EDA on Meteo Data.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["## Introduction\n","\n","This Notebook shows how to perform a quick analysis on simple time series by using basic Pandas and Seaborn commands to generate heatmaps.\n","The similar techniques can be used on any dataset containing just a date and value columns (number of sales, users accesses…).  \n","I hope the same steps can be useful when analyzing any time series dataset."]},{"cell_type":"markdown","metadata":{},"source":["Let's start by importing the needed libraries, also setting retina resolution for plots and ggplot style."]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import calendar\n","import matplotlib.style as style\n","import missingno as msno\n","\n","%config InlineBackend.figure_format = 'retina'\n","%matplotlib inline\n","\n","style.use('ggplot')"]},{"cell_type":"markdown","metadata":{},"source":["### Dataset  \n","\n","I'm using a dataset downloaded from the National Centers for Environmental Information (NCEI), the data is in the public domain and can be used freely.  \n","In case using the same dataset or generating a new one from NCEI you need to cite the origin.  \n","The Dataset covers each day from 2010 to 2019 and the station used is located at Heathrow Airport in London.\n","\n","**DATE**: is the year of the record (4 digits) followed by month (2 digits) and day (2 digits).  \n","**PRCP**: Precipitation (mm)  \n","**TAVG**: Average temperature (°C)  "]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"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>elevation</th>\n","      <th>datetime</th>\n","      <th>speed</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>171494</th>\n","      <td>600.557762</td>\n","      <td>2023-06-24 18:54:47</td>\n","      <td>56.655034</td>\n","    </tr>\n","    <tr>\n","      <th>76070</th>\n","      <td>928.510528</td>\n","      <td>2024-04-17 00:21:40</td>\n","      <td>78.758860</td>\n","    </tr>\n","    <tr>\n","      <th>151617</th>\n","      <td>951.542025</td>\n","      <td>2023-09-21 17:31:29</td>\n","      <td>77.815828</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["         elevation            datetime      speed\n","171494  600.557762 2023-06-24 18:54:47  56.655034\n","76070   928.510528 2024-04-17 00:21:40  78.758860\n","151617  951.542025 2023-09-21 17:31:29  77.815828"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv('/Volumes/DataLakeActive/RZR Rider Profile Dashboard/data/gpx_rzr_all/gpx_rzr_all-single_export_polars_df.csv', \n","                               usecols=['timestamp', 'speed', 'elevation'], parse_dates=['timestamp'])\n","#df['DATE'] = df['DATE'].astype('datetime64[ns]') # needed if date format is not standard\n","df.columns = ['elevation', 'datetime', 'speed']\n","df.sample(3)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"text/plain":["elevation           float64\n","datetime     datetime64[ns]\n","speed               float64\n","dtype: object"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.dtypes"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"text/plain":["241752"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["len(df)"]},{"cell_type":"markdown","metadata":{},"source":["Let's check if there are missing values"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/plain":["elevation    0\n","datetime     0\n","speed        0\n","dtype: int64"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["df.isnull().sum()"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/plain":["elevation    0.0\n","datetime     0.0\n","speed        0.0\n","dtype: float64"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["round(df.isnull().mean() * 100, 2)"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"image/png":"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size 2500x1000 with 2 Axes>"]},"metadata":{"image/png":{"height":904,"width":2085}},"output_type":"display_data"}],"source":["_ = msno.matrix(df)"]},{"cell_type":"markdown","metadata":{},"source":["In this case we will fill the null values with 0.0 (in this case meaning no rain), this is to make the rest of the analysis simpler (like when summing up) but this may change depending on your dataset."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df['speed'].fillna(0, inplace=True)"]},{"cell_type":"markdown","metadata":{},"source":["We also want to check if there are missing days in the range"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["print(f\"Data Available from {= df['datetime']..min()} to {= df['datetime']..max()}\")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["idx = pd.date_range(= df['datetime']..min(), = df['datetime']..max())\n","print(f\"Days present {len(df)} out of {len(idx)}\")"]},{"cell_type":"markdown","metadata":{},"source":["Some days are missing, let's create a DataFrame to visualize them."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["pd.DataFrame(data=idx.difference(= df['datetime'].), columns=['dates']).sample(3)"]},{"cell_type":"markdown","metadata":{},"source":["According to the MET Office a day can be considered dry if precipitation is less than 1mm.  \n","Let's see what is the percentage of dry days in the entire dataset."]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["SLOW_SPEED = 20.0"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"data":{"text/plain":["14.9"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["round((len(df[df['speed'] < SLOW_SPEED]) / len(df)) * 100, 2)"]},{"cell_type":"markdown","metadata":{},"source":["Find day with the highest precpitation."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df[df.speed == df.speed.max()][['date', 'speed']]"]},{"cell_type":"markdown","metadata":{},"source":["And highest temperature"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df[df.avg_temp == df.avg_temp.max()][['date', 'avg_temp']]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["sns.distplot(df.speed)"]},{"cell_type":"markdown","metadata":{},"source":["In order to create the needed visualizations it can be useful to augment the dataframe with additional columns representing date infos.  \n","This step is not strictly needed because we could just use the same methods when later grouping."]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/80/5h9bppcj40s4vt1zyzlcwcbc0000gn/T/ipykernel_12096/158982096.py:6: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.