facebook_likes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              timestamp                                               data  \\\n",
      "0   2020-03-25 23:35:54  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...   \n",
      "1   2019-12-21 16:17:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
      "2   2019-08-12 12:40:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
      "3   2019-06-20 18:54:47  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...   \n",
      "4   2019-06-07 20:55:51  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
      "..                  ...                                                ...   \n",
      "157 2015-07-04 11:39:07  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
      "158 2015-04-26 11:52:57  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
      "159 2015-04-07 12:22:44  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
      "160 2015-04-02 09:26:46  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
      "161 2015-03-04 15:48:14  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
      "\n",
      "                                                 title  \n",
      "0    Pablo FernÃ¡ndez RodrÃ­guez ha reaccionado a u...  \n",
      "1    A Pablo FernÃ¡ndez RodrÃ­guez le gusta la publ...  \n",
      "2    A Pablo FernÃ¡ndez RodrÃ­guez le ha gustado la...  \n",
      "3    Pablo FernÃ¡ndez RodrÃ­guez ha reaccionado a u...  \n",
      "4    A Pablo FernÃ¡ndez RodrÃ­guez le gusta la publ...  \n",
      "..                                                 ...  \n",
      "157  A Pablo FernÃ¡ndez RodrÃ­guez le gusta la foto...  \n",
      "158  A Pablo FernÃ¡ndez RodrÃ­guez le gusta la publ...  \n",
      "159  A Pablo FernÃ¡ndez RodrÃ­guez le gusta la foto...  \n",
      "160  A Pablo FernÃ¡ndez RodrÃ­guez le ha gustado un...  \n",
      "161  A Pablo FernÃ¡ndez RodrÃ­guez le gusta la foto...  \n",
      "\n",
      "[162 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_json('data/likes_and_reactions/posts_and_comments.json')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['timestamp', 'data', 'title'], dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2020-03-25 23:35:54\n",
       "1     2019-12-21 16:17:35\n",
       "2     2019-08-12 12:40:35\n",
       "3     2019-06-20 18:54:47\n",
       "4     2019-06-07 20:55:51\n",
       "              ...        \n",
       "157   2015-07-04 11:39:07\n",
       "158   2015-04-26 11:52:57\n",
       "159   2015-04-07 12:22:44\n",
       "160   2015-04-02 09:26:46\n",
       "161   2015-03-04 15:48:14\n",
       "Name: date, Length: 162, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'timestamp': 'date'}, inplace=True)\n",
    "df = df.drop(['title'], axis=1)\n",
    "pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'data'], dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   date                                               data\n",
      "0   2020-03-25 23:35:54  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...\n",
      "1   2019-12-21 16:17:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
      "2   2019-08-12 12:40:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
      "3   2019-06-20 18:54:47  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...\n",
      "4   2019-06-07 20:55:51  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
      "..                  ...                                                ...\n",
      "157 2015-07-04 11:39:07  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
      "158 2015-04-26 11:52:57  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
      "159 2015-04-07 12:22:44  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
      "160 2015-04-02 09:26:46  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
      "161 2015-03-04 15:48:14  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
      "\n",
      "[162 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2015-03-01     1\n",
       "2015-04-01     3\n",
       "2015-05-01     0\n",
       "2015-06-01     0\n",
       "2015-07-01    25\n",
       "              ..\n",
       "2019-11-01     0\n",
       "2019-12-01     1\n",
       "2020-01-01     0\n",
       "2020-02-01     0\n",
       "2020-03-01     1\n",
       "Freq: MS, Name: data, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.set_index('date')\n",
    "likes_counts = df2['data'].resample('MS').size()\n",
    "likes_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# set figure size and font size\n",
    "sns.set(rc={'figure.figsize':(40,20)})\n",
    "sns.set(font_scale=3)\n",
    "\n",
    "# set x labels\n",
    "x_labels = likes_counts.index\n",
    "\n",
    "#create bar plot\n",
    "sns.barplot(x_labels, likes_counts, color=\"blue\")\n",
    "\n",
    "# only show x-axis labels for Jan 1 of every other year\n",
    "tick_positions = np.arange(5, len(x_labels), step=12)\n",
    "\n",
    "#reformat date to display year onlyplt.ylabel(\"post counts\")\n",
    "plt.xticks(tick_positions, x_labels[tick_positions].strftime(\"%Y\"))\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1= pd.DataFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(162, 2)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'data'], dtype='object')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-25 23:35:54</th>\n",
       "      <td>[{'reaction': {'reaction': 'LOVE', 'actor': 'P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-21 16:17:35</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-12 12:40:35</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 18:54:47</th>\n",
       "      <td>[{'reaction': {'reaction': 'LOVE', 'actor': 'P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 20:55:51</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  data\n",
       "date                                                                  \n",
       "2020-03-25 23:35:54  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...\n",
       "2019-12-21 16:17:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
       "2019-08-12 12:40:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...\n",
       "2019-06-20 18:54:47  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...\n",
       "2019-06-07 20:55:51  [{'reaction': {'reaction': 'LIKE', 'actor': 'P..."
