Facebook Posts Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import date\n",
    "import calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>data</th>\n",
       "      <th>title</th>\n",
       "      <th>attachments</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-09-20 14:08:32</td>\n",
       "      <td>[{'post': 'For whom it may concern..'}]</td>\n",
       "      <td>Shaon Sikder posted in Department of ECE,EWU.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-09-20 14:01:13</td>\n",
       "      <td>[{'post': 'For whom it may concern..'}]</td>\n",
       "      <td>Shaon Sikder posted in EWU ECE Telecom Rocker's.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-03 07:29:59</td>\n",
       "      <td>[{'post': '**Data Science in Python**\n",
       "\n",
       "à¦¯à¦à...</td>\n",
       "      <td>Shaon Sikder posted in Data Science Bangladesh...</td>\n",
       "      <td>[{'data': [{'external_context': {'url': 'https...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-01 16:49:35</td>\n",
       "      <td>[{'post': 'Hello data enthusiasts people, I'm ...</td>\n",
       "      <td>Shaon Sikder updated his status.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-09-01 13:57:00</td>\n",
       "      <td>[{'update_timestamp': 1598968620}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "            timestamp                                               data  \\\n",
       "0 2020-09-20 14:08:32            [{'post': 'For whom it may concern..'}]   \n",
       "1 2020-09-20 14:01:13            [{'post': 'For whom it may concern..'}]   \n",
       "2 2020-09-03 07:29:59  [{'post': '**Data Science in Python**\n",
       "\n",
       "à¦¯à¦à...   \n",
       "3 2020-09-01 16:49:35  [{'post': 'Hello data enthusiasts people, I'm ...   \n",
       "4 2020-09-01 13:57:00                 [{'update_timestamp': 1598968620}]   \n",
       "\n",
       "                                               title  \\\n",
       "0      Shaon Sikder posted in Department of ECE,EWU.   \n",
       "1   Shaon Sikder posted in EWU ECE Telecom Rocker's.   \n",
       "2  Shaon Sikder posted in Data Science Bangladesh...   \n",
       "3                   Shaon Sikder updated his status.   \n",
       "4                                                NaN   \n",
       "\n",
       "                                         attachments tags  \n",
       "0                                                NaN  NaN  \n",
       "1                                                NaN  NaN  \n",
       "2  [{'data': [{'external_context': {'url': 'https...  NaN  \n",
       "3                                                NaN  NaN  \n",
       "4                                                 []  NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mypost = pd.read_json(\"your_posts_1.json\")\n",
    "df_mypost.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weekday(x):\n",
    "    d=calendar.day_name[x]\n",
    "    return d \n",
    "\n",
    "def month_name(x):\n",
    "    m =calendar.month_name[x]\n",
    "    return m "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#I need to convert this into a readable datetime \n",
    "#create month, day, year and time columns\n",
    "df_mypost[\"year\"] = pd.DatetimeIndex(df_mypost['timestamp']).year\n",
    "\n",
    "df_mypost[\"month\"] = pd.DatetimeIndex(df_mypost['timestamp']).month\n",
    "df_mypost[\"month\"] = df_mypost[\"month\"].apply(lambda x: month_name(x))\n",
    "\n",
    "df_mypost[\"week_day\"] = pd.DatetimeIndex(df_mypost['timestamp']).weekday\n",
    "df_mypost[\"week_day\"] = df_mypost[\"week_day\"].apply(lambda x: weekday(x))\n",
    "\n",
    "df_mypost[\"day\"] = pd.DatetimeIndex(df_mypost['timestamp']).day\n",
    "df_mypost[\"time\"] = pd.DatetimeIndex(df_mypost['timestamp']).time\n",
    "\n",
    "df_mypost['month_year'] = pd.to_datetime(df_mypost['timestamp']).dt.to_period('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>data</th>\n",
       "      <th>title</th>\n",
       "      <th>attachments</th>\n",
       "      <th>tags</th>\n",
       "      <th>year</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-09-20 14:08:32</td>\n",
       "      <td>[{'post': 'For whom it may concern..'}]</td>\n",
       "      <td>Shaon Sikder posted in Department of ECE,EWU.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>September</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>20</td>\n",
       "      <td>14:08:32</td>\n",
       "      <td>2020-09</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-09-20 14:01:13</td>\n",
       "      <td>[{'post': 'For whom it may concern..'}]</td>\n",
       "      <td>Shaon Sikder posted in EWU ECE Telecom Rocker's.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
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       "      <td>20</td>\n",
       "      <td>14:01:13</td>\n",
       "      <td>2020-09</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-03 07:29:59</td>\n",
       "      <td>[{'post': '**Data Science in Python**\n",
       "\n",
       "à¦¯à¦à...</td>\n",
       "      <td>Shaon Sikder posted in Data Science Bangladesh...</td>\n",
       "      <td>[{'data': [{'external_context': {'url': 'https...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>September</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>3</td>\n",
