Project2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 0,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "# Project: Wrangling and Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Gathering\n",
    "In the cell below, gather **all** three pieces of data for this project and load them in the notebook. **Note:** the methods required to gather each data are different.\n",
    "1. Directly download the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import os\n",
    "import tweepy\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "#reading and checking the csv file\n",
    "df1=pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Use the Requests library to download the tweet image prediction (image_predictions.tsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#downloading the image_predictions file\n",
    "folder='twitter'\n",
    "if not os.path.exists(folder):\n",
    "    os.makedirs(folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "url=' https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response=requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#writing the downloaded doc to a file so we can access it\n",
    "with open(os.path.join(folder, url.split('/')[-1]), mode='wb')as file:\n",
    "    file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accessing and viewing the tsv file\n",
    "df2= pd.read_csv('image-predictions.tsv', sep='\\t')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Use the Tweepy library to query additional data via the Twitter API (tweet_json.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the json file into a dataframe\n",
    "new_list = []  #an empty list\n",
    "\n",
    "with open('tweet-json.txt') as file:\n",
    "    for tweet in file:\n",
    "        data = json.loads(tweet)\n",
    "        tweet_id=data['id']\n",
    "        retweet_count=data['retweet_count']\n",
    "        favorite_count=data['favorite_count']\n",
    "        \n",
    "        new_list.append({\"tweet_id\": tweet_id, \"retweet_count\": int(retweet_count),\n",
    "                         \"favourite_count\": favorite_count})\n",
    "    \n",
    "df3=pd.DataFrame(new_list, columns= ['tweet_id', 'retweet_count', 'favourite_count'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 28,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Assessing Data\n",
    "In this section, detect and document at least **eight (8) quality issues and two (2) tidiness issue**. You must use **both** visual assessment\n",
    "programmatic assessement to assess the data.\n",
    "\n",
    "**Note:** pay attention to the following key points when you access the data.\n",
    "\n",
    "* You do not need to gather the tweets beyond August 1st, 2017. You can, but note that you won't be able to gather the image predictions for these tweets since you don't have access to the algorithm used.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#viewing the twitter-archive-enhanced dataframe\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2075 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "0     666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1     666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2     666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3     666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4     666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "...                  ...                                              ...   \n",
       "2070  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0           1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1           1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2           1         German_shepherd  0.596461    True            malinois   \n",
       "3           1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4           1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "...       ...                     ...       ...     ...                 ...   \n",
       "2070        2                  basset  0.555712    True    English_springer   \n",
       "2071        1             paper_towel  0.170278   False  Labrador_retriever   \n",
       "2072        1               Chihuahua  0.716012    True            malamute   \n",
       "2073        1               Chihuahua  0.323581    True            Pekinese   \n",
       "2074        1                  orange  0.097049   False               bagel   \n",
       "\n",
       "       p2_conf  p2_dog                           p3   p3_conf  p3_dog  \n",
       "0     0.156665    True            Shetland_sheepdog  0.061428    True  \n",
       "1     0.074192    True          Rhodesian_ridgeback  0.072010    True  \n",
       "2     0.138584    True                   bloodhound  0.116197    True  \n",
       "3     0.360687    True           miniature_pinscher  0.222752    True  \n",
       "4     0.243682    True                     Doberman  0.154629    True  \n",
       "...        ...     ...                          ...       ...     ...  \n",
       "2070  0.225770    True  German_short-haired_pointer  0.175219    True  \n",
       "2071  0.168086    True                      spatula  0.040836   False  \n",
       "2072  0.078253    True                       kelpie  0.031379    True  \n",
       "2073  0.090647    True                     papillon  0.068957    True  \n",
       "2074  0.085851   False                       banana  0.076110   False  \n",
       "\n",
       "[2075 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#viewing the image-predictions dataframe\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>tweet_id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favourite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>8853</td>\n",
       "      <td>39467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>6514</td>\n",
       "      <td>33819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>4328</td>\n",
       "      <td>25461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>8964</td>\n",
       "      <td>42908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>9774</td>\n",