\n","  df['week'] = df['datetime'].dt.week\n"]}],"source":["df['month'] = df['datetime'].dt.month\n","df['year'] = df['datetime'].dt.year\n","df['day'] = df['datetime'].dt.day\n","df['weekdayName'] = df['datetime'].dt.day_name() # = df['datetime']..dt.weekday_name on older Pandas\n","df['weekday'] = df['datetime'].dt.weekday\n","df['week'] = df['datetime'].dt.week\n","df['weekend'] = df['datetime'].dt.weekday // 5 == 1"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[],"source":["df['fast'] = df['speed'].gt(SLOW_SPEED).astype('int')"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"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>elevation</th>\n","      <th>datetime</th>\n","      <th>speed</th>\n","      <th>month</th>\n","      <th>year</th>\n","      <th>day</th>\n","      <th>weekdayName</th>\n","      <th>weekday</th>\n","      <th>week</th>\n","      <th>weekend</th>\n","      <th>fast</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>85540</th>\n","      <td>803.576141</td>\n","      <td>2024-04-08 15:53:06</td>\n","      <td>67.912257</td>\n","      <td>4</td>\n","      <td>2024</td>\n","      <td>8</td>\n","      <td>Monday</td>\n","      <td>0</td>\n","      <td>15</td>\n","      <td>False</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>56938</th>\n","      <td>931.102392</td>\n","      <td>2024-05-17 22:50:37</td>\n","      <td>5.240414</td>\n","      <td>5</td>\n","      <td>2024</td>\n","      <td>17</td>\n","      <td>Friday</td>\n","      <td>4</td>\n","      <td>20</td>\n","      <td>False</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8675</th>\n","      <td>962.565648</td>\n","      <td>2024-05-17 23:44:45</td>\n","      <td>78.070187</td>\n","      <td>5</td>\n","      <td>2024</td>\n","      <td>17</td>\n","      <td>Friday</td>\n","      <td>4</td>\n","      <td>20</td>\n","      <td>False</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        elevation            datetime      speed  month  year  day  \\\n","85540  803.576141 2024-04-08 15:53:06  67.912257      4  2024    8   \n","56938  931.102392 2024-05-17 22:50:37   5.240414      5  2024   17   \n","8675   962.565648 2024-05-17 23:44:45  78.070187      5  2024   17   \n","\n","      weekdayName  weekday  week  weekend  fast  \n","85540      Monday        0    15    False     1  \n","56938      Friday        4    20    False     0  \n","8675       Friday        4    20    False     1  "]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["df.sample(3)"]},{"cell_type":"markdown","metadata":{},"source":["We could create a heatmap representing the average precipitation for every month in the dataset.  \n","To do so we can use the pivot_table function:"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"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>year</th>\n","      <th>2023</th>\n","      <th>2024</th>\n","      <th>All</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Feb</th>\n","      <td>0.000000</td>\n","      <td>101.016441</td>\n","      <td>101.016441</td>\n","    </tr>\n","    <tr>\n","      <th>Mar</th>\n","      <td>0.000000</td>\n","      <td>60.337728</td>\n","      <td>60.337728</td>\n","    </tr>\n","    <tr>\n","      <th>Apr</th>\n","      <td>0.000000</td>\n","      <td>53.509269</td>\n","      <td>53.509269</td>\n","    </tr>\n","    <tr>\n","      <th>May</th>\n","      <td>63.502747</td>\n","      <td>63.783500</td>\n","      <td>63.765066</td>\n","    </tr>\n","    <tr>\n","      <th>Jun</th>\n","      <td>56.122585</td>\n","      <td>60.091597</td>\n","      <td>57.942600</td>\n","    </tr>\n","    <tr>\n","      <th>Jul</th>\n","      <td>47.241887</td>\n","      <td>0.000000</td>\n","      <td>47.241887</td>\n","    </tr>\n","    <tr>\n","      <th>Aug</th>\n","      <td>55.586169</td>\n","      <td>0.000000</td>\n","      <td>55.586169</td>\n","    </tr>\n","    <tr>\n","      <th>Sep</th>\n","      <td>58.345198</td>\n","      <td>0.000000</td>\n","      <td>58.345198</td>\n","    </tr>\n","    <tr>\n","      <th>Oct</th>\n","      <td>66.343591</td>\n","      <td>0.000000</td>\n","      <td>66.343591</td>\n","    </tr>\n","    <tr>\n","      <th>Nov</th>\n","      <td>55.845563</td>\n","      <td>0.000000</td>\n","      <td>55.845563</td>\n","    </tr>\n","    <tr>\n","      <th>Dec</th>\n","      <td>61.472147</td>\n","      <td>0.000000</td>\n","      <td>61.472147</td>\n","    </tr>\n","    <tr>\n","      <th>All</th>\n","      <td>56.775214</td>\n","      <td>62.129041</td>\n","      <td>60.155593</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["year       2023        2024         All\n","Feb    0.000000  101.016441  101.016441\n","Mar    0.000000   60.337728   60.337728\n","Apr    0.000000   53.509269   53.509269\n","May   63.502747   63.783500   63.765066\n","Jun   56.122585   60.091597   57.942600\n","Jul   47.241887    0.000000   47.241887\n","Aug   55.586169    0.000000   55.586169\n","Sep   58.345198    0.000000   58.345198\n","Oct   66.343591    0.000000   66.343591\n","Nov   55.845563    0.000000   55.845563\n","Dec   61.472147    0.000000   61.472147\n","All   56.775214   62.129041   60.155593"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["all_month_year_df = pd.pivot_table(df, values=\"speed\",index=[\"month\"],\n","                                   columns=[\"year\"],\n","                                   fill_value=0,\n","                                   margins=True)\n","named_index = [[calendar.month_abbr[i] if isinstance(i, int) else i for i in list(all_month_year_df.index)]]\n","all_month_year_df = all_month_year_df.set_index(named_index)\n","all_month_year_df"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[],"source":["def plot_heatmap(df, title):\n","    plt.figure(figsize = (14, 10))\n","    ax = sns.heatmap(df, cmap='RdYlGn_r',\n","                     robust=True,\n","                     fmt='.2f', annot=True,\n","                     linewidths=.5, annot_kws={'size':11},\n","                     cbar_kws={'shrink':.8, 'label':'Speed (mm)'})\n","    \n","    ax.set_yticklabels(ax.get_yticklabels(), rotation=0, fontsize=10)\n","    ax.set_xticklabels(ax.get_xticklabels(), rotation=0, fontsize=10)\n","    plt.title(title, fontdict={'fontsize':18}, pad=14);"]},{"cell_type":"markdown","metadata":{},"source":["robust or vmin, vmax"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"data":{"image/png":"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size 1400x1000 with 2 Axes>"]},"metadata":{"image/png":{"height":874,"width":1069}},"output_type":"display_data"}],"source":["plot_heatmap(all_month_year_df, 'Average Speed')"]},{"cell_type":"markdown","metadata":{},"source":["By changing the aggfunc on pivot_table creation we can have a different aggregation, for instance we can use aggfunc=np.sum to calculate the total amount of rainfall per month."]