       "      <td>07:29:59</td>\n",
       "      <td>2020-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-01 16:49:35</td>\n",
       "      <td>[{'post': 'Hello data enthusiasts people, I'm ...</td>\n",
       "      <td>Shaon Sikder updated his status.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>September</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>16:49:35</td>\n",
       "      <td>2020-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-09-01 13:57:00</td>\n",
       "      <td>[{'update_timestamp': 1598968620}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>September</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>13:57:00</td>\n",
       "      <td>2020-09</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4152</th>\n",
       "      <td>2013-12-16 05:46:33</td>\n",
       "      <td>[{'post': 'HAPPPPY BIRTHDAY 2 U'}]</td>\n",
       "      <td>Shaon Sikder wrote on à¦à§à¦§à§à¦²à§ à¦¬à¦...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Monday</td>\n",
       "      <td>16</td>\n",
       "      <td>05:46:33</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4153</th>\n",
       "      <td>2013-12-15 18:40:03</td>\n",
       "      <td>[{'post': 'à¦à¦ à¦¸à§à¦ à¦à¦¤à¦¿à¦¹à¦¾à¦¸...</td>\n",
       "      <td>Shaon Sikder updated his status.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>15</td>\n",
       "      <td>18:40:03</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4154</th>\n",
       "      <td>2013-12-15 18:04:11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'data': [{'media': {'uri': 'photos_and_video...</td>\n",
       "      <td>[Panna Das, Halder Shuvo, Tapu Mondal]</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>15</td>\n",
       "      <td>18:04:11</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4155</th>\n",
       "      <td>2013-12-14 03:51:15</td>\n",
       "      <td>[{'post': 'Happpy Birthday 2 u'}]</td>\n",
       "      <td>Shaon Sikder wrote on Suvankar Mondal's timeline.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>14</td>\n",
       "      <td>03:51:15</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4156</th>\n",
       "      <td>2013-12-01 03:08:42</td>\n",
       "      <td>[{'post': 'Happpppy BIRTHday\r\n",
       " Like this page&gt;...</td>\n",
       "      <td>Shaon Sikder wrote on à¦¸à¦¬à§à¦ à¦à¦¹à¦®à§...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>03:08:42</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4157 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               timestamp                                               data  \\\n",
       "0    2020-09-20 14:08:32            [{'post': 'For whom it may concern..'}]   \n",
       "1    2020-09-20 14:01:13            [{'post': 'For whom it may concern..'}]   \n",
       "2    2020-09-03 07:29:59  [{'post': '**Data Science in Python**\n",
       "\n",
       "à¦¯à¦à...   \n",
       "3    2020-09-01 16:49:35  [{'post': 'Hello data enthusiasts people, I'm ...   \n",
       "4    2020-09-01 13:57:00                 [{'update_timestamp': 1598968620}]   \n",
       "...                  ...                                                ...   \n",
       "4152 2013-12-16 05:46:33                 [{'post': 'HAPPPPY BIRTHDAY 2 U'}]   \n",
       "4153 2013-12-15 18:40:03  [{'post': 'à¦à¦ à¦¸à§à¦ à¦à¦¤à¦¿à¦¹à¦¾à¦¸...   \n",
       "4154 2013-12-15 18:04:11                                                NaN   \n",
       "4155 2013-12-14 03:51:15                  [{'post': 'Happpy Birthday 2 u'}]   \n",
       "4156 2013-12-01 03:08:42  [{'post': 'Happpppy BIRTHday\n",
       " Like this page>...   \n",
       "\n",
       "                                                  title  \\\n",
       "0         Shaon Sikder posted in Department of ECE,EWU.   \n",
       "1      Shaon Sikder posted in EWU ECE Telecom Rocker's.   \n",
       "2     Shaon Sikder posted in Data Science Bangladesh...   \n",
       "3                      Shaon Sikder updated his status.   \n",
       "4                                                   NaN   \n",
       "...                                                 ...   \n",
       "4152  Shaon Sikder wrote on à¦à§à¦§à§à¦²à§ à¦¬à¦...   \n",
       "4153                   Shaon Sikder updated his status.   \n",
       "4154                                                NaN   \n",
       "4155  Shaon Sikder wrote on Suvankar Mondal's timeline.   \n",
       "4156  Shaon Sikder wrote on à¦¸à¦¬à§à¦ à¦à¦¹à¦®à§...   \n",
       "\n",
       "                                            attachments  \\\n",
       "0                                                   NaN   \n",
       "1                                                   NaN   \n",
       "2     [{'data': [{'external_context': {'url': 'https...   \n",
       "3                                                   NaN   \n",
       "4                                                    []   \n",
       "...                                                 ...   \n",
       "4152                                                NaN   \n",
       "4153                                                NaN   \n",
       "4154  [{'data': [{'media': {'uri': 'photos_and_video...   \n",
       "4155                                                NaN   \n",
       "4156                                                NaN   \n",
