date  avg_temp_c\n",
      "country     city                          \n",
      "Afghanistan Kabul   2000-01-01       3.326\n",
      "            Kabul   2000-02-01       3.454\n",
      "            Kabul   2000-03-01       9.612\n",
      "            Kabul   2000-04-01      17.925\n",
      "            Kabul   2000-05-01      24.658\n",
      "...                        ...         ...\n",
      "Zimbabwe    Harare  2013-05-01      18.298\n",
      "            Harare  2013-06-01      17.020\n",
      "            Harare  2013-07-01      16.299\n",
      "            Harare  2013-08-01      19.232\n",
      "            Harare  2013-09-01         NaN\n",
      "\n",
      "[16500 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv')\n",
    "print(temperatures.head())\n",
    "\n",
    "\n",
    "\n",
    "temperature_ind = temperatures.set_index(['country', 'city'])\n",
    "print(temperatures_ind.head())\n",
    "\n",
    "\n",
    "# Sort temperatures_ind by index values\n",
    "print(temperatures_ind.sort_index())\n",
    "\n",
    "# Sort temperatures_ind by index values at the city level\n",
    "print(temperatures_ind.sort_index(level='city'))\n",
    "\n",
    "# Sort temperatures_ind by country then descending city\n",
    "#####################################################################################################################\n",
    "print(temperatures_ind.sort_index(level=['country', 'city'], ascending=[True, False]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e070031a-26cf-432b-b537-93eeddf77b0f",
   "metadata": {},
   "source": [
    "## Slicing and subsetting with .loc and .iloc\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Slicing is a technique for selecting consecutive elements from objects.  Below is a dog breed list, to slice a list, you pass first and last positioon indexes separated by a colon into a pair of square brackets breeds[2:4] - meaning selecting 3th to 4th elements in list.  Doing slicing from the begining of the list, we can use 0 or just leave it empty - breeds[:4] or breeds[0:4] - meaning selecting first 4 elements.  also breeds[4:] or breeds[:] meaning selecting 5th till last element, and selecting all elements.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# We can also slicing DataFrames, but we need to sort the index first.  \n",
    "\n",
    "Here the dogs dataset has been given a multi-level index of breed and color, then the index is sorted with .sort_index() method.  To slice rows at the outer level of an index, you call .loc[] and passing first and last values separated by a colon.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "There are two differences compared to slicing lists.  Rather that specifying row nembers, you specifying index values.  Secondly, notice that the final value is included.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "The same technique doen't work on inner index levels, Here trying to slice from Tan to Grey using dogs_sort.loc['Tan': 'Gray'] returns an empty DataFrame instead of 6 dogs info we wanted.  Thus its important to understand the danger here, Pandas doesn't throw an error to let you know that there is a problem, so be careful when doing so.  \n",
    "* The correst approach to slicing an inner index levels is to pass the first and last positions as tuples - dogs_sort.loc[('Chihuahua', 'Tan'): ('Schnauzer', 'Gray')].  \n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# Since DataFrames are two-dimensional objects, you can also slice columns.  We do this by passing two arguments to .loc[] method.  The simplest case involves subsetting columns but keeping all rows.  like here using dogs_sorted.loc[:, 'name': 'weight_kg'].  As with slicing lists, a colon by itself means keep everything.  \n",
    "\n",
    "We can also slice on rows and columns at the same time, simply pass the appropriate slice to each argument.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# An important use case of slicing is to subset DataFrame by a range of dates.  \n",
    "To demostrate this, lets set the date_of_birth column as the index and applying .sort_index().  \n",
    "* You slice dates with the same syntax as other types.  \n",
    "One helpful feature is that you can slice by partical dates.  Like this dogs_dates.loc['2015': '2017'].  The Pandas interprets this as slicing from the start of 2015 to the end of 2017.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# You can also slice DataFrames by row or column number using the .iloc[] method.  This uses similar syntax to slicing lists, except that there are two arguments: one for rows and one for columns.  \n",
    "\n",
    "Notice that, like list slicing but unlike .loc[], the final values aren't included in the slice.  \n",
    "\n",
    "\n",
    "\n",
    "breeds = ['Labrador', 'Poodle', 'Chow Chow', 'Schnauzer', 'Labrador', 'Chihuahua', 'St. Bernard']\n",
    "\n",
    "breeds[1:3]\n",
    "# Remember 0 indexing, and last value in square bracket are not included\n",
    "\n",
    "\n",
    "\n",
    "dogs_sort = dogs.set_index(['breed', 'color']).sort_index\n",
    "\n",
    "\n",
    "dogs_dates = dogs.set_index('date_of_birth').sort_index()\n",
    "\n",
    "dogs_dates.loc['2015': '2017']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b2571bc9-8dc8-4ff8-98c2-e119d5c77b15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     name  height_cn  weight_km data_of_birth\n",
      "breed      color                                             \n",
      "Chihuahua  Tan     Stella         18          2    2015-04-20\n",
      "Chow Chow  Brown     Lucy         46         24    2014-08-25\n",
      "Labrador   Black      Max         59         29    2017-01-20\n",
      "           Brown    Bella         56         24    2013-07-01\n",
      "Poodle     Black  Charlie         43         24    2016-09-16\n",
      "Schnauzer  Gray    Cooper         49         17    2011-12-11\n",
      "St.Bernard White   Bernie         77         74    2018-02-27\n",
      "                    name  height_cn  weight_km data_of_birth\n",
      "breed     color                                             \n",
      "Chihuahua Tan     Stella         18          2    2015-04-20\n",
      "Chow Chow Brown     Lucy         46         24    2014-08-25\n",
      "Labrador  Black      Max         59         29    2017-01-20\n",
      "          Brown    Bella         56         24    2013-07-01\n",
      "Poodle    Black  Charlie         43         24    2016-09-16\n",
      "                    name  height_cn  weight_km data_of_birth\n",
      "breed     color                                             \n",
      "Chihuahua Tan     Stella         18          2    2015-04-20\n",
      "Chow Chow Brown     Lucy         46         24    2014-08-25\n",
      "Labrador  Black      Max         59         29    2017-01-20\n",
      "          Brown    Bella         56         24    2013-07-01\n",
      "Poodle    Black  Charlie         43         24    2016-09-16\n",
      "Schnauzer Gray    Cooper         49         17    2011-12-11\n",
      "                     name  height_cn  weight_km\n",
      "breed      color                               \n",
      "Chihuahua  Tan     Stella         18          2\n",
      "Chow Chow  Brown     Lucy         46         24\n",
      "Labrador   Black      Max         59         29\n",
      "           Brown    Bella         56         24\n",
      "Poodle     Black  Charlie         43         24\n",
      "Schnauzer  Gray    Cooper         49         17\n",
      "St.Bernard White   Bernie         77         74\n",
      "                    name  height_cn  weight_km\n",
      "breed     color                               \n",
      "Chihuahua Tan     Stella         18          2\n",
      "Chow Chow Brown     Lucy         46         24\n",
      "Labrador  Black      Max         59         29\n",
      "          Brown    Bella         56         24\n",
      "Poodle    Black  Charlie         43         24\n",
      "Schnauzer Gray    Cooper         49         17\n",
      "                  name       breed  color  height_cn  weight_km\n",
      "data_of_birth                                                  \n",
      "2011-12-11      Cooper   Schnauzer   Gray         49         17\n",
      "2013-07-01       Bella    Labrador  Brown         56         24\n",
      "2014-08-25        Lucy   Chow Chow  Brown         46         24\n",
      "2015-04-20      Stella   Chihuahua    Tan         18          2\n",
      "2016-09-16     Charlie      Poodle  Black         43         24\n",
      "2017-01-20         Max    Labrador  Black         59         29\n",
      "2018-02-27      Bernie  St.Bernard  White         77         74\n",
      "                  name      breed  color  height_cn  weight_km\n",
      "data_of_birth                                                 \n",
      "2013-07-01       Bella   Labrador  Brown         56         24\n",
      "2014-08-25        Lucy  Chow Chow  Brown         46         24\n",
      "2015-04-20      Stella  Chihuahua    Tan         18          2\n",
      "2016-09-16     Charlie     Poodle  Black         43         24\n",
      "2017-01-20         Max   Labrador  Black         59         29\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "\n",
    "dogs = pd.read_csv('dogs2.csv')\n",
    "\n",
    "dogs_sorted = dogs.set_index(['breed', 'color']).sort_index()\n",
    "print(dogs_sorted)\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "print(dogs_sorted.loc['Chihuahua': 'Poodle'])\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "print(dogs_sorted.loc[('Chihuahua', 'Tan'): ('Schnauzer', 'Gray')])\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "print(dogs_sorted.loc[:, 'name':'weight_km'])\n",
    "\n",
    "\n",
    "# Just not elegant, not Pythonic at all\n",
    "#####################################################################################################################\n",
    "print(dogs_sorted.loc[('Chihuahua', 'Tan'): ('Schnauzer', 'Gray'), 'name': 'weight_km'])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "dogs_dates = dogs.set_index('data_of_birth').sort_index()\n",
    "print(dogs_dates)\n",
    "\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "date2013_2017 = dogs_dates.loc['2013-01-01': '2017-12-30']\n",
    "\n",
    "print(date2013_2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91fbfcda-3ef8-4021-9db6-168aa7dcc34e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2d708daf-4570-4c0c-a2c3-c5c86dfc3e1f",
   "metadata": {},
   "source": [
    "## Slicing index values\n",
    "\n",
    "Slicing lets you select consecutive elements of an object using first:last syntax. DataFrames can be sliced by index values or by row/column number; we'll start with the first case. This involves slicing inside the .loc[] method.\n",
    "\n",
    "# Compared to slicing lists, there are a few things to remember.\n",
    "\n",
    "    You can only slice an index if the index is sorted (using .sort_index()).\n",
    "    To slice at the outer level, first and last can be strings.\n",
    "    To slice at inner levels, first and last should be tuples.\n",
    "    If you pass a single slice to .loc[], it will slice the rows.\n",
    "\n",
    "pandas is loaded as pd. temperatures_ind has country and city in the index, and is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Sort the index of temperatures_ind.\n",
    "    Use slicing with .loc[] to get these subsets:\n",
    "        from Pakistan to Russia.\n",
    "        from Lahore to Moscow. (This will return nonsense.)\n",
    "        from Pakistan, Lahore to Russia, Moscow.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fcf04a2c-1d18-49b6-b5d1-a44fcc6b617c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0        date     city        country  avg_temp_c\n",
      "0           0  2000-01-01  Abidjan  Côte D'Ivoire      27.293\n",
      "1           1  2000-02-01  Abidjan  Côte D'Ivoire      27.685\n",
      "2           2  2000-03-01  Abidjan  Côte D'Ivoire      29.061\n",
      "3           3  2000-04-01  Abidjan  Côte D'Ivoire      28.162\n",
      "4           4  2000-05-01  Abidjan  Côte D'Ivoire      27.547\n",
      "                   Unnamed: 0        date  avg_temp_c\n",
      "country     city                                     \n",
      "Afghanistan Kabul        7260  2000-01-01       3.326\n",
      "            Kabul        7261  2000-02-01       3.454\n",
      "            Kabul        7262  2000-03-01       9.612\n",
      "            Kabul        7263  2000-04-01      17.925\n",
      "            Kabul        7264  2000-05-01      24.658\n",
      "                           Unnamed: 0        date  avg_temp_c\n",
      "country  city                                                \n",
      "Pakistan Faisalabad              4785  2000-01-01      12.792\n",
      "         Faisalabad              4786  2000-02-01      14.339\n",
      "         Faisalabad              4787  2000-03-01      20.309\n",
      "         Faisalabad              4788  2000-04-01      29.072\n",
      "         Faisalabad              4789  2000-05-01      34.845\n",
      "...                               ...         ...         ...\n",
      "Russia   Saint Petersburg       13360  2013-05-01      12.355\n",
      "         Saint Petersburg       13361  2013-06-01      17.185\n",
      "         Saint Petersburg       13362  2013-07-01      17.234\n",
      "         Saint Petersburg       13363  2013-08-01      17.153\n",
      "         Saint Petersburg       13364  2013-09-01         NaN\n",
      "\n",
      "[1155 rows x 3 columns]\n",
      "                 Unnamed: 0        date  avg_temp_c\n",
      "country  city                                      \n",
      "Pakistan Lahore        8415  2000-01-01      12.792\n",
      "         Lahore        8416  2000-02-01      14.339\n",
      "         Lahore        8417  2000-03-01      20.309\n",
      "         Lahore        8418  2000-04-01      29.072\n",
      "         Lahore        8419  2000-05-01      34.845\n",
      "...                     ...         ...         ...\n",
      "Russia   Moscow       10885  2013-05-01      16.152\n",
      "         Moscow       10886  2013-06-01      18.718\n",
      "         Moscow       10887  2013-07-01      18.136\n",
      "         Moscow       10888  2013-08-01      17.485\n",
      "         Moscow       10889  2013-09-01         NaN\n",
      "\n",
      "[660 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv')\n",
    "print(temperatures.head())\n",
    "\n",
    "\n",
    "\n",
    "temperatures_ind = temperatures.set_index(['country', 'city']).sort_index()\n",