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAACFIAAAbUCAYAAADW1qAhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzdd3SUVf7H8c+k905MQkmA0HtviTRBBBRU7L1gAzu76upP1+5iV6yIuiqrgsoCCyoo0qS30AMJIQkppJHek/n9kWHIMOkJJJj36xzOeeY+97n3+4zDkZn5zL0Go9FoFAAAAAAAAAAAAAAAAGTT3AUAAAAAAAAAAAAAAAC0FAQpAAAAAAAAAAAAAAAATAhSAAAAAAAAAAAAAAAAmBCkAAAAAAAAAAAAAAAAMCFIAQAAAAAAAAAAAAAAYEKQAgAAAAAAAAAAAAAAwIQgBQAAAAAAAAAAAAAAgAlBCgAAAAAAAAAAAAAAABOCFAAAAAAAAAAAAAAAACYEKQAAAAAAAAAAAAAAAEwIUgAAAAAAAAAAAAAAAJgQpAAAAAAAAAAAAAAAADAhSAEAAAAAAAAAAAAAAGBCkAIAAAAAAAAAAAAAAMCEIAUAAAAAAAAAAAAAAIAJQQoAAAAAAAAAAAAAAAATghQAAAAAAAAAAAAAAAAmBCkAAAAAAAAAAAAAAABMCFIAAAAAAAAAAAAAAACYEKQAAAAAADTY8ePHZTAYLP58+eWXzV0WIEkKCQmxeG3efvvtzV0SAAAAAAC4ABCkAAAAAAAAAAAAAAAAMCFIAQAAAKDFqWqVg/P1Z8yYMc19+wAAAAAAAACaEUEKAAAAAGgGt99+u0WAIyQkpLlLAgAAAAAAACCCFAAAAAAAAAAAAAAAAGZ2zV0AAAAAAJzNwcFB/fr1q9c1ubm5io6OtmhzdXVVaGhovcapb38AAAAAAAAAfy0EKQAAAAC0OEFBQdqzZ0+9rlm7dq3Gjh1r0TZ48GCtXbu26QoDAAAAAAAA8JfH1h4AAAAAAAAAAAAAAAAmBCkAAAAAAAAAAAAAAABMCFIAAAAAAAAAAAAAAACY2DV3AQAAAADQEpWVlWnXrl2KiYlRamqqsrKy5OPjozZt2qhr167q06dPc5dYq9zcXB0+fFhHjhxRenq6cnJy5OjoKG9vb/n7+2vw4MEKCAho7jKbzMmTJxUREaHY2FhlZ2eroKBATk5OcnFxUUBAgEJCQtS1a1e5ubk1+dw5OTnasmWLjh49qszMTLm4uCgoKEi9evVSr169mny+09LS0rRjxw6lpKQoNTVVZWVl8vPz00UXXaThw4fL19f3nM0dFRWlgwcPKjU1VampqXJ0dJSfn5/atWun4cOHy9nZucnnTEhI0I4dO5SYmKiMjAy5urqqY8eOGjJkiIKCgpp8vtMyMjK0d+9eRUdHKzs7W3l5eXJwcJCLi4v8/f3Nry0vL69zVgMAAAAAADh/CFIAAAAAQCU7duzQm2++qVWrVikjI6PafkFBQZoyZYqeeOIJde7cuU5jh4SEKDY2tspzsbGxMhgMtY7xxx9/aMyYMVWeKy0t1Zo1a7RixQqtXbtW+/btk9ForHG8zp0768Ybb9RDDz0kPz+/WudvaTIzM/Xhhx9q4cKFOnjwYK39bW1t1bt3b40dO1YzZszQyJEja3zev/zyS91xxx0WbTExMQoJCZEk7dmzRy+99JKWL1+u4uLiKsfo3r277rrrLj388MOyt7ev+81VIzs7W/PmzdOSJUu0c+fOav8bGwwGDRgwQHfddZfuvvtuOTg4NHrumJgYvfXWW1q5cqWOHTtWbT8nJyeFh4fr0Ucf1WWXXdboeZcsWaK3335bGzdurPJ+DQaDRowYoSeeeEJXXHFFo+eTpMLCQs2fP19ff/21tm/fXmt/g8Ggbt266eKLL9aMGTM0duxY2dnxsQsAAAAAABcitvYAAAAAAFX8sv/666/X0KFD9d1339UYopCkxMREzZ8/Xz169NDDDz+soqKi81Rp1RYtWqTAwEBdeumleu+997R3795aQxSSFB0drRdffFHBwcH64IMPzkOlTWfx4sXq2rWrnn766TqFKKSKlUYiIiL0zjvvKCwsTJGRkQ2e/+WXX9aQIUP0448/VhuikKTDhw/rb3/7mwYOHKjdu3c3eD6j0ah3331XnTp10tNPP60dO3bU+N/YaDRq165dmjVrlrp27ao1a9Y0eO7s7GzNnj1b3bp107x582oMUUgVIYTVq1dr8uTJCg8PV1xcXIPmTUtL0/Tp03XVVVdpw4YN1d6v0WjUpk2bNG3aNF177bXKz89v0HynrV27Vj179tRDDz1UpxDF6RoOHz6sTz/9VBMnTtRvv/3WqBoAAAAAAEDzIUgBAAAAoNU7duyYRo4cqe+//75O4YPKSkpK9N5772nChAk6derUOaqwdgcPHlRaWlqDr8/Pz9fs2bN1//33N2FV585nn32m6667Tqmpqc0y/xNPPKFnnnlGpaWldb5m//79Gjt2rHbs2FHv+QoLC3X99dfrkUceUXp6er2vj42N1aWXXqr58+c36NpRo0bpgw8+UElJSb2v37hxo4YOHaotW7bU67r09HSNHz9eS5curdd1ixcv1mWXXVZjuKUmK1eu1KRJkxQTE9Og6wEAAAAAwIWPNSYBAAAAtGopKSkKCwtTUlKS1bl27drpqquuUo8ePeTj46OUlBRFRERoyZIlVl9mb9iwQZdccok2bdokR0fHKufq2bOnvLy8JElxcXEWwQt7e3v17Nmz1nrd3NzqdF/BwcEaMGCAevbsqXbt2snd3V3Ozs7Kzc1VYmKi9uzZo19//VVZWVkW13388cfq06ePHnjggTrN0xyOHDmi2bNnW4VenJ2dNW7cOA0ZMkTBwcFyc3NTaWmpsrOzlZSUpAMHDmj79u06fvx4o+ZfvHix5s6da37s5OSkyy67TOHh4QoMDFRubq6io6O1ZMkSqxUvsrKyNGHCBO3cuVOdOnWq03wlJSWaOHGiNmzYYHWuc+fOGjt2rPr27SsfHx/Z2dkpLS1N27dv18qVKy2CJqWlpbr33nsVEBCgyy+/vE5zx8bGatiwYTp58qTVuaFDh2rUqFHq1q2bvL29VVxcrKSkJG3atEk///yzxSotJ0+e1JQpU7Rr1y4FBwfX6Z4vvfRS7d271+pc27ZtdfXVV5v/Xp48eVK7d+/Wf//7X/PfqfXr1+vxxx+v0z1Wlp6erttuu81qhRk7OztdfPHFGjlypEJCQuTu7i6pYqWOlJQUHThwQLt27dLhw4frPScAAAAAAGh5CFIAAAAAaLWMRqNuv/12qxCFs7OzXn75ZT300EOytbW1uu69997Tc889pzfffFPl5eXm9l27dunJJ5/U22+/XeV8K1euNB/ffvvt+ve//21+HBQUpD179jTqfvr06aO77rpLkydPVpcuXWrtX1RUpE8//VTPPPOMsrOzze2PPfaYpk2bprZt2zaqnnNl7ty5Vl9033zzzXrrrbfUpk2bWq8/ePCgfvjhB3366acNmv/ZZ581H0+ZMkWffvqpgoKCrPq9+uqr+uqrr/Twww8rMzPT3J6Zmam7775bv//+uwwGQ63zzZkzxypE0bNnT7399tuaMGFCtWMUFBTo3Xff1bPPPmteScJoNOrWW29VRESEOnToUOO8xcXFuuaaa6xCFFOnTtXcuXPVo0ePaq9NTk7W3/72N33zzTfmtoyMDF1zzTXavHlzlX+vKnv55Ze1c+dOizZHR0c9//zzmjNnTpXXv//++3r66af17rvvSpI++OADOTg41DjP2T766COrlV0mTJigzz77rNbnS5KOHz+uJUuW6KOPPqrXvAAAAAAAoGVhaw8AAAAArda3336rn3/+2aLNyclJy5cv16OPPlrtl73Ozs6aO3dulV+Wvvvuuw3auqGxHn30Ue3du1cPP/xwnUIUUsUX0w8++KA2bNggDw8Pc3tRUZE++OCDc1Vqo5291cPYsWP11Vdf1SlEIVWEEJ599lkdP368zqtCVFZYWChJuvHGG7Vs2bIqQxSn3XrrrVq1apV5BYPT/vjjD4uQQXV+/fVXvffeexZt06ZN0+7duzVx4sQagxjOzs568skntWLFCtnb25vbMzMz9eqrr9Y69z//+U9t377dou21117T8uXLawxRSFJAQIC+/vprPffccxbt27dv1w8//FDjtdHR0XrllVcs2uzs7PTdd9/piSeeqPbvpaurq9555x299dZbkipCI2cHbmpz9mure/fuWr58eZ1CFJIUEhKiRx99VJGRkRo9enS95gYAAAAAAC0HQQoAAAAArdbpL1wrmzt3rsaPH1+n6++55x7dd999Fm1Go7HKcc81T0/PBl/bt29fqy+uFyxY0NiSzomcnByrFQPuvvvuOq3scDY7O7t6r1hwWteuXfXFF1/Ixqb2t9VDhgzR+++/b9V+euWEmrzwwgsWj/v27avFixfXq+4JEyZYBRq++OKLKrfrOO3UqVNWNd9333164okn6jyvVBHGmDBhgkXba6+9VuM1H374oXkFjdOeeuopTZ8+vU5zPvroo7rhhhvqVedpx44ds3h8yy23VLtVT00MBoOcnZ0bVAMAAAAAAGh+BCkAAAAAtEpbtmyx2jqgT58+mjVrVr3GefXVV+Xt7W3R9sMPP9T4JXVLdPPNN1uEEVJSUnTkyJFmrKhqOTk5Vm2+vr7nvY4333yzXmGGW2+9VYMHD7Zo27lzZ42rl2zcuFGbNm2yaHv77bctVpeoq8cee8xiVYyioiKr1Vgq++CDD5Sbm2t+7Obmpn/961/1nley3ApFkvbs2aPjx49X2beoqEhffvmlRVtQUJCeeuqpes355ptvNigAcfbrqzleWwAAAAAAoPkRpAAAAADQKq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size 1400x1000 with 2 Axes>"]},"metadata":{"image/png":{"height":874,"width":1065}},"output_type":"display_data"}],"source":["all_month_year_sum_df = pd.pivot_table(df, values=\"speed\",index=[\"month\"], columns=[\"year\"], aggfunc=np.sum, fill_value=0)\n","all_month_year_sum_df = all_month_year_sum_df.set_index([[calendar.month_abbr[i] if isinstance(i, int) else i for i in list(all_month_year_sum_df.index)]])\n","plot_heatmap(all_month_year_sum_df, 'Total speeds')"]},{"cell_type":"markdown","metadata":{},"source":["And if we wanted to calculate the average amount of rainfall per weekday we just need to change index."]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[{"data":{"image/png":"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size 1400x1000 with 2 Axes>"]},"metadata":{"image/png":{"height":874,"width":1112}},"output_type":"display_data"}],"source":["all_weekday_year_df = pd.pivot_table(df, values=\"speed\",index=[\"weekday\"], columns=[\"year\"], fill_value=0.0)\n","all_weekday_year_df = all_weekday_year_df.set_index([[calendar.day_name[i] for i in list(all_weekday_year_df.index)]])\n","plot_heatmap(all_weekday_year_df, 'Average speed per weekday')"]},{"cell_type":"markdown","metadata":{},"source":["The aggfunc can be a custom function, it could be interesting to calculate the percentage of days in the month with rain."]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[],"source":["all_month_year_percentage_df = pd.pivot_table(df, values=\"speed\",index=[\"month\"], columns=[\"year\"],\n","                                              aggfunc=lambda x: (x>SLOW_SPEED).sum()/len(x),\n","                                              fill_value=0,\n","                                              margins=True)\n","all_month_year_percentage_df = all_month_year_percentage_df.set_index([[calendar.month_abbr[i] if isinstance(i, int)\n","                                                                        else i for i in