       "\n",
       "                                        tags  year      month  week_day  day  \\\n",
       "0                                        NaN  2020  September    Sunday   20   \n",
       "1                                        NaN  2020  September    Sunday   20   \n",
       "2                                        NaN  2020  September  Thursday    3   \n",
       "3                                        NaN  2020  September   Tuesday    1   \n",
       "4                                        NaN  2020  September   Tuesday    1   \n",
       "...                                      ...   ...        ...       ...  ...   \n",
       "4152                                     NaN  2013   December    Monday   16   \n",
       "4153                                     NaN  2013   December    Sunday   15   \n",
       "4154  [Panna Das, Halder Shuvo, Tapu Mondal]  2013   December    Sunday   15   \n",
       "4155                                     NaN  2013   December  Saturday   14   \n",
       "4156                                     NaN  2013   December    Sunday    1   \n",
       "\n",
       "          time month_year  \n",
       "0     14:08:32    2020-09  \n",
       "1     14:01:13    2020-09  \n",
       "2     07:29:59    2020-09  \n",
       "3     16:49:35    2020-09  \n",
       "4     13:57:00    2020-09  \n",
       "...        ...        ...  \n",
       "4152  05:46:33    2013-12  \n",
       "4153  18:40:03    2013-12  \n",
       "4154  18:04:11    2013-12  \n",
       "4155  03:51:15    2013-12  \n",
       "4156  03:08:42    2013-12  \n",
       "\n",
       "[4157 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mypost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4157 entries, 0 to 4156\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   timestamp    4157 non-null   datetime64[ns]\n",
      " 1   data         3777 non-null   object        \n",
      " 2   title        3082 non-null   object        \n",
      " 3   attachments  2552 non-null   object        \n",
      " 4   tags         51 non-null     object        \n",
      " 5   year         4157 non-null   int64         \n",
      " 6   month        4157 non-null   object        \n",
      " 7   week_day     4157 non-null   object        \n",
      " 8   day          4157 non-null   int64         \n",
      " 9   time         4157 non-null   object        \n",
      " 10  month_year   4157 non-null   period[M]     \n",
      "dtypes: datetime64[ns](1), int64(2), object(7), period[M](1)\n",
      "memory usage: 357.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_mypost.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making the assumption that if 95% of the rows are NaN values, the column doesn’t contain valuable info for the purposes of our analysis\n",
    "#use dropna and specify threshhold of na values\n",
    "df_mypost = df_mypost.dropna(thresh=len(df_mypost)*0.05, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>data</th>\n",
       "      <th>title</th>\n",
       "      <th>attachments</th>\n",
       "      <th>year</th>\n",
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       "      <th>0</th>\n",
       "      <td>2020-09-20 14:08:32</td>\n",
       "      <td>[{'post': 'For whom it may concern..'}]</td>\n",
       "      <td>Shaon Sikder posted in Department of ECE,EWU.</td>\n",
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       "      <td>2020</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-09-20 14:01:13</td>\n",
       "      <td>[{'post': 'For whom it may concern..'}]</td>\n",
       "      <td>Shaon Sikder posted in EWU ECE Telecom Rocker's.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>September</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>20</td>\n",
       "      <td>14:01:13</td>\n",
       "      <td>2020-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-09-03 07:29:59</td>\n",
       "      <td>[{'post': '**Data Science in Python**\n",
       "\n",
       "à¦¯à¦à...</td>\n",
       "      <td>Shaon Sikder posted in Data Science Bangladesh...</td>\n",
       "      <td>[{'data': [{'external_context': {'url': 'https...</td>\n",
       "      <td>2020</td>\n",
       "      <td>September</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>3</td>\n",
       "      <td>07:29:59</td>\n",
       "      <td>2020-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-09-01 16:49:35</td>\n",
       "      <td>[{'post': 'Hello data enthusiasts people, I'm ...</td>\n",
       "      <td>Shaon Sikder updated his status.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>September</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>16:49:35</td>\n",
       "      <td>2020-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-09-01 13:57:00</td>\n",
       "      <td>[{'update_timestamp': 1598968620}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>2020</td>\n",
       "      <td>September</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>13:57:00</td>\n",
       "      <td>2020-09</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4152</th>\n",
       "      <td>2013-12-16 05:46:33</td>\n",
       "      <td>[{'post': 'HAPPPPY BIRTHDAY 2 U'}]</td>\n",
       "      <td>Shaon Sikder wrote on à¦à§à¦§à§à¦²à§ à¦¬à¦...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Monday</td>\n",
       "      <td>16</td>\n",