    "print(temperatures_ind.head())\n",
    "\n",
    "\n",
    "\n",
    "# Subset rows from Pakistan to Russia\n",
    "print(temperatures_ind.loc['Pakistan': 'Russia'])\n",
    "\n",
    "# Try to subset rows from Lahore to Moscow\n",
    "print(temperatures_ind.loc[('Pakistan', 'Lahore'): ('Russia', 'Moscow')])\n",
    "\n",
    "# Subset rows from Pakistan, Lahore to Russia, Moscow\n",
    "#print(____)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1aeeb4a-6afd-4fc8-900b-c8194b2c2784",
   "metadata": {},
   "source": [
    "## Slicing in both directions\n",
    "\n",
    "# You've seen slicing DataFrames by rows and by columns, but since DataFrames are two-dimensional objects, it is often natural to slice both dimensions at once. That is, by passing two arguments to .loc[], you can subset by rows and columns in one go.\n",
    "\n",
    "pandas is loaded as pd. temperatures_srt is indexed by country and city, has a sorted index, and is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Use .loc[] slicing to subset rows from India, Hyderabad to Iraq, Baghdad.\n",
    "    Use .loc[] slicing to subset columns from date to avg_temp_c.\n",
    "    Slice in both directions at once from Hyderabad to Baghdad, and date to avg_temp_c.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e7b9a146-2ad4-46fc-8173-c747f8ce09e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Unnamed: 0        date  avg_temp_c\n",
      "country     city                                     \n",
      "Afghanistan Kabul        7260  2000-01-01       3.326\n",
      "            Kabul        7261  2000-02-01       3.454\n",
      "            Kabul        7262  2000-03-01       9.612\n",
      "            Kabul        7263  2000-04-01      17.925\n",
      "            Kabul        7264  2000-05-01      24.658\n",
      "                   Unnamed: 0        date  avg_temp_c\n",
      "country city                                         \n",
      "India   Hyderabad        5940  2000-01-01      23.779\n",
      "        Hyderabad        5941  2000-02-01      25.826\n",
      "        Hyderabad        5942  2000-03-01      28.821\n",
      "        Hyderabad        5943  2000-04-01      32.698\n",
      "        Hyderabad        5944  2000-05-01      32.438\n",
      "...                       ...         ...         ...\n",
      "Iraq    Baghdad          1150  2013-05-01      28.673\n",
      "        Baghdad          1151  2013-06-01      33.803\n",
      "        Baghdad          1152  2013-07-01      36.392\n",
      "        Baghdad          1153  2013-08-01      35.463\n",
      "        Baghdad          1154  2013-09-01         NaN\n",
      "\n",
      "[2145 rows x 3 columns]\n",
      "                          date  avg_temp_c\n",
      "country     city                          \n",
      "Afghanistan Kabul   2000-01-01       3.326\n",
      "            Kabul   2000-02-01       3.454\n",
      "            Kabul   2000-03-01       9.612\n",
      "            Kabul   2000-04-01      17.925\n",
      "            Kabul   2000-05-01      24.658\n",
      "...                        ...         ...\n",
      "Zimbabwe    Harare  2013-05-01      18.298\n",
      "            Harare  2013-06-01      17.020\n",
      "            Harare  2013-07-01      16.299\n",
      "            Harare  2013-08-01      19.232\n",
      "            Harare  2013-09-01         NaN\n",
      "\n",
      "[16500 rows x 2 columns]\n",
      "                         date  avg_temp_c\n",
      "country city                             \n",
      "India   Hyderabad  2000-01-01      23.779\n",
      "        Hyderabad  2000-02-01      25.826\n",
      "        Hyderabad  2000-03-01      28.821\n",
      "        Hyderabad  2000-04-01      32.698\n",
      "        Hyderabad  2000-05-01      32.438\n",
      "...                       ...         ...\n",
      "Iraq    Baghdad    2013-05-01      28.673\n",
      "        Baghdad    2013-06-01      33.803\n",
      "        Baghdad    2013-07-01      36.392\n",
      "        Baghdad    2013-08-01      35.463\n",
      "        Baghdad    2013-09-01         NaN\n",
      "\n",
      "[2145 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv')\n",
    "\n",
    "temperatures_srt = temperatures.set_index(['country', 'city']).sort_index()\n",
    "print(temperatures_srt.head())\n",
    "\n",
    "\n",
    "\n",
    "# slicing to subset rows from India, Hyderabad to Iraq, Baghdad\n",
    "print(temperatures_srt.loc[('India', 'Hyderabad'): ('Iraq', 'Baghdad')])\n",
    "\n",
    "\n",
    "# slicing to subset columns from date to avg_temp_c\n",
    "print(temperatures_srt.loc[:, 'date': 'avg_temp_c'])\n",
    "\n",
    "\n",
    "\n",
    "print(temperatures_srt.loc[('India', 'Hyderabad'): ('Iraq', 'Baghdad'), 'date': 'avg_temp_c'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ea376b5-70a0-444b-84a3-1c0ef27f7f48",
   "metadata": {},
   "source": [
    "## Slicing time series\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Slicing is particularly useful for time series since it's a common thing to want to filter for data within a date range. Add the date column to the index, then use .loc[] to perform the subsetting. The important thing to remember is to keep your dates in ISO 8601 format, that is, \"yyyy-mm-dd\" for year-month-day, \"yyyy-mm\" for year-month, and \"yyyy\" for year.\n",
    "\n",
    "# Recall from Chapter 1 that you can combine multiple Boolean conditions using logical operators, such as &. To do so in one line of code, you'll need to add parentheses () around each condition.\n",
    "\n",
    "pandas is loaded as pd and temperatures, with no index, is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Use Boolean conditions, not .isin() or .loc[], and the full date \"yyyy-mm-dd\", to subset temperatures for rows in 2010 and 2011 and print the results.\n",
    "    Set the index to the date column and sort it.\n",
    "    Use .loc[] to subset temperatures_ind for rows in 2010 and 2011.\n",
    "    Use .loc[] to subset temperatures_ind for rows from Aug 2010 to Feb 2011.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d4dc381f-63ca-477f-bb38-065f156be034",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 city        country  avg_temp_c\n",
      "date                                            \n",
      "2000-01-01    Abidjan  Côte D'Ivoire      27.293\n",
      "2000-01-01     Lahore       Pakistan      12.792\n",
      "2000-01-01   Tangshan          China      -5.406\n",
      "2000-01-01      Gizeh          Egypt      12.669\n",
      "2000-01-01    Lakhnau          India      15.152\n",
      "...               ...            ...         ...\n",
      "2013-09-01    Nanjing          China         NaN\n",
      "2013-09-01  New Delhi          India         NaN\n",
      "2013-09-01   New York  United States      17.408\n",
      "2013-09-01     Peking          China         NaN\n",
      "2013-09-01       Xian          China         NaN\n",
      "\n",
      "[16500 rows x 3 columns]\n",
      "             date     city        country  avg_temp_c\n",
      "120    2010-01-01  Abidjan  Côte D'Ivoire      28.270\n",
      "121    2010-02-01  Abidjan  Côte D'Ivoire      29.262\n",
      "122    2010-03-01  Abidjan  Côte D'Ivoire      29.596\n",
      "123    2010-04-01  Abidjan  Côte D'Ivoire      29.068\n",
      "124    2010-05-01  Abidjan  Côte D'Ivoire      28.258\n",
      "...           ...      ...            ...         ...\n",
      "16462  2010-08-01     Xian          China      23.434\n",
      "16463  2010-09-01     Xian          China      19.630\n",
      "16464  2010-10-01     Xian          China      12.292\n",
      "16465  2010-11-01     Xian          China       6.742\n",
      "16466  2010-12-01     Xian          China       0.845\n",
      "\n",
      "[1200 rows x 4 columns]\n",
      "                  city    country  avg_temp_c\n",
      "date                                         \n",
      "2010-01-01  Faisalabad   Pakistan      11.810\n",
      "2010-01-01   Melbourne  Australia      20.016\n",
      "2010-01-01   Chongqing      China       7.921\n",
      "2010-01-01   São Paulo     Brazil      23.738\n",
      "2010-01-01   Guangzhou      China      14.136\n",
      "...                ...        ...         ...\n",
      "2010-12-01     Jakarta  Indonesia      26.602\n",
      "2010-12-01       Gizeh      Egypt      16.530\n",
      "2010-12-01      Nagpur      India      19.120\n",
      "2010-12-01      Sydney  Australia      19.559\n",
      "2010-12-01    Salvador     Brazil      26.265\n",
      "\n",
      "[1200 rows x 3 columns]\n",
      "                city        country  avg_temp_c\n",
      "date                                           \n",
      "2010-08-01  Calcutta          India      30.226\n",
      "2010-08-01      Pune          India      24.941\n",
      "2010-08-01     Izmir         Turkey      28.352\n",
      "2010-08-01   Tianjin          China      25.543\n",
      "2010-08-01    Manila    Philippines      27.101\n",
      "...              ...            ...         ...\n",
      "2011-02-01     Kabul    Afghanistan       3.914\n",
      "2011-02-01   Chicago  United States       0.276\n",
      "2011-02-01    Aleppo          Syria       8.246\n",
      "2011-02-01     Delhi          India      18.136\n",
      "2011-02-01   Rangoon          Burma      26.631\n",
      "\n",
      "[700 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv', index_col=0)\n",
    "#print(temperatures.head())\n",
    "\n",
    "temperatures_bool = temperatures.set_index('date').sort_index()\n",
    "print(temperatures_bool)\n",
    "\n",
    "\n",
    "\n",
    "# Use Boolean conditions to subset temperatures for rows in 2010 and 2011\n",
    "#####################################################################################################################\n",
    "temperatures_bool = temperatures[(temperatures['date'] >= '2010') & (temperatures['date'] <='2011')]\n",
    "print(temperatures_bool)\n",
    "\n",
    "# Set date as the index and sort the index\n",
    "temperatures_ind = temperatures.set_index('date').sort_index()\n",
    "\n",
    "# Use .loc[] to subset temperatures_ind for rows in 2010 and 2011\n",
    "print(temperatures_ind.loc['2010': '2011'])\n",
    "\n",
    "# Use .loc[] to subset temperatures_ind for rows from Aug 2010 to Feb 2011\n",
    "print(temperatures_ind.loc['2010-08-01': '2011-02-29'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee74bbf-3ca3-45a5-b641-746a1e8e5b6b",
   "metadata": {},
   "source": [
    "## Subsetting by row/column number\n",
    "\n",
    "The most common ways to subset rows are the ways we've previously discussed: using a Boolean condition or by index labels. However, it is also occasionally useful to pass row numbers.\n",
    "\n",
    "This is done using .iloc[], and like .loc[], it can take two arguments to let you subset by rows and columns.\n",
    "\n",
    "pandas is loaded as pd. temperatures (without an index) is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "Use .iloc[] on temperatures to take subsets.\n",
    "\n",
    "    Get the 23rd row, 2nd column (index positions 22 and 1).\n",
    "    Get the first 5 rows (index positions 0 to 5).\n",
    "    Get all rows, columns 3 and 4 (index positions 2 to 4).\n",
    "    Get the first 5 rows, columns 3 and 4.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7698c6d6-f538-4db1-aa2b-c94e19ae31c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Abidjan\n",
      "         date     city        country  avg_temp_c\n",
      "0  2000-01-01  Abidjan  Côte D'Ivoire      27.293\n",
      "1  2000-02-01  Abidjan  Côte D'Ivoire      27.685\n",
      "2  2000-03-01  Abidjan  Côte D'Ivoire      29.061\n",
      "3  2000-04-01  Abidjan  Côte D'Ivoire      28.162\n",
      "4  2000-05-01  Abidjan  Côte D'Ivoire      27.547\n",
      "             country  avg_temp_c\n",
      "0      Côte D'Ivoire      27.293\n",
      "1      Côte D'Ivoire      27.685\n",
      "2      Côte D'Ivoire      29.061\n",
      "3      Côte D'Ivoire      28.162\n",
      "4      Côte D'Ivoire      27.547\n",
      "...              ...         ...\n",
      "16495          China      18.979\n",
      "16496          China      23.522\n",
      "16497          China      25.251\n",
      "16498          China      24.528\n",
      "16499          China         NaN\n",
      "\n",
      "[16500 rows x 2 columns]\n",
      "         country  avg_temp_c\n",
      "0  Côte D'Ivoire      27.293\n",
      "1  Côte D'Ivoire      27.685\n",
      "2  Côte D'Ivoire      29.061\n",
      "3  Côte D'Ivoire      28.162\n",
      "4  Côte D'Ivoire      27.547\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv', index_col=0)\n",
    "\n",
    "\n",
    "# 23rd row, 2nd column\n",
    "print(temperatures.iloc[22, 1])\n",
    "\n",
    "\n",
    "# first 5 rows\n",
    "print(temperatures.iloc[:5])\n",
    "\n",
    "\n",
    "# all rows, columns 3 and 4\n",
    "print(temperatures.iloc[:, 2:4])\n",
    "\n",
    "# first 5 rows, columns 3 and 4.\n",
    "print(temperatures.iloc[:5, 2:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5b0f683-f2a6-42b1-88ed-a671b0172453",
   "metadata": {},
   "source": [
    "## Working with pivot tables\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# **We have experienced how to create pivot_tables with Pandas in chapter 2.  In this lesson, we'll perform subsetting and calculations on pivot tables.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Here is a larger version of the dogs dataset, the extra dogs mean we have something to compute on.  \n",
    "\n",
    "**Recall that we create a pivot_table(), its another way of calculating grouped summary statistics.  The first argument is the column name containing the values to aggregate.  The index= argument lists the columns to group by and display in rows, and the columns= argument lists the columns to group by and display in columns.  We'll use the default aggregation function which is mean.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "The pivot_table are just DataFrames with sorted indexes.  That means that all the fun stuff you've learned so far this chapter can be used on them.  In particular, the .loc[] and slicing combination is ideal for subsetting pivot tables, like so.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "The methods for calculating summary statistics on a DataFrame such as .mean(), have an axis= argument.  The default value is index, which means calculate the statistics across rows.  Here the mean is calculated for each color thats across the breeds.  Same way, to calculate a summary statistics for each row, we set axis='columns'.  For most DataFrames, setting the axis= argument doesn't make any sense, since you have different data types in each columns.  Pivot tables are a special case, since every column contains the same data type.  \n",
