wrangle_act_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NDFDS II\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib.pyplot import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('df_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2175 entries, 0 to 2174\n",
      "Data columns (total 27 columns):\n",
      "Unnamed: 0               2175 non-null int64\n",
      "tweet_id                 2175 non-null int64\n",
      "in_reply_to_status_id    78 non-null float64\n",
      "in_reply_to_user_id      78 non-null float64\n",
      "timestamp                2175 non-null object\n",
      "source                   2175 non-null object\n",
      "text                     2175 non-null object\n",
      "expanded_urls            2117 non-null object\n",
      "rating_numerator         2175 non-null int64\n",
      "rating_denominator       2175 non-null int64\n",
      "name                     2175 non-null object\n",
      "id                       2172 non-null float64\n",
      "favorite_count           2172 non-null float64\n",
      "retweet_count            2172 non-null float64\n",
      "display_text_range       2172 non-null object\n",
      "jpg_url                  1994 non-null object\n",
      "img_num                  1994 non-null float64\n",
      "p1                       1994 non-null object\n",
      "p1_conf                  1994 non-null float64\n",
      "p1_dog                   1994 non-null object\n",
      "p2                       1994 non-null object\n",
      "p2_conf                  1994 non-null float64\n",
      "p2_dog                   1994 non-null object\n",
      "p3                       1994 non-null object\n",
      "p3_conf                  1994 non-null float64\n",
      "p3_dog                   1994 non-null object\n",
      "dog_stage                2175 non-null object\n",
      "dtypes: float64(9), int64(4), object(14)\n",
      "memory usage: 458.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['id', 'Unnamed: 0', 'in_reply_to_status_id', 'in_reply_to_user_id'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>...</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",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>680055455951884288</td>\n",
       "      <td>2015-12-24 16:00:30</td>\n",
       "      <td>&lt;a href=\"https://about.twitter.com/products/tw...</td>\n",
       "      <td>Meet Sammy. At first I was like \"that's a snow...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/680055455...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Sammy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>samoyed</td>\n",
       "      <td>0.995466</td>\n",
       "      <td>True</td>\n",
       "      <td>great_pyrenees</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>True</td>\n",
       "      <td>pomeranian</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>811985624773361665</td>\n",
       "      <td>2016-12-22 17:23:53</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Say hello to Ted. He accidentally opened the f...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/811985624...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Ted</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>staffordshire_bullterrier</td>\n",
       "      <td>0.610573</td>\n",
       "      <td>True</td>\n",
       "      <td>french_bulldog</td>\n",
       "      <td>0.159935</td>\n",
       "      <td>True</td>\n",
       "      <td>doormat</td>\n",
       "      <td>0.058672</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1582</th>\n",
       "      <td>754011816964026368</td>\n",
       "      <td>2016-07-15 17:56:40</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Charlie. He pouts until he gets to go ...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/754011816...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>french_bulldog</td>\n",
       "      <td>0.600985</td>\n",
       "      <td>True</td>\n",
       "      <td>boston_bull</td>\n",
       "      <td>0.273176</td>\n",
       "      <td>True</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.056772</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id            timestamp  \\\n",
       "975   680055455951884288  2015-12-24 16:00:30   \n",
       "1166  811985624773361665  2016-12-22 17:23:53   \n",
       "1582  754011816964026368  2016-07-15 17:56:40   \n",
       "\n",
       "                                                 source  \\\n",
       "975   <a href=\"https://about.twitter.com/products/tw...   \n",
       "1166  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1582  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  \\\n",
       "975   Meet Sammy. At first I was like \"that's a snow...   \n",
       "1166  Say hello to Ted. He accidentally opened the f...   \n",
       "1582  This is Charlie. He pouts until he gets to go ...   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "975   https://twitter.com/dog_rates/status/680055455...                10   \n",
       "1166  https://twitter.com/dog_rates/status/811985624...                11   \n",
       "1582  https://twitter.com/dog_rates/status/754011816...                12   \n",
       "\n",
