{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc555008",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b427dcf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa29e3d",
   "metadata": {},
   "source": [
    "## Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1340c69f",
   "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>show_id</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>director</th>\n",
       "      <th>cast</th>\n",
       "      <th>country</th>\n",
       "      <th>date_added</th>\n",
       "      <th>release_year</th>\n",
       "      <th>rating</th>\n",
       "      <th>duration</th>\n",
       "      <th>listed_in</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s1</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Dick Johnson Is Dead</td>\n",
       "      <td>Kirsten Johnson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>September 25, 2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>90 min</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>As her father nears the end of his life, filmm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Blood &amp; Water</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, TV Dramas, TV Mysteries</td>\n",
       "      <td>After crossing paths at a party, a Cape Town t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s3</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Ganglands</td>\n",
       "      <td>Julien Leclercq</td>\n",
       "      <td>Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Crime TV Shows, International TV Shows, TV Act...</td>\n",
       "      <td>To protect his family from a powerful drug lor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s4</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Jailbirds New Orleans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>1 Season</td>\n",
       "      <td>Docuseries, Reality TV</td>\n",
       "      <td>Feuds, flirtations and toilet talk go down amo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s5</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>Kota Factory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...</td>\n",
       "      <td>India</td>\n",
       "      <td>September 24, 2021</td>\n",
       "      <td>2021</td>\n",
       "      <td>TV-MA</td>\n",
       "      <td>2 Seasons</td>\n",
       "      <td>International TV Shows, Romantic TV Shows, TV ...</td>\n",
       "      <td>In a city of coaching centers known to train I...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  show_id     type                  title         director  \\\n",
       "0      s1    Movie   Dick Johnson Is Dead  Kirsten Johnson   \n",
       "1      s2  TV Show          Blood & Water              NaN   \n",
       "2      s3  TV Show              Ganglands  Julien Leclercq   \n",
       "3      s4  TV Show  Jailbirds New Orleans              NaN   \n",
       "4      s5  TV Show           Kota Factory              NaN   \n",
       "\n",
       "                                                cast        country  \\\n",
       "0                                                NaN  United States   \n",
       "1  Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...   South Africa   \n",
       "2  Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...            NaN   \n",
       "3                                                NaN            NaN   \n",
       "4  Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...          India   \n",
       "\n",
       "           date_added  release_year rating   duration  \\\n",
       "0  September 25, 2021          2020  PG-13     90 min   \n",
       "1  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "2  September 24, 2021          2021  TV-MA   1 Season   \n",
       "3  September 24, 2021          2021  TV-MA   1 Season   \n",
       "4  September 24, 2021          2021  TV-MA  2 Seasons   \n",
       "\n",
       "                                           listed_in  \\\n",
       "0                                      Documentaries   \n",
       "1    International TV Shows, TV Dramas, TV Mysteries   \n",
       "2  Crime TV Shows, International TV Shows, TV Act...   \n",
       "3                             Docuseries, Reality TV   \n",
       "4  International TV Shows, Romantic TV Shows, TV ...   \n",
       "\n",
       "                                         description  \n",
       "0  As her father nears the end of his life, filmm...  \n",
       "1  After crossing paths at a party, a Cape Town t...  \n",
       "2  To protect his family from a powerful drug lor...  \n",
       "3  Feuds, flirtations and toilet talk go down amo...  \n",
       "4  In a city of coaching centers known to train I...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./DataSets/netflix_titles.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c63e65d9",
   "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>type</th>\n",
       "      <th>release_year</th>\n",
       "      <th>Movie</th>\n",
       "      <th>TV Show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1925</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1942</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1943</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1944</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1945</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "type  release_year  Movie  TV Show\n",
       "0             1925    0.0      1.0\n",
       "1             1942    2.0      0.0\n",
       "2             1943    3.0      0.0\n",
       "3             1944    3.0      0.0\n",
       "4             1945    3.0      1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = df.groupby(['release_year', 'type'], as_index=False).show_id.count()\n",
    "n.columns = ['release_year', 'type', 'count']\n",
    "pivot = n_data.pivot(index='release_year', columns='type', values='count').reset_index()\n",
    "pivot.fillna(0, inplace = True)\n",
    "pivot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1392729f",
   "metadata": {},
   "source": [
    "## Creating a Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61f55f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1850x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Declare a chart with one Axis\n",
    "fig, ax = plt.subplots()  \n",
    "# Creating some  dummy data\n",
    "ax.plot(pivot.release_year, pivot.Movie, label = 'Movies')\n",
    "ax.plot(pivot.release_year, pivot['TV Show'], label = 'Tv Shows') \n",
    "ax.set_ylabel('Number of Movies/TV Shows') \n",
    "ax.set_xlabel('Year of Release')\n",
    "ax.set_title(\"Trend Chart\")\n",
    "plt.text(x=1970, y=400, s=r'Majority Movies are released after 2000 and Tv Shows mostly after 2010')#, fontsize = 12)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "plt.grid()\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ac7521e",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}