    "\n",
    "\n",
    "\n",
    "dogs_height_by_breed_vs_color = dogs_pack.pivot_table('height_cm', index='breed', columns='color')\n",
    "\n",
    "\n",
    "dogs_height_by_breed_vs_color.loc['Chow Chow': 'Poodle']\n",
    "\n",
    "dogs_height_by_breed_vs_color.mean(axis='index')\n",
    "dogs_height_by_breed_vs_color.mean(axis='columns')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "51865443-8373-434c-906f-fb7d9bc1c3fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "color       Black  Brown  Gray  Tan  White        All\n",
      "breed                                                \n",
      "Chihuahua     0.0      0     0    2      0   2.000000\n",
      "Chow Chow     0.0     24     0    0      0  24.000000\n",
      "Labrador     29.0     24     0    0      0  26.500000\n",
      "Poodle       24.0      0     0    0      0  24.000000\n",
      "Schnauzer     0.0      0    17    0      0  17.000000\n",
      "St.Bernard    0.0      0     0    0     74  74.000000\n",
      "All          26.5     24    17    2     74  27.714286\n",
      "color      Black  Brown  Gray  Tan  White   All\n",
      "breed                                          \n",
      "Chow Chow    0.0     24     0    0      0  24.0\n",
      "Labrador    29.0     24     0    0      0  26.5\n",
      "Poodle      24.0      0     0    0      0  24.0\n",
      "Schnauzer    0.0      0    17    0      0  17.0\n",
      "color\n",
      "Black    11.357143\n",
      "Brown    10.285714\n",
      "Gray      4.857143\n",
      "Tan       0.571429\n",
      "White    21.142857\n",
      "All      27.887755\n",
      "dtype: float64\n",
      "color\n",
      "Black    11.357143\n",
      "Brown    10.285714\n",
      "Gray      4.857143\n",
      "Tan       0.571429\n",
      "White    21.142857\n",
      "All      27.887755\n",
      "dtype: float64\n",
      "breed\n",
      "Chihuahua      0.666667\n",
      "Chow Chow      8.000000\n",
      "Labrador      13.250000\n",
      "Poodle         8.000000\n",
      "Schnauzer      5.666667\n",
      "St.Bernard    24.666667\n",
      "All           28.535714\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dogs = pd.read_csv('dogs2.csv')\n",
    "#print(dogs)\n",
    "\n",
    "\n",
    "dogs_h_by_breed_color = dogs.pivot_table('weight_km', \n",
    "                                         index='breed', \n",
    "                                         columns='color', \n",
    "                                         fill_value=0, \n",
    "                                         margins=True)\n",
    "\n",
    "print(dogs_h_by_breed_color)\n",
    "\n",
    "print(dogs_h_by_breed_color.loc['Chow Chow': 'Schnauzer'])\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "print(dogs_h_by_breed_color.mean())\n",
    "print(dogs_h_by_breed_color.mean(axis='index'))\n",
    "print(dogs_h_by_breed_color.mean(axis='columns'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7af5f8b2-2ab3-43bd-9852-8ba4885f73f8",
   "metadata": {},
   "source": [
    "## Pivot temperature by city and year\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# It's interesting to see how temperatures for each city change over time—looking at every month results in a big table, which can be tricky to reason about. Instead, let's look at how temperatures change by year.\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "You can access the components of a date (year, month and day) using code of the form dataframe[\"column\"].dt.component. For example, the month component is dataframe[\"column\"].dt.month, and the year component is dataframe[\"column\"].dt.year.\n",
    "\n",
    "Once you have the year column, you can create a pivot table with the data aggregated by city and year, which you'll explore in the coming exercises.\n",
    "\n",
    "pandas is loaded as pd. temperatures is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Add a year column to temperatures, from the year component of the date column.\n",
    "#    Make a pivot table of the avg_temp_c column, with country and city as rows, and year as columns. Assign to temp_by_country_city_vs_year, and look at the result.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "952907a3-adcb-414c-aaf6-4808a99c9c59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date     city        country  avg_temp_c\n",
      "0  2000-01-01  Abidjan  Côte D'Ivoire      27.293\n",
      "1  2000-02-01  Abidjan  Côte D'Ivoire      27.685\n",
      "2  2000-03-01  Abidjan  Côte D'Ivoire      29.061\n",
      "3  2000-04-01  Abidjan  Côte D'Ivoire      28.162\n",
      "4  2000-05-01  Abidjan  Côte D'Ivoire      27.547\n",
      "        date     city        country  avg_temp_c  year\n",
      "0 2000-01-01  Abidjan  Côte D'Ivoire      27.293  2000\n",
      "1 2000-02-01  Abidjan  Côte D'Ivoire      27.685  2000\n",
      "2 2000-03-01  Abidjan  Côte D'Ivoire      29.061  2000\n",
      "3 2000-04-01  Abidjan  Côte D'Ivoire      28.162  2000\n",
      "4 2000-05-01  Abidjan  Côte D'Ivoire      27.547  2000\n",
      "                      avg_temp_c                                              \\\n",
      "year                        2000       2001       2002       2003       2004   \n",
      "country     city                                                               \n",
      "Afghanistan Kabul      15.822667  15.847917  15.714583  15.132583  16.128417   \n",
      "Angola      Luanda     24.410333  24.427083  24.790917  24.867167  24.216167   \n",
      "Australia   Melbourne  14.320083  14.180000  14.075833  13.985583  13.742083   \n",
      "            Sydney     17.567417  17.854500  17.733833  17.592333  17.869667   \n",
      "Bangladesh  Dhaka      25.905250  25.931250  26.095000  25.927417  26.136083   \n",
      "\n",
      "                                                                              \\\n",
      "year                        2005       2006       2007       2008       2009   \n",
      "country     city                                                               \n",
      "Afghanistan Kabul      14.847500  15.798500  15.518000  15.479250  15.093333   \n",
      "Angola      Luanda     24.414583  24.138417  24.241583  24.266333  24.325083   \n",
      "Australia   Melbourne  14.378500  13.991083  14.991833  14.110583  14.647417   \n",
      "            Sydney     18.028083  17.749500  18.020833  17.321083  18.175833   \n",
      "Bangladesh  Dhaka      26.193333  26.440417  25.951333  26.004500  26.535583   \n",
      "\n",
      "                                                                   \n",
      "year                        2010       2011       2012       2013  \n",
      "country     city                                                   \n",
      "Afghanistan Kabul      15.676000  15.812167  14.510333  16.206125  \n",
      "Angola      Luanda     24.440250  24.150750  24.240083  24.553875  \n",
      "Australia   Melbourne  14.231667  14.190917  14.268667  14.741500  \n",
      "            Sydney     17.999000  17.713333  17.474333  18.089750  \n",
      "Bangladesh  Dhaka      26.648167  25.803250  26.283583  26.587000  \n",
      "\n",
      "\n",
      "year                                 2000       2001       2002       2003  \\\n",
      "country       city                                                           \n",
      "Afghanistan   Kabul             15.822667  15.847917  15.714583  15.132583   \n",
      "Angola        Luanda            24.410333  24.427083  24.790917  24.867167   \n",
      "Australia     Melbourne         14.320083  14.180000  14.075833  13.985583   \n",
      "              Sydney            17.567417  17.854500  17.733833  17.592333   \n",
      "Bangladesh    Dhaka             25.905250  25.931250  26.095000  25.927417   \n",
      "...                                   ...        ...        ...        ...   \n",
      "United States Chicago           11.089667  11.703083  11.532083  10.481583   \n",
      "              Los Angeles       16.643333  16.466250  16.430250  16.944667   \n",
      "              New York           9.969083  10.931000  11.252167   9.836000   \n",
      "Vietnam       Ho Chi Minh City  27.588917  27.831750  28.064750  27.827667   \n",
      "Zimbabwe      Harare            20.283667  20.861000  21.079333  20.889167   \n",
      "\n",
      "year                                 2004       2005       2006       2007  \\\n",
      "country       city                                                           \n",
      "Afghanistan   Kabul             16.128417  14.847500  15.798500  15.518000   \n",
      "Angola        Luanda            24.216167  24.414583  24.138417  24.241583   \n",
      "Australia     Melbourne         13.742083  14.378500  13.991083  14.991833   \n",
      "              Sydney            17.869667  18.028083  17.749500  18.020833   \n",
      "Bangladesh    Dhaka             26.136083  26.193333  26.440417  25.951333   \n",
      "...                                   ...        ...        ...        ...   \n",
      "United States Chicago           10.943417  11.583833  11.870500  11.448333   \n",
      "              Los Angeles       16.552833  16.431417  16.623083  16.699917   \n",
      "              New York          10.389500  10.681417  11.519250  10.627333   \n",
      "Vietnam       Ho Chi Minh City  27.686583  27.884000  28.044000  27.866667   \n",
      "Zimbabwe      Harare            20.307667  21.487417  20.699750  20.746250   \n",
      "\n",
      "year                                 2008       2009       2010       2011  \\\n",
      "country       city                                                           \n",
      "Afghanistan   Kabul             15.479250  15.093333  15.676000  15.812167   \n",
      "Angola        Luanda            24.266333  24.325083  24.440250  24.150750   \n",
      "Australia     Melbourne         14.110583  14.647417  14.231667  14.190917   \n",
      "              Sydney            17.321083  18.175833  17.999000  17.713333   \n",
      "Bangladesh    Dhaka             26.004500  26.535583  26.648167  25.803250   \n",
      "...                                   ...        ...        ...        ...   \n",
      "United States Chicago           10.242417  10.298333  11.815917  11.214250   \n",
      "              Los Angeles       17.014750  16.677000  15.887000  15.874833   \n",
      "              New York          10.641667  10.141833  11.357583  11.272250   \n",
      "Vietnam       Ho Chi Minh City  27.611417  27.853333  28.281750  27.675417   \n",
      "Zimbabwe      Harare            20.680500  20.523833  21.165833  20.781750   \n",
      "\n",
      "year                                 2012       2013  \n",
      "country       city                                    \n",
      "Afghanistan   Kabul             14.510333  16.206125  \n",
      "Angola        Luanda            24.240083  24.553875  \n",
      "Australia     Melbourne         14.268667  14.741500  \n",
      "              Sydney            17.474333  18.089750  \n",
      "Bangladesh    Dhaka             26.283583  26.587000  \n",
      "...                                   ...        ...  \n",
      "United States Chicago           12.821250  11.586889  \n",
      "              Los Angeles       17.089583  18.120667  \n",
      "              New York          11.971500  12.163889  \n",
      "Vietnam       Ho Chi Minh City  28.248750  28.455000  \n",
      "Zimbabwe      Harare            20.523333  19.756500  \n",
      "\n",
      "[100 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv', index_col=0)\n",
    "print(temperatures.head())\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "temperatures['date'] = pd.to_datetime(temperatures['date'])\n",
    "#print(temperatures.head())\n",
    "\n",
    "\n",
    "\n",
    "# Add a year column to temperatures, form the year component of the date column\n",
    "temperatures['year'] = temperatures['date'].dt.year\n",
    "print(temperatures.head())\n",
    "\n",
    "\n",
    "temp_by_country_city_vs_year = temperatures.pivot_table(index=['country', 'city'], columns='year')\n",
    "print(temp_by_country_city_vs_year.head())\n",
    "\n",
    "print()\n",
    "print()\n",
    "#####################################################################################################################\n",
    "temp_by_country_city_vs_year = temperatures.pivot_table('avg_temp_c', \n",
    "                                                        index=['country', 'city'], \n",
    "                                                        columns='year')\n",
    "\n",
    "\n",
    "print(temp_by_country_city_vs_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37118890-7ff3-4a96-a9d3-fab3d175051c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ea7a7455-4142-40bc-8eca-edec46f97329",
   "metadata": {},
   "source": [
    "## Subsetting pivot tables\n",
    "\n",
    "# A pivot table is just a DataFrame with sorted indexes, \n",
    "so the techniques you have learned already can be used to subset them. In particular, the .loc[] + slicing combination is often helpful.\n",
    "\n",
    "pandas is loaded as pd. temp_by_country_city_vs_year is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "Use .loc[] on temp_by_country_city_vs_year to take subsets.\n",
    "\n",
    "    From Egypt to India.\n",
    "    From Egypt, Cairo to India, Delhi.\n",
    "    From Egypt, Cairo to India, Delhi, and 2005 to 2010.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c6a2bed8-60e6-4476-b5cf-cb6412cfc910",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year                       2000       2001       2002       2003       2004  \\\n",
      "country  city                                                                 \n",
      "Egypt    Alexandria   20.744500  21.454583  21.456167  21.221417  21.064167   \n",
      "         Cairo        21.486167  22.330833  22.414083  22.170500  22.081917   \n",
      "         Gizeh        21.486167  22.330833  22.414083  22.170500  22.081917   \n",
      "Ethiopia Addis Abeba  18.241250  18.296417  18.469750  18.320917  18.292750   \n",
      "France   Paris        11.739667  11.371250  11.871333  11.909500  11.338833   \n",
      "Germany  Berlin       10.963667   9.690250  10.264417  10.065750   9.822583   \n",
      "India    Ahmadabad    27.436000  27.198083  27.719083  27.403833  27.628333   \n",
      "         Bangalore    25.337917  25.528167  25.755333  25.924750  25.252083   \n",