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['reaction']='no yet'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>data</th>\n",
       "      <th>reaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-25 23:35:54</th>\n",
       "      <td>[{'reaction': {'reaction': 'LOVE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-21 16:17:35</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-12 12:40:35</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 18:54:47</th>\n",
       "      <td>[{'reaction': {'reaction': 'LOVE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 20:55:51</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04 11:39:07</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-26 11:52:57</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-07 12:22:44</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-02 09:26:46</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-04 15:48:14</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>no yet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  data  \\\n",
       "date                                                                     \n",
       "2020-03-25 23:35:54  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...   \n",
       "2019-12-21 16:17:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2019-08-12 12:40:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2019-06-20 18:54:47  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...   \n",
       "2019-06-07 20:55:51  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "...                                                                ...   \n",
       "2015-07-04 11:39:07  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2015-04-26 11:52:57  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2015-04-07 12:22:44  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2015-04-02 09:26:46  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2015-03-04 15:48:14  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "\n",
       "                    reaction  \n",
       "date                          \n",
       "2020-03-25 23:35:54   no yet  \n",
       "2019-12-21 16:17:35   no yet  \n",
       "2019-08-12 12:40:35   no yet  \n",
       "2019-06-20 18:54:47   no yet  \n",
       "2019-06-07 20:55:51   no yet  \n",
       "...                      ...  \n",
       "2015-07-04 11:39:07   no yet  \n",
       "2015-04-26 11:52:57   no yet  \n",
       "2015-04-07 12:22:44   no yet  \n",
       "2015-04-02 09:26:46   no yet  \n",
       "2015-03-04 15:48:14   no yet  \n",
       "\n",
       "[162 rows x 2 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['data']= df1['data'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### These are the common Facebook reactions: \n",
    "#### love, like, wow, haha, sorry, anger\n",
    "\n",
    "I can extract the reaction by selecting the dictionary and going inside the value, but as I already did it in the Post notebook, I will try something different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc[df1['data'].str.contains(pat='LOVE'), 'reaction'] = 'Love'\n",
    "df1.loc[df1['data'].str.contains(pat='LIKE'), 'reaction'] = 'Like' \n",
    "df1.loc[df1['data'].str.contains(pat='WOW'), 'reaction'] = 'Wow' \n",
    "df1.loc[df1['data'].str.contains(pat='HAHA'), 'reaction'] = 'Haha' \n",
    "df1.loc[df1['data'].str.contains(pat='SORRY'), 'reaction'] = 'Sorry'\n",
    "df1.loc[df1['data'].str.contains(pat='ANGER'), 'reaction'] = 'Anger' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>reaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-25 23:35:54</th>\n",
       "      <td>[{'reaction': {'reaction': 'LOVE', 'actor': 'P...</td>\n",
       "      <td>Love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-21 16:17:35</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>Like</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-12 12:40:35</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>Like</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 18:54:47</th>\n",
       "      <td>[{'reaction': {'reaction': 'LOVE', 'actor': 'P...</td>\n",
       "      <td>Love</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 20:55:51</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>Like</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04 11:39:07</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>Like</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-26 11:52:57</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>Like</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-07 12:22:44</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>Like</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-02 09:26:46</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>Like</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-04 15:48:14</th>\n",
       "      <td>[{'reaction': {'reaction': 'LIKE', 'actor': 'P...</td>\n",
       "      <td>Like</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  data  \\\n",
       "date                                                                     \n",
       "2020-03-25 23:35:54  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...   \n",
       "2019-12-21 16:17:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2019-08-12 12:40:35  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2019-06-20 18:54:47  [{'reaction': {'reaction': 'LOVE', 'actor': 'P...   \n",
       "2019-06-07 20:55:51  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "...                                                                ...   \n",