       "      <td>41048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>41</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>147</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>47</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>48</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>532</td>\n",
       "      <td>2535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2354 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  retweet_count  favourite_count\n",
       "0     892420643555336193           8853            39467\n",
       "1     892177421306343426           6514            33819\n",
       "2     891815181378084864           4328            25461\n",
       "3     891689557279858688           8964            42908\n",
       "4     891327558926688256           9774            41048\n",
       "...                  ...            ...              ...\n",
       "2349  666049248165822465             41              111\n",
       "2350  666044226329800704            147              311\n",
       "2351  666033412701032449             47              128\n",
       "2352  666029285002620928             48              132\n",
       "2353  666020888022790149            532             2535\n",
       "\n",
       "[2354 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the tweet-json file\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking the information of df1\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking the information of df2\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column           Non-Null Count  Dtype\n",
      "---  ------           --------------  -----\n",
      " 0   tweet_id         2354 non-null   int64\n",
      " 1   retweet_count    2354 non-null   int64\n",
      " 2   favourite_count  2354 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "#checking the information of df3\n",
    "df3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>tweet_id</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the descriptive statistics of df2\n",
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    tweet_id\n",
       "29    tweet_id\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for duplicated columns across the 3 dataframes\n",
    "columns=pd.Series(list(df1)+ list(df2)+ list(df3))\n",
    "columns[columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>888202515573088257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-21 01:02:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Canela. She attempted s...</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-07-19 00:47:34 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887473957...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Canela</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>886054160059072513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-15 02:45:48 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @Athletics: 12/10 #BATP https://t.co/WxwJmv...</td>\n",
       "      <td>8.860537e+17</td>\n",
       "      <td>1.960740e+07</td>\n",
       "      <td>2017-07-15 02:44:07 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886053434...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>885311592912609280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-13 01:35:06 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Lilly. She just paralle...</td>\n",
       "      <td>8.305833e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-02-12 01:04:29 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/830583320...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Lilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>879130579576475649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-26 00:13:58 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Emmy. She was adopted t...</td>\n",
       "      <td>8.780576e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-06-23 01:10:23 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/878057613...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Emmy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>878404777348136964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-24 00:09:53 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Meet Shadow. In an attempt to r...</td>\n",
       "      <td>8.782815e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-06-23 16:00:04 +0000</td>\n",
       "      <td>https://www.gofundme.com/3yd6y1c,https://twitt...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Shadow</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>746521445350707200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-25 01:52:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Shaggy. He knows exactl...</td>\n",
       "      <td>6.678667e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-21 00:46:50 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667866724...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Shaggy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>743835915802583040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-17 16:01:16 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Extremely intelligent dog here....</td>\n",
       "      <td>6.671383e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-19 00:32:12 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667138269...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>711998809858043904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-21 19:31:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @twitter: @dog_rates Awesome Tweet! 12/10. ...</td>\n",
       "      <td>7.119983e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>2016-03-21 19:29:52 +0000</td>\n",
       "      <td>https://twitter.com/twitter/status/71199827977...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2259</th>\n",
       "      <td>667550904950915073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 03:51:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @dogratingrating: Exceptional talent. Origi...</td>\n",
       "      <td>6.675487e+17</td>\n",
       "      <td>4.296832e+09</td>\n",
       "      <td>2015-11-20 03:43:06 +0000</td>\n",