      "         Bombay       27.203667  27.243667  27.628667  27.578417  27.318750   \n",
      "         Calcutta     26.491333  26.515167  26.703917  26.561333  26.634333   \n",
      "         Delhi        26.048333  25.862917  26.634333  25.721083  26.239917   \n",
      "         Hyderabad    27.231833  27.555167  27.665167  27.844583  27.228500   \n",
      "         Jaipur       26.430250  26.023000  27.031667  26.027417  26.642167   \n",
      "         Kanpur       25.353917  25.326500  26.116833  25.409083  25.586500   \n",
      "         Lakhnau      25.353917  25.326500  26.116833  25.409083  25.586500   \n",
      "         Madras       28.811667  29.162917  29.246500  29.273083  28.810917   \n",
      "         Nagpur       26.181417  26.321667  26.753000  26.503833  26.406333   \n",
      "         New Delhi    26.048333  25.862917  26.634333  25.721083  26.239917   \n",
      "         Pune         25.110917  25.337833  25.583000  25.748417  25.316167   \n",
      "         Surat        27.029000  26.897250  27.347750  27.230917  27.290583   \n",
      "\n",
      "year                       2005       2006       2007       2008       2009  \\\n",
      "country  city                                                                 \n",
      "Egypt    Alexandria   21.082333  21.148167  21.507750  21.739000  21.670500   \n",
      "         Cairo        22.006500  22.050000  22.361000  22.644500  22.625000   \n",
      "         Gizeh        22.006500  22.050000  22.361000  22.644500  22.625000   \n",
      "Ethiopia Addis Abeba  18.312833  18.427083  18.142583  18.165000  18.765333   \n",
      "France   Paris        11.552917  11.788500  11.750833  11.278250  11.464083   \n",
      "Germany  Berlin        9.919083  10.545333  10.883167  10.657750  10.062500   \n",
      "India    Ahmadabad    26.828083  27.282833  27.511167  27.048500  28.095833   \n",
      "         Bangalore    25.476500  25.418250  25.464333  25.352583  25.725750   \n",
      "         Bombay       27.035750  27.381500  27.634667  27.177750  27.844500   \n",
      "         Calcutta     26.729167  26.986250  26.584583  26.522333  27.153250   \n",
      "         Delhi        25.716083  26.365917  26.145667  25.675000  26.554250   \n",
      "         Hyderabad    27.488833  27.471000  27.545333  27.446667  28.026500   \n",
      "         Jaipur       25.772250  26.429833  26.267000  25.877583  26.919500   \n",
      "         Kanpur       25.621500  25.848833  25.436083  25.109750  25.987083   \n",
      "         Lakhnau      25.621500  25.848833  25.436083  25.109750  25.987083   \n",
      "         Madras       29.099417  29.073250  28.970167  29.024083  29.417417   \n",
      "         Nagpur       26.229167  26.374833  26.393667  26.188000  27.138917   \n",
      "         New Delhi    25.716083  26.365917  26.145667  25.675000  26.554250   \n",
      "         Pune         25.085583  25.310167  25.584833  25.230250  25.868250   \n",
      "         Surat        26.724500  27.149417  27.311750  26.828750  27.819833   \n",
      "\n",
      "year                       2010       2011       2012       2013  \n",
      "country  city                                                     \n",
      "Egypt    Alexandria   22.459583  21.181500  21.552583  21.438500  \n",
      "         Cairo        23.718250  21.986917  22.484250  22.907000  \n",
      "         Gizeh        23.718250  21.986917  22.484250  22.907000  \n",
      "Ethiopia Addis Abeba  18.298250  18.606750  18.448583  19.539000  \n",
      "France   Paris        10.409833  12.325750  11.219917  11.011625  \n",
      "Germany  Berlin        8.606833  10.556417   9.964333  10.121500  \n",
      "India    Ahmadabad    28.017833  27.290417  27.027250  27.608625  \n",
      "         Bangalore    25.705250  25.362083  26.042333  26.610500  \n",
      "         Bombay       27.765417  27.384917  27.192500  26.713000  \n",
      "         Calcutta     27.288833  26.406917  26.935083  27.369250  \n",
      "         Delhi        26.520250  25.629500  25.889417  26.709250  \n",
      "         Hyderabad    27.693417  27.408833  28.018583  28.851250  \n",
      "         Jaipur       26.818417  25.916500  25.884500  26.844125  \n",
      "         Kanpur       26.021750  25.062083  25.445417  26.121250  \n",
      "         Lakhnau      26.021750  25.062083  25.445417  26.121250  \n",
      "         Madras       29.047333  29.063167  29.778417  30.411750  \n",
      "         Nagpur       26.927000  26.004667  26.327917  27.112375  \n",
      "         New Delhi    26.520250  25.629500  25.889417  26.709250  \n",
      "         Pune         25.749167  25.161417  25.296833  25.847625  \n",
      "         Surat        27.681667  27.016583  26.889250  27.437750  \n",
      "year                       2000       2001       2002       2003       2004  \\\n",
      "country  city                                                                 \n",
      "Egypt    Cairo        21.486167  22.330833  22.414083  22.170500  22.081917   \n",
      "         Gizeh        21.486167  22.330833  22.414083  22.170500  22.081917   \n",
      "Ethiopia Addis Abeba  18.241250  18.296417  18.469750  18.320917  18.292750   \n",
      "France   Paris        11.739667  11.371250  11.871333  11.909500  11.338833   \n",
      "Germany  Berlin       10.963667   9.690250  10.264417  10.065750   9.822583   \n",
      "India    Ahmadabad    27.436000  27.198083  27.719083  27.403833  27.628333   \n",
      "         Bangalore    25.337917  25.528167  25.755333  25.924750  25.252083   \n",
      "         Bombay       27.203667  27.243667  27.628667  27.578417  27.318750   \n",
      "         Calcutta     26.491333  26.515167  26.703917  26.561333  26.634333   \n",
      "         Delhi        26.048333  25.862917  26.634333  25.721083  26.239917   \n",
      "\n",
      "year                       2005       2006       2007       2008       2009  \\\n",
      "country  city                                                                 \n",
      "Egypt    Cairo        22.006500  22.050000  22.361000  22.644500  22.625000   \n",
      "         Gizeh        22.006500  22.050000  22.361000  22.644500  22.625000   \n",
      "Ethiopia Addis Abeba  18.312833  18.427083  18.142583  18.165000  18.765333   \n",
      "France   Paris        11.552917  11.788500  11.750833  11.278250  11.464083   \n",
      "Germany  Berlin        9.919083  10.545333  10.883167  10.657750  10.062500   \n",
      "India    Ahmadabad    26.828083  27.282833  27.511167  27.048500  28.095833   \n",
      "         Bangalore    25.476500  25.418250  25.464333  25.352583  25.725750   \n",
      "         Bombay       27.035750  27.381500  27.634667  27.177750  27.844500   \n",
      "         Calcutta     26.729167  26.986250  26.584583  26.522333  27.153250   \n",
      "         Delhi        25.716083  26.365917  26.145667  25.675000  26.554250   \n",
      "\n",
      "year                       2010       2011       2012       2013  \n",
      "country  city                                                     \n",
      "Egypt    Cairo        23.718250  21.986917  22.484250  22.907000  \n",
      "         Gizeh        23.718250  21.986917  22.484250  22.907000  \n",
      "Ethiopia Addis Abeba  18.298250  18.606750  18.448583  19.539000  \n",
      "France   Paris        10.409833  12.325750  11.219917  11.011625  \n",
      "Germany  Berlin        8.606833  10.556417   9.964333  10.121500  \n",
      "India    Ahmadabad    28.017833  27.290417  27.027250  27.608625  \n",
      "         Bangalore    25.705250  25.362083  26.042333  26.610500  \n",
      "         Bombay       27.765417  27.384917  27.192500  26.713000  \n",
      "         Calcutta     27.288833  26.406917  26.935083  27.369250  \n",
      "         Delhi        26.520250  25.629500  25.889417  26.709250  \n",
      "year                       2005       2006       2007       2008       2009  \\\n",
      "country  city                                                                 \n",
      "Egypt    Cairo        22.006500  22.050000  22.361000  22.644500  22.625000   \n",
      "         Gizeh        22.006500  22.050000  22.361000  22.644500  22.625000   \n",
      "Ethiopia Addis Abeba  18.312833  18.427083  18.142583  18.165000  18.765333   \n",
      "France   Paris        11.552917  11.788500  11.750833  11.278250  11.464083   \n",
      "Germany  Berlin        9.919083  10.545333  10.883167  10.657750  10.062500   \n",
      "India    Ahmadabad    26.828083  27.282833  27.511167  27.048500  28.095833   \n",
      "         Bangalore    25.476500  25.418250  25.464333  25.352583  25.725750   \n",
      "         Bombay       27.035750  27.381500  27.634667  27.177750  27.844500   \n",
      "         Calcutta     26.729167  26.986250  26.584583  26.522333  27.153250   \n",
      "         Delhi        25.716083  26.365917  26.145667  25.675000  26.554250   \n",
      "\n",
      "year                       2010  \n",
      "country  city                    \n",
      "Egypt    Cairo        23.718250  \n",
      "         Gizeh        23.718250  \n",
      "Ethiopia Addis Abeba  18.298250  \n",
      "France   Paris        10.409833  \n",
      "Germany  Berlin        8.606833  \n",
      "India    Ahmadabad    28.017833  \n",
      "         Bangalore    25.705250  \n",
      "         Bombay       27.765417  \n",
      "         Calcutta     27.288833  \n",
      "         Delhi        26.520250  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv')\n",
    "\n",
    "temperatures['date'] = pd.to_datetime(temperatures['date'])\n",
    "\n",
    "temperatures['year'] = temperatures['date'].dt.year\n",
    "\n",
    "\n",
    "temp_by_country_city_vs_year = temperatures.pivot_table('avg_temp_c', \n",
    "                                                        index=['country', 'city'], \n",
    "                                                        columns='year').sort_index()\n",
    "\n",
    "#print(temp_by_country_city_vs_year.tail())\n",
    "\n",
    "\n",
    "\n",
    "print(temp_by_country_city_vs_year.loc['Egypt': 'India'])\n",
    "\n",
    "\n",
    "print(temp_by_country_city_vs_year.loc[('Egypt', 'Cairo'): ('India', 'Delhi')])\n",
    "\n",
    "\n",
    "\n",
    "print(temp_by_country_city_vs_year.loc[('Egypt', 'Cairo'): ('India', 'Delhi'), '2005': '2010'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e67eb53-b104-4168-b87c-8fefec13410d",
   "metadata": {},
   "source": [
    "## Calculating on a pivot table\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# Pivot tables are filled with summary statistics, but they are only a first step to finding something insightful. \n",
    "Often you'll need to perform further calculations on them. A common thing to do is to find the rows or columns where the highest or lowest value occurs.\n",
    "\n",
    "# Recall from Chapter 1 that you can easily subset a Series or DataFrame to find rows of interest using a logical condition inside of square brackets. For example: series[series > value].\n",
    "\n",
    "pandas is loaded as pd and the DataFrame temp_by_country_city_vs_year is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Calculate the mean temperature for each year, assigning to mean_temp_by_year.\n",
    "#    Filter mean_temp_by_year for the year that had the highest mean temperature.\n",
    "    Calculate the mean temperature for each city (across columns), assigning to mean_temp_by_city.\n",
    "    Filter mean_temp_by_city for the city that had the lowest mean temperature.\n",
    "\n",
    "Hint\n",
    "\n",
    "    Call .mean() without arguments to get the mean of each column.\n",
    "#    Use Boolean filtering of the form mean_temp_by_year equal to mean_temp_by_year.max().\n",
    "    Call .mean(), setting axis to \"columns\" to get the mean of each row.\n",
    "    Use Boolean filtering of the form mean_temp_by_city equal to mean_temp_by_city.min().\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fee1a314-6d33-48f6-9e88-1e4be243f51b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year                        2000       2001       2002       2003       2004  \\\n",
      "country     city                                                               \n",
      "Afghanistan Kabul      15.822667  15.847917  15.714583  15.132583  16.128417   \n",
      "Angola      Luanda     24.410333  24.427083  24.790917  24.867167  24.216167   \n",
      "Australia   Melbourne  14.320083  14.180000  14.075833  13.985583  13.742083   \n",
      "            Sydney     17.567417  17.854500  17.733833  17.592333  17.869667   \n",
      "Bangladesh  Dhaka      25.905250  25.931250  26.095000  25.927417  26.136083   \n",
      "\n",
      "year                        2005       2006       2007       2008       2009  \\\n",
      "country     city                                                               \n",
      "Afghanistan Kabul      14.847500  15.798500  15.518000  15.479250  15.093333   \n",
      "Angola      Luanda     24.414583  24.138417  24.241583  24.266333  24.325083   \n",
      "Australia   Melbourne  14.378500  13.991083  14.991833  14.110583  14.647417   \n",
      "            Sydney     18.028083  17.749500  18.020833  17.321083  18.175833   \n",
      "Bangladesh  Dhaka      26.193333  26.440417  25.951333  26.004500  26.535583   \n",
      "\n",
      "year                        2010       2011       2012       2013  \n",
      "country     city                                                   \n",
      "Afghanistan Kabul      15.676000  15.812167  14.510333  16.206125  \n",
      "Angola      Luanda     24.440250  24.150750  24.240083  24.553875  \n",
      "Australia   Melbourne  14.231667  14.190917  14.268667  14.741500  \n",
      "            Sydney     17.999000  17.713333  17.474333  18.089750  \n",
      "Bangladesh  Dhaka      26.648167  25.803250  26.283583  26.587000  \n",
      "year\n",
      "2000    19.506243\n",
      "2001    19.679352\n",
      "2002    19.855685\n",
      "2003    19.630197\n",
      "2004    19.672204\n",
      "2005    19.607239\n",
      "2006    19.793993\n",
      "2007    19.854270\n",
      "2008    19.608778\n",
      "2009    19.833752\n",
      "2010    19.911734\n",
      "2011    19.549197\n",
      "2012    19.668239\n",
      "2013    20.312285\n",
      "dtype: float64\n",
      "year\n",
      "2013    20.312285\n",
      "dtype: float64\n",
      "country        city            \n",
      "Afghanistan    Kabul               15.541955\n",
      "Angola         Luanda              24.391616\n",
      "Australia      Melbourne           14.275411\n",