       "      rating_denominator     name  favorite_count  retweet_count    ...     \\\n",
       "975                   10    Sammy             NaN            NaN    ...      \n",
       "1166                  10      Ted             NaN            NaN    ...      \n",
       "1582                  10  Charlie             NaN            NaN    ...      \n",
       "\n",
       "                             p1   p1_conf  p1_dog              p2   p2_conf  \\\n",
       "975                     samoyed  0.995466    True  great_pyrenees  0.001834   \n",
       "1166  staffordshire_bullterrier  0.610573    True  french_bulldog  0.159935   \n",
       "1582             french_bulldog  0.600985    True     boston_bull  0.273176   \n",
       "\n",
       "     p2_dog          p3   p3_conf p3_dog dog_stage  \n",
       "975    True  pomeranian  0.000667   True      None  \n",
       "1166   True     doormat  0.058672  False      None  \n",
       "1582   True       boxer  0.056772   True      None  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.favorite_count.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(8, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight 1\n",
    "\n",
    "What kind of dog has more retweets and favorites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_stage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>1.347023e+21</td>\n",
       "      <td>24902.0</td>\n",
       "      <td>19382.0</td>\n",
       "      <td>14886404.0</td>\n",
       "      <td>4482346.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>996.878392</td>\n",
       "      <td>226.507324</td>\n",
       "      <td>102.936025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doggo</th>\n",
       "      <td>5.959498e+19</td>\n",
       "      <td>889.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1340488.0</td>\n",
       "      <td>468787.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>36.354634</td>\n",
       "      <td>9.912485</td>\n",
       "      <td>3.911219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floofer</th>\n",
       "      <td>7.656406e+18</td>\n",
       "      <td>118.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>111718.0</td>\n",
       "      <td>38361.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.619160</td>\n",
       "      <td>1.264771</td>\n",
       "      <td>0.467682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pupper</th>\n",
       "      <td>1.687385e+20</td>\n",
       "      <td>2532.0</td>\n",
       "      <td>2340.0</td>\n",
       "      <td>1680550.0</td>\n",
       "      <td>559292.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>130.253893</td>\n",
       "      <td>27.252383</td>\n",
       "      <td>11.763012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>puppo</th>\n",
       "      <td>2.022401e+19</td>\n",
       "      <td>302.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>546905.0</td>\n",
       "      <td>164742.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>16.212114</td>\n",
       "      <td>3.095430</td>\n",
       "      <td>1.057526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  rating_numerator  rating_denominator  favorite_count  \\\n",
       "dog_stage                                                                       \n",
       "None       1.347023e+21           24902.0             19382.0      14886404.0   \n",
       "doggo      5.959498e+19             889.0               750.0       1340488.0   \n",
       "floofer    7.656406e+18             118.0               100.0        111718.0   \n",
       "pupper     1.687385e+20            2532.0              2340.0       1680550.0   \n",
       "puppo      2.022401e+19             302.0               250.0        546905.0   \n",
       "\n",
       "           retweet_count  img_num     p1_conf     p2_conf     p3_conf  \n",
       "dog_stage                                                              \n",
       "None           4482346.0   2012.0  996.878392  226.507324  102.936025  \n",
       "doggo           468787.0     81.0   36.354634    9.912485    3.911219  \n",
       "floofer          38361.0      9.0    4.619160    1.264771    0.467682  \n",
       "pupper          559292.0    264.0  130.253893   27.252383   11.763012  \n",
       "puppo           164742.0     33.0   16.212114    3.095430    1.057526  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('dog_stage').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dog = df.groupby('dog_stage').sum()[['favorite_count', 'retweet_count']]\n",
    "df_dog.drop(df_dog.index[[0]]).plot(kind='bar', figsize=(12, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight 2\n",
    "\n",
    "Correlation between retweets and favorites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots()\n",
    "df.plot(kind = 'scatter', x = 'favorite_count', y = 'retweet_count', figsize=(12, 8), ax=ax);\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight 3\n",
    "\n",
    "Which type of dog is most prevalent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dogg = df.groupby('dog_stage').count()['tweet_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dogg.drop(df_dog.index[[0]]).plot(kind='barh', figsize=(8, 8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}