       "      <td>https://twitter.com/dogratingrating/status/667...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2260</th>\n",
       "      <td>667550882905632768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 03:51:47 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @dogratingrating: Unoriginal idea. Blatant ...</td>\n",
       "      <td>6.675484e+17</td>\n",
       "      <td>4.296832e+09</td>\n",
       "      <td>2015-11-20 03:41:59 +0000</td>\n",
       "      <td>https://twitter.com/dogratingrating/status/667...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "19    888202515573088257                    NaN                  NaN   \n",
       "32    886054160059072513                    NaN                  NaN   \n",
       "36    885311592912609280                    NaN                  NaN   \n",
       "68    879130579576475649                    NaN                  NaN   \n",
       "73    878404777348136964                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "1023  746521445350707200                    NaN                  NaN   \n",
       "1043  743835915802583040                    NaN                  NaN   \n",
       "1242  711998809858043904                    NaN                  NaN   \n",
       "2259  667550904950915073                    NaN                  NaN   \n",
       "2260  667550882905632768                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "19    2017-07-21 01:02:36 +0000   \n",
       "32    2017-07-15 02:45:48 +0000   \n",
       "36    2017-07-13 01:35:06 +0000   \n",
       "68    2017-06-26 00:13:58 +0000   \n",
       "73    2017-06-24 00:09:53 +0000   \n",
       "...                         ...   \n",
       "1023  2016-06-25 01:52:36 +0000   \n",
       "1043  2016-06-17 16:01:16 +0000   \n",
       "1242  2016-03-21 19:31:59 +0000   \n",
       "2259  2015-11-20 03:51:52 +0000   \n",
       "2260  2015-11-20 03:51:47 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "19    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "32    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "36    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "68    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "73    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "1023  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1043  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1242  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2259  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2260  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "19    RT @dog_rates: This is Canela. She attempted s...         8.874740e+17   \n",
       "32    RT @Athletics: 12/10 #BATP https://t.co/WxwJmv...         8.860537e+17   \n",
       "36    RT @dog_rates: This is Lilly. She just paralle...         8.305833e+17   \n",
       "68    RT @dog_rates: This is Emmy. She was adopted t...         8.780576e+17   \n",
       "73    RT @dog_rates: Meet Shadow. In an attempt to r...         8.782815e+17   \n",
       "...                                                 ...                  ...   \n",
       "1023  RT @dog_rates: This is Shaggy. He knows exactl...         6.678667e+17   \n",
       "1043  RT @dog_rates: Extremely intelligent dog here....         6.671383e+17   \n",
       "1242  RT @twitter: @dog_rates Awesome Tweet! 12/10. ...         7.119983e+17   \n",
       "2259  RT @dogratingrating: Exceptional talent. Origi...         6.675487e+17   \n",
       "2260  RT @dogratingrating: Unoriginal idea. Blatant ...         6.675484e+17   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "19                4.196984e+09  2017-07-19 00:47:34 +0000   \n",
       "32                1.960740e+07  2017-07-15 02:44:07 +0000   \n",
       "36                4.196984e+09  2017-02-12 01:04:29 +0000   \n",
       "68                4.196984e+09  2017-06-23 01:10:23 +0000   \n",
       "73                4.196984e+09  2017-06-23 16:00:04 +0000   \n",
       "...                        ...                        ...   \n",
       "1023              4.196984e+09  2015-11-21 00:46:50 +0000   \n",
       "1043              4.196984e+09  2015-11-19 00:32:12 +0000   \n",
       "1242              7.832140e+05  2016-03-21 19:29:52 +0000   \n",
       "2259              4.296832e+09  2015-11-20 03:43:06 +0000   \n",
       "2260              4.296832e+09  2015-11-20 03:41:59 +0000   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "19    https://twitter.com/dog_rates/status/887473957...                13   \n",
       "32    https://twitter.com/dog_rates/status/886053434...                12   \n",
       "36    https://twitter.com/dog_rates/status/830583320...                13   \n",
       "68    https://twitter.com/dog_rates/status/878057613...                14   \n",
       "73    https://www.gofundme.com/3yd6y1c,https://twitt...                13   \n",
       "...                                                 ...               ...   \n",
       "1023  https://twitter.com/dog_rates/status/667866724...                10   \n",
       "1043  https://twitter.com/dog_rates/status/667138269...                10   \n",
       "1242  https://twitter.com/twitter/status/71199827977...                12   \n",
       "2259  https://twitter.com/dogratingrating/status/667...                12   \n",
       "2260  https://twitter.com/dogratingrating/status/667...                 5   \n",
       "\n",
       "      rating_denominator    name doggo floofer pupper puppo  \n",
       "19                    10  Canela  None    None   None  None  \n",
       "32                    10    None  None    None   None  None  \n",
       "36                    10   Lilly  None    None   None  None  \n",
       "68                    10    Emmy  None    None   None  None  \n",
       "73                    10  Shadow  None    None   None  None  \n",
       "...                  ...     ...   ...     ...    ...   ...  \n",
       "1023                  10  Shaggy  None    None   None  None  \n",
       "1043                  10    None  None    None   None  None  \n",
       "1242                  10    None  None    None   None  None  \n",