      "               Sydney              17.799250\n",
      "Bangladesh     Dhaka               26.174440\n",
      "                                     ...    \n",
      "United States  Chicago             11.330825\n",
      "               Los Angeles         16.675399\n",
      "               New York            10.911034\n",
      "Vietnam        Ho Chi Minh City    27.922857\n",
      "Zimbabwe       Harare              20.699000\n",
      "Length: 100, dtype: float64\n",
      "country  city  \n",
      "China    Harbin    4.876551\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "temperatures = pd.read_csv('temperatures.csv')\n",
    "\n",
    "temperatures['date'] = pd.to_datetime(temperatures['date'])\n",
    "\n",
    "temperatures['year'] = temperatures['date'].dt.year\n",
    "\n",
    "\n",
    "temp_by_country_city_vs_year = temperatures.pivot_table('avg_temp_c', \n",
    "                                                        index=['country', 'city'], \n",
    "                                                        columns='year').sort_index()\n",
    "\n",
    "print(temp_by_country_city_vs_year.head())\n",
    "\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "# Calculate the mean temperature for each year, assigning to mean_temp_by_year\n",
    "mean_temp_by_year = temp_by_country_city_vs_year.mean(axis='index')\n",
    "print(mean_temp_by_year)\n",
    "\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "# Filter mean_temp_by_year for the year that had the highest mean temperature.\n",
    "#####################################################################################################################\n",
    "print(mean_temp_by_year[mean_temp_by_year==mean_temp_by_year.max()])\n",
    "\n",
    "\n",
    "\n",
    "# Call .mean(), setting axis to \"columns\" to get the mean of each row.\n",
    "mean_temp_by_city = temp_by_country_city_vs_year.mean(axis='columns')\n",
    "print(mean_temp_by_city)\n",
    "\n",
    "\n",
    "\n",
    "print(mean_temp_by_city[mean_temp_by_city==mean_temp_by_city.min()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d149970-eaf2-474c-957e-bd5847935480",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d1e6f703-3d65-41c5-b07f-ca8c79dd15a9",
   "metadata": {},
   "source": [
    "## Visualizing your data\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Plots are powerful way to share the insights you've gained from your data.  In this lesson, we'll use a bigger dataset of dogs, called dog_pack, to make visualization easier.  \n",
    "\n",
    "Remember when we talked about matplotlib at the beginning of the course?  We'll need to import matplotlib.pyplot as plt in order to display our visualizations.  Just like pd is the standard alias for Pandas, plt is the standard alias for matplotlib.pyplot.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Lets create a histogram, which shows the distribution of a numeric variable.  We can create a histogram of the height variable by selecting the column and calling tt() method.  (not using plt.hist() function on the np array).  In order to show the plt, we need to call plt.show().  The x-axis represents the heights of the dogs, and the y-axis represents the number of dogs in each height range.  By grouping observations into ranges, the histogram allows us to see that there are a lot of dogs around 50-60 centimeters tall.  \n",
    "\n",
    "We can adjust the number of bars, or bins, using the bins= argument.  Increasing or decreasing this can give us a better idea of what the distribution looks like.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Bar plots can reveal relationships between a categorical variable and a numeric variable, like breed and weight.  To compute the average weight of each breed, we group by breed select the weight column and take the mean, giving us the average weight of each breed.  \n",
    "**Now we can create a bar .plot() from the mean weights using the .plot() method, setting kind='bar'.  \n",
    "To add a title to our plot, we can use the title= argument of the .plot() method.  \n",
    "\n",
    "It looks like Saint Bernards are the heavest breed on average.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Line plots are great for visualizing changes in numeric variables over time.  Lucky for us a Labrador names Sully has been weighted by his owner every month - lets see how his weight has changed over the year.  We can use the .plot() method again, but this time, we pass 3 arguments: x='date', y='weight', kind='bar'.  The outcome show Sully's weight has fluctuated quite a bit over time.  \n",
    "\n",
    "**We may want to rotate the x-asix lebels to make the text easier to read.  This can be done by passing an angle in degrees with the rot= argument.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**Scatter plots are great for visualizing relationship between 2 numeric variables.  To plot each dog's height versus their weight, we call the .plot() method with argument x='height', y='weight', kind='scatter'.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**Plot can also be layered on top of one another.  For example, we can create a histogram of female dog's heights, and put a histogram of male dog's heights on top, than call plt.show().  However we can't tell which color represents which sex.  We can use plt.legend(['', '']) method, passing in a list of labels, and then call plt.show().  \n",
    "\n",
    "**Now we know which color is which, but we can't see whats going on behind the covered histogram.  Lets fix this problem by making the histograms translucant, using hist's alpha= argument.  \n",
    "\n",
    "\n",
    "# In this chapter, you'll be working with a dataset that contains weekly US avocado sales data, broken down by avocado size, and whether or not the avocado were organic.  \n",
    "\n",
    "\n",
    "\n",
    "avg_weight_by_breed = dog_pack.groupby('breed')['weight'].mean()\n",
    "or                    dog_pack.pivot_table('weight', index='breed').mean()\n",
    "\n",
    "avg_weight_by_breed.plot(kind='bar', title='Avg_weight_by_breed')\n",
    "\n",
    "\n",
    "\n",
    "sully.plot(x='date', y='weight', kind='line', rot=45)\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "dog_pack[dog_pack['sex']=='F']['height_cm'].hist(alpha=0.7)\n",
    "dog_pack[dog_pack['sex']=='M']['height_cm'].hist(alpha=0.7)\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "plt.legend(['F', 'M'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6fabd906-b476-4eb2-bc41-403cfde44df6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name       breed  color  height_cn  weight_km data_of_birth\n",
      "0    Bella    Labrador  Brown         56         24    2013-07-01\n",
      "1  Charlie      Poodle  Black         43         24    2016-09-16\n",
      "2     Lucy   Chow Chow  Brown         46         24    2014-08-25\n",
      "3   Cooper   Schnauzer   Gray         49         17    2011-12-11\n",
      "4      Max    Labrador  Black         59         29    2017-01-20\n",
      "5   Stella   Chihuahua    Tan         18          2    2015-04-20\n",
      "6   Bernie  St.Bernard  White         77         74    2018-02-27\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            weight_km\n",
      "breed                \n",
      "Chihuahua         2.0\n",
      "Chow Chow        24.0\n",
      "Labrador         26.5\n",
      "Poodle           24.0\n",
      "Schnauzer        17.0\n",
      "St.Bernard       74.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "dogs = pd.read_csv('dogs2.csv')\n",
    "print(dogs)\n",
    "\n",
    "dogs['height_cn'].hist()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "avg_weight_by_breed = dogs.groupby('breed')['weight_km'].mean()\n",
    "avg_weight_by_breed = dogs.pivot_table('weight_km', index='breed')\n",
    "#####################################################################################################################\n",
    "\n",
    "print(avg_weight_by_breed)\n",
    "\n",
    "\n",
    "avg_weight_by_breed.hist()\n",
    "plt.show()\n",
    "\n",
    "#####################################################################################################################\n",
    "dogs.pivot_table('weight_km', index='breed').hist()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "avg_weight_by_breed.plot(kind='bar', title='Avg_weight_by_breed')\n",
    "plt.show()\n",
    "\n",
    "#####################################################################################################################\n",
    "dogs.pivot_table(values='weight_km', index='breed').plot(kind='bar')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bf15823a-4c50-44c9-ab33-0a6ab173c6fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "year = [1950, 1970, 1990, 2010]\n",
    "pop = [2.519, 3.692, 5.263, 6.972]\n",
    "\n",
    "plt.plot(year, pop)\n",
    "plt.show()\n",
    "\n",
    "plt.plot(year)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "385500cd-847f-4f98-b9d2-4f5820cf2824",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.84 67.12]\n",
      " [ 1.97 60.01]\n",
      " [ 1.72 64.15]\n",
      " ...\n",
      " [ 1.57 57.04]\n",
      " [ 1.86 63.56]\n",
      " [ 1.56 62.52]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "height = np.round(np.random.normal(1.75, 0.20, 5000),2)\n",
    "weight = np.round(np.random.normal(60.32, 5, 5000),2)\n",
    "\n",
    "#####################################################################################################################\n",
    "np_city = np.column_stack((height, weight))\n",
    "print(np_city)\n",
    "\n",
    "np_weight = np.array(weight)\n",
    "\n",
    "# ***************************************************************************************************************** #\n",
    "#fig = plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.hist(height, 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "39dc9c6b-cdaf-4594-b7e9-ae2182afe3ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            date          type  year  avg_price         size     nb_sold\n",
      "0     2015-12-27  conventional  2015       0.95        small  9626901.09\n",
      "1     2015-12-20  conventional  2015       0.98        small  8710021.76\n",
      "2     2015-12-13  conventional  2015       0.93        small  9855053.66\n",
      "3     2015-12-06  conventional  2015       0.89        small  9405464.36\n",
      "4     2015-11-29  conventional  2015       0.99        small  8094803.56\n",
      "...          ...           ...   ...        ...          ...         ...\n",
      "1009  2018-02-04       organic  2018       1.53  extra_large     1703.52\n",
      "1010  2018-01-28       organic  2018       1.61  extra_large     1270.61\n",
      "1011  2018-01-21       organic  2018       1.63  extra_large     1490.02\n",
      "1012  2018-01-14       organic  2018       1.59  extra_large     1580.01\n",
      "1013  2018-01-07       organic  2018       1.51  extra_large     1289.07\n",
      "\n",
      "[1014 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "avacado = pd.read_pickle('avoplotto.pkl')\n",
    "print(avacado)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "356089c9-083d-44c7-bc85-917ebf6d59d7",
   "metadata": {},
   "source": [
    "## Which avocado size is most popular?\n",
    "\n",
    "Avocados are increasingly popular and delicious in guacamole and on toast. The Hass Avocado Board keeps track of avocado supply and demand across the USA, including the sales of three different sizes of avocado. In this exercise, you'll use a bar plot to figure out which size is the most popular.\n",
    "\n",
    "# Bar plots are great for revealing relationships between categorical (size) and numeric (number sold) variables, but you'll often have to manipulate your data first in order to get the numbers you need for plotting.\n",
    "\n",
    "pandas has been imported as pd, and avocados is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Print the head of the avocados dataset. What columns are available?\n",
    "    For each avocado size group, calculate the total number sold, storing as nb_sold_by_size.\n",
    "    Create a bar plot of the number of avocados sold by size.\n",
    "    Show the plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d809b241-fd62-438a-b45b-5c31d738eab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date          type  year  avg_price   size     nb_sold\n",
      "0  2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1  2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2  2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3  2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4  2015-11-29  conventional  2015       0.99  small  8094803.56\n",
      "                  nb_sold\n",
      "size                     \n",
      "extra_large  1.561752e+08\n",
      "large        2.015012e+09\n",
      "small        2.054936e+09\n",
      "size\n",
      "extra_large    1.561752e+08\n",
      "large          2.015012e+09\n",
      "small          2.054936e+09\n",
      "Name: nb_sold, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "avocado = pd.read_pickle('avoplotto.pkl')\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Print the head of the avocados dataset\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "# For each avocado size group, calculate the total number sold, storing as nb_sold_by_size\n",
    "nb_sold_by_size = avocado.pivot_table('nb_sold', index='size', aggfunc=np.sum)   ### Do Not add .sum() in the end\n",
    "print(nb_sold_by_size)                                                           ### It sum up everything\n",
    "#####################################################################################################################\n",
    "#####################################################################################################################\n",
    "nb_sold_by_size = avocado.groupby('size')['nb_sold'].sum()\n",
    "print(nb_sold_by_size)\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "plt.rcParams['font.size'] = '12'\n",
    "\n",
    "\n",
    "#Create a bar plot of the number of avocados sold by size\n",
    "#####################################################################################################################\n",
    "nb_sold_by_size.plot(kind='bar', rot=45, title='Avocado_sold_by_size', alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "60e826da-873e-4d3e-ac44-8b2e9cc74381",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(help(avocado.pivot_table))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34dfdac-36cf-4c18-b5a9-5100a90f37b0",
   "metadata": {},