       "      <td>05:46:33</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4153</th>\n",
       "      <td>2013-12-15 18:40:03</td>\n",
       "      <td>[{'post': 'à¦à¦ à¦¸à§à¦ à¦à¦¤à¦¿à¦¹à¦¾à¦¸...</td>\n",
       "      <td>Shaon Sikder updated his status.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>15</td>\n",
       "      <td>18:40:03</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4154</th>\n",
       "      <td>2013-12-15 18:04:11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'data': [{'media': {'uri': 'photos_and_video...</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>15</td>\n",
       "      <td>18:04:11</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4155</th>\n",
       "      <td>2013-12-14 03:51:15</td>\n",
       "      <td>[{'post': 'Happpy Birthday 2 u'}]</td>\n",
       "      <td>Shaon Sikder wrote on Suvankar Mondal's timeline.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>14</td>\n",
       "      <td>03:51:15</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4156</th>\n",
       "      <td>2013-12-01 03:08:42</td>\n",
       "      <td>[{'post': 'Happpppy BIRTHday\r\n",
       " Like this page&gt;...</td>\n",
       "      <td>Shaon Sikder wrote on à¦¸à¦¬à§à¦ à¦à¦¹à¦®à§...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>December</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>03:08:42</td>\n",
       "      <td>2013-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4157 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               timestamp                                               data  \\\n",
       "0    2020-09-20 14:08:32            [{'post': 'For whom it may concern..'}]   \n",
       "1    2020-09-20 14:01:13            [{'post': 'For whom it may concern..'}]   \n",
       "2    2020-09-03 07:29:59  [{'post': '**Data Science in Python**\n",
       "\n",
       "à¦¯à¦à...   \n",
       "3    2020-09-01 16:49:35  [{'post': 'Hello data enthusiasts people, I'm ...   \n",
       "4    2020-09-01 13:57:00                 [{'update_timestamp': 1598968620}]   \n",
       "...                  ...                                                ...   \n",
       "4152 2013-12-16 05:46:33                 [{'post': 'HAPPPPY BIRTHDAY 2 U'}]   \n",
       "4153 2013-12-15 18:40:03  [{'post': 'à¦à¦ à¦¸à§à¦ à¦à¦¤à¦¿à¦¹à¦¾à¦¸...   \n",
       "4154 2013-12-15 18:04:11                                                NaN   \n",
       "4155 2013-12-14 03:51:15                  [{'post': 'Happpy Birthday 2 u'}]   \n",
       "4156 2013-12-01 03:08:42  [{'post': 'Happpppy BIRTHday\n",
       " Like this page>...   \n",
       "\n",
       "                                                  title  \\\n",
       "0         Shaon Sikder posted in Department of ECE,EWU.   \n",
       "1      Shaon Sikder posted in EWU ECE Telecom Rocker's.   \n",
       "2     Shaon Sikder posted in Data Science Bangladesh...   \n",
       "3                      Shaon Sikder updated his status.   \n",
       "4                                                   NaN   \n",
       "...                                                 ...   \n",
       "4152  Shaon Sikder wrote on à¦à§à¦§à§à¦²à§ à¦¬à¦...   \n",
       "4153                   Shaon Sikder updated his status.   \n",
       "4154                                                NaN   \n",
       "4155  Shaon Sikder wrote on Suvankar Mondal's timeline.   \n",
       "4156  Shaon Sikder wrote on à¦¸à¦¬à§à¦ à¦à¦¹à¦®à§...   \n",
       "\n",
       "                                            attachments  year      month  \\\n",
       "0                                                   NaN  2020  September   \n",
       "1                                                   NaN  2020  September   \n",
       "2     [{'data': [{'external_context': {'url': 'https...  2020  September   \n",
       "3                                                   NaN  2020  September   \n",
       "4                                                    []  2020  September   \n",
       "...                                                 ...   ...        ...   \n",
       "4152                                                NaN  2013   December   \n",
       "4153                                                NaN  2013   December   \n",
       "4154  [{'data': [{'media': {'uri': 'photos_and_video...  2013   December   \n",
       "4155                                                NaN  2013   December   \n",
       "4156                                                NaN  2013   December   \n",
       "\n",
       "      week_day  day      time month_year  \n",
       "0       Sunday   20  14:08:32    2020-09  \n",
       "1       Sunday   20  14:01:13    2020-09  \n",
       "2     Thursday    3  07:29:59    2020-09  \n",
       "3      Tuesday    1  16:49:35    2020-09  \n",
       "4      Tuesday    1  13:57:00    2020-09  \n",
       "...        ...  ...       ...        ...  \n",
       "4152    Monday   16  05:46:33    2013-12  \n",
       "4153    Sunday   15  18:40:03    2013-12  \n",
       "4154    Sunday   15  18:04:11    2013-12  \n",
       "4155  Saturday   14  03:51:15    2013-12  \n",
       "4156    Sunday    1  03:08:42    2013-12  \n",
       "\n",