       "2259                  10    None  None    None   None  None  \n",
       "2260                  10    None  None    None   None  None  \n",
       "\n",
       "[181 rows x 17 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for which values in the df1 that are retweets\n",
    "df1[df1.retweeted_status_id.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for duplicated values in df1\n",
    "sum(df1.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for duplicated values in df2\n",
    "sum(df2.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for duplicated values in df1\n",
    "sum(df3.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "in_reply_to_status_id         2278\n",
       "in_reply_to_user_id           2278\n",
       "timestamp                        0\n",
       "source                           0\n",
       "text                             0\n",
       "retweeted_status_id           2175\n",
       "retweeted_status_user_id      2175\n",
       "retweeted_status_timestamp    2175\n",
       "expanded_urls                   59\n",
       "rating_numerator                 0\n",
       "rating_denominator               0\n",
       "name                             0\n",
       "doggo                            0\n",
       "floofer                          0\n",
       "pupper                           0\n",
       "puppo                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of missing values in each column of df1\n",
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id    0\n",
       "jpg_url     0\n",
       "img_num     0\n",
       "p1          0\n",
       "p1_conf     0\n",
       "p1_dog      0\n",
       "p2          0\n",
       "p2_conf     0\n",
       "p2_dog      0\n",
       "p3          0\n",
       "p3_conf     0\n",
       "p3_dog      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of missing values in each column of df2\n",
    "df2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id           0\n",
       "retweet_count      0\n",
       "favourite_count    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the number of missing values in each column of df3\n",
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality issues\n",
    "#### df1 (twiiter-archive)\n",
    "- Erroneous datatypes in some columns.\n",
    "- Some values in the name column are in lowercase and appear to not be dog names.\n",
    "- Some tweet-id's have values in the *retweeted_status_id, retweeted_status_user_id and retweeted_status_timestamp* column.\n",
    "- Missing data in the *in_reply_to_status, in_reply_to_user_id, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_url*.\n",
    "\n",
    "#### df2 (image-prediction)\n",
    "- Erroneous datatype in tweet_id column.\n",
    "- Outliers in *p2_conf and p3_conf* column.\n",
    "- Non-descriptive column names i.e *p1, p2, p3, p1_conf* e.t.c.\n",
    "- Inconsistent alphabet case in *p1, p2, p3*.\n",
    "\n",
    "#### df3 (tweet-json)\n",
    "- Erroneous datatype in *tweet_id* column.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 7,
        "hidden": false,
        "row": 40,
        "width": 12
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "### Tidiness issues\n",
    "- Dog stages (doggo, popper, puppo, and floofer) should be in one column.\n",
    "- *tweet_id* in twitter-archive dataframe is duplicated in the other two dataframe.\n",
    "- Merging the three dataframe as one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 32,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Cleaning Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of original pieces of data\n",
    "df1_clean=df1.copy()\n",
    "df2_clean=df2.copy()\n",
    "df3_clean=df3.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality\n",
    "### df1\n",
    "### Issue #1: Erroneous datatypes in the *tweet_id, timestamp, retweeted_status_timestamp*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Tweet_id would be changed from *int* to *string* datatype, while the other two would be changed to *datetime*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean['tweet_id']=df1_clean['tweet_id'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "date=['timestamp', 'retweeted_status_timestamp']\n",
    "for value in date:\n",
    "    df1_clean[value]=df1_clean[value].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2356 non-null   object             \n",
      " 1   in_reply_to_status_id       78 non-null     float64            \n",
      " 2   in_reply_to_user_id         78 non-null     float64            \n",
      " 3   timestamp                   2356 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2356 non-null   object             \n",
      " 5   text                        2356 non-null   object             \n",
      " 6   retweeted_status_id         181 non-null    float64            \n",
      " 7   retweeted_status_user_id    181 non-null    float64            \n",
      " 8   retweeted_status_timestamp  181 non-null    datetime64[ns, UTC]\n",
      " 9   expanded_urls               2297 non-null   object             \n",
      " 10  rating_numerator            2356 non-null   int64              \n",
      " 11  rating_denominator          2356 non-null   int64              \n",
      " 12  name                        2356 non-null   object             \n",
      " 13  doggo                       2356 non-null   object             \n",
      " 14  floofer                     2356 non-null   object             \n",
      " 15  pupper                      2356 non-null   object             \n",
      " 16  puppo                       2356 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](2), float64(4), int64(2), object(9)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df1_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #2: Some values in the name column are in lowercase and appear to not be dog names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "#### Define: Replace the values that have lower case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['such', 'a', 'quite', 'not', 'one', 'incredibly', 'mad', 'an', 'very', 'just', 'my', 'his', 'actually', 'getting', 'this', 'unacceptable', 'all', 'old', 'infuriating', 'the', 'by', 'officially', 'life', 'light', 'space']\n"