   "source": [
    "## Changes in sales over time\n",
    "\n",
    "# Line plots are designed to visualize the relationship between two numeric variables, where each data values is connected to the next one. \n",
    "They are especially useful for visualizing the change in a number over time since each time point is naturally connected to the next time point. In this exercise, you'll visualize the change in avocado sales over three years.\n",
    "\n",
    "pandas has been imported as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Get the total number of avocados sold on each date. The DataFrame has two rows for each date -- one for organic, and one for conventional. Save this as nb_sold_by_date.\n",
    "    Create a line plot of the number of avocados sold.\n",
    "    Show the plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "372e5440-9e71-488d-b7bb-4440c8bf27a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date          type  year  avg_price   size     nb_sold\n",
      "0  2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1  2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2  2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3  2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4  2015-11-29  conventional  2015       0.99  small  8094803.56\n",
      "                nb_sold\n",
      "date                   \n",
      "2015-01-04  27279606.03\n",
      "2015-01-11  25081927.33\n",
      "2015-01-18  24961540.48\n",
      "2015-01-25  24094678.66\n",
      "2015-02-01  39838734.08\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "avocado = pd.read_pickle('avoplotto.pkl')\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "nb_sold_by_date = avocado.pivot_table('nb_sold', index='date', aggfunc=np.sum)\n",
    "print(nb_sold_by_date.head())\n",
    "\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "nb_sold_by_date.plot(kind='line', rot=45, title='total_avocado_sold_by_dates')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c651d3b5-4138-4f69-8a7f-6f1c2954d449",
   "metadata": {},
   "source": [
    "## Avocado supply and demand\n",
    "\n",
    "# Scatter plots are ideal for visualizing relationships between numerical variables. \n",
    "In this exercise, you'll compare the number of avocados sold to average price and see if they're at all related. If they're related, you may be able to use one number to predict the other.\n",
    "\n",
    "matplotlib.pyplot has been imported as plt and pandas has been imported as pd.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Create a scatter plot with nb_sold on the x-axis and avg_price on the y-axis. Title it \"Number of avocados sold vs. average price\".\n",
    "    Show the plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9300203d-f5ff-415c-a52c-cf0c0a2d50af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date          type  year  avg_price   size     nb_sold\n",
      "0  2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1  2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2  2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3  2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4  2015-11-29  conventional  2015       0.99  small  8094803.56\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "avocado = pd.read_pickle('avoplotto.pkl')\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "plt.rcParams['font.size'] = '14'\n",
    "#####################################################################################################################\n",
    "\n",
    "\n",
    "# Scatter plot of nb_sold vs avg_price with title\n",
    "avocado.plot(kind='scatter', x='nb_sold', y='avg_price', title='Number of avocados sold vs. average price')\n",
    "\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e2c1b93-55ad-4b8f-a4f7-afd380519efd",
   "metadata": {},
   "source": [
    "## Price of conventional vs. organic avocados\n",
    "\n",
    "Creating multiple plots for different subsets of data allows you to compare groups. In this exercise, you'll create multiple histograms to compare the prices of conventional and organic avocados.\n",
    "\n",
    "matplotlib.pyplot has been imported as plt and pandas has been imported as pd.\n",
    "Instructions 1/3\n",
    "35 XP\n",
    "\n",
    "    Question 1\n",
    "#    Subset avocados for the conventional type, and the average price column. Create a histogram.\n",
    "    Create a histogram of avg_price for organic type avocados.\n",
    "    Add a legend to your plot, with the names \"conventional\" and \"organic\".\n",
    "    Show your plot.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    Modify your code to adjust the transparency of both histograms to 0.5 to see how much overlap there is between the two distributions.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 3\n",
    "    Modify your code to use 20 bins in both histograms.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "11e422b2-6060-4e96-9edb-17178e10c98b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date          type  year  avg_price   size     nb_sold\n",
      "0  2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1  2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2  2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3  2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4  2015-11-29  conventional  2015       0.99  small  8094803.56\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "avocado = pd.read_pickle('avoplotto.pkl')\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "avocado[avocado['type']=='conventional']['avg_price'].hist(alpha=0.6, bins=20)\n",
    "avocado[avocado['type']=='organic']['avg_price'].hist(alpha=0.6, bins=20)\n",
    "#####################################################################################################################\n",
    "\n",
    "\n",
    "plt.xlabel('avg_price_USD')\n",
    "plt.ylabel('number of counts')\n",
    "plt.title('conventional_&_organic_avg_price_counts')\n",
    "#####################################################################################################################\n",
    "\n",
    "\n",
    "plt.legend(['conventional', 'organic'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a279307d-7583-473a-92f0-083ccc9d643b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date          type  year  avg_price   size     nb_sold\n",
      "0  2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1  2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2  2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3  2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4  2015-11-29  conventional  2015       0.99  small  8094803.56\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "avocado = pd.read_pickle('avoplotto.pkl')\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "#####################################################################################################################\n",
    "avocado.pivot_table('nb_sold', index='size', aggfunc=np.sum).plot(kind='bar', alpha=0.6, rot=45)\n",
    "plt.title('Size_NBSold_Sum', fontsize=16)\n",
    "plt.xlabel('Size Categories')\n",
    "plt.ylabel('Sum_of_NBSold')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "avocado[avocado['type']=='organic']['avg_price'].hist(bins=20, alpha=0.6)\n",
    "plt.title('Type_Category_AvgPrice_Hist', fontsize=16)\n",
    "plt.xlabel('AvgPrice in USD')\n",
    "plt.ylabel('Number_of_counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3adcedea-22c2-4d37-8155-2769ecf9968a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date          type  year  avg_price   size     nb_sold\n",
      "0  2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1  2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2  2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3  2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4  2015-11-29  conventional  2015       0.99  small  8094803.56\n",
      "size\n",
      "extra_large    1.319024\n",
      "large          1.319024\n",
      "small          1.319024\n",
      "Name: avg_price, dtype: float64\n",
      "             avg_price\n",
      "size                  \n",
      "extra_large     445.83\n",
      "large           445.83\n",
      "small           445.83\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "avocado = pd.read_pickle('avoplotto.pkl')\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "print(avocado.groupby('size')['avg_price'].mean())\n",
    "print(avocado.pivot_table('avg_price', index='size', aggfunc=np.sum))\n",
    "\n",
    "\n",
    "avocado[avocado['size']=='small']['avg_price'].hist(alpha=0.6, bins=30)\n",
    "#avocado[avocado['size']=='large']['avg_price'].hist(alpha=0.6, bins=30)\n",
    "#avocado[avocado['size']=='extra_large']['avg_price'].hist(alpha=0.6)\n",
    "\n",
    "plt.xlabel('number_sold')\n",
    "plt.ylabel('number of counts')\n",
    "plt.title('each_size_weekly_nb_sold_counts')\n",
    "#####################################################################################################################\n",
    "\n",
    "\n",
    "plt.legend(['small', 'large', 'extra_large'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e52668-3c76-4026-948d-ec15145e39f3",
   "metadata": {},
   "source": [
    "## Missing values\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**You could be given a DataFrame that has missing values, so its important to know how to handle them.  Most data is not perfect - there is always a possibility that there are some pieces missing from your dataset.  In a Pandas DataFrame, missing values are indicated with NaN, which stands for not a number.  \n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "When you first get a DataFrame, its a good idea to get a sence of wheather it contains any missing values, and if so, how many.  That's where .isna() method comes in,  when we call .isna() on a DataFrame, we get a Boolean for every single value(interesting, on value level instead of colun level) indicating whether the value is missing or not, but this isn't very helpful when working with a lot of data.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# If we chain .isna() with .any() method together, we get one value for each variable that tells us if there are any missing values in that column.  \n",
    "# Since taking the .sum() of booleans is the same thing as counting the number of Trues, we can combine .sum() with .isna() to count the number of NaNs in each column.  \n",
    "\n",
    "# *******************************************************************************************************************\n",
    "We can use those counts to visualize the missing values in the dataset using bar plot.  Plots like this are more interesting when you have missing data across different variables, while here, only weights are missing.  \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "# Now that we know there are missing values in the dataset, what can we do about them.  ?\n",
    "\n",
    "\n",
    "One option is to remove the rows in the DataFrame that contain missing values.  This can be done using .dropna() method.  However this may not be idea if you have lots of missing values in one of the columns.  Since that means removing lots of observations.  \n",
    "\n",
    "**Another option is to replace missing values with another value.  The .fillna() method takes in a value, and all NaNs will be replaced with this value.  \n",
    "\n",
    "There are also many sophisticated techniques for replacing missing values, which you can learn more about in our course about missing data.   \n",
    "\n",
    "\n",
    "\n",
    "   dogs.isna().sum().plot(kind='bar', rot=45, title='missing_value_count_columns')\n",
    "\n",
    "   dogs['weight_cm'].fillna(dogs.groupby('breed')['weight_cm'].mean())\n",
    "   \n",
    "# *******************************************************************************************************************\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Hard working does not means good outcome\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "cada5aaa-b516-486a-867b-165b18f60a0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name             False\n",
      "breed            False\n",
      "color            False\n",
      "height_cn        False\n",
      "weight_km        False\n",
      "data_of_birth    False\n",
      "dtype: bool\n",
      "name             0\n",
      "breed            0\n",
      "color            0\n",
      "height_cn        0\n",
      "weight_km        0\n",
      "data_of_birth    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "dogs = pd.read_csv('dogs2.csv')\n",
    "\n",
    "\n",
    "#print(dogs.isna())\n",
    "\n",
    "print(dogs.isna().any())\n",
    "\n",
    "print(dogs.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ecc2867-d83d-41cc-9ca9-117df91240c8",
   "metadata": {},
   "source": [
    "## Finding missing values\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Missing values are everywhere, and you don't want them interfering with your work. Some functions ignore missing data by default, but that's not always the behavior you might want. Some functions can't handle missing values at all, so these values need to be taken care of before you can use them. If you don't know where your missing values are, or if they exist, you could make mistakes in your analysis. In this exercise, you'll determine if there are missing values in the dataset, and if so, how many.\n",
    "\n",
    "pandas has been imported as pd and avocados_2016, a subset of avocados that contains only sales from 2016, is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Print a DataFrame that shows whether each value in avocados_2016 is missing or not.\n",
    "    Print a summary that shows whether any value in each column is missing or not.\n",
    "    Create a bar plot of the total number of missing values in each column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "dc3eb78e-49c9-468d-9521-a9a93bf1ce69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date          type  year  avg_price   size     nb_sold\n",
      "0  2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1  2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2  2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3  2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4  2015-11-29  conventional  2015       0.99  small  8094803.56\n",
      "        date          type  year  avg_price   size     nb_sold\n",
      "0 2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1 2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2 2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3 2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4 2015-11-29  conventional  2015       0.99  small  8094803.56\n",