       "[4157 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mypost #removed tag columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\shaon\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "c:\\users\\shaon\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df_mypost['title'] = df_mypost['title'].astype(str)\n",
    "df_mypost['data'] = df_mypost['data'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total no of post per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x232f61cc548>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vT86dO+excePG+gAQExNz6ejRo1779u2rCwDLly+/MioOCwvL//XXX+sBwMaNG32PHTvmBQBpaWl1/P39zZMmTcp47LHHTv3666++AFCvXr2izMxMl+c8jnSJiGqrMt7i4ygllfb78MMPAwcPHny+ffv20a1bt74UHR2dCwB+fn46Z86cwwMGDAgPDAws7Ny580XrdqNHjz7/8ccfB0VGRkZdf/31F1u1anUJAHbs2OHz1FNPhZhMJnh6eurbb799GADGjBlzduDAgeFNmjQp2LZtW6oz37ctlvYj98YqQ0R2uVNpv2vJzMw0NWjQwGw2mzF69OjQ8PDwSzNmzDjt6riuhaX9qMYKu7TE4X2mObxHIqqoN954o9HSpUsbFRQUSHR0dO6UKVPc6ktDcUy6RERUbc2YMeN0dR/ZlofLTyoTERHVFky6RERETsKkS0RE5CRMukRERE5S6oVUIvIegEEATqtqB6NtNoDBAC4DOAjgflW9YCx7CsA4AEUAJqvqKqM9FsAHAHwAfAvgUa3u9ysREdVgHRd1dGhpv6QxSde879fDwyM2PDw8r7CwUDw8PPSee+4597e//e2Uh4eHI8MoE19f3865ubmJzt5vWUa6HwAYUKztBwAdVLUTgFQATwGAiEQBGAEg2tjmbRGxfprvAJgAINx4FO+TiIhqMC8vL/PevXtTDhw4kLx27drU77//vsHUqVNbuDqu8iooKKjwtqUmXVX9CUBGsbbvVdVaqWErgBDj+RAAy1Q1X1UPATgAIE5EmgOor6pbjNHtYgC3VzhqIiJya8HBwYXvvvtu2vvvv9/EbDajsLAQDzzwQEiHDh3aR0RERM2ePbuRdd1nn322aURERFS7du2iJk2aFAwAycnJXj179gyPjo5uHxsb2y4xMdEbAIYOHRr217/+NbRbt24RISEhHb/55hu/YcOGhV133XXRQ4cODbONYfz48SFRUVHtu3fvHnH8+HHP0vqNj48P6datW8SkSZNCUEGOuE93LIDlxvNgWJKwVbrRVmA8L95ORES1VFRU1GWz2Yxjx455Ll++vGGDBg2Kdu/evScvL0/+9Kc/RQ4ePDhr165d3t98803Ajh079vr7+5tPnTrlAQDx8fGtFixYcLhjx475a9eurTdx4sTQrVu3pgJAZmam55YtW1KXLFnScPjw4eFr167dGxsbm9epU6f2mzdv9unRo0deXl6eqUuXLrkLFy5Mnzp1avPp06e3WLx48ZFr9Xvw4EHvTZs2pXp6Vjx1VirpisgzAAoBfGxtsrOaXqO9pH4nwHIoGqGhoZUJkYiIqjHrpT2rV6+uv3fvXt+vvvoqAACys7M9UlJSvH/44Yf6995771l/f38zADRt2rQoMzPTlJiY6Dds2LA21n4uX758Jc/ceuutF0wmE7p06ZIbFBRUEBcXlwcAEREReQcPHvTq0aNHnslkQnx8fAYAjB079tydd97ZtrR+77zzzvOVSbhAJZKuiIyB5QKrvjYXRKUDaGmzWgiA40Z7iJ12u1R1AYAFgGXu5YrGSERE1VdKSkpdDw8PBAcHF6qqvP7660eGDh2aZbvOt99+W1/kj+O2oqIi+Pv7F+7duzfFXr/e3t4KAB4eHqhbt+6VHGIymVBYWGhvEAgRKbVfPz8/s7328qjQLUMiMgDAkwBuU9Vcm0VfARghIl4i0hqWC6a2q+oJANki8mexfHqjAXxZydiJiMhNHT9+3HP8+PGt7r///tMmkwn9+/fPfOeddxpb6+Lu2rXLKysryzRgwICsDz/8sFF2drYJAE6dOuURGBhoDgkJufzee+8FAIDZbMaWLVt8yrN/s9mM999/PwAAPvjgg6C4uLhsR/RbmrLcMrQUQC8AjUQkHcAMWK5W9gLwg/ENZKuqPqiqySKyAkAKLIedH1LVIqOrifj9lqHvjAcREblIabf4OFp+fr4pMjIyynrL0PDhw8/NmDHjFAA8/vjjZ9PS0rw6duzYXlUlMDCw4Ntvvz141113Zf3yyy++119/ffs6depov379Mt96661jS5cu/W38+PGtXnnlleaFhYVyxx13ZHTv3j2vrLH4+PiYk5OTfaKjo5v5+/sXffbZZ78BQGX7LQ1L+5FbC5v+jcP7TJt1q8P7JHK2mlLazx1dq7QfZ6QiIiJyEiZdIiIiJ2HSJSIichImXSIiIidh0iUiInISJl0iIiInccTcy0RE5Ib2RLZ3aGm/9nv3lKm0n/X1l19+eaBdu3aX7a0bFxfX7rXXXjt600035dpb7q6YdImIyCmspf0c1V9BQQHq1KlTqT4KCwtR2fmUy4OHl4mIyGU2bNjg+6c//alddHR0+xtvvDH88OHDV7LoBx98ENS5c+fI8PDw6B9//NEXAKZMmdLinnvuaXXDDTeE33nnna3nzp0bNHr06CuVcXr37t125cqV/gDw17/+NbRDhw7t27ZtG/34449fqdsbHBzccerUqc1jY2PbPfPMM82ioqLaW5clJSV5RUdHX3ntaEy6RETkFNZpICMjI6P69+/fJj8/XyZPnhz65ZdfHkxOTt4zZsyYs1OnTr1S9jU3N9eUmJi4d+7cuYcnTJjQ2tq+a9cu31WrVh34+uuvD11rf3PmzDm2e/fuPXv37k3etGmT/7Zt267Mo+zt7W3esWPHvldeeeWkv79/0ebNm30AYP78+Y1Gjhx5rireP8DDy0RE5CTFDy///PPP3vv37/fp06dPBGApMNC4ceMC6/KRI0dmAMDAgQNzcnJyTGfPnvUAgAEDBlzw8/MrdQ7jRYsWBX7wwQeNCgsL5cyZM3V27tzp3a1btzwAGD169Hnrevfdd9/ZhQsXNoqLizv65ZdfBvz88897HPeu/4hJl4iIXEJVpW3btnm//vrrXnvLi5f0s76uV6/elRJ7np6eajb/XnEvPz/fBAB79+6t+9ZbbzXdsWPHnsaNGxcNHTo07NKlS1eO7lrr8wLAmDFjzr/yyistli1blt2xY8fcZs2aFaGK8PAyERG5RKdOnS5lZGR4rl69uh4A5OfnS0JCgrd1+dKlSwMAYNWqVX7+/v5FQUFBVyXDNm3aXE5OTvYtKirCgQMH6uzataseAJw/f97Dx8fHHBgYWHT06FHPdevWNSgpDl9fX7355pszp0yZEnrfffdVaUEIjnSJiGqp0m7xqWre3t66bNmyg5MnTw7Nzs72KCoqkokTJ57q2rXrJQAICAgo6ty5c2ROTo7HggUL7J6/7d+/f86//vWv/Hbt2kW3a9cuLyoqKhcAunfvntehQ4fc8PDw6NDQ0PzY2Nica8UyevTojO+++y7gzjvvzHL8O/0dky4RETlFbm5uYvG2Hj165CUkJOwr3r59+/ar2gBgzpw5x21fm0wmfPXVV3YT8qeffppmr/3YsWNJxdvWr1/vd88995yt6tuHmHSJiKhW69+/f5vDhw97rV+/PrWq98WkS27t0i3Bpa9ERHQNP/zww0Fn7YtJl8gZZpZ4DUcF+8t0bH9E5BS8epmIiMhJmHSJiIichEmXiIjISXhOl8gJwi4tcWh/aQ7tjWqrfz241qGl/R6a16fU+359fX0727t1qLYodaQrIu+JyGkR2W3TFigiP4jIfuPfAJtlT4nIARHZJyK32LTHikiSsWyuFJ/fi4iIqAoUFha6OoQrynJ4+QMAA4q1TQewRlXDAawxXkNEogCMABBtbPO2iHgY27wDYAKAcONRvE8iIqoFMjMzTd27d4+IiopqHxEREfXRRx81BIB9+/bVve6666JHjBjRqm3bttE33HBDeE5OjgCWovY//fSTLwCcOHHCMzg4uKN1m9jY2HZRUVHto6Ki2v/www/1AGDlypX+3bp1ixg8eHDrdu3aRT/66KMt/v73vzexxvDII48Ev/jii02ujq5qlZp0VfUnABnFmocAWGQ8XwTgdpv2Zaqar6qHABwAECcizQHUV9UtqqoAFttsQ0REtYivr6/5m2++OZCSkrJn/fr1qU8//XSItWjBkSNHvCdPnnz6wIEDyQ0aNChavHhxwLX6atGiReGGDRtSU1JS9ixfvvy3xx9//Ept3V27dtWbPXv2sYMHDyZPmjTp7NKlS4MAoKioCF988UVAfHx8lZXwK0lFz+k2VdUTAKCqJ0TE+m0hGMBWm/XSjbYC43nxdrtEZAIso2KEhoaWtBoREbkhs9ksjz32WMjWrVv9TCYTTp8+XTc9Pd0TAIKDg/N79OiRBwCdO3fOTUtL87pWX5cvX5Zx48a1SklJ8TGZTDh8+PCV9Tt16nQxMjLyMgC0a9fucsOGDQs3bdrkc+LEiTrR0dFVWk2oJI6+kMreeVq9RrtdqroAwAIA6Nq1a6k1E4mIyH3Mnz8/8Ny5c55JSUl7vLy8NDg4uGNeXp4JAOrWrXvlb76Hh4da2z09PbWoyJIjc3Nzr+SUl156qWmTJk0KPv3000Nmsxk+Pj5XLg7z9fX9veYfgPvvv//su+++2+j06dN17r//fqePcoGKJ91TItLcGOU2B3DaaE8H0NJmvRAAx432EDvtRLUCp6sk+l1mZqZHo0aNCry8vPTrr7/2P378eN3StmnZsmX+9u3b6/Xu3Tv3448/vnLIOTMz0yMkJOSyh4cH3nrrrSBrYrZn1KhRF1566aXgwsJCGTp06G8OejvlUtGk+xWAMQBmGf9+adO+RETmAGgBywVT21W1SESyReTPALYBGA3g/yoVORERVUpZbvFxpIKCAtStW1fj4+MzBg4c2LZDhw7to6Ojc1u3bn2ptG2nT59+avjw4dctW7YsqGfPnlfK7z322GOnhw4d2uaLL74IuPHGG7N9fHzMJfXh7e2tPXr0yGrYsGFRVVcTKolYrmu6xgoiSwH0AtAIwCkAMwB8AWAFgFAARwAMU9UMY/1nAIwFUAjgMVX9zmjvCsuV0D4AvgPwiJa2c1gOLyckJFTgrVFt0OzHXx3e58ne1zu8zzVr2zi0v759nDY/O7kpEdmhql1t23bu3JkWExNTpUXar2XLli0+EyZMCEtKStrjiv0XFRUhOjo66pNPPjnYsWPH/Kraz86dOxvFxMSE2VtWaqpX1XtKWNS3hPVfAvCSnfYEAB1K2x8REdU8r776auP58+c3mT179lFX7H/Hjh3eQ4YMCR84cOD5qky4peGMVEREVOWmTZt2Ztq0aWdctf/Y2NhL6enpVxWvdzbOvUxEROQkTLpEREROwqRLRETkJEy6RERETsILqYiIaqnXhw9yaGm/J5avrHRpv7i4uHavvfba0ZtuuinXkbFVFxzpEhEROQmTLhEROdXKlSv9e/fu3db6evTo0aFz584Nsl3nn//8Z6Nx48ZdmVb49ddfbxQfH287nbBbYtIlIqJqZ9y4cRnff/99g/z8fAGAjz76qNGECRNcUqTAkXhOl8gJNvw0yqH99e3j0O6Iqp369eubb7jhhuzly5c36Nix46WCggKJi4vLc3VclcWkS0RETlWnTh21Fq0HAOtotrgJEyacfemll5pFRERcuvfee102Z7Qj8fAyERE5VZs2bfIPHDjgk5eXJ+fOnfPYuHFjfXvr9enT5+KJEyfqfv7550Hjxo3LcHacVYEjXSKiWqost/g4krW0X9u2bQsGDx58vn379tGtW7e+FB0dXeLtQbfffvv5Xbt2+TZu3LjkQrluhEmXiIicIiEhwadly5b5ADBv3rx0AOnF19m+ffs+29dbtmzxe+yxx045KcQqx8PLRERU5V599dXG995773XPP//8sbKsf/bsWY+wsLAO3t7e5iFDhmRXdXzOwpEuERFVufKW9mvUqFFRWlra7qqMyRU40iUiInISJl0iIiInYdIlIiJyEiZdIiIiJ+GFVEREtVT69A0OLe0XMqtnpUv71XSVGumKyOMikiwiu0VkqYh4i0igiPwgIvuNfwNs1n9KRA6IyD4RuaXy4RMREbmPCiddEQkGMBlAV1XtAMADwAgA0wGsUdVwAGuM1xCRKGN5NIABAN4WEY/KhU9ERO7mWqX9goODOz7++OMtoqKi2kdEREQlJiZ6A0BWVpZp2LBhYR06dGjfvn37qI8++qihq+KvjMqe0/UE4CMingB8ARwHMATAImP5IgC3G8+HAFimqlmbM30AACAASURBVPmqegjAAQBxldw/ERHVMI0aNSpMSUnZM3bs2DOzZs1qCgBPP/108969e2ft3r17z4YNG/Y9++yzIVlZWW53XVKFA1bVYwBeA3AEwAkAmar6PYCmqnrCWOcEgCbGJsEAjtp0kW60ERERXTFy5MjzABAXF5d79OhRLwBYt25d/X/+85/NIyMjo2688cZ2+fn5cuDAgbqujbT8KnwhlXGudgiA1gAuAPhERO691iZ22rSEvicAmAAAoaGhFQ2RiIiqodJK+3l7eysAeHp6amFhoQCAquI///nPgZiYmHynButglRma9wNwSFXPqGoBgM8A9ABwSkSaA4Dx72lj/XQALW22D4HlcPRVVHWBqnZV1a6NGzeuRIhERFTdlLW0n63evXtnvf76602tyXrTpk0+VR5oFajMLUNHAPxZRHwB5AHoCyABwEUAYwDMMv790lj/KwBLRGQOgBYAwgFsr8T+iYioEspyi48jVaS0n9WsWbOOT5gwITQyMjJKVSUkJCT/xx9/POCMuB2pwklXVbeJyH8A/AKgEEAigAUA/ACsEJFxsCTmYcb6ySKyAkCKsf5Dqloj6iMSEVHpylLa79ixY0nW5zfddFOutdSfn5+fLlmy5LDTgq0ilZocQ1VnAJhRrDkfllGvvfVfAvBSZfZJRETu59VXX208f/78JrNnzz5a+to1F2ekIiKiKlfe0n41ldvd40REROSumHSJiIichEmXiIjISZh0iYiInIQXUhER1VIzZ850aGm/mTNnXvO+XxGJHTJkSMYXX3xxCLDct9ukSZOY66+//qIj7rl1h7KBHOkSEZFT+Pj4mPft2+eTk5MjAPD555/Xb9q0aUF5+igoKNfq1Q6TLhEROU3fvn0zP/nkk4YAsHTp0sChQ4dmWJf9+OOPvp07d45s3759VOfOnSN37tzpBQBz584NGjhw4HV9+vRp27Nnz4jMzEzTXXfdFRYREREVERER9cEHH1wp8/fII48Et2vXLiomJiby6NGj1e5oLpMuERE5zahRozKWL18ekJubK3v27PHt3r37ReuymJiYS9u3b9+7Z8+elBkzZhybNm1aiHXZL7/84rd06dJDW7duTZ0+fXrz+vXrF6Wmpqakpqam3HrrrdkAkJeXZ+revXvOvn37Urp3757zf//3f9Vu8v5q9y2AiIhqrm7duuWlp6d7LVy4MLBfv36ZtssyMjI8hg8f3jotLc1bRLSgoOBK9aGePXtmNW3atAgAfvrpp/rLli37zbqscePGRYCletGIESMyASA2Nvbi6tWrSy2k4Gwc6RIRkVMNGDDgwowZM1qOHj06w7b9ySefDL755puz9+/fn/z1118fuHz58pUc5evre6UWoKpC5OpqsZ6enmoymazPYS0LWJ1wpEvkBPGX7E5HTlQrTZw48WyDBg2K4uLi8lauXOlvbc/KyvIICQm5DADz589vVNL2vXr1ypozZ06T99577ygAnDlzxsM62q3umHSJiGqp0m7xqSpt2rQp+Nvf/na6ePuTTz55Mj4+vvXcuXOb9ezZM6uk7f/xj3+cuP/++0PDw8OjTSaTPv3008fHjBlzoWqjdgxRVVfHcE1du3bVhIQEV4dB1VSzH391eJ8ne1/v8D7Tp29waH8hs3o6tD+qeURkh6p2tW3buXNnWkxMzFlXxVRb7Ny5s1FMTEyYvWU8p0tEROQkPLzsbDMbVEGfmaWvQ0RELseRLhERkZNwpOtkYZeWOLzPNIf3SEREVYEjXSIiIidh0iUiInISHl4mIqql1qxt49DSfn37HHRpaT93UKmkKyINAbwLoAMABTAWwD4AywGEwXK68W5VPW+s/xSAcQCKAExW1VWV2T/Rxzq0Cno9WAV9EpFtaT8/Pz+tSGk/d1fZw8tvAvivqkYCiAGwB8B0AGtUNRzAGuM1RCQKwAgA0QAGAHhbRDwquX8iInIjFSntFxsb227z5s0+1vW6dOkSuW3bNp+re6/+KjzSFZH6AG4CcB8AqOplAJdFZAiAXsZqiwCsA/AkgCEAlqlqPoBDInIAQByALRWNwR1duiXY1SEQEbnMqFGjMmbMmNF8+PDhF/bs2eM7bty4c5s3b/YDfi/tV6dOHXzxxRf+06ZNC1m1atXB++677+y7777bqEePHkd37drldfnyZenWrVueq99LRVTm8PJ1AM4AeF9EYgDsAPAogKaqegIAVPWEiDQx1g8GsNVm+3SjjYiIaomKlPa77777zs+ePbt5fn5++rx58xqNHDnSbaeyrMzhZU8AXQC8o6qdAVyEcSi5BPZKLNmd+FlEJohIgogknDlzphIhEhFRdVPe0n7+/v7mnj17Zi1ZsqThV199FThu3LgM+z1Xf5UZ6aYDSFfVbcbr/8CSdE+JSHNjlNscwGmb9VvabB8C4Li9jlV1AYAFgKXgQSViJKoWlh96xaH9PQEWPCD3VZHSfg8++ODZoUOHtv3Tn/6UYy1m744qnHRV9aSIHBWRdqq6D0BfACnGYwyAWca/XxqbfAVgiYjMAdACQDiA7ZUJnoiIKq60W3yqSkVK+/Xs2TO3Xr16Rffff7/bHloGKn+f7iMAPhaRugB+A3A/LIesV4jIOABHAAwDAFVNFpEVsCTlQgAPqarbflshIqLyyc3NTSzeNmjQoOxBgwZlA0C/fv0upqWl7bYue/PNN68cDU1LS6ujqnLHHXeUWGfXHVQq6arqrwC62lnUt4T1XwLwUmX2SUREtctbb70V9OKLLwa//PLLRz083PtOU85I5WSczIGIqHwefvjhcw8//PA5V8fhCJx7mYiIyEmYdImIiJyEh5fJrW34aZTD++zbx+FdEhEB4EiXiIjIaTjSJSKqpZr9+KtDS/ud7H19qaX94uPjTy1cuDAdAJ577rmmOTk5HnPmzLE7UVJV8vX17WzvFqaqxpEuERE5Rd26dfXbb78NOHHihFsP+AoKKl6NkEmXiIicwsPDQ0ePHn3m5Zdfblp8WWpqat3u3btHRERERHXv3j1i//79dc+dO+cRHBzcsajIMo9Sdna2qVmzZp3y8/MlOTnZq2fPnuHR0dHtY2Nj2yUmJnoDwNChQ8P++te/hnbr1i0iJCSk4zfffOM3bNiwsOuuuy566NChYbb7HD9+fEhUVFT77t27Rxw/ftwTAK7Vb3x8fEi3bt0iJk2aFFLRz8Ctv224I174Uzt5B0xxdQhE1cL//u//nu7YsWP0zJkzT9q2P/jgg6EjR44898gjj5x74403giZOnNhy9erVByMjI3O//fZb/8GDB2cvW7aswc0335zp5eWl8fHxrRYsWHC4Y8eO+WvXrq03ceLE0K1bt6YCQGZmpueWLVtSlyxZ0nD48OHha9eu3RsbG5vXqVOn9ps3b/bp0aNHXl5enqlLly65CxcuTJ86dWrz6dOnt1i8ePGRa/V78OBB702bNqV6elY8dTLpEhGR0wQGBpqHDRt2btasWU18fHzM1vbExMR633333UEAmDhxYsbzzz8fAgDDhg07v3Tp0oDBgwdnr1ixInDSpElnMjMzTYmJiX7Dhg1rY93+8uXLVyrZ3XrrrRdMJhO6dOmSGxQUVBAXF5cHABEREXkHDx706tGjR57JZEJ8fHwGAIwdO/bcnXfe2ba0fu+8887zlUm4AJMuERE52VNPPXWqS5cuUSNGjCi1eME999xz4YUXXgg+deqUx+7du30HDx6clZWVZfL39y/cu3dvir1tvL29FQA8PDxQt27dK5XqTCYTCgsL7ZWZhYigqKgI1+rXz8/PbK+9PHhOl4iInKpp06ZFgwcPPr9kyZIr5fs6d+588d133w0AgPnz5wd27do1BwAaNGhgjomJufjAAw+E9u3bN9PT0xOBgYHmkJCQy++9914AAJjNZmzZssWnPDGYzWa8//77AQDwwQcfBMXFxWU7ot/ScKRLRFRLlXaLT1V65plnTi5atKix9fU777xzZMyYMWFvvvlms6CgoMLFixenWZfdfffd58eOHXvdypUr91nbli5d+tv48eNbvfLKK80LCwvljjvuyOjevXteWffv4+NjTk5O9omOjm7m7+9f9Nlnn/3miH5LI6rVu0Z8165dNSEhwdVhOMzMmTPdok93kT59g8P7DJnl+ALx/3pwrUP7e2ger56jaxORHar6hypwO3fuTIuJiXHrerTuYOfOnY1iYmLC7C3jSJeILGY2qII+Mx3fJ5EbY9J1svhLdksNExFRLcALqYiIiJyEI10iAgCEXVri8D7THN4jkXvjSJeIiMhJmHSJiIichIeXiYhqqbDp3zi0tF/arFtLve/34MGDdSZMmBB64MABH7PZjH79+mW+88476dZZpIp74YUXmjz++ONn/f39rzkblKtK9ZUXR7pEROQUZrMZt99+e9vbbrvtwuHDh3cfOnRo98WLF02PPvpocEnbzJ8/v2lOTk6V5qrKlOorr0qPdEXEA0ACgGOqOkhEAgEsBxAGy3UUd6vqeWPdpwCMA1AEYLKqrqrs/ql2W37oFYf3+QQcPzmGO7h0S4l/94gc4uuvv/b38vIyP/roo+cAwNPTE/PmzTt63XXXdZo9e/bxqVOntli3bl19ABgzZsxZVcXp06fr3HzzzREBAQGF27ZtS50/f37g66+/3kxVpV+/fhfeeeedY9b+x48fH7Jp0yb/Bg0aFH366ae/tWjRojA5OdnrwQcfDM3IyPD09vY2v/vuu4c7d+58aejQoWEBAQGFSUlJvp06dcpduHBhujM+A0d8e3gUwB6b19MBrFHVcABrjNcQkSgAIwBEAxgA4G0jYRMRUS2QlJTkExMTk2vbFhgYaG7evPnlN998s9Hhw4e9kpOTU1JTU1Pi4+PPPfvss6ebNGlSsH79+tRt27alpqWl1Zk5c2bwunXrUlNSUpITExPrffjhhw0BwFqqLyUlZc8NN9yQPX369BYAEB8f3+rtt98+kpycvGf27NnpEydODLXu21qqz1kJF6hk0hWREAC3AnjXpnkIgEXG80UAbrdpX6aq+ap6CMABAHGV2T8REbkPVYWIXHXuVlWxceNG/wcffPBMnTp1AFiKIhRfb+PGjfX+/Oc/Z7do0aKwTp06GD58eMb69ev9AEsFIdtSfdu3b/ezLdUXGRkZNWnSpFanT5+uY+3PEaX6yquye3sDwDQA/jZtTVX1BACo6gkRaWK0BwPYarNeutFGRES1QMeOHfO+/PLLANu2jIwM08mTJ+uGhobm20vItspTK8BZpfrKq8JJV0QGATitqjtEpFdZNrHTZvcTFJEJACYAQGhoqL1ViAAA3gFTXB0CEZXRbbfdlv3ss8+a3nrrraCHH374XGFhISZNmtRy2LBhZ8PDw/PnzZvX+NZbb82uU6cOTp065dG0adOievXqFWVmZpqaN2+Om2666eKTTz7Z8sSJE56NGzcu/OSTTwInTZp0Gvi9VN+ECRPO2yvVN3bs2PNmsxnbtm3zcWTVoPKqzEj3BgC3ichfAHgDqC8iHwE4JSLNjVFucwCnjfXTAbS02T4EwHF7HavqAgALAEuVoUrEWO3wwh8iqi7KcouPI5lMJnzxxRcHJkyY0Gr27NnNzWYz+vTpkzl37txjnp6empqa6hUZGRnt6empY8aMOfP000+fGTNmzNmBAweGN2nSpGDbtm2pzz333LGbb745QlWlb9++mffee+8FwHWl+srLIaX9jJHuVOPq5dkAzqnqLBGZDiBQVaeJSDSAJbCcx20By0VW4ap61XF7WzWttN/rwwc5vM8nlq90eJ/uwtEl84CqKZvnDqX9mv34q8P7PNn7eof3SWXD0n6u4+zSfrMArBCRcQCOABgGAKqaLCIrAKQAKATwUGkJl4iIqCZxSNJV1XUA1hnPzwGwW79OVV8C8JIj9umueA6SqquPdWgV9HqwCvokcl+cBpLcWp91D1VBr3tKX4WIqAI4DSQREZGTcKTrZByZ1U6O/7nzZ07kjjjSJSIichKOdImIaquZDRxa2g8zM0u97/fJJ59s9umnnwaZTCY1mUx4++23D/fp0+dieXazcuVKfy8vL3P//v3LtV1JgoODOyYkJOxp3rx5oSP6uxYmXSIicorVq1fXW7VqVcOkpKQUHx8fPXHihGd+fr692Qqvae3atf5+fn5Fjkq6lVFQUADrfNFlwaRLREROcezYsTqBgYGFPj4+CgDWkeWGDRt8p0yZ0jI3N9cUEBBQ+PHHH6e1atWqIC4url2HDh1yExMT6+Xk5HgsWLDgUIsWLQoXL17c2GQy6YoVK4LeeOONI506dbp0//33tzp27FhdAJgzZ86R//mf/7k4ZcqUFmlpaXVPnTpVJy0tzfvll18+umXLFr+1a9fWb9q0acHq1asPeHl5KQC88MILTTdu3FgfsMxi1aFDh/zjx497ltTviRMn6hw5cqRuYGBg4ddff32orJ8Bk66T3f2U4z/yJIf36D74eTrOhp9GObzPvo6fOIvc2O233571j3/8o0VYWFiHG2+8Meuee+7J6Nev38XJkyeHfvPNNwdatGhRuHDhwoCpU6cGf/LJJ2kAkJuba0pMTNz73Xff+U2YMKH1/v37k0ePHn3Gz8+v6IUXXjgFAIMHD249ZcqUU7fcckvO/v37695yyy3hv/32WzIAHD582Gvz5s2pv/zyi3efPn0iFy1adHDevHnp/fv3b7NixYoGo0aNugAA9evXL0pKStrz1ltvBT3yyCMtf/zxxwMPPPBAy5L63bVrl++2bdv2+vn5lWtaRyZdIiJyigYNGph3796d8t///td/zZo1/mPGjGkzZcqU4/v37/fp06dPBGApXNC4ceMC6zYjR47MAICBAwfm5OTkmM6ePXtVHfZNmzbV379/v4/1dU5Ojsf58+dNANCvX79MLy8vjYuLyysqKpK77rorCwCio6PzDh06VNe6zZgxYzIAYPz48RnPPvtsy9L6HTBgwIXyJlyASZdKMrNBFfSZ6fg+iciteHp6YtCgQdmDBg3K7tSpU968efMat23bNu/XX3/da299Ebnma8BS8i8hIWGPvSRoPXzs4eEBT09PNZksN+2YTCYUFhZe6czabuxDS+u3Xr16FSoLyFuGiIjIKXbu3OmVlJTkZX2dmJjoEx4efikjI8Nz9erV9QAgPz9fEhISvK3rLF26NAAAVq1a5efv718UFBRU5O/vX5SdnX1lxHvjjTdmvfLKK9ba7di8efOV0WlZLV68OBAA/v3vfwd07tz5oqP6LY4jXSdLOnTE1SEQEVmU4RYfR8rKyvKYPHlyaFZWloeHh4eGhYXlL1q06PChQ4fOTJ48OTQ7O9ujqKhIJk6ceKpr166XACAgIKCoc+fOkdYLqQBg6NChF+6666423333XcM33njjyIIFC47Gx8eHRkRERBUVFUm3bt2ye/ToUa4/tvn5+dKpU6dIs9ksy5Yt+w0AHNFvcUy6RETkFD179sxNTEy86jBy8+bNCxMSEvbZ22b48OHn//Wvfx2zbevUqVN+ampqim3bN99881vxbefMmfOHmu25ubmJ9pYdO3bMev3kieJxlaXf8uDhZSIiIifhSJeIiKql7du32x39ujMmXScLu7TE4X2mObxHIqqhzGazWUwmU7lvdaGyMZvNAqDEK5uZdIkIABB/qa+rQ6Cqt/vMmTNRjRs3zmTidTyz2SxnzpxpAGB3Sesw6ZJdHJET1TyFhYXxJ0+efPfkyZMdwGt6qoIZwO7CwsL4klZg0iUiAMDyQ684vM8n0NPhfVLFxcbGngZwm6vjqM34TYeIiMhJONIluy7dEuzqEIiIapwKJ10RaQlgMYBmsBzHXqCqb4pIIIDlAMJgOY13t6qeN7Z5CsA4AEUAJqvqqkpFT0S1D+cFJzdWmcPLhQCeUNX2AP4M4CERiQIwHcAaVQ0HsMZ4DWPZCADRAAYAeFtErqoWQUREVFNVOOmq6glV/cV4ng1gD4BgAEMALDJWWwTgduP5EADLVDVfVQ8BOAAgrqL7JyIicjcOOacrImEAOgPYBqCpqp4ALIlZRKwVGoIBbLXZLN1oIyIqM97ORu6s0klXRPwAfArgMVXNslfr0LqqnTa7N2eLyAQAEwAgNDS0siESURl4B0xxdQhENV6lkq6I1IEl4X6sqp8ZzadEpLkxym0O4LTRng6gpc3mIQDsVmpQ1QUAFgBA165dOWsKEV3BK+vJnVXm6mUB8G8Ae1R1js2irwCMATDL+PdLm/YlIjIHQAsA4QC2V3T/RORYfdY9VAW97nF4jw+u/8LhfaL39Y7vk8iOyox0bwAwCkCSiPxqtD0NS7JdISLjABwBMAwAVDVZRFYASIHlyueHVLWoEvsnIiJyKxVOuqq6EfbP0wKA3ZnTVfUlAC9VdJ/kPB/r0Cro9WAV9EmOcvdTjp8rJ6n0VcqNhRnInXFGKiJyK19eKHB4n1VxYJ3IHiZdIidoP8LuNYNEVMuw4AEREZGTcKRLdm34aZTD++zbx+FdEhG5FY50iYiInIQjXXJrSYeOuDoEIqIy40iXiIjISTjSJbt4L2Ttw6MGRFWPSZfsWn7oFYf3+QR6OrxPd+HoyjhpDu2NiJyFh5eJiIichEmXiIjISZh0iYiInIRJl4iIyEl4IRXZ5R0wxdUhEBHVOEy6RORW+qyrippAe6qgT6Kr8fAyERGRk3CkS3ZxNEFE5Hgc6RIRETkJR7pk191POf5XI8nhPRJVYzMbVEGfmY7vk5yKSZfscpd5eB09vSJQe6dY5GfpWDPxeBX0Se6Oh5eJiIichCNdInIr7nLqw39PQhX0Su7O6UlXRAYAeBOAB4B3VXWWI/rtuKijI7r5g6QxPAtJVN24y6mPv+w86OoQqBpyatIVEQ8A/wLQH0A6gJ9F5CtVTXFmHFQ6nt8jqpz2I467OgSqhpw90o0DcEBVfwMAEVkGYAgAJl0iKhN+ISR3JqrqvJ2J3AVggKrGG69HAeimqg8XW28CgAnGy3YA9jk4lEYAzjq4T0dzhxgBxulojNOxanOcrVS1sYP7pEpy9khX7LRdlfVVdQGABVUWhEiCqnatqv4dwR1iBBinozFOx2KcVN04+5ahdAAtbV6HAOCJDyIiqhWcnXR/BhAuIq1FpC6AEQC+cnIMRERELuHUw8uqWigiDwNYBcstQ++parIzYzBU2aFrB3KHGAHG6WiM07EYJ1UrTr2QioiIqDbjNJBEREROwqRLRETkJEy6RERETsKkS0RE5CQ1usqQiAiAYbBMwPEfAH1gmXZyL4B5qmp2YXjXJCJrVbWPq+OwJSKNVPWszet7YZnaczeAhVpNrsoTkTsArFfVDBFpDOB1AJ1hmW70CVVNd2mABhGZA+BTVd3k6liuRUQCATwMyz31/wbwNIDuAPYAeFlVz7swvCtEpDeAobDMBVAIYD8sRVUOuDSwYkTkFgC3AwiG5W/TcQBfqup/XRoYOUWNvnpZRN4G0ARAXQBZALwAfA3gLwBOqeqjLgzvChHZVbwJQASM6S9VtZPTg7JDRH5R1S7G82cB9ASwBMAgAOmq6viq3RUgIimqGmU8Xw5gK4BPAPQD8FdV7e/K+KxE5AyAwwAaA1gOYKmqJro2qquJyLewVL+rD6C98XwFLIVLYlR1iAvDAwCIyCwATQGsgSWhHQKQCmASLF8MPnFheFeIyBuw/N9eDMtkQYBlkqDRAPZXl79JVHVqetJNUtWOIlIHwEkAzVX1soh4AkhUVcfXA6wAEfkKli8FLwLIgyXpbgBwIwCo6mHXRfc7EUlU1c7G818A9FTVi8bn+0s1+jz3qWo74/kOVY21Wfarql7vuuh+Z/08RSQcloliRsBy//pSWBJwqksDNFg/M+PIUbqqBhdf5sLwrHEkWX//jP/f61X1BhEJALBBVTu4NkILEUlV1Qg77QIgVVXDXRAWOVFNP6dbCACqWgDgZ1W9bLwuBFDkysBsqeptAD6F5Qb5GFVNA1CgqoerS8I1+IhIZxGJBeChqheBK59vtfk8AawTkRdExMd4fjtw5fBjpmtD+wMFAFXdr6p/V9VoAHcD8AbwrUsj+yOTkbxaAvATkTAAEJEgWI4iVQdm4zA4ALSA5csLjEPf9uZ8d5VLIhJnp/1PAC45Oxhyvhp9ThfASRHxU9UcVR1gbRSRZgAuuzCuq6jq5yLyPYC/i0g8qs8fM1snAMwxnmeISHNVPWH88S10YVzFPQzgGfxenepxEbkIy6mFUS6L6mpXJQNV3QVgF4CnnB9Oif4By3UQADAWwLsiogCiADzvsqj+6GUAiSKyD0AkgIkAYJzT3+nKwIq5D8A7IuKP3w8vt4TlSNd9LoqJnKhGH14uiYjUA1BPVU+7OhZ7RCQGQHdVnefqWMpCRDwAeKlqrqtjKU5EGgDwVNVzro6lOOsXQlfHURbGz1iMqVw9AVwP4JiqnnBxaFcYI93rYKnZfcHV8VyL8cU/GJYvXumqetLFIZGT1MqkCwAiEqmqe0tf07UYp2MxTsdyhzirY4wiUsc4LWPb9oe7A6hmqunndK/le1cHUEaM07EYp2O5Q5zVJkYR6S0i6QCOi8j31vPjhmoTJ1WdGn1OV0TmlrQIQENnxnItjNOxGKdjuUOc7hCj4VUAt6hqsojcBeAHERmlqltRvS74oipSow8vi0g2gCcA5NtZ/LqqNnJySHYxTsdinI7lDnG6Q4wAICI7VTXG5nU0gM8ATAfwN+t98FRz1eiRLoCfAexW1c3FF4jITOeHUyLG6ViM07HcIU53iBEACkSkmfXCKWPE2xfASgBtXBsaOUNNH+kGArhUHa+qtcU4HYtxOpY7xOkOMQKAiPQDcEZVdxZrbwjgIVV9yTWRkbPU6KRLRERUndToq5dFpIGIzBKRvSJyznjsMdqqzcUVjNOxGKdjuUOc7hAj4D5xUtWp0UkXlknZzwPopapBqhoEoLfRVi0mQDcwTsdiEciLTwAAAxlJREFUnI7lDnG6Q4yA+8RJVaRGH14Wm4nvy7PM2RinYzFOx3KHON0hRsB94qSqU9NHuodFZJqINLU2iEhTEXkSwFEXxlUc43QsxulY7hCnO8QIuE+cVEVqetIdDiAIwHoROS8iGQDWAQiEpZpLdcE4HYtxOpY7xOkOMQLuEydVkRp9eBmwzLsKS5HorbaTy4vIAFX9r+si+yPG6ViM07HcIU53iBFwnzipatToka6ITAbwJSyl3naLyBCbxS+7JqqrMU7HYpyO5Q5xukOMgPvESVWnps9INR5ArKrmiGVi8f+ISJiqvonqNc8p43QsxulY7hCnO8QIuE+cVEVqetL1sB6+UdU0EekFyy95K1SvX3DG6ViM07HcIU53iBFwnzipitTow8sATorI9dYXxi/7IACNAHR0WVRXY5yOxTgdyx3idIcYAfeJk6pIjb6QSkRCABRaJxcvtuwGVd3kgrCuwjgdi3E6ljvE6Q4xAu4TJ1WdGp10iYiIqpOafniZiIio2mDSJSIichImXSIiIidh0iWqYiLi4eoYiKh6YNIlsiEifxeRR21evyQik0Xkf0XkZxHZJSLP2yz/QkR2iEiyiEywac8RkRdEZBuA7k5+G0RUTTHpEv3RvwGMAQARMQEYAeAUgHAAcQCuBxArIjcZ649V1VgAXQFMFpEgo70egN2q2k1VNzrzDRBR9VXTZ6QiKhdjlqBzItIZQFMAiQD+BOB/jOcA4AdLEv4JlkR7h9He0mg/B6AIwKfOjJ2Iqj8mXaKrvQvgPgDNALwHoC+Af6jqfNuVjCn8+gHorqq5IrIOgLex+JKqFjkrYCJyDzy8THS1zwEMgGWEu8p4jBURPwAQkWARaQKgAYDzRsKNBPBnVwVMRO6BI12iYlT1soj8COCCMVr9XkTaA9giIgCQA+BeAP8F8KCI7AKwD8BWV8VMRO6B00ASFWNcQPULgGGqut/V8RBRzcHDy0Q2RCQKwAEAa5hwicjRONIlIiJyEo50iej/26tjAQAAAIBB/taz2FUSARPpAsBEugAwkS4ATKQLABPpAsAkrCcWT86jTZgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_mypost.groupby([\"year\",\"month\"]).size().unstack().plot(kind='bar',title=\"Total no of facebook post per year\",stacked=True) \n",
    "plt.legend(bbox_to_anchor=(1.0, 1.04))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total post by month in 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x232f9e18f08>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_mypost[df_mypost['year']==2020].groupby([\"month_year\",\"week_day\"]).size().unstack().plot(kind='bar',title=\"Total post by month in 2020\",stacked=True) \n",
    "plt.legend(bbox_to_anchor=(1.0,1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Avergae post per month in 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.333333333333333"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mypost[df_mypost['year']==2020].groupby([\"month_year\"]).data.count().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['timestamp', 'data', 'title', 'attachments', 'year', 'month',\n",
       "       'week_day', 'day', 'time', 'month_year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mypost.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average no of post in every week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": [
    "# Average posts per day \n",
    "weighted = df_mypost.groupby('year').week_day.value_counts(normalize=True).unstack().sum().sort_values().plot(kind='bar',title=\"Average no of post per day\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Daily fb post by hour period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x232f61dfc08>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#facebook posts by hour, I need to split the first 2 digits from time\n",
    "df_mypost['hour'] = df_mypost['time'].apply(lambda x: x.hour)\n",
    "time_weight = df_mypost.groupby('year').hour.value_counts(normalize=True).unstack()\n",
    "time_weight.sum().sort_index().plot(kind='bar',title=\"Daily fb post by hour period\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most frequent post by months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Jan'),\n",
       " Text(0, 0, 'Feb'),\n",
       " Text(0, 0, 'Mar'),\n",
       " Text(0, 0, 'Apr'),\n",
       " Text(0, 0, 'May'),\n",
       " Text(0, 0, 'Jun'),\n",
       " Text(0, 0, 'Jul'),\n",
       " Text(0, 0, 'Aug'),\n",
       " Text(0, 0, 'Sept'),\n",
       " Text(0, 0, 'Oct'),\n",
       " Text(0, 0, 'Nov'),\n",
       " Text(0, 0, 'Dec')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "month_weight = df_mypost.groupby('year').month.value_counts(normalize=True).unstack()\n",
    "labels = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sept','Oct','Nov','Dec']\n",
    "fig, ax  = plt.subplots()\n",
    "month_weight.sum().sort_index().plot(kind='bar')\n",
    "plt.title('Most frequent post by months')\n",
    "plt.ylabel('Weighted Average')\n",
    "plt.xlabel('Months')\n",
    "ax.set_xticklabels(labels)"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}