     ]
    }
   ],
   "source": [
    "#we define a function that shows us the values in lowercase\n",
    "list=[]\n",
    "\n",
    "for lower in df1_clean.name:\n",
    "    if lower.islower() and lower not in list:\n",
    "        list.append(lower)\n",
    "        \n",
    "print(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace the values in [list] with  'None' since there are also other values that have None\n",
    "df1_clean['name'].replace(list, 'None', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "595         None\n",
       "552        Rusty\n",
       "492        Atlas\n",
       "74      Terrance\n",
       "1826        None\n",
       "879         Theo\n",
       "37          None\n",
       "1002        None\n",
       "361          Leo\n",
       "1710       Penny\n",
       "572         None\n",
       "1949        None\n",
       "87        Nugget\n",
       "621         None\n",
       "1267        Olaf\n",
       "1714        None\n",
       "254      Charlie\n",
       "741         Bell\n",
       "1862        None\n",
       "172         None\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean.name.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #3: Some tweet-id's have values in the retweeted_status_id, retweeted_status_user_id and retweeted_status_timestamp column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: We remove these columns since what we need is original tweets and not retweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "181\n",
      "181\n",
      "181\n"
     ]
    }
   ],
   "source": [
    "#lets check the sum of the values in these columns\n",
    "print(df1_clean.retweeted_status_id.notnull().sum())\n",
    "print(df1_clean.retweeted_status_user_id.notnull().sum())\n",
    "print(df1_clean.retweeted_status_timestamp.notnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the column\n",
    "df1_clean=df1_clean[df1_clean.retweeted_status_id.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#cross check to be sure the columns have dropped\n",
    "print(df1_clean.retweeted_status_id.notnull().sum())\n",
    "print(df1_clean.retweeted_status_user_id.notnull().sum())\n",
    "print(df1_clean.retweeted_status_timestamp.notnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #4: Missing data in the *in_reply_to_status, in_reply_to_user_id, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_url*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Dropping off these columns as the amount of data missing is enormous and the columns won't be used for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#group all columns into a list and drop them\n",
    "column=['in_reply_to_status_id', 'in_reply_to_user_id', 'retweeted_status_id', 'retweeted_status_user_id',\n",
    "        'retweeted_status_timestamp', 'expanded_urls']\n",
    "\n",
    "df1_clean.drop(column, axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id              0\n",
       "timestamp             0\n",
       "source                0\n",
       "text                  0\n",
       "rating_numerator      0\n",
       "rating_denominator    0\n",
       "name                  0\n",
       "doggo                 0\n",
       "floofer               0\n",
       "pupper                0\n",
       "puppo                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### df2\n",
    "### Issue #5: Erroneous datatype in tweet_id column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Chane the datatype from int to string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing the datatype to string using .astype()\n",
    "df2_clean['tweet_id']=df2_clean.tweet_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   object \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(1), object(5)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df2_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #6: Outliers in *p2_conf and p3_conf* column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Dropping the columns as they would not be used in further analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "list=['p2_conf', 'p3_conf']\n",
    "\n",
    "df2_clean.drop(list, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 10 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   object \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_dog    2075 non-null   bool   \n",
      " 8   p3        2075 non-null   object \n",
      " 9   p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(1), int64(1), object(5)\n",
      "memory usage: 119.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df2_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #7: Non-descriptive column names i.e p1, p2, p3, p1_conf e.t.c."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Rename the columns with the following descriptive names- prediction1, pred1_confidence, pred1_asdog, prediction2, pred2_asdog, prediction3, pred3_asdog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_clean=df2_clean.rename(columns={'p1': 'prediction1', 'p1_conf': 'p1_confidence', 'p1_dog': 'p1_asdog', 'p2':'prediction2', \n",
    "                                    'p2_dog':'p2_asdog', 'p3':'prediction3', 'p3_dog':'p3_asdog'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>prediction1</th>\n",
       "      <th>p1_confidence</th>\n",
       "      <th>p1_asdog</th>\n",
       "      <th>prediction2</th>\n",
       "      <th>p2_asdog</th>\n",
       "      <th>prediction3</th>\n",
       "      <th>p3_asdog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "\n",
       "   img_num             prediction1  p1_confidence  p1_asdog prediction2  \\\n",
       "0        1  Welsh_springer_spaniel       0.465074      True      collie   \n",
       "\n",
       "   p2_asdog        prediction3  p3_asdog  \n",
       "0      True  Shetland_sheepdog      True  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_clean.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #8: Inconsistent alphabet case in *p1, p2, p3*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: we change the values in the 3 columns to lowercase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_clean['prediction1']=df2_clean.prediction1.str.lower()\n",