      "        date          type  year  avg_price   size     nb_sold\n",
      "0 2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1 2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2 2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3 2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4 2015-11-29  conventional  2015       0.99  small  8094803.56\n",
      "         date          type  year  avg_price   size      nb_sold\n",
      "52 2016-12-25  conventional  2016       1.00  small   9255125.20\n",
      "53 2016-12-18  conventional  2016       0.96  small   9394065.91\n",
      "54 2016-12-11  conventional  2016       0.98  small   9009996.11\n",
      "55 2016-12-04  conventional  2016       1.00  small  11043350.90\n",
      "56 2016-11-27  conventional  2016       1.21  small   7891487.94\n",
      "(312, 6)\n",
      "312\n",
      "date         0\n",
      "type         0\n",
      "year         0\n",
      "avg_price    0\n",
      "size         0\n",
      "nb_sold      0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "avocado = pd.read_pickle('avoplotto.pkl')\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "avocado['date'] = pd.to_datetime(avocado['date'])\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "avocado['year'] = avocado['date'].dt.year\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "\n",
    "avocados_2016 = avocado[avocado['year']==2016]\n",
    "print(avocados_2016.head())\n",
    "\n",
    "print(avocados_2016.shape)\n",
    "print(len(avocados_2016['date']))\n",
    "\n",
    "#####################################################################################################################\n",
    "print(avocados_2016.isna().sum())\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "avocados_2016.isna().sum().plot(kind='bar', rot=45)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "avocados_2016[['date', 'type', 'avg_price', 'size']].isna().sum().plot(kind='bar', rot=45)\n",
    "#####################################################################################################################\n",
    "#####################################################################################################################\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4996c228-3950-4bb2-8cd0-fe5c46e2fb8a",
   "metadata": {},
   "source": [
    "## Removing missing values\n",
    "\n",
    "Now that you know there are some missing values in your DataFrame, you have a few options to deal with them. One way is to remove them from the dataset completely. In this exercise, you'll remove missing values by removing all rows that contain missing values.\n",
    "\n",
    "pandas has been imported as pd and avocados_2016 is available.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Remove the rows of avocados_2016 that contain missing values and store the remaining rows in avocados_complete.\n",
    "    Verify that all missing values have been removed from avocados_complete. Calculate each column that has NAs and print.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da1fa3d1-057e-46c1-8223-75da618b813f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date         0\n",
      "type         0\n",
      "year         0\n",
      "avg_price    0\n",
      "size         0\n",
      "nb_sold      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "avocados_complete = avocados_2016.dropna()\n",
    "\n",
    "\n",
    "print(avocados_complete.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2abfad79-4993-4f81-bfef-a8bb5fa94721",
   "metadata": {},
   "source": [
    "## Replacing missing values\n",
    "\n",
    "Another way of handling missing values is to replace them all with the same value. For numerical variables, one option is to replace values with 0— you'll do this here. However, when you replace missing values, you make assumptions about what a missing value means. In this case, you will assume that a missing number sold means that no sales for that avocado type were made that week.\n",
    "\n",
    "In this exercise, you'll see how replacing missing values can affect the distribution of a variable using histograms. You can plot histograms for multiple variables at a time as follows:\n",
    "\n",
    "# dogs[[\"height_cm\", \"weight_kg\"]].hist()\n",
    "\n",
    "pandas has been imported as pd and matplotlib.pyplot has been imported as plt. The avocados_2016 dataset is available.\n",
    "Instructions 1/2\n",
    "50 XP\n",
    "\n",
    "    Question 1\n",
    "# *******************************************************************************************************************\n",
    "#    A list has been created, cols_with_missing, containing the names of columns with missing values: \"small_sold\", \"large_sold\", and \"xl_sold\".\n",
    "    Create a histogram of those columns.\n",
    "    Show the plot.\n",
    "    \n",
    "Hint\n",
    "    Call .hist() on a DataFrame to create a histogram.\n",
    "    To plot distributions of multiple columns, call .hist() on a subset of columns (df['col_a', 'col_b'].hist()).\n",
    "# I just cant imagine this, what would the hist looks like after our code is ran.  \n",
    "# *******************************************************************************************************************\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    Replace the missing values of avocados_2016 with 0s and store the result as avocados_filled.\n",
    "    Create a histogram of the cols_with_missing columns of avocados_filled.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "286be1f7-c854-4a43-a316-fd0810b69021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['small' 'large' 'extra_large']\n",
      "\n",
      "\n",
      "           date          type  year  avg_price         size      nb_sold\n",
      "441  2016-01-03  conventional  2016       0.86        large  16054083.86\n",
      "779  2016-01-03  conventional  2016       0.86  extra_large   1560068.62\n",
      "610  2016-01-03       organic  2016       1.43        large    246580.95\n",
      "272  2016-01-03       organic  2016       1.43        small    124799.63\n",
      "103  2016-01-03  conventional  2016       0.86        small  11616506.17\n",
      "..          ...           ...   ...        ...          ...          ...\n",
      "559  2016-12-25       organic  2016       1.34        large    250138.66\n",
      "728  2016-12-25  conventional  2016       1.00  extra_large    541972.42\n",
      "897  2016-12-25       organic  2016       1.34  extra_large      3415.27\n",
      "390  2016-12-25  conventional  2016       1.00        large  10282925.61\n",
      "52   2016-12-25  conventional  2016       1.00        small   9255125.20\n",
      "\n",
      "[312 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "cols_with_missing = ['small', 'extra_large']\n",
    "\n",
    "\n",
    "\n",
    "#print(avocados_2016[avocados_2016['size'].isin(cols_with_missing)])\n",
    "#####################################################################################################################\n",
    "\n",
    "\n",
    "print(avocados_2016['size'].unique())\n",
    "#####################################################################################################################\n",
    "print('\\n')\n",
    "print(avocados_2016.sort_values('date'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e6bd0c1f-ec1a-45be-a082-1e3926d027f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List the columns with missing values\n",
    "cols_with_missing = [\"small_sold\", \"large_sold\", \"xl_sold\"]\n",
    "cols_with_missing = ['small', 'large', 'extra_large']\n",
    "\n",
    "\n",
    "avocado = pd.read_pickle('avoplotto.pkl')\n",
    "#print(avocado.head())\n",
    "avocados_2016 = avocado[avocado['year']==2016]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "avocados_2016[avocados_2016['size'].isin(cols_with_missing)].isna().sum().plot(kind='bar', rot=45)\n",
    "#####################################################################################################################\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "ba46ac6e-06d2-4ac4-b1be-b5f2858c07e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date          type  year  avg_price   size     nb_sold\n",
      "0  2015-12-27  conventional  2015       0.95  small  9626901.09\n",
      "1  2015-12-20  conventional  2015       0.98  small  8710021.76\n",
      "2  2015-12-13  conventional  2015       0.93  small  9855053.66\n",
      "3  2015-12-06  conventional  2015       0.89  small  9405464.36\n",
      "4  2015-11-29  conventional  2015       0.99  small  8094803.56\n",
      "         date          type  year  avg_price   size     nb_sold  small_sold  \\\n",
      "0  2015-12-27  conventional  2015       0.95  small  9626901.09         NaN   \n",
      "1  2015-12-20  conventional  2015       0.98  small  8710021.76         NaN   \n",
      "2  2015-12-13  conventional  2015       0.93  small  9855053.66         NaN   \n",
      "3  2015-12-06  conventional  2015       0.89  small  9405464.36         NaN   \n",
      "4  2015-11-29  conventional  2015       0.99  small  8094803.56         NaN   \n",
      "\n",
      "   large_sold  xl_sold  \n",
      "0         NaN      NaN  \n",
      "1         NaN      NaN  \n",
      "2         NaN      NaN  \n",
      "3         NaN      NaN  \n",
      "4         NaN      NaN  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             avg_price                 nb_sold                          \n",
      "size       extra_large large small extra_large        large        small\n",
      "date                                                                    \n",
      "2015-01-04        2.41  2.41  2.41   848464.31  13840694.25  12590447.47\n",
      "2015-01-11        2.43  2.43  2.43   870405.08  12395744.98  11815777.27\n",
      "2015-01-18        2.45  2.45  2.45   835322.75  12013758.81  12112458.92\n",
      "2015-01-25        2.57  2.57  2.57   772773.92  10946964.84  12374939.90\n",
      "2015-02-01        2.25  2.25  2.25  1387368.39  19215766.18  19235599.51\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "avocado = pd.read_pickle('avoplotto.pkl')\n",
    "print(avocado.head())\n",
    "\n",
    "#####################################################################################################################\n",
    "#####################################################################################################################\n",
    "#####################################################################################################################\n",
    "avocado['small_sold'] = avocado[avocado['size']=='small'].isna().sum()\n",
    "avocado['large_sold'] = avocado[avocado['size']=='large'].isna().sum()\n",
    "avocado['xl_sold'] = avocado[avocado['size']=='extra_large'].isna().sum()\n",
    "\n",
    "print(avocado.head())\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "#####################################################################################################################\n",
    "#####################################################################################################################\n",
    "avocado[['small_sold', 'large_sold', 'xl_sold']].hist()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Fill in missing values with 0\n",
    "#avocados_filled = avocados_2016.fillna(0)\n",
    "\n",
    "# Create histograms of the filled columns\n",
    "#avocados_filled[cols_with_missing].hist()\n",
    "\n",
    "# Show the plot\n",
    "#plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# List the columns with missing values\n",
    "cols_with_missing = [\"small_sold\", \"large_sold\", \"xl_sold\"]\n",
    "\n",
    "\n",
    "date_zise_avocado = avocado.pivot_table(values=['avg_price', 'nb_sold'], \n",
    "                                        index='date', \n",
    "                                        columns='size', \n",
    "                                        aggfunc=np.sum).sort_index()\n",
    "print(date_zise_avocado.head())\n",
    "\n",
    "\n",
    "\n",
    "#date_zise_avocado2 = avocado.pivot_table(avocado.groupby(['date', 'size'])[:, :].isna(), index='date', columns='size', aggfunc=np.sum)\n",
    "#print(date_zise_avocado2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd98395f-cde8-46bc-b67d-2de2dd287c25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a46ffc9-0ea1-402d-9066-a56fa0175c5c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4e122896-4ee2-4ee9-9e88-83b7cf0eedf4",
   "metadata": {},
   "source": [
    "## Creating DataFrames\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**How do you get data into DataFrame in the first place?   DO I still remember the two ways to manually put data into a DF?  A list of dictionaries, just like what we got with csv.DictReader() on a csv file.  And a dictionary with column names as keys and values in list.  \n",
    "\n",
    "\n",
    "Before creating you own DFs, lets talk about dictionaries.  Dictionary is a way of storing data in Python.  It holds a set of key/value pairs.  You can access values of a dictionary via their keys in square brackets.  \n",
    "\n",
    "\n",
    "# There are many ways of creating a DF, this course will discuss two ways: \n",
    "from a list of dictionaries, and from a dictionary of lists.  \n",
    "# *******************************************************************************************************************\n",
    "In the first method, the DF is built up row by row,  while in the second method, the DF is built up column by column.  \n",
    "\n",
    "\n",
    "# Its exactly as what I have pre-learned before, in DataCamp Intermediate Python course.  \n",
    "\n",
    "# I think this is understanding, cause I don't need to take notes, I have searched and learned it, and I can recall it at anytime, even explain it to anyone.  Thats good experience.  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c36f1bd4-b408-474a-bf64-e5feb6d37be2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "63739689-1a3a-49fa-aa37-3e6d7ff4ea2e",
   "metadata": {},
   "source": [
    "## List of dictionaries\n",
    "\n",
    "You recently got some new avocado data from 2019 that you'd like to put in a DataFrame using the list of dictionaries method. Remember that with this method, you go through the data row by row.\n",
    "date \tsmall_sold \tlarge_sold\n",
    "\"2019-11-03\" \t10376832 \t7835071\n",
    "\"2019-11-10\" \t10717154 \t8561348\n",
    "\n",
