Coursera Social Media Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5dbb1b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',\n",
       "               '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',\n",
       "               '2018-01-09', '2018-01-10', '2018-01-11', '2018-01-12',\n",
       "               '2018-01-13', '2018-01-14', '2018-01-15', '2018-01-16',\n",
       "               '2018-01-17', '2018-01-18', '2018-01-19', '2018-01-20'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    " \n",
    "per1 = pd.date_range(start ='1-1-2018', periods= 20)\n",
    "per1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8504886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Movie',\n",
       " 'Sport',\n",
       " 'Fashion',\n",
       " 'Fashion',\n",
       " 'Movie',\n",
       " 'Sport',\n",
       " 'Fashion',\n",
       " 'Movie',\n",
       " 'Sport',\n",
       " 'Sport',\n",
       " 'Sport',\n",
       " 'Sport',\n",
       " 'Fashion',\n",
       " 'Fashion',\n",
       " 'Fashion',\n",
       " 'Sport',\n",
       " 'Sport',\n",
       " 'Movie',\n",
       " 'Movie',\n",
       " 'Movie',\n",
       " 'Movie']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "from numpy import random\n",
    "categories = ['Sport','Fashion','Movie']\n",
    "\n",
    "l = [random.choice(categories) for i in range(0,21,1)]\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6be25c6c",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "np.random.randint(0, 10000, size=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3f52498",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "594afb67",
   "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>Date</th>\n",
       "      <th>Category</th>\n",
       "      <th>Likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>Sport</td>\n",
       "      <td>1114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>9066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>Sport</td>\n",
       "      <td>7569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>1604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>9071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Sport</td>\n",
       "      <td>9889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>4884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>Sport</td>\n",
       "      <td>3176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>6037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>Sport</td>\n",
       "      <td>5372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>2998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>2773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>Sport</td>\n",
       "      <td>2181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>8202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>6442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>Sport</td>\n",
       "      <td>6616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>5879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>Sport</td>\n",
       "      <td>9557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>7957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>5231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Sport</td>\n",
       "      <td>6760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>4671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>2706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>Sport</td>\n",
       "      <td>8181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>Politcs</td>\n",
       "      <td>5072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>1702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Category  Likes\n",
       "0  2019-01-01    Sport   1114\n",
       "1  2019-01-01  Politcs   9066\n",
       "2  2019-01-01  Fashion    871\n",
       "3  2019-01-02    Sport   7569\n",
       "4  2019-01-02  Politcs   1604\n",
       "5  2019-01-02  Fashion   9071\n",
       "6  2019-01-03    Sport   9889\n",
       "7  2019-01-03  Politcs    849\n",
       "8  2019-01-03  Fashion   4884\n",
       "9  2019-01-04    Sport   3176\n",
       "10 2019-01-04  Politcs   6037\n",
       "11 2019-01-04  Fashion     66\n",
       "12 2019-01-05    Sport   5372\n",
       "13 2019-01-05  Politcs   2998\n",
       "14 2019-01-05  Fashion   2773\n",
       "15 2019-01-06    Sport   2181\n",
       "16 2019-01-06  Politcs   8202\n",
       "17 2019-01-06  Fashion   6442\n",
       "18 2019-01-07    Sport   6616\n",
       "19 2019-01-07  Politcs   5879\n",
       "20 2019-01-07  Fashion    975\n",
       "21 2019-01-08    Sport   9557\n",
       "22 2019-01-08  Politcs   7957\n",
       "23 2019-01-08  Fashion   5231\n",
       "24 2019-01-09    Sport   6760\n",
       "25 2019-01-09  Politcs   4671\n",
       "26 2019-01-09  Fashion   2706\n",
       "27 2019-01-10    Sport   8181\n",
       "28 2019-01-10  Politcs   5072\n",
       "29 2019-01-10  Fashion   1702"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a list containing some categories of socail media posts we can see in real life\n",
    "list_category = ['Sport','Politcs','Fashion']\n",
    "\n",
    "data_diction = {\"Date\":[],\"Category\":[],\"Likes\":[]}\n",
    "\n",
    "for i in pd.date_range('2019-01-01',periods=10):\n",
    "    for j in list_category:\n",
    "        data_diction['Date'].append(i)\n",
    "        data_diction['Category'].append(j)\n",
    "        data_diction['Likes'].append(np.random.randint(0, 10000))\n",
    "df1 = pd.DataFrame(data_diction)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "652608fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 871],\n",
       "       [9066],\n",
       "       [1114],\n",
       "       [9071],\n",
       "       [1604],\n",
       "       [7569],\n",
       "       [4884],\n",
       "       [ 849],\n",
       "       [9889],\n",
       "       [  66],\n",
       "       [6037],\n",
       "       [3176],\n",
       "       [2773],\n",
       "       [2998],\n",
       "       [5372],\n",
       "       [6442],\n",
       "       [8202],\n",
       "       [2181],\n",
       "       [ 975],\n",
       "       [5879],\n",
       "       [6616],\n",
       "       [5231],\n",
       "       [7957],\n",
       "       [9557],\n",
       "       [2706],\n",
       "       [4671],\n",
       "       [6760],\n",
       "       [1702],\n",
       "       [5072],\n",
       "       [8181]], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "likes = df1.groupby(by = ['Date','Category']).sum().values\n",
    "likes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b3c7beb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_likes =pd.DataFrame(likes.reshape(10,3),index= range(1,11),columns= ['Fashion','Politics','Sport'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d99bdd54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11e63275",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mplcursors in c:\\users\\admin\\anaconda3\\lib\\site-packages (0.5.3)\n",
      "Requirement already satisfied: matplotlib!=3.7.1,>=3.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from mplcursors) (3.7.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib!=3.7.1,>=3.1->mplcursors) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib!=3.7.1,>=3.1->mplcursors) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib!=3.7.1,>=3.1->mplcursors) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib!=3.7.1,>=3.1->mplcursors) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib!=3.7.1,>=3.1->mplcursors) (1.24.3)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib!=3.7.1,>=3.1->mplcursors) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib!=3.7.1,>=3.1->mplcursors) (10.0.1)\n",
      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib!=3.7.1,>=3.1->mplcursors) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from matplotlib!=3.7.1,>=3.1->mplcursors) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.7.1,>=3.1->mplcursors) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install mplcursors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6eed7880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mplcursors as mpc\n",
    "from matplotlib.container import BarContainer\n",
    "fig, ax = plt.subplots(figsize = (8,5))\n",
    "df_likes.plot(kind = 'bar',ax=ax,stacked =True)\n",
    "mpc.cursor(hover = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "043465a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}