list(all_month_year_percentage_df.index)]])"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"data":{"image/png":"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PyNdxvt6HNHKfv7LyS3q8K5MReP8Wvnr1oqz/49ZYyWHC3bBBR8FG7yLxaxRETK0aKNbPUbyBIWLm9GutyH9su9fUsQ38afa/tm2ZMbF927TYdyxzpLXHeVci9rvUTFXnGj0c788E35sjNDjgkAANQcOdOnKv6me4x29Hmjlf39FMlVcR4Ue8nRedDv8uzbXeb4ojzIv+CjcKP/rqaWiEg5WrYtkQcdliflgFzbNwfzdfy4tpXIg9p2LHesfx4UeC9rQn3FXnGT0c744E15yYMAoFawWC1mhwDUGKZtNTF48GCjgGPFihXKyspSTExMqWMXLlyogr+2RIuLi1P37t1DutfMmTP10UcfBTW2sLBQs2bN0sKFCzVu3Dj16dOn1HEJCQnq1auXFi9eLEmaP3++xowZE/TOIF6vV7/++qvRbtOmDcUbAMrkyUwP6LPVSwhpDXtC4Hhny5aVjKh65SxfqpjBQyRJ4e07yNmipQp3bA8YF9a2ncLbtjPaucuXBoxJvu9B2aKLzkHNW/2nDn81NWAMAACofbK/nazIoefI2bq9JMneuJkSHnhKaS89LrkCi12PiBl9nSIHjzDaPpdLGe++XO69HO38C0q9+Xly7y46js7Rsq2iLxmjiH5DAh5uSEVveuYvmaesKR/KvSe4I+8K1qxUxKlFLyo4W7WTvUnzUufaW7SRo0WbEvNWBIyJv/FuWSOLcqHCjWuU89OxOa8eAAAcP1nffKbIYX8z8iBH42ZKfOBppb74j/LzoEuvV+TpZxptn8ul9HdeKvdezvad/NpFedD2ovu2aqeYUdeUnQelF+VBmZ9PCj4PWr1CEX0rzoMcR+dBq8vIg6JK5EE/TgsqBgAAgJrEataNTzrpJGOHC4/Ho/nz55c5du7cucbngQMHymqtfNgRERHq2LGjhg4dqvPPP1+jRo3S+eefrz59+ig2tvhM5Ly8PL3yyivatGlTmWudeWZx8ltQUOAXZ0WWLl2q1NRUo33GGWeE+E0A1CXulBR5j9qFI+KkTmWMLl14KeNtcaGdmXqsZHz3rXwl3hpJvveBUscl31d8lrvXVaiM7771ux7Zs5fi/3aeJMnn9Wrfs09JPt8xiBgAABx3brcO/eNOuXZuNboiB56h5AmfKepvl8jepIXkDJPsdtmSkhUxaISSXnhXcdeOM8b7XC6lvfh/cm0r++c8SbLX99+N0ZOaIvl8ihl9rRq89rGihp5T6kMLSbLF1VPUGecr+Z0pir12nGSp+C2i3F+my+d2G+246+8qdVz8DcX9PpdLubN+8Lvu7NJDkUPOLrru9erwWy+QCwEAcCJwu5Xyf7fLtaNEHjRohBq+PVnR546SvWmLop1X7XbZkhoqYvAIJb04UfHX3W6M97lcSn3h0QrzIFv9hn5tIw+69Dolv/ZJ+XlQfD1FjThfDd+dqrjrbg8qD8qZNV2+EjuqldxxraS4EjuQ+Fwu5fwy3e96WJdTFDX0nKLrXq8Ov/kceRAAAKiVTNuBQyoqxjhyhMicOXN01llnBYxJSUnR2rXFW7SFenyKJEVGRmrEiBEaMGCA2rVrJ5vNVuo4r9erefPmadKkScrLy5PH49Fbb72l8ePHy1JKstmlSxc1bdpUu3cXbTn3888/l/odSjNz5ky/+Pr37x/y9wJQd/gKC5W/ZrUie5xi9MWcPlSpn3wY9Boxpw8N6LM6nLI4HH7FE2ZwH0pR2pTPlHhl0baesUOGqekL43Xw7Tfl2r1LzhYt1eC2OxTTf6AxJ+3T/8qdeqh4EatVjf7+mNFM/3qq8lb/edy+AwAAOPa8aYd08K4xir1yrKL+domsEZFyNG2heuMernBu4aZ1OjzhObk2rqlwrCXaf3dI7+FDir36FsVefmMZM0pZw2ZX7Ojr5GjSQqn/eljyessc6z2cquzpXyjmgsslSRGnDVbCw88q87/vyr1vj+xNmivuqpsV3qufMSf7u8/lPVz8UoCsVsXfWlzsmvPTN0F9VwAAUDt40w7pwJ1XKfaqmxV97qi/8qCWqnf73yucW7hpnQ6/8awKg8gNrH/tanqEJ+2QYsfcqrgSR5NUxGKzK/bS62Vv2lKpzzxYfh6UdkjZ33+hmAuvkCRFnHa6Ev/+nDL+82+59++Wo3FzxY65VREl86BvJwfmQeMeMpo5P04L6rsCAADURKYWcAwePFhffvmlfD6ftmzZot27d6tp06Z+Y+bMmSPfX5WyLVq0UMtKbPc/ZMgQDRkypMJxVqtVgwcPVqNGjfSPf/xDXq9Xe/bs0apVq8o8tmXEiBGaNGmSJGnXrl1av369OnYs/6y+gwcPatWqVUZ70KBBCgsLC/4LAaiTshf85lfAEdW7j8I7dVH+2tUVzg1r3UYxA0ovgLNGRsmTkV5dYVbagddfUWSPnoro1FmSFHfWOYo765xSx+b+uUoH33zVry/h8isV3r7oLFR3RroOvDb+mMYLAADM4SvIV8ak15W/dL7ib3tIjublH0XpyUhXxsRXlHvUW5rlOXIEyRH25q0V1qU4D3Mf3KesqZ+oYPlCuQ8dlMVul71JC0UOGKao8y6VNTzcGBvRf6hix9yqzA8nlHvPzA8nKKxTdzn/Or4lctAIRQ4aUerYwg2rlfHx23590eeNlrNV0VFz3qwMZX74ZtDfFwAA1A6+gnxlvP+a8n+fr3rjHpajRUV56vjaKQABAABJREFU0GGlv/eKcmd+H/Q9jhxBcoSjRWuFdy2RBx3Yp6ypHyt/2QJ5Dh2U7A45mjZXxIDhij7/Ur/dOSL7D5X7mtuU8UH5eUnGB28qrHOP4jxo8Ai/I/BKKlj/pzI+esuvL/q8S408yJOVUeH9AAAAajLTjlCRpOTkZHXo0MFol3YEScm+wYMHH5e42rdv71ewsXz58jLHDh48WBERxUnpzz//XOH6M2fONIpSJI5PARCctC8my5uf79fX5OlnA96MOJolIkJN/vmcLPbSa/Ys4TWjgMyXl6ftN12nzNmzyh2XOfMn7Rh7vXwFBUafLSFRDW69w2gffONVedLTA+ZG9jhFjZ98Ru2m/6STFq9Qx/m/q81X3yr5vofkaNKk2r4LAAA4dhztOyvppfeV9Ny/KyzekCRbXLwS7ntS9Z99S86OXYO6hyUi0n+N2Hjjc97iuTpw8yXK+X6K3Ht3SYUF8uXmyLVprTI+eEMH77hC7gN7/ebHXHKNHG06qDy+gnylPHqb8haVfzRn3vxZSnlknFRYnAtZ4xMUe+XNRjvjo7fkzcwImOvs1E317v6HGr4/TY2/+k2Nv5it5Lc+V9yNd8uW3Ljc+wIAAPM523dWg5cnqcEL71ZYvCEVHe2WeP9TSvrXO3KedHJQ97CEl5MHLZqr/WMvVvZ3n8u9d5d8hQXy5WarcONaZUx6XQfGXR6YB426Vo625b/w6CvI18G/36K8RXPKHZc7f5ZSHrlNvqPyoLirbzHaGR9OkDczPWCus3N31bvncTWa9I2aTJuvJl/OUcN3pij+pnvIgwAAQI1i6g4cUlEBxPr16yVJ8+bN02WXXSartaiuZMOGDdq/f7+kot0xBgwYcNziat68uVG4sWlT2ecCRkREaODAgfrpp58kSYsWLdK1116rmJiYUse73W79+uuvRrtjx45q1qxZNUYO4ETlSUtT6n8/VtINY42+8Hbt1fL9j7Tn0YdVsDnw3ypni5Zq8s/nFNGl7IcV3tzcYxJvZXizMrXrztsU2bOXYkecrfB27WWLjZUn4//Zu+84qaq7j+Pfe6dsbyy9Su9F7KgUafaOvfeCsUVjNIkaY9oTNYm9t0QNaqxoVCyoKKIIKiC99wW2t5m59z5/LDu7d2dmd7bAsPB5v177euacOW15XmvOnPnd3ylUxdIlKvzgPZXPnxfRr+NNt8iTmSlJKv95ofJf/Y+7gderzr+5Szmnnh7R15ORoeQ+/dTmzLO1+W9/Vv60V3bJ7wYAAJov7ehTlH3NryICUwPLFqlywbyqO9qtkMysNvL3G6SkoQfI8PkkSckjDlbS0JEqevFxFU97tv6JgoHo1auXa/u9v5JCsa+fC21Yq2133qAO/3yx6i56SYZpKuPMS7Tjj7+K2U+SnJJibf/9TfIP2V+pR06Ub7/eMtIz5RQXKbBqmcq/+EiBRT9E9Mu65Bcyd177Eli+WKXv/9fdwONRztTblTb5pIi+Zlq6fPv1Vvpxp6vgyQdU+t7r9a4RAAAkRtoxpyrn2l/J8Ppc9YGli1S54HtZ2/PkhELyZO/cBw07sGYftP/BSho2UoUvPKbi/zxT7zxOjH1QYNUybbv3Fqmea3hDG9Yq73e/UMcH/+3aB2WeeUnVVSr1zVtSrG133aikISOVMnqifPv1kZmRKbu4UMFVy1Q288Oo+6DsS6+vtQ/6OXIv4/Eq5xe3K33yyRF9zbQM+fbro7Tjp6jgiftVOv21etcIANiFPEaiVwDsMRIewHHYYYfpmWeeUTAY1Pbt27Vw4UINHVr1RePMmTURt8OHD1d2dnaz51u9erVmz56tVatWaePGjSorK1N5ebnsOvfw1c6QsWPHjnrHnDx5cjiAIxgM6rPPPtMJJ5wQte2cOXNUWFjzJNSkSdFTwQFANFsf/qfSDjxYqcNHhOtSBg5W72lvqPTbb1Q2f56sggJ5MjOVMmy40g49TKbPH25bPOsLZRx+ZLjs2LbskpLd+SvEpWzudyqb+11cbVOG76+s40+UVPX7bLr3HqnWf8Mlqeuf/k9Zk48Jl51gUJWrV8lISlJS9x6SJDM5WZ1/c5dkO8p/rU4ACAAASLiUw49S9tRfyzBrEkkGVi1T/t/vUXDZoqh9PG07KOuKm5R6xHhJVfexZ110rZxQUCX//VfMuezy8qj1BU8+UG/wRrXQ2pUqmf66Mk45p2b9h42RkZIqp7zh4NnAgnkKLIgMWo3GP3CYUo+qunbOsW0VPPKXiL1Qm1v/oNQjazI/OqGQQutXy/Anydu56oECIylZOVN/LTlOZAAIAABIqJTDj1LOdbdH7oMeuFuBpbH3QdlX3qzUIydIqtoHZV88VQoFVfz6izHncirq2QfVE7xRLbRmpUrefU0Zp55bs/5RY+PeB1Uu+F6VC2JnxK7NP3CYUscfV7Vu21b+w5H7oNxf/cF1LZ0TCiq4brWMpGT5du6DzKRktbnu9qp9EMGsAAAgwRIewJGamqqDDjpIX331laSqoI2hQ4cqEAjo66+/Drdr7vUp69ev11NPPaVFi6JvaOtT0sCXm926ddOgQYPCY8+YMUPHH3+8DCMyWqz2FSsZGRk65JBDGr0eSdq+fXtc7XJzc5s0PoA9VCiktVOvUre/P6i0Aw4KVxter9IPO1zphx0es+u2Z59ScONGVwCHXVIS8cG2VTFNdbr9t+EDjIK331T5j/NdTbJOOMkVvFH85efa+NvbFdq+TZKUPHCwut33d/m7Vn1o73jLbSr95msF1q3dPb8DAABokJGapuypdb60WLZIebdeIaeyImY/a9sW7fjjr2Rfe5vSj6vJxJV14bWqmPu1QmtWRO3nlJdG1IXyNqty3jdxr7n0o7dcARyGxyv/oOGqnPt1Pb0ayTSrMpLs/Hcp+/hdBRb/5GqSetRxruCNiu9maccDv5edX/WZ0tdngHJ//Wd5O3WVJGVdfpMq5s+RtWl9y60TAAA0mZGarpxf3OHeBy1dpK23XNbgPmj7vbfKnvprpR8/JVyfddFUVXz3lYIx9kF2WYx90Pez415z6YdvuQI4DI9XSYNGqGLuV3GP0SDTVM61t9Xsg2a8q8DPP7qapI4/zhW8Uf7tLO24/65a+6CBanvHX8L7oOwrblLlvDkKbVrXcusEAABoJLPhJrte7eCMb775RhUVFfruu+9UWlq1WUxNTdWBBx7Y5PEXL16s22+/vUnBG1JVVo2GTJ48Ofx606ZNWrBgQUSbjRs3auHCheHyuHHj5PP5ItrF4+qrr47rB8Dexyos0JrLL9HWxx6WVRr5obquUGGB1v3qZm154G/ytmvnei+4ZfOuWuZu0WbKWUoZOEiSZBUVacvf/xbRpvaVM5WrVmrdDVPDwRuSVPHzQq2depWcnf+tN1NSlHvBRbt24QAAoFHSJp0kT1Z2uOxYIe3462/r/dKitoLH/6bQ5g3hsuHzKWPKhTHb2yXFEXWBRT9GaRlbaPWKiHH8vfo1aoyGpB17mvy9+0uqWnPhMw9GtMk446Lw6+C61dp2zy3hLy0kKbh8sbbddaOcUEhSVVayjFPOrTsMAABIkLTJJ8mTlRMuO1ZI2/96R9z7oPzH/i9yH1Rrf1BX1H3QwsirS+oTXL08Yhxf75bdB6Ufd7r8fQZIqlpzwdP/iGiTeeYlNWtat0rbfn9znX3Qz8q783o5OzOsmckprsATAACAREh4Bg6p5nqUgoICVVZW6ptvvgln5JCqrlnx+/31jBBbeXm57r//flVU1Gxou3btqiOPPFJ9+/ZV+/btlZmZKa/XK2+te5SnTZum116L/867gw8+WDk5OcrPz5dUlWmj+iqYajNmzAi/NgxDEyZMaNLvBABOKKi8Rx7Ujpf/pcxJRyv90FFK6tNX3pw2Mvx+hbblKbBmjQo/+p+KPvxf+JqUpF69XeOUL4wMNmstPNnZajf1F+Hy1of/KavOlVfJAwe5fudtzz4lJxB5l2vlyhUq/OgDZR97vCQp65jjtOne3++ilQMAgMZKPvgIV7ly3jcKbVgT/wChkErf/6+yLr4uXJVyyGjlm6ZU5zpNSQqtjxw7tGVj/PNV98nbLP/OO9klyczIavQYsZiZWco6/6pwufDFx2QX5rva+Hr3l697z3C5+LUXpCj32ofWrVL5lzOUOvZoSVLqmMlVV7EAAICESzn4SFe54vtvou5VYgqFVPLe68q+pOYMJeXQ0VLMfdDqyLqW2AdlZjd6jFjMzGxlXlDz8GLhC49E7oP6DHDvg16NsQ9au0plX8xQ2riq7K2pY49W/sN/brG1AgAANNYeEcBhmqaOOOIIvfvuu5Kk9957T2vW1GxCm3N9yocffqiCgoJw+dhjj9UFF1wg06w/+UjtgI94eDweTZgwQa+++qok6dtvv1VBQYGys7MlSYFAQDNnzgy3Hzp0qDp27NioOWp79NFHm9wXwN7Dys9X/n9eVv5/Xo6rfcrQ4a5y+cKfYrTc83W44WZ5dz6JW7FksXb856WINqnDRrjKJV9+EXO8ks8/CwdweDKzlNSrtypXRk8nCgAAdi/ffn1c5cpGZsOo6uN+ctRMS5e3U1eFNkRemxZcuzKiLp472yP61ElBbqSlN3qMWLIuvi4cEBJYuVSl01+NaOMfOMxVrvhuVszxyud8EQ7gMDMy5e3WU6F1q1psvQAAoGl8Pd37oMCi+Y0eo3Jh3X1QRqP2QXaT9kHua8nN1BbcB11ynTy19kEl70bug5Lq7IPK69kHVXzzRTiAw8zIlLd7T4XWsg8CAACJsUdcoSJJY8eODb9etWqV7J3Rvx06dNCAAQOaPO7cuXPDrzt06BBX8IYkFRYWNnquCRMmyOPxSJIsy9Inn3wSfm/27NkqLq5JGzdx4sSI/o2Rm5sb1w8AVPN17SZfhw6uurJ53ydoNc2TMmSosk8+LVze9MffR31qxNelS/i1VVKi0La8mGNWrnJ/MPd17twCKwUAAC3BTM90le2CHTFaxhatT6yMGKGNa+XUeULTSElp9JxGsrtP3YCOpvL1G6zUiSeGywWP/CXqXsjbvlP4tV1W4koZXlfdJ3m9HTrFaAkAAHYns1YWC0myCvJjtIzNLojcA8TKiBHasC4ie6nZpH1QqnsNdQI6msrfb7DSJp0ULuc//Oeo+yBPh5pzHbu0RPaObRFtqgXr7oPasw8CgN3ONPhp7A/2WntMAEf37t213377RdQ3J/uGJG3cWJPebfjw4XEFb0jSypWRkcYNycnJ0cEHHxwuf/zxx+FAlI8++sjV7sADD2z0+ADQHNknnuwqly/+WZVLlyRmMc1hGOp0++9k7PzvecE7b8YMRPFk1BxyRLvDtba673syWy7FOQAAaJ662S+MpKRGj2EkJUeOG+vueMtS5YJ5ripP2w7R29ajbh+7sKDRY0QwDOVcc2t4L1T68XQF6mQXCTet9YWPXVr/lyZ13zfqfFkEAAASwykvd5UNfwvtgyrKo7SUZIVUucB9zuJp1/hM0p627V3lulecNIlhKGfqbTX7oBnvKrBwftSmZq3MZw0Fjzh19kF1g4cBAAB2pz0mgEOSRo8e7SobhhFR11ilpTVPOKWlpcXVZ+3atdqwYUOT5ps8eXL4dV5enubPn6+1a9dqyZKaL0nHjx8fztQBALuD4fMp+6RTXHUFr0eml2wNck47QylDhkqSrOJibbn/bzHb2oFg+LXh89U7ruH3u/tWVjZjlQAAoCVZRe4Df2/n7o0ew9slso9VVBCzffmsT1zlumm4G56vhzw7r3urFli1tFFjRJN29Cny9xssqSroovCZf8ZuHKy1F/I2sBfyufdCdZ+8BQAAiWHVCXyItqdpiLdLj4g6u559UFlz90Fde8iTleOqC65c1qgxokk75tRa+6BiFTz9j5htnUbsg+R3v183ExsAAMDutEcFcIwZM0YXXnhh+Ofqq69W+/btG+5Yj5Ra6d3y8mKnzq/t9ddfb/J8gwYNUrdu3cLlDz/80JV9wzRNjR8/vsnjA0BTtL3sSvk71aSODO3YoYLpbydwRU3jycxS++tuCJfzHn1Ioe2xU2BatZ5y9WRlRwRp1OZt735C1iooiN4QAADsdsHl7qxhyYccKZmNC4pPGXWUq2wV7JC9PfZnxPKvP5NjhcJlb6eu8g/ZP+75Uice7yo7waACdbJ6NJaRnqnMC64Jl4v+/US916LU/mLGzMySfLH3Qp7cdnX6Nv5aUQAA0PKCyxe7yimHjG70Pij18Mh9kNXIfVDSkJFxz5c28QRX2QkGI7J6NJaZnqmsC2v2QYX/amgfVLOXaXgfVCdbSD3BLQAAALvaHhXAkZGRoeOOOy78M3bs2GaPWTuYYu7cuSpo4Au5Tz75RF9//XWz5qydhWPevHmaOXNmuDxy5Ejl5uY2a3wAaIy0ww5X20uvcNVt/tufZZe0zN2ju1P7X9wob07VExwVy5dq+8v/qrd9xeKfw68Nr1cpQ2M/MZI6ouYLGce2VdEar5cBAGAvVfG9+zOat30npZ9wRtz9/f2HKOUIdyB9xfez6+1j529X2Yx3XXXZl14vxXEtp6djF6WfcKarrnzWx7GvbIlT1kXXhrN6BFevUMnb/6m3fWBlTcYPw+OVv//gmG2TBg0Pv3ZsW8HVzX9KFgAANF/F3Dr7oA6dlH7imTFaR/IPGKqUIye4x2xoH7Rjm0o/esdVl3VZI/ZBJ57lqiv7ckbz90EXTw1n9QiuXq6St16pt31wZc25juHxKqn/kJht6+6DAqvYBwEAgMTZowI4doWDDjoo/Lq8vFx/+ctftG1b5NPagUBA06ZN0+OPPy5JSmrCncrVRo8eHc784TiOKipqNqcTJ05s8rgA4M1tq9SDDom7fdYJJ6n7Px6WWSvzRMlXs1T4buvLvpE8cLByTpsSLm/64x8ky6q3T9n338mulfYy55TTo7YzvD5ln3hyuFzx8yLZxUXNWzAAAGgx5Z9/KGuH+3Nc1qXXK2Xs5Bg9avj6DlLub/8mo841lqXv1B/8IEmF/3pcdq3Pc/7+Q5Rzw+/q/fLCzMlV29/dLzMlNVznWJaKXn66wfnq4+szQGmTTw6XCx79q2TXvxcKLJznSh+eNumk6A29XqWOPy5cDK5YLKekuFnrBQAALaPs8w9l7XBny8i+7Hqljj26wb7+foPU9nf3ReyDGgp+kKSiFx+TXVEeLicNGKo2N95Z/z6oTVu1u+uBKPugpxqcrz6+PgOVdnTN1cD5j/ylwX1Q5YI6+6DJsfdBaRNqMqcFl7MPAgAAieVN9AJ2tQkTJmj69Onavr0qndqKFSt0/fXXa/jw4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size 1400x1000 with 2 Axes>"]},"metadata":{"image/png":{"height":879,"width":1080}},"output_type":"display_data"}],"source":["plt.figure(figsize = (14, 10))\n","ax = sns.heatmap(all_month_year_percentage_df, cmap = 'RdYlGn_r', annot=True, fmt='.0%',\n","                 vmin=0, vmax=1, linewidths=.5, annot_kws={\"size\": 16})\n","cbar = ax.collections[0].colorbar\n","cbar.set_ticks([0, .25, .50,.75, 1])\n","cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])\n","ax.set_yticklabels(ax.get_yticklabels(), rotation = 0, fontsize = 14)\n","ax.set_xticklabels(ax.get_xticklabels(), rotation = 0, fontsize = 14)\n","ax.tick_params(rotation = 0)\n","plt.title('Percentage of days in the month with low speeds', fontdict={'fontsize':18}, pad=14);"]},{"cell_type":"markdown","metadata":{},"source":["In a similar fashion we can create an aggregation for each day of the year."]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[],"source":["def plot_heatmap_year(year):\n","    plt.figure(figsize = (16, 10))\n","    allByYear_df = df.loc[df['year'] == year]\n","    allByYear_df = pd.pivot_table(allByYear_df, values=\"speed\",\n","                                  index=[\"month\"], columns=[\"day\"], fill_value=None)\n","    allByYear_df = allByYear_df.set_index([[calendar.month_abbr[i] for i in list(allByYear_df.index)]])\n","    ax = sns.heatmap(allByYear_df, cmap = 'RdYlGn_r',\n","                     vmin=0, vmax=20,\n","                     annot=False, linewidths=.1,\n","                     annot_kws={\"size\": 8}, square=True, cbar_kws={\"shrink\": .48, 'label': 'Rain (mm)'})\n","    ax.set_yticklabels(ax.get_yticklabels(), rotation = 0, fontsize = 12)\n","    ax.set_xticklabels(ax.get_xticklabels(), rotation = 0, fontsize = 12)\n","    ax.tick_params(rotation = 0)\n","    plt.title(f'speeds {year}', fontdict={'fontsize':18}, pad=14);"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/homebrew/Caskroom/miniconda/base/envs/skmob-dev/lib/python3.11/site-packages/seaborn/matrix.py:309: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n","  ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))\n","/opt/homebrew/Caskroom/miniconda/base/envs/skmob-dev/lib/python3.11/site-packages/seaborn/matrix.py:309: UserWarning: Attempting to set identical low and high ylims makes transformation singular; automatically expanding.\n","  ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))\n"]},{"data":{"image/png":"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size 1600x1000 with 2 Axes>"]},"metadata":{"image/png":{"height":850,"width":933}},"output_type":"display_data"}],"source":["plot_heatmap_year(2019)"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/homebrew/Caskroom/miniconda/base/envs/skmob-dev/lib/python3.11/site-packages/seaborn/matrix.py:309: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n","  ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))\n","/opt/homebrew/Caskroom/miniconda/base/envs/skmob-dev/lib/python3.11/site-packages/seaborn/matrix.py:309: UserWarning: Attempting to set identical low and high ylims makes transformation singular; automatically expanding.\n","  ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAB0oAAAalCAYAAACxKUufAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAB7CAAAewgFu0HU+AACulElEQVR4nOzdeZjVZf0//teZYUdkFRVRIRBE+ojIgIqiYKIppmZIfio1SktCM5fSvvZRzH3JJbfKTD+ZmtsHV1wyEzdQQCFTWWYkVLZhB1mGbX5/GPNjOMNhZhgYnPvxuC6u65z73Pf7dZ/D2668nt6vd6a0tLQ0AAAAAAAAABKSV9sbAAAAAAAAANjeBKUAAAAAAABAcgSlAAAAAAAAQHIEpQAAAAAAAEByBKUAAAAAAABAcgSlAAAAAAAAQHIEpQAAAAAAAEByBKUAAAAAAABAcgSlAAAAAAAAQHIEpQAAAAAAAEByBKUAAAAAAABAcgSlAAAAAAAAQHIEpQAAAAAAAEByBKUAAAAAAABAcgSlAAAAAAAAQHIEpQAAAAAAAEByBKUAAAAAAABAcgSlAAAAAAAAQHIEpQAAAAAAAEByBKUAAJCwDh06RCaTKfvz/e9/v7a3BAAAALBd1KvtDQAAAGxPc+fOjX/9618xY8aMWLx4caxatSp23nnnaNmyZXTu3Dl69uwZDRo02Ob7mDdvXowbNy6Kiopi6dKlUb9+/WjdunXst99+UVBQEPXr19/mewAAAICUCUoBAIA6bcaMGfHCCy/EK6+8EqNHj465c+fmnN+wYcM45JBD4kc/+lF861vfqvHQ9PHHH4/bbrst3nzzzSgtLa1wTrNmzWLIkCHxi1/8Irp06VJjtWfPnh0TJkyI8ePHl/3Z9PfYe++949///neN1ays4uLi6N69e8yfPz/rs8svvzxGjBix3fcEAABA3SYoBQAA6qSbb745Hn300Xj77bertK6kpCReffXVePXVV2PPPfeMP/7xj3H00Udv9X5mzpwZ3/3ud2P06NFbnLts2bK4995744EHHohf/epX8atf/SoymUyVa44dOzb+9re/lYWis2bNqs7Wt4thw4ZVGJICAADAtiIoBQAA6qQLL7xwq6/x6aefxjHHHBMXX3xxXHfdddW+ztSpU6N///4xe/bsKq1bvXp1XHbZZfHRRx/FAw88EPn5+VVaf91118VTTz1VpTW14aGHHor/+7//q+1tAAAAkBhBKQAAkJS99tor+vXrF927d49ddtklmjdvHosXL44PPvggXnjhhZgyZUrWmuuvvz7y8/Pj6quvrnK9BQsWxMCBAysMSXv16hUnnnhidOzYMVauXBlTp06Nhx56KOvk58MPPxxt27aNW2+9tcr1d3Rz5syJc889t7a3AQAAQIIEpQAAQJ239957