    "df2_clean['prediction2']=df2_clean.prediction2.str.lower()\n",
    "df2_clean['prediction3']=df2_clean.prediction3.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "171                      hog\n",
       "1747             cowboy_boot\n",
       "988       miniature_pinscher\n",
       "143             crash_helmet\n",
       "984                      ram\n",
       "1529                malamute\n",
       "493               bath_towel\n",
       "331              tennis_ball\n",
       "421                    robin\n",
       "23          golden_retriever\n",
       "1093            home_theater\n",
       "1297                 upright\n",
       "2049                 samoyed\n",
       "786                      pug\n",
       "1434    old_english_sheepdog\n",
       "619             space_heater\n",
       "508            walking_stick\n",
       "127                 pembroke\n",
       "253              wood_rabbit\n",
       "899           border_terrier\n",
       "Name: prediction1, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_clean.prediction1.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### df3\n",
    "### Issue #9: Erroneous datatype in tweet_id column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Change the datatype from int to string using the astype() method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_clean['tweet_id']=df3_clean.tweet_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   tweet_id         2354 non-null   object\n",
      " 1   retweet_count    2354 non-null   int64 \n",
      " 2   favourite_count  2354 non-null   int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 55.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df3_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness\n",
    "### Issue #1: Dog stages (doggo, popper, puppo, and floofer) should be in one column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Using pd.melt, we convert the four columns of dog stages into one column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the pd.melt method\n",
    "df1_clean=pd.melt(df1_clean, id_vars=['tweet_id', 'timestamp', 'source', 'text', 'rating_numerator', 'rating_denominator', 'name'],\n",
    "            var_name='types', value_name='dog_stages')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing the var_name column as this just repeats the types of dog_stages and also to get rid of duplicates\n",
    "df1_clean.drop(columns='types', inplace=True)\n",
    "df1_clean.drop_duplicates(inplace=True)\n",
    "\n",
    "#replacing the 'None' values with NaN\n",
    "df1_clean['dog_stages']=df1_clean.dog_stages.replace('None', np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper     234\n",
       "doggo       87\n",
       "puppo       25\n",
       "floofer     10\n",
       "Name: dog_stages, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean['dog_stages'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #2: *tweet_id* in twitter-archive (df1) dataframe is duplicated in the other two dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Merge the favorite count and retweet count to the twitter-archive table, joining on tweet_id."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean=pd.merge(df1_clean, df3_clean, on='tweet_id', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2531 entries, 0 to 2530\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2531 non-null   object             \n",
      " 1   timestamp           2531 non-null   datetime64[ns, UTC]\n",
      " 2   source              2531 non-null   object             \n",
      " 3   text                2531 non-null   object             \n",
      " 4   rating_numerator    2531 non-null   int64              \n",
      " 5   rating_denominator  2531 non-null   int64              \n",
      " 6   name                2531 non-null   object             \n",
      " 7   dog_stages          356 non-null    object             \n",
      " 8   retweet_count       2531 non-null   int64              \n",
      " 9   favourite_count     2531 non-null   int64              \n",
      "dtypes: datetime64[ns, UTC](1), int64(4), object(5)\n",
      "memory usage: 217.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df1_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #3: Merging the dataframes into one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Before merging the image prediction table, we would be dropping some columns as they would not be used for further analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the prediction2, p2_asdog, prediction3, p3_asdog columns\n",
    "list = ['prediction2', 'p2_asdog', 'prediction3', 'p3_asdog']\n",
    "df2_clean.drop(list, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#then we merge the image-prediction dataframe with the twitter-archive on tweet_id\n",
    "df1_clean=pd.merge(df1_clean, df2_clean, on='tweet_id', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2531 entries, 0 to 2530\n",
      "Data columns (total 15 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2531 non-null   object             \n",
      " 1   timestamp           2531 non-null   datetime64[ns, UTC]\n",
      " 2   source              2531 non-null   object             \n",
      " 3   text                2531 non-null   object             \n",
      " 4   rating_numerator    2531 non-null   int64              \n",
      " 5   rating_denominator  2531 non-null   int64              \n",
      " 6   name                2531 non-null   object             \n",
      " 7   dog_stages          356 non-null    object             \n",
      " 8   retweet_count       2531 non-null   int64              \n",
      " 9   favourite_count     2531 non-null   int64              \n",
      " 10  jpg_url             2311 non-null   object             \n",
      " 11  img_num             2311 non-null   float64            \n",
      " 12  prediction1         2311 non-null   object             \n",
      " 13  p1_confidence       2311 non-null   float64            \n",
