03.Using Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data ={\n",
    "    'year':[2008,2012,2016],\n",
    "    'attendees':[112,321,729],\n",
    "    'average age':[24,43,31]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>year</th>\n",
       "      <th>attendees</th>\n",
       "      <th>average age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>112</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>321</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>729</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  attendees  average age\n",
       "0  2008        112           24\n",
       "1  2012        321           43\n",
       "2  2016        729           31"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## boolean indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlier_than_2013 = df['year'] < 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "Name: year, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earlier_than_2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>year</th>\n",
       "      <th>attendees</th>\n",
       "      <th>average age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>112</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>321</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  attendees  average age\n",
       "0  2008        112           24\n",
       "1  2012        321           43"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[earlier_than_2013]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### checking attendes per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "plt.plot(df['year'],df['attendees'])\n",
    "plt.plot(df['year'],df['average age'])\n",
    "plt.legend(['number of attendees', 'average age'])\n",
    "plt.title('Attendees Vs Age per year')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}