       "2015-07-04 11:39:07  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2015-04-26 11:52:57  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2015-04-07 12:22:44  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2015-04-02 09:26:46  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "2015-03-04 15:48:14  [{'reaction': {'reaction': 'LIKE', 'actor': 'P...   \n",
       "\n",
       "                    reaction  \n",
       "date                          \n",
       "2020-03-25 23:35:54     Love  \n",
       "2019-12-21 16:17:35     Like  \n",
       "2019-08-12 12:40:35     Like  \n",
       "2019-06-20 18:54:47     Love  \n",
       "2019-06-07 20:55:51     Like  \n",
       "...                      ...  \n",
       "2015-07-04 11:39:07     Like  \n",
       "2015-04-26 11:52:57     Like  \n",
       "2015-04-07 12:22:44     Like  \n",
       "2015-04-02 09:26:46     Like  \n",
       "2015-03-04 15:48:14     Like  \n",
       "\n",
       "[162 rows x 2 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc[df1['reaction'].str.contains(pat='Love'), 'Love'] = '1'\n",
    "df1.loc[df1['reaction'].str.contains(pat='Like'), 'Like'] = '1' \n",
    "df1.loc[df1['reaction'].str.contains(pat='Wow'), 'Wow'] = '1' \n",
    "df1.loc[df1['reaction'].str.contains(pat='Haha'), 'Haha'] = '1' \n",
    "df1.loc[df1['reaction'].str.contains(pat='Sorry'), 'Sorry'] = '1'\n",
    "df1.loc[df1['reaction'].str.contains(pat='Anger'), 'Anger'] = '1' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df5=df1.drop(['data'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reaction</th>\n",
       "      <th>Love</th>\n",
       "      <th>Like</th>\n",
       "      <th>Wow</th>\n",
       "      <th>Haha</th>\n",
       "      <th>Sorry</th>\n",
       "      <th>Anger</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-25 23:35:54</th>\n",
       "      <td>Love</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-21 16:17:35</th>\n",
       "      <td>Like</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-12 12:40:35</th>\n",
       "      <td>Like</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 18:54:47</th>\n",
       "      <td>Love</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-07 20:55:51</th>\n",
       "      <td>Like</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-04 11:39:07</th>\n",
       "      <td>Like</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-26 11:52:57</th>\n",
       "      <td>Like</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-07 12:22:44</th>\n",
       "      <td>Like</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-02 09:26:46</th>\n",
       "      <td>Like</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-04 15:48:14</th>\n",
       "      <td>Like</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>162 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    reaction Love Like Wow Haha Sorry Anger\n",
       "date                                                       \n",
       "2020-03-25 23:35:54     Love    1    0   0    0     0     0\n",
       "2019-12-21 16:17:35     Like    0    1   0    0     0     0\n",
       "2019-08-12 12:40:35     Like    0    1   0    0     0     0\n",
       "2019-06-20 18:54:47     Love    1    0   0    0     0     0\n",
       "2019-06-07 20:55:51     Like    0    1   0    0     0     0\n",
       "...                      ...  ...  ...  ..  ...   ...   ...\n",
       "2015-07-04 11:39:07     Like    0    1   0    0     0     0\n",
       "2015-04-26 11:52:57     Like    0    1   0    0     0     0\n",
       "2015-04-07 12:22:44     Like    0    1   0    0     0     0\n",
       "2015-04-02 09:26:46     Like    0    1   0    0     0     0\n",
       "2015-03-04 15:48:14     Like    0    1   0    0     0     0\n",
       "\n",
       "[162 rows x 7 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.fillna('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x576 with 0 Axes>"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 429.1x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set figure size and font size\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(40, 8)\n",
    "\n",
    "sns.set(font_scale=1)\n",
    "sns.displot(df5, x=\"date\", hue=\"reaction\",multiple=\"stack\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 429.1x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set figure size and font size\n",
    "sns.set()\n",
    "\n",
    "sns.displot(df5, x=\"date\", hue=\"reaction\",kind=\"kde\",fill=True)\n",
    "\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2015-03-01     1\n",
       "2015-04-01     3\n",
       "2015-05-01     0\n",
       "2015-06-01     0\n",
       "2015-07-01    25\n",
       "              ..\n",
       "2019-11-01     0\n",
       "2019-12-01     1\n",
       "2020-01-01     0\n",
       "2020-02-01     0\n",
       "2020-03-01     1\n",
       "Freq: MS, Name: reaction, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reaction_counts = (df1['reaction']).resample('MS').size()\n",
    "reaction_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set figure size and font size\n",
    "sns.set(rc={'figure.figsize':(40,20)})\n",
    "sns.set(font_scale=3)\n",
    "\n",
    "# set x labels\n",
    "x_labels = reaction_counts.index\n",
    "\n",
    "#create bar plot\n",
    "sns.barplot(x_labels, reactions_counts, color=\"red\")\n",
    "\n",
    "# only show x-axis labels for Jan 1 of every other year\n",
    "tick_positions = np.arange(5, len(x_labels), step=12)\n",
    "\n",
    "#reformat date to display year onlyplt.ylabel(\"post counts\")\n",
    "plt.xticks(tick_positions, x_labels[tick_positions].strftime(\"%Y\"))\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}