xxlnnBGnnXZadO7cebPzSktLY+TIkTFs2LAoLi4u99k111wTAwcOjP79+1ep9o9+9KP45JNPyo01a9YsHnjggTjxxBOz5l9zzTVxzTXXZD2T87bbbotjjjkmjj322CrVr0jjxo2jR48eUVBQEHfcccdWX29r/PjHP46FCxeWvT/ooIOq3C4ZAAAAqkNQCgAA1FkHHnhgjBgxIo4//vhKPeMzk8nEySefHH369IlDDz00K+A899xz4/333690/b/97W9ZLWUbNGgQr7zyShQUFFS4pn79+nH55ZdHixYt4mc/+1lW/cmTJ0e9epX/V7mGDRvGf/3Xf0VBQUHZn+7du5ddozaD0j//+c/x9NNPl73v169fDB06VFAKAADAdiEoBQAA6qQnn3yywhObldG+fft44oknok+fPlFaWlo2/q9//Svee++96NmzZ6Wuc+WVV2aNXX755ZsNSTd23nnnxXPPPRd/+9vfysaKiorioYceitNPP71S9W+//fbYbbfdon79+pWavz3NmjUrzjvvvLL3jRs3jnvvvTfefPPNWtwVAAAAKcmr7Q0AAABsC9UNSTcoKCiI448/Pmv82WefrdT6Dz/8MF5//fVyY23atIkLL7yw0nu49tprs8buvvvuSq/fc889d8iQNCLirLPOisWLF5e9//Wvfx377LNP7W0IAACA5DhRCgCQkLlz58akSZNixowZsXTp0li5cmU0atQomjRpErvttlt06NAhunTpEjvttFON1162bFmMHTs2pk2bFosXL44mTZpEu3btonv37tG9e/car7fB/PnzY/z48VFcXBzz5s2LdevWRZs2bWLXXXeNgw8+OFq3br3NahcWFsaHH34Y8+bNi3nz5kXDhg2jTZs20b59+zj44IOjcePGNV5z5syZMX78+Jg1a1YsXLgwmjZtGh07dozevXtHu3btarzeBgsXLox//vOfUVRUFEuXLo3ly5dHgwYNokmTJtG2bduye6tFixbbbA/bwnHHHRfPPPNMubGPP/64UmsffvjhrLGhQ4dGw4YNK12/V69e0atXr5gwYULZ2NixY2P69OnRsWPHSl9nR/OnP/0pRo0aVfa+T58+cf7559fijgAAAEiRoBQAoI5bvHhx3HXXXfHggw/Ghx9+uMX5+fn58dWvfjUGDBgQgwcPjr59++Z8tuP9998fQ4cOLTc2ffr06NChQ0RETJw4Ma666qp45plnYvXq1RVeY999940f/vCHcd5559XI6belS5fGHXfcESNHjowJEyaUa526sUwmEz179owf/vCHceaZZ0aDBg22uvb06dPj5ptvjlGjRuUM1Bo1ahT9+vWL888/P4499titrjty5Mi45ZZb4o033qjw+2YymTjkkEPi4osvjhNOOGGr60VErFq1Ku6555544IEHYty4cVucn8lkomvXrnH44YfH4MGDY8CAAVV61mZt2GuvvbLG5syZU6m1L7zwQtbY4MGDq7yHwYMHlwtKN1x72LBhVb7WjuCzzz6LCy64oOx9gwYN4k9/+lPk5+fX4q4AAABIkda7AAB12GOPPRZdunSJSy+9tFIhaUTEunXrYtKkSXHrrbfGYYcdFlOmTKl2/auvvjp69+4dTzzxxGZD0oiIyZMnx89//vM48MAD47333qt2vdLS0rjtttviK1/5Slx66aUxfvz4zYakG+a/++67MXz48OjSpUu88sor1a69dOnSOOecc6Jr165xxx13bPHU4apVq+Jvf/tbHHfccdGvX7/45JNPqlV3/vz5cdJJJ8XJJ58cr7/++ma/b2lpabz11ltx4oknxpAhQ2LFihXVqrfBq6++Gvvtt1/89Kc/rVRIumEPkydPjj/84Q9x9NFHx8svv7xVe9geKvqdKnMSePny5fHuu++WG2vSpEmln226sX79+mWNbdrS98vkzDPPjCVLlpS9/9WvfrVNT5UDAADA5ghKAQDqqD/+8Y/x7W9/O+bNm1cr9S+++OL41a9+FWvXrq30mn/9618xYMCAGD9+fJXrrVq1Kk499dT42c9+FgsWLKjy+hkzZsQxxxwT99xzT7XWHnrooXHnnXfGmjVrqrz+jTfeiD59+sTYsWOrtG7BggXxta99LZ566qkqrXvsscfi2GOPzRle5zJq1Kj4+te/HtOnT6/W+i+TwsLCrLHdd999i+smTpwY69evLzdWUFBQrRPTvXv3zlq36QnTL4t77rknXnzxxbL3PXr0iEsuuaQWdwQAAEDKduw+VwAAVMvUqVPjnHPOyTpd2Lhx4zjyyCOjd+/esffee8dOO+0Ua9eujaVLl8bs2bPjgw8+iHHjxsW///3vrar/2GOPxQ033FD2vlGjRnHsscdGv379Yvfdd4/PP/88ioqKYuTIkVknVpcsWRIDBw6MCRMmxFe+8pVK1VuzZk0cffTRFZ6y69SpUwwYMCD233//aNWqVdSrVy/mz58f48aNi1GjRpULkteuXRs//vGPY7fddotvfOMblao9Y8aMOOigg2Lu3LlZn/Xp0ycOPfTQ6Nq1a7Rs2TJWr14ds2fPjrfeeiuef/75KCkpKZs7d+7cGDRoULz77rux9957V+o7H3PMMfHPf/4z67M99tgjvvWtb0W3bt2iVatWMXfu3HjvvffiySefjEWLFkVExGuvvRYXXnhhpb7jxhYsWBBnnHFGub1HRNSrVy8OP/zw6Nu3b3To0CGaNWsWEV+ctC0uLo4PPvgg3n333Zg8eXKVa9amxx9/PGusd+/eW1xX0ffs3LlztfbQoEGDaN++fblguqioKNauXbvDty7e2CeffFLunqtXr1786U9/qpF22wAAAFAdX55/qwYAoNJuuOGGrCDre9/7Xtx8882xyy67bHH9hx9+GI8//nj84Q9/qFb9yy67rOz1oEGD4g9/+EO0a9cua961114bf/7zn+O8886LxYsXl40vXrw4zjzzzPj73/+e8/moG1x00UVZIel+++0Xt9xySwwcOHCz11i5cmXcdtttcdlll5WdBC0tLY3TTz89Jk2aVOHzKTe2evXqOOWUU7JC0uOPPz5uuOGG6Nat22bXzpkzJ37+85/HX/7yl7KxhQsXximnnBJjxozZ4vMar7766qxThQ0bNowrrrgiLrroogrX33777XHppZfGbbfdFhERd955Z5Wfy3r33XfH/Pnzy40NHDgw/vjHP27x94qI+Pe//x0jR46Mu+++u0p1a8O7776b9Rvn5eXF8ccfv8W1Ff3HBpUJwDdnr732KheUrlu3Lj755JNK/8cEta20tDR++MMfxrJly8rGLrroojjwwANrcVcAAACkTutdAIA6aNNWrAMGDIg///nPlQpJI74IGS+77LL497//Xa0gZtWqVRER8Z3vfCeefvrpCkPSDU4//fR46aWXyk4gbvCPf/yjXIi4OS+++GL89re/LTd24oknxnvvvRdHH310zqC1cePGcckll8Rzzz1X7lTb4sWL49prr91i7REjRmQ9n/O6666LZ555JmdIGhGx2267xQMPPBCXX355ufFx48ZVeIpxY0VFRXHNNdeUG6tXr1789a9/jYsvvnizIWvTpk3j1ltvjZtvvjkivgivNg3Ut2TTe2vfffeNZ555plIhaUREhw4d4vzzz48pU6bEEUccUaXa21NpaWlccMEFWePf/OY3o02bNltcP2fOnKyxPffcs9r7qWhtRaeYd1S/+93vyj2Tdt99940RI0bU3oYAAAAgBKUAAHXOsmXLsk78nXnmmZU6mbmpevXqVfnE4QZdunSJ++67L/Lytvx/OXv37h2333571viGk4+5/PrXvy73fv/994/HHnusSvseOHBgVmB533335QyiFi1alLXns88+Oy6++OJK1434ImwdOHBgubHrrrsu55q77ror61mov/zlL+Okk06qVM3zzz8//vu//7tK+9zg448/Lvf+tNNOi4YNG1b5OplMJho3blytPWwPv/3tb2P06NHlxurXrx9XXnllpdYvXLgwa2ynnXaq9n4qWludZ/HWhunTp8cvfvGLsvd5eXlx7733Vuu+AQAAgJokKAUAqGM2bm25QevWrbf7Pn7zm99UKaw8/fTTo6CgoNzYhAkTYvz48Ztd88Ybb8Rbb71VbuyWW26p1jMPL7jggnKnWktKSuL555/f7Pw777wzPv/887L3O+20U1x//fVVrhtRvlVxRMTEiRM3+5zYkpKSuP/++8uNtWvXLn75y19WqeZvfvObagVVm95ftXFvbWtjx44tF+xtcMkll2zxpPAGy5cvzxrbmmC4orUrVqyo9vW2l9LS0vjBD35Q7p+Vc889N/r27VuLuwIAAIAvCEoBAOqYVq1aZZ0efeONN7brHtq1axfHHXdcldZkMpk466yzssZzhZWbtqjt2LFjHHnkkVWqu0Hjxo1jwIAB5cY2PVGYq/Ypp5wSO++8c7Vq9+3bN1q0aFGp2m+//XbWacXvfe97VQ7hdt999/jGN75RpTUR2cHo9r63trUZM2bESSedFKtXry43fvDBB2cF2rlseuI3IqJRo0bV3ldFf7+b7nFHdMcdd8Srr75a9r5jx45x9dVX196GAAAAYCOCUgCAOqZRo0ax//77lxu76aab4oknnthuezjhhBMq1XJ3UyeffHLW2NixYzc7f9MwcWtPqXXs2LHc+/fee6/CeYsWLYr333+/xmrn5eXF3nvvXanaFf0eFf1ulVGddQcddFC59w8++GDcddddUVpaWq097EgWLFgQxx57bFbL5V133TUee+yxqFev3lZdvzrtr3Ot3dF/86KiorjkkkvK3mcymfjjH/8YTZs2rcVdAQAAwP9v6/5NHwCAHdLQoUPjZz/7Wdn7VatWxeDBg+Pggw+OM844I44//vho3779Nqvfq1evaq1r06ZN7LnnnvHpp5+WjU2YMKHCucuWLYt//vOf5cb+9re/xQEHHFCt2hERc+bMKfd+02e9bjBmzJhYv359ubFrr7027rjjjmrXLiwsrFTtTX+P/Pz86NGjR7VqVufvaejQofHUU0+VvS8tLY3hw4fHXXfdFUOHDo0TTjgh9tlnn2rtpzYtXbo0vv71r8dHH31Ubrx58+bx/PPPV/mfl4raP69cubLa+6tobXWfH7w9rF+/PoYOHVquPfBZZ51V7RPfAAAAsC0ISgEA6qCzzz47HnrooXjnnXfKjY8dOzbGjh0bw4YNi86dO8dhhx0WBQUFcdhhh8X++++/VSfeNta1a9dqr913333LBaXz5s2L0tLSrL3Nnj07K6wsLi6O4uLiatfe1IIFCyoc/+yzz7LGPv744xqrm6v2pt9v7733rnZL13322Sfy8/Nj3bp1lV5z4oknxkknnRRPPvlkufEPPvggLrroorjoootizz33jMMOOyx69+4dffv2jV69em31acxtafny5TFo0KCs5+E2bdo0Ro0aFT179qzyNZs0aZI1VtNB6Y58MvO2226L119/vex9+/bt48Ybb6zFHQEAAEA2rXcBAOqghg0bxnPPPRdHHXXUZucUFhbG/fffH+ecc04ccMAB0aZNmzjttNNi1KhRWQFkVTVv3rzG1q5fvz6WLVuWNW9zQWJNWrVqVYXj26P25kK1xYsXl3u/Nb91JpOJZs2aVXndgw8+GN/5znc2+/mnn34aDz/8cFxwwQVx8MEHR8uWLePkk0+ORx99NEpKSqq9321h5cqVccIJJ2Q9a7Vx48bx7LPPVrul8qbPco2I+Pzzz6t1rc2trajGjmDatGlx6aWXlhv7/e9/X+1n+AIAAMC2IigFAKij2rRpEy+99FI88sgjlWqxunDhwvjLX/4SgwYNiv3222+rnmm6NSfdKlpbUVC6aNGiatfYWrVZe9PfYmtPFVZnfZMmTeLBBx+Ml156Kfr377/Fk8iff/55jBw5Mr797W9Hp06d4ve///0O8XzNkpKS+OY3vxmvvPJKufGGDRvGU089Ff3796/2tXfdddessYpOIlfWxqesc9XYEZx33nnlgv7TTjstjjvuuFrcEQAAAFRsx+1/BQDAVstkMjFkyJAYMmRIfPTRRzFq1KgYPXp0vPXWWzlPRU6ZMiUGDx4cZ599dtx1111Vbsm7fPnyau+5orUVnXps3Lhx1titt94a5513XrVrV1ZFtSdOnFjtZ4VWxaa/xdb81lu7fuDAgTFw4MCYMWNGPPvsszF69Oh48803Y9asWZtdM3PmzDj77LPjueeei8cff7zWnrO5evXqOPnkk+PFF18sN96gQYMYOXJkDBw4cKuu37Fjx6yxGTNmVPt6n3zySbn3+fn5sddee1X7etvSpn//Y8aMqdKzgxcuXJg19rvf/S6r3XNBQUH88Y9/rM4WAQAAICIEpQAAyejWrVt069YtLrzwwoj4oj3m66+/Hq+++mo8//zzMX/+/Kw1v/vd76J9+/ZZbTS3ZMmSJdXe56Zr8/LyKgxK27RpkzU2ffr0atetis3V3h5BaYsWLcq935rfurS0tMLTulW19957x/Dhw2P48OER8UWo9/rrr8drr70Wo0aNqvAk5TPPPBPDhw+Pe+65Z6vrV9WaNWvilFNOiVGjRpUbr1+/fjz++ONx7LHHbnWNip7TW1hYWK1rrV69OutEaadOnXbo575urLrfe2Nz586NuXPnlhvb9J8FAAAAqCqtdwEAErXPPvvED37wg/jzn/8cc+bMiWeeeSZ69+6dNe+aa66pMETNZerUqdXe15QpU8q932WXXSo80VpR29F//vOf1a5bFbVZu23btuXef/LJJ5t9luqWTJs2LdatW1cT2ypnr732iu9+97vx+9//Pj799NP4xz/+EUcffXTWvHvvvTc++OCDGq+fy5o1a2LIkCHx9NNPlxuvX79+PProo/GNb3yjRur07Nkz8vLK/+vW+PHjY+3atVW+1vjx42PNmjXlxg488MCt2h8AAAAgKAUAIL5o43n88cfHW2+9lXWabsWKFfHCCy9U6XoTJkyo1j7mz5+f1WJ0c