    "pandas as pd is imported.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Create a list of dictionaries with the new data called avocados_list.\n",
    "    Convert the list into a DataFrame called avocados_2019.\n",
    "    Print your new DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80044a1b-31c6-4695-a38a-2183058746e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of dictionaries with new data\n",
    "avocados_list = [\n",
    "    {____: ____, ____: ____, ____: ____},\n",
    "    {____: ____, ____: ____, ____: ____},\n",
    "]\n",
    "\n",
    "# Convert list into DataFrame\n",
    "avocados_2019 = ____\n",
    "\n",
    "# Print the new DataFrame\n",
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3295715b-61c7-483b-83b8-487623754d55",
   "metadata": {},
   "source": [
    "## Dictionary of lists\n",
    "\n",
    "Some more data just came in! This time, you'll use the dictionary of lists method, parsing the data column by column.\n",
    "date \tsmall_sold \tlarge_sold\n",
    "\"2019-11-17\" \t10859987 \t7674135\n",
    "\"2019-12-01\" \t9291631 \t6238096\n",
    "\n",
    "pandas as pd is imported.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Create a dictionary of lists with the new data called avocados_dict.\n",
    "    Convert the dictionary to a DataFrame called avocados_2019.\n",
    "    Print your new DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea0869e2-d768-43be-913e-b11424019491",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary of lists with new data\n",
    "avocados_dict = {\n",
    "  \"____\": [____],\n",
    "  \"____\": [____],\n",
    "  \"____\": [____]\n",
    "}\n",
    "\n",
    "# Convert dictionary into DataFrame\n",
    "avocados_2019 = ____\n",
    "\n",
    "# Print the new DataFrame\n",
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd6092e-28ad-4caf-9a28-40ddd444ae03",
   "metadata": {},
   "source": [
    "## Reading and writing CSVs\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**In this video, you'll learn how to pull data from a .csv file.  The .csv is designed to store tabular data just like Pandas' DF.  \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "\n",
    "# Duplicated content, learnt for before.  \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# To convert a DF into a csv file, we use .to_csv() method, and pass in a new file path.  \n",
    "\n",
    "DF.to_csv('my_new_csv.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3808ca2a-4b7e-40f1-9e86-de297726c477",
   "metadata": {},
   "source": [
    "## CSV to DataFrame\n",
    "\n",
    "You work for an airline, and your manager has asked you to do a competitive analysis and see how often passengers flying on other airlines are involuntarily bumped from their flights. You got a CSV file (airline_bumping.csv) from the Department of Transportation containing data on passengers that were involuntarily denied boarding in 2016 and 2017, but it doesn't have the exact numbers you want. In order to figure this out, you'll need to get the CSV into a pandas DataFrame and do some manipulation!\n",
    "\n",
    "pandas is imported for you as pd. \"airline_bumping.csv\" is in your working directory.\n",
    "Instructions 1/4\n",
    "25 XP\n",
    "\n",
    "    Question 1\n",
    "    Read the CSV file \"airline_bumping.csv\" and store it as a DataFrame called airline_bumping.\n",
    "    Print the first few rows of airline_bumping.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 2\n",
    "    For each airline group, select the nb_bumped, and total_passengers columns, and calculate the sum (for both years). Store this as airline_totals.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 3\n",
    "    Create a new column of airline_totals called bumps_per_10k, which is the number of passengers bumped per 10,000 passengers in 2016 and 2017.\n",
    "    \n",
    "    \n",
    "    \n",
    "    Question 4\n",
    "    Print airline_totals to see the results of your manipulations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90045686-6706-4965-92df-4241b40f9d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read CSV as DataFrame called airline_bumping\n",
    "airline_bumping = ____\n",
    "\n",
    "# Take a look at the DataFrame\n",
    "print(____)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#####################################################################################################################\n",
    "# For each airline, select nb_bumped and total_passengers and sum\n",
    "airline_totals = airline_bumping.groupby('airline')[['nb_bumped', 'total_passengers']].sum()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "airline_totals[\"bumps_per_10k\"] = airline_totals['nb_bumped'] / airline_totals['total_passengers'] * 10000\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708127ac-4baa-4d90-a873-6d2f6517cffb",
   "metadata": {},
   "source": [
    "## DataFrame to CSV\n",
    "\n",
    "You're almost there! To make things easier to read, you'll need to sort the data and export it to CSV so that your colleagues can read it.\n",
    "\n",
    "pandas as pd has been imported for you.\n",
    "Instructions\n",
    "100 XP\n",
    "\n",
    "    Sort airline_totals by the values of bumps_per_10k from highest to lowest, storing as airline_totals_sorted.\n",
    "    Print your sorted DataFrame.\n",
    "    Save the sorted DataFrame as a CSV called \"airline_totals_sorted.csv\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18440cac-2a6b-42a2-888e-21122e8f7950",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create airline_totals_sorted\n",
    "airline_totals_sorted = ____\n",
    "\n",
    "# Print airline_totals_sorted\n",
    "____\n",
    "\n",
    "# Save as airline_totals_sorted.csv\n",
    "____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c1f68ac-968e-4e07-8334-f29a9597f739",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4679bd78-d0ca-46f7-92fd-797cb8905227",
   "metadata": {},
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  },
  {
   "cell_type": "markdown",
   "id": "10f8f14e-2677-4630-89f4-e2ed1e03d1ba",
   "metadata": {},
   "source": [
    "## Wrap-up\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "**Congradulations, you've now covered the basics of using Pandas.  \n",
    "\n",
    "\n",
    "In chapter1, you saw how to subset and sort DF and how to add new columns.  \n",
    "\n",
    "In chapter2, you saw several methods for aggregating and grouping data to calculate summary statistics.  \n",
    "#    (.pivot_table(values=, index=, columns=, aggfunc=, fill_value=, margins=))\n",
    "#     df.groupby('column')['another_column'].sum() or df.groupby('column')['another_column'].agg(np.sum, np.mean)\n",
    "\n",
    "\n",
    "In chapter3, you saw how indexing and slicing allows us for simpler subsetting ?? Do I still remember\n",
    "\n",
    "\n",
    "In chapter4, you saw how to visualize a DF, and how to read data from and write to csv file.  \n",
    "\n",
    "\n",
    "\n",
    "# *******************************************************************************************************************\n",
    "Pandas is powerful tool to analyze tabular data, many features are not covered in this course.  \n",
    "\n",
    "\n",
    "# In this course, everything we did involved only one DF, but sometimes, you need to join or merge several DFs. \n",
    "\n",
    "\n",
    "\n",
    "# Reading form csv bearly scratches the surface of the option for importing data into Pandas. \n",
    "\n",
    "\n",
    "\n",
    "You can also perform very sophisticated exploratory data analyze using Pandas package.  \n"
   ]
  },
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   "execution_count": 98,
   "id": "ca2aa6ca-488f-4144-8c46-746ef4212e5e",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pivot_table in module pandas.core.frame:\n",
      "\n",
      "pivot_table(self, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True) -> 'DataFrame'\n",
      "    Create a spreadsheet-style pivot table as a DataFrame.\n",
      "    \n",
      "    The levels in the pivot table will be stored in MultiIndex objects\n",
      "    (hierarchical indexes) on the index and columns of the result DataFrame.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    values : column to aggregate, optional\n",
      "    index : column, Grouper, array, or list of the previous\n",
      "        If an array is passed, it must be the same length as the data. The\n",
      "        list can contain any of the other types (except list).\n",
      "        Keys to group by on the pivot table index.  If an array is passed,\n",
      "        it is being used as the same manner as column values.\n",
      "    columns : column, Grouper, array, or list of the previous\n",
      "        If an array is passed, it must be the same length as the data. The\n",
      "        list can contain any of the other types (except list).\n",
      "        Keys to group by on the pivot table column.  If an array is passed,\n",
      "        it is being used as the same manner as column values.\n",
      "    aggfunc : function, list of functions, dict, default numpy.mean\n",
      "        If list of functions passed, the resulting pivot table will have\n",
      "        hierarchical columns whose top level are the function names\n",
      "        (inferred from the function objects themselves)\n",
      "        If dict is passed, the key is column to aggregate and value\n",
      "        is function or list of functions.\n",
      "    fill_value : scalar, default None\n",
      "        Value to replace missing values with (in the resulting pivot table,\n",
      "        after aggregation).\n",
      "    margins : bool, default False\n",
      "        Add all row / columns (e.g. for subtotal / grand totals).\n",
      "    dropna : bool, default True\n",
      "        Do not include columns whose entries are all NaN.\n",
      "    margins_name : str, default 'All'\n",
      "        Name of the row / column that will contain the totals\n",
      "        when margins is True.\n",
      "    observed : bool, default False\n",
      "        This only applies if any of the groupers are Categoricals.\n",
      "        If True: only show observed values for categorical groupers.\n",
      "        If False: show all values for categorical groupers.\n",
      "    \n",
      "        .. versionchanged:: 0.25.0\n",
      "    \n",
      "    sort : bool, default True\n",
      "        Specifies if the result should be sorted.\n",
      "    \n",
      "        .. versionadded:: 1.3.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    DataFrame\n",
      "        An Excel style pivot table.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    DataFrame.pivot : Pivot without aggregation that can handle\n",
      "        non-numeric data.\n",
      "    DataFrame.melt: Unpivot a DataFrame from wide to long format,\n",
      "        optionally leaving identifiers set.\n",
      "    wide_to_long : Wide panel to long format. Less flexible but more\n",
      "        user-friendly than melt.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> df = pd.DataFrame({\"A\": [\"foo\", \"foo\", \"foo\", \"foo\", \"foo\",\n",
      "    ...                          \"bar\", \"bar\", \"bar\", \"bar\"],\n",
      "    ...                    \"B\": [\"one\", \"one\", \"one\", \"two\", \"two\",\n",
      "    ...                          \"one\", \"one\", \"two\", \"two\"],\n",
      "    ...                    \"C\": [\"small\", \"large\", \"large\", \"small\",\n",
      "    ...                          \"small\", \"large\", \"small\", \"small\",\n",
      "    ...                          \"large\"],\n",
      "    ...                    \"D\": [1, 2, 2, 3, 3, 4, 5, 6, 7],\n",
      "    ...                    \"E\": [2, 4, 5, 5, 6, 6, 8, 9, 9]})\n",
      "    >>> df\n",
      "         A    B      C  D  E\n",
      "    0  foo  one  small  1  2\n",
      "    1  foo  one  large  2  4\n",
      "    2  foo  one  large  2  5\n",
      "    3  foo  two  small  3  5\n",
      "    4  foo  two  small  3  6\n",
      "    5  bar  one  large  4  6\n",
      "    6  bar  one  small  5  8\n",
      "    7  bar  two  small  6  9\n",
      "    8  bar  two  large  7  9\n",
      "    \n",
      "    This first example aggregates values by taking the sum.\n",
      "    \n",
      "    >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],\n",
      "    ...                     columns=['C'], aggfunc=np.sum)\n",
      "    >>> table\n",
      "    C        large  small\n",
      "    A   B\n",
      "    bar one    4.0    5.0\n",
      "        two    7.0    6.0\n",
      "    foo one    4.0    1.0\n",
      "        two    NaN    6.0\n",
      "    \n",
      "    We can also fill missing values using the `fill_value` parameter.\n",
      "    \n",
      "    >>> table = pd.pivot_table(df, values='D', index=['A', 'B'],\n",
      "    ...                     columns=['C'], aggfunc=np.sum, fill_value=0)\n",
      "    >>> table\n",
      "    C        large  small\n",
      "    A   B\n",
      "    bar one      4      5\n",
      "        two      7      6\n",
      "    foo one      4      1\n",
      "        two      0      6\n",
      "    \n",
      "    The next example aggregates by taking the mean across multiple columns.\n",
      "    \n",
      "    >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],\n",
      "    ...                     aggfunc={'D': np.mean,\n",
      "    ...                              'E': np.mean})\n",
      "    >>> table\n",
      "                    D         E\n",
      "    A   C\n",
      "    bar large  5.500000  7.500000\n",
      "        small  5.500000  8.500000\n",
      "    foo large  2.000000  4.500000\n",
      "        small  2.333333  4.333333\n",
      "    \n",
      "    We can also calculate multiple types of aggregations for any given\n",
      "    value column.\n",
      "    \n",
      "    >>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],\n",
      "    ...                     aggfunc={'D': np.mean,\n",
      "    ...                              'E': [min, max, np.mean]})\n",
      "    >>> table\n",
      "                    D    E\n",
      "                mean  max      mean  min\n",
      "    A   C\n",
      "    bar large  5.500000  9.0  7.500000  6.0\n",
      "        small  5.500000  9.0  8.500000  8.0\n",
      "    foo large  2.000000  5.0  4.500000  4.0\n",
      "        small  2.333333  6.0  4.333333  2.0\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(help(pd.DataFrame.pivot_table))"
   ]
  },
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