      " 14  p1_asdog            2311 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(4), object(8)\n",
      "memory usage: 316.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df1_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The datatype of the *img_num* column so we would need to change it back to integer. First we convert all the NaN values to 0 to avoid errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean['img_num']=df1_clean.img_num.fillna(0)\n",
    "df1_clean['img_num']=df1_clean.img_num.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2531 entries, 0 to 2530\n",
      "Data columns (total 15 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2531 non-null   object             \n",
      " 1   timestamp           2531 non-null   datetime64[ns, UTC]\n",
      " 2   source              2531 non-null   object             \n",
      " 3   text                2531 non-null   object             \n",
      " 4   rating_numerator    2531 non-null   int64              \n",
      " 5   rating_denominator  2531 non-null   int64              \n",
      " 6   name                2531 non-null   object             \n",
      " 7   dog_stages          356 non-null    object             \n",
      " 8   retweet_count       2531 non-null   int64              \n",
      " 9   favourite_count     2531 non-null   int64              \n",
      " 10  jpg_url             2311 non-null   object             \n",
      " 11  img_num             2531 non-null   int32              \n",
      " 12  prediction1         2311 non-null   object             \n",
      " 13  p1_confidence       2311 non-null   float64            \n",
      " 14  p1_asdog            2311 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(1), int32(1), int64(4), object(8)\n",
      "memory usage: 306.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking to see that the datatype has changed\n",
    "df1_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Data\n",
    "Save gathered, assessed, and cleaned master dataset to a CSV file named \"twitter_archive_master.csv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean.to_csv('twitter_archive_master.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing and Visualizing Data\n",
    "In this section, analyze and visualize your wrangled data. You must produce at least **three (3) insights and one (1) visualization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stages</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favourite_count</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>prediction1</th>\n",
       "      <th>p1_confidence</th>\n",
       "      <th>p1_asdog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8853</td>\n",
       "      <td>39467</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6514</td>\n",
       "      <td>33819</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4328</td>\n",
       "      <td>25461</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8964</td>\n",
       "      <td>42908</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                  timestamp  \\\n",
       "0  892420643555336193  2017-08-01 16:23:56+00:00   \n",
       "1  892177421306343426  2017-08-01 00:17:27+00:00   \n",
       "2  891815181378084864  2017-07-31 00:18:03+00:00   \n",
       "3  891689557279858688  2017-07-30 15:58:51+00:00   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  rating_numerator  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                13   \n",
       "1  This is Tilly. She's just checking pup on you....                13   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                12   \n",
       "3  This is Darla. She commenced a snooze mid meal...                13   \n",
       "\n",
       "   rating_denominator     name dog_stages  retweet_count  favourite_count  \\\n",
       "0                  10  Phineas        NaN           8853            39467   \n",
       "1                  10    Tilly        NaN           6514            33819   \n",
       "2                  10   Archie        NaN           4328            25461   \n",
       "3                  10    Darla        NaN           8964            42908   \n",
       "\n",
       "                                           jpg_url  img_num  prediction1  \\\n",
       "0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg        1       orange   \n",
       "1  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg        1    chihuahua   \n",
       "2  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg        1    chihuahua   \n",
       "3  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg        1  paper_towel   \n",
       "\n",
       "   p1_confidence p1_asdog  \n",
       "0       0.097049    False  \n",
       "1       0.323581     True  \n",
       "2       0.716012     True  \n",
       "3       0.170278    False  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the new csv into a dataframe and view it\n",
    "df_new=pd.read_csv('twitter_archive_master.csv')\n",
    "\n",
    "df_new.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>tweet_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favourite_count</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1_confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.531000e+03</td>\n",
       "      <td>2531.000000</td>\n",
       "      <td>2531.000000</td>\n",
       "      <td>2531.000000</td>\n",
       "      <td>2531.000000</td>\n",
       "      <td>2531.000000</td>\n",
       "      <td>2311.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384206e+17</td>\n",
       "      <td>12.927301</td>\n",
       "      <td>10.423548</td>\n",
       "      <td>2899.792967</td>\n",
       "      <td>9048.932043</td>\n",
       "      <td>1.105492</td>\n",
       "      <td>0.597116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.693796e+16</td>\n",
       "      <td>44.252208</td>\n",
       "      <td>6.508795</td>\n",
       "      <td>5085.331743</td>\n",
       "      <td>12668.514176</td>\n",
       "      <td>0.644476</td>\n",