89Xbdu2bXTo0KHc2JtvvlkjJyS35KCDDsoae/7557d53Yjs32Pt2rUxadKkal2run9PVdW/f/948cUX40c/+lG58dLS0hg5cuR22UPEF7/VqaeemtXCtV69evHXv/41TjrppBqr1bRp0+jZs2e5seXLl8d7771X5Wu98cYbWWOHH354tfcGAAAAfEFQCgBAmXr16sW1116bNV7V05JPP/10rF+/vsr1/+///i9r7OCDD97s/KOOOqrc+9WrV8ejjz5a5bpVtffee0fnzp3Ljb3zzjtbdZK2sir6PaobNlb0e29L11xzTeTn55cb214ncdetWxff/e53s75zfn5+PPjgg3HyySfXeM2vf/3rWWNPPPFEla/z+OOPV+raO4qJEydGaWlptf/cd999Wde8/PLLs+a9+uqr2//LAQAAUKcISgEAKGfffffNGqvqczBnzpxZrROW9957b9ZYrudFnnjiiVljV199daxevbrKtatq09rr16+PX//619u87kEHHRStWrUqN/bAAw9Uuf3uhnbL21Pr1q1jl112KTe2Nc9Yraz169fHaaedlhWi5+fnx1/+8pcYMmTINqn73//931lj9913X5Xuz/feey/GjRtXbuyggw6Kjh07bvX+AAAAIHWCUgAAypk9e3bW2KbhVmVcdNFFWc9VzOWBBx6Id955p9zYgQceGAUFBZtdM2jQoDjggAPKjU2fPj3OO++8Ku21Oi688MJo1KhRubEHH3wwHnnkkW1at2HDhvH973+/3NisWbPiuuuuq9J1LrrooigpKanBnW3ZqlWrYtGiReXGqnNvVcX69etj6NCh8fDDD5cbz8vLi//93/+NU089dZvV7t69exx22GHlxoqLi+OWW26p9DX+3//7f1ljw4YN2+q9AQAAAIJSAIA656233orbbrut2s/qvO2227LGevToUeXrTJ48OX74wx9GaWnpFue+++67cc4552SNbynwzGQyceWVV2aN/+53v4tf/vKX1Wr/GxExZsyY+M53vpNzzu677x7Dhw/PGv/BD35QrfaqEV+0h33kkUfi0ksvzTlv2LBhUb9+/XJjV199dTz77LOVqvPb3/42HnzwwSrvr7CwMK688sqYN29elddGRPz+97/PCmerc29VVmlpafzoRz+KP//5z+XG8/Ly4v7774/vfve726z2Br/61a+yxi6//PJ49913t7j2jjvuyHo+cMeOHbd4bwIAAACVIygFAKhjiouL42c/+1m0b98+zj777HjppZcqdbLz888/j4svvjhuvfXWcuPNmzeP448/vkp72HDS8oEHHoiTTjqpwlOqGzz44INx1FFHxdKlS8uNDxgwIE477bQt1jr++OPj3HPPzRq/7rrrYsCAAfHaa69Vas+zZ8+O22+/Pfr27Rt9+/aNp59+eotrrrrqqujTp0+5sRUrVsTgwYPjzDPPjKKiokrV/te//hWXXXZZdOnSJU499dSYNGlSzvmdO3fOOmm4du3aGDx4cNx0002xbt26CtetWLEiLrzwwvjZz34WEV8EzQ0b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size 1600x1000 with 2 Axes>"]},"metadata":{"image/png":{"height":850,"width":933}},"output_type":"display_data"}],"source":["plot_heatmap_year(2014)"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"ename":"SyntaxError","evalue":"invalid syntax (2109960825.py, line 1)","output_type":"error","traceback":["\u001b[0;36m  Cell \u001b[0;32mIn[35], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    all_days_avg_df = df.groupby([= df['datetime']..dt.month, = df['datetime']..dt.day])['speed'].mean()\u001b[0m\n\u001b[0m                                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"]}],"source":["all_days_avg_df = df.groupby([= df['datetime']..dt.month, = df['datetime']..dt.day])['speed'].mean()\n","all_days_avg_df = all_days_avg_df.unstack()\n","all_days_avg_df = all_days_avg_df.set_index([[calendar.month_abbr[i] for i in list(all_days_avg_df.index)]])"]},{"cell_type":"markdown","metadata":{},"source":["Another possibility is to get the mean precipitation for each day of the year considering all the years in the dataset.\n","In the heatmap a custom colormap was used in order to have greenish cells for very low values."]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[],"source":["import matplotlib.pyplot as plt\n","import matplotlib.colors as mcolors\n","\n","def make_colormap(seq):\n","    \"\"\"\n","    Return a LinearSegmentedColormap\n","    seq: list\n","        a sequence of floats and RGB-tuples. \n","        The floats should be increasing and in the interval (0,1).\n","    \"\"\"\n","    seq = [(None,) * 3, 0.0] + list(seq) + [1.0, (None,) * 3]\n","    cdict = {'red': [], 'green': [], 'blue': []}\n","    for i, item in enumerate(seq):\n","        if isinstance(item, float):\n","            r1, g1, b1 = seq[i - 1]\n","            r2, g2, b2 = seq[i + 1]\n","            cdict['red'].append([item, r1, r2])\n","            cdict['green'].append([item, g1, g2])\n","            cdict['blue'].append([item, b1, b2])\n","    return mcolors.LinearSegmentedColormap('CustomMap', cdict)"]},{"cell_type":"code","execution_count":37,"metadata":{},"outputs":[],"source":["import matplotlib.colors as colors\n","c = colors.ColorConverter().to_rgb\n","gyr = make_colormap([c('green'), c('yellow'), 0.25, c('yellow'), c('red')])"]},{"cell_type":"code","execution_count":38,"metadata":{},"outputs":[{"ename":"NameError","evalue":"name 'all_days_avg_df' is not defined","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn[38], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m14\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m ax \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mheatmap(\u001b[43mall_days_avg_df\u001b[49m, cmap \u001b[38;5;241m=\u001b[39m gyr, annot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, fmt\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      3\u001b[0m                  vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, linewidths\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.1\u001b[39m,\n\u001b[1;32m      4\u001b[0m                  annot_kws\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m8\u001b[39m}, square\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,  \u001b[38;5;66;03m# <-- square cell\u001b[39;00m\n\u001b[1;32m      5\u001b[0m                  cbar_kws\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshrink\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m.5\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRain (mm)\u001b[39m\u001b[38;5;124m'\u001b[39m})\n\u001b[1;32m      6\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_yticklabels(ax\u001b[38;5;241m.\u001b[39mget_yticklabels(), rotation \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, fontsize \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m12\u001b[39m)\n\u001b[1;32m      7\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_xticklabels(ax\u001b[38;5;241m.\u001b[39mget_xticklabels(), rotation \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, fontsize \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m12\u001b[39m)\n","\u001b[0;31mNameError\u001b[0m: name 'all_days_avg_df' is not defined"]},{"data":{"text/plain":["<Figure size 2000x1400 with 0 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize = (20, 14))\n","ax = sns.heatmap(all_days_avg_df, cmap = gyr, annot=True, fmt='.2f',\n","                 vmin=0, linewidths=.1,\n","                 annot_kws={\"size\": 8}, square=True,  # <-- square cell\n","                 cbar_kws={\"shrink\": .5, 'label': 'Rain (mm)'})\n","ax.set_yticklabels(ax.get_yticklabels(), rotation = 0, fontsize = 12)\n","ax.set_xticklabels(ax.get_xticklabels(), rotation = 0, fontsize = 12)\n","ax.tick_params(rotation = 0)\n","_ = plt.title('speeds Average 2010-2019', fontdict={'fontsize':18}, pad=14)"]},{"cell_type":"markdown","metadata":{},"source":["Another way to customize the colormap to make highest values more prominent."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["custom_palette = sns.color_palette(\"GnBu\", 6)\n","custom_palette[5] = sns.color_palette(\"OrRd\", 6)[5]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["sns.palplot(custom_palette)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["plt.figure(figsize = (20, 14))\n","ax = sns.heatmap(all_days_avg_df, cmap = custom_palette, annot=True, fmt='.2f',\n","                 vmin=0, linewidths=.1,\n","                 annot_kws={\"size\": 8}, square=True,\n","                 cbar_kws={\"shrink\": .5, 'label': 'Rain (mm)'})\n","ax.set_yticklabels(ax.get_yticklabels(), rotation = 0, fontsize = 12)\n","ax.set_xticklabels(ax.get_xticklabels(), rotation = 0, fontsize = 12)\n","ax.tick_params(rotation = 0)\n","_ = plt.title('speeds Average 2010-2019', fontdict={'fontsize':18}, pad=14)"]},{"cell_type":"markdown","metadata":{},"source":["### CountPlot with customized text"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.groupby('year')['raining'].sum()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["plt.figure(figsize = (14, 6))\n","ax = sns.countplot(x=\"year\", hue=\"raining\", data=df.sort_values(by='year'))\n","ax.legend(loc='upper right', frameon=True, labels=['Dry', 'Rain'])\n","\n","for p in ax.patches:\n","    ax.annotate(format(p.get_height()),\n","                (p.get_x()+p.get_width()/2., p.get_height()-4),\n","                ha = 'center', va = 'center',\n","                xytext = (0, 10), textcoords = 'offset points')\n","\n","_ = ax.set_title(\"Dry and Wet Days per Year\")"]},{"cell_type":"markdown","metadata":{},"source":["Something that may be needed when analyzing time series is to calculate the number of consecutive days satisfying a specific condition.\n","In this case we can try to find the longest spells of dry and rainy days.\n","One way to do so is to use a combination of diff, cumsum and groupby.\n","First we need to label each row with an increasing number per each spell."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df['value_grp'] = (df['raining'].diff() != 0).astype('int').cumsum()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["(df['raining'].diff() != 0).astype('int')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.head(10)[['date', 'speed', 'raining', 'value_grp']]"]},{"cell_type":"markdown","metadata":{},"source":["Then we can create a new DataFrame with a row for each label using groupby."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["grouped_values = df.groupby('value_grp')\n","consecutive_df = pd.DataFrame({'BeginDate' : grouped_values.date.first(), \n","              'EndDate' : grouped_values.date.last(),\n","              'Consecutive' : grouped_values.size(),\n","              'condition': grouped_values.raining.max() }).reset_index(drop=True)\n","consecutive_df['condition'].replace({0: 'Dry', 1: 'Rain'}, inplace=True)\n","consecutive_df.sort_values(by='Consecutive', ascending=False).head(10)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["plt.figure(figsize = (14, 6))\n","ax = sns.countplot(x='Consecutive', hue='condition', data=consecutive_df.query('Consecutive >= 2'))\n","ax.set_title('Consecutive days on a specific condition 2012-2019 (> 2 days)', pad=14)\n","ax.set(xlabel='Consecutive days', ylabel='Count')\n","_ = plt.legend(loc='upper right')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["consecutive_df['DateRange'] = consecutive_df[\"BeginDate\"].astype(str) + ' -> ' + consecutive_df[\"EndDate\"].astype(str)\n","ax = sns.barplot(x=\"Consecutive\", y=\"DateRange\", hue=\"condition\", data=consecutive_df.sort_values(by='Consecutive', ascending=False).head(14))\n","\n","for p in ax.patches:\n"," width = p.get_width()\n"," ax.text(width -1.6, p.get_y() + p.get_height()/2. + 0.2,'{:1.0f}'.format(width), ha=\"center\")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df_top10_per_condition = consecutive_df.sort_values(by='Consecutive',ascending = False).groupby('condition').head(10)\n","\n","d = {'color': ['g', 'r']}\n","g = sns.FacetGrid(df_top10_per_condition, row=\"condition\",\n","                      hue='condition',\n","                      hue_kws=d,\n","                      sharey=False)\n","\n","g.fig.set_figheight(8)\n","g.fig.set_figwidth(10)\n","    \n","_ = g.map(sns.barplot, \"Consecutive\", \"DateRange\")\n","_ = g.set(ylabel='')\n","\n","# This is just to add the numbers inside the bars\n","for ax in g.axes.flat:\n"," for p in ax.patches:\n","  width = p.get_width()\n","  _ = ax.text(width -1.6, p.get_y() + p.get_height()/2. + 0.1,'{:1.0f}'.format(width), ha=\"center\")"]},{"cell_type":"markdown","metadata":{},"source":["Create custom palette to use blue for high precipitation values"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["custom_palette = sns.diverging_palette(128, 240, n=10)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["sns.palplot(custom_palette)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["plt.figure(figsize = (20, 14))\n","ax = sns.heatmap(all_days_avg_df, cmap = custom_palette, annot=True, fmt='.2f',\n","                 vmin=0, linewidths=.1,\n","                 annot_kws={\"size\": 8}, square=True,\n","                 cbar_kws={\"shrink\": .5, 'label': 'Rain (mm)'})\n","ax.set_yticklabels(ax.get_yticklabels(), rotation = 0, fontsize = 12)\n","ax.set_xticklabels(ax.get_xticklabels(), rotation = 0, fontsize = 12)\n","ax.tick_params(rotation = 0)\n","_ = plt.title('speeds Average 2010-2019', fontdict={'fontsize':18}, pad=14)"]},{"cell_type":"markdown","metadata":{},"source":["Running average to identify periods with high precipitations."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["plt.figure(figsize = (18, 6))\n","plt.title('Avg Rainfall (40 days window)', pad=14)\n","_ = df.set_index('date')['speed'].rolling(40).mean().plot()"]},{"cell_type":"markdown","metadata":{},"source":["Experimenting with the average temperature"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ops_month_df= df.groupby(['month', 'year']).mean()['avg_temp'].reset_index()\n","plt.figure(figsize = (14, 6))\n","ax = sns.boxplot(x = \"month\", y = \"avg_temp\", data = ops_month_df)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["df.groupby(['month', 'year']).mean()['avg_temp'].reset_index()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def plotHeatmap(df, title):\n","    plt.figure(figsize = (20, 8))\n","\n","    ax = sns.heatmap(df, cmap = 'RdYlBu_r', fmt='.2f', annot=True,\n","                     linewidths=.2, annot_kws={\"size\": 8}, square=True,\n","                     cbar_kws={\"shrink\": .9, 'label': 'Temperature °C'})\n","    cbar = ax.collections[0].colorbar\n","    ax.set_yticklabels(ax.get_yticklabels(), rotation = 0, fontsize = 10)\n","    ax.set_xticklabels(ax.get_xticklabels(), rotation = 0, fontsize = 10)\n","    ax.tick_params(rotation = 0)\n","    plt.title(title, fontdict={'fontsize':18}, pad=14);"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["allMonthYear_df = pd.pivot_table(df, values=\"avg_temp\",index=[\"month\"], columns=[\"year\"], fill_value=None, margins=True)\n","allMonthYear_df = allMonthYear_df.set_index([[calendar.month_abbr[i] if isinstance(i, int) else i for i in list(allMonthYear_df.index)]])\n","plotHeatmap(allMonthYear_df, 'Average Temperature')"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"skmob-dev","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":1}