       "      <td>0.271582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783890e+17</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>642.000000</td>\n",
       "      <td>2093.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.367945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.124382e+17</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1408.000000</td>\n",
       "      <td>4228.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.596796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.904598e+17</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3263.000000</td>\n",
       "      <td>11309.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>79515.000000</td>\n",
       "      <td>132810.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  rating_numerator  rating_denominator  retweet_count  \\\n",
       "count  2.531000e+03       2531.000000         2531.000000    2531.000000   \n",
       "mean   7.384206e+17         12.927301           10.423548    2899.792967   \n",
       "std    6.693796e+16         44.252208            6.508795    5085.331743   \n",
       "min    6.660209e+17          0.000000            0.000000       0.000000   \n",
       "25%    6.783890e+17         10.000000           10.000000     642.000000   \n",
       "50%    7.124382e+17         11.000000           10.000000    1408.000000   \n",
       "75%    7.904598e+17         12.000000           10.000000    3263.000000   \n",
       "max    8.924206e+17       1776.000000          170.000000   79515.000000   \n",
       "\n",
       "       favourite_count      img_num  p1_confidence  \n",
       "count      2531.000000  2531.000000    2311.000000  \n",
       "mean       9048.932043     1.105492       0.597116  \n",
       "std       12668.514176     0.644476       0.271582  \n",
       "min          52.000000     0.000000       0.044333  \n",
       "25%        2093.500000     1.000000       0.367945  \n",
       "50%        4228.000000     1.000000       0.596796  \n",
       "75%       11309.500000     1.000000       0.846807  \n",
       "max      132810.000000     4.000000       1.000000  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shows the descriptive statistics of our dataset\n",
    "df_new.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1973\n",
       "2     226\n",
       "0     220\n",
       "3      75\n",
       "4      37\n",
       "Name: img_num, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['img_num'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>img_num</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p1_asdog</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>526</td>\n",
       "      <td>42</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1447</td>\n",
       "      <td>184</td>\n",
       "      <td>54</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "img_num      1    2   3   4\n",
       "p1_asdog                   \n",
       "False      526   42  21   7\n",
       "True      1447  184  54  30"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.groupby('p1_asdog')['img_num'].value_counts().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper     234\n",
       "doggo       87\n",
       "puppo       25\n",
       "floofer     10\n",
       "Name: dog_stages, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['dog_stages'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "1. Tweets that have one image were mostly predicted and we have more of them as dogs.\n",
    "\n",
    "2. The mean of the first prediction confidence is approximately 60%.\n",
    "\n",
    "3. We have more favorite_count than retweet_count.\n",
    "\n",
    "4. We have more dogs as pupper (smaller and younger dogs) than the rest of the other dog stages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q1. What dog stage has the highest count?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we would show the distribution of the dogstages on a barchart\n",
    "stages=df_new['dog_stages'].value_counts()\n",
    "stages.plot(kind='bar', figsize = (5,6))\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"Dog Stages\")\n",
    "plt.xlabel(\"Stages\")\n",
    "plt.ylabel(\"Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most dogs were classified as a pupper, which is a smaller and usually younger type of dogs. But the data for the dog stages is incomplete so we cannot acertain the accuracy of this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q2. What image number occured most and how accurate was it prediction as a dog?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the image numbers with the prediction as dog \n",
    "pred=df_new.groupby('p1_asdog')['img_num'].value_counts().unstack()\n",
    "pred.index=['False', 'True']\n",
    "pred.plot(kind='bar', figsize= (5,6))\n",
    "plt.legend(['1', '2', '3', '4'])\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"Image Number Count with Prediction as Dog\")\n",
    "plt.xlabel(\"Prediction as Dog\")\n",
    "plt.ylabel(\"Image Number Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Tweet with one image were mostly predicted through the neural network and more 50% of the prediction were dogs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q3. What correlation exists between the favourite count and retweet count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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"
    }
   ],
   "source": [
    "#we want to see the correlation between the favorite and retweet count\n",
    "df_new.plot(x='favourite_count', y='retweet_count', kind='scatter')\n",
    "plt.title('Favorite and Retweet Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From the above, we can see a positive correlation between the retweet and favorite count."
   ]
  }
 ],
 "metadata": {
  "extensions": {
   "jupyter_dashboards": {
    "activeView": "report_default",
    "version": 1,
    "views": {
     "grid_default": {
      "cellMargin": 10,
      "defaultCellHeight": 20,
      "maxColumns": 12,
      "name": "grid",
      "type": "grid"
     },
     "report_default": {
      "name": "report",
      "type": "report"
     }
    }
   }
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}