notebook10_Soumonos&Kavesh.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms and mustache boxes\n",
    "#  Coded by Soumonos Mukherjee "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>FILM</th>\n",
       "      <th>RT_user_norm</th>\n",
       "      <th>Metacritic_User</th>\n",
       "      <th>IMDB_norm</th>\n",
       "      <th>Fandango_Ratingvalue</th>\n",
       "      <th>Fandango_Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avengers: Age of Ultron (2015)</td>\n",
       "      <td>4.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cinderella (2015)</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.55</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ant-Man (2015)</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.1</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Do You Believe? (2015)</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2.70</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hot Tub Time Machine 2 (2015)</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.55</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             FILM  RT_user_norm  Metacritic_User  IMDB_norm  \\\n",
       "0  Avengers: Age of Ultron (2015)           4.3              7.1       3.90   \n",
       "1               Cinderella (2015)           4.0              7.5       3.55   \n",
       "2                  Ant-Man (2015)           4.5              8.1       3.90   \n",
       "3          Do You Believe? (2015)           4.2              4.7       2.70   \n",
       "4   Hot Tub Time Machine 2 (2015)           1.4              3.4       2.55   \n",
       "\n",
       "   Fandango_Ratingvalue  Fandango_Stars  \n",
       "0                   4.5             5.0  \n",
       "1                   4.5             5.0  \n",
       "2                   4.5             5.0  \n",
       "3                   4.5             5.0  \n",
       "4                   3.0             3.5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "os.chdir(\"C:\\\\Users\\\\User\\\\Downloads\\\\Soumonos & Kavesh\")\n",
    "review = pd.read_csv('fandango_scores.csv')\n",
    "norm_reviews = review[['FILM','RT_user_norm','Metacritic_User','IMDB_norm','Fandango_Ratingvalue','Fandango_Stars']]\n",
    "norm_reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency distribution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Use the value_counts() method to return the frequency count of the 'IMDB_norm' column. Sort the resulting Series object by its index and assign the result to the variable imdb_distribution.\n",
    "- Display the result.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.60    10\n",
       "3.30     9\n",
       "3.15     9\n",
       "3.90     9\n",
       "3.70     8\n",
       "3.45     7\n",
       "3.55     7\n",
       "3.35     7\n",
       "3.75     6\n",
       "3.20     6\n",
       "2.75     5\n",
       "3.65     5\n",
       "3.50     4\n",
       "2.70     4\n",
       "3.05     4\n",
       "4.10     4\n",
       "3.25     4\n",
       "3.85     4\n",
       "3.80     3\n",
       "2.95     3\n",
       "2.60     2\n",
       "4.20     2\n",
       "2.45     2\n",
       "2.30     2\n",
       "3.95     2\n",
       "2.80     2\n",
       "3.00     2\n",
       "4.00     1\n",
       "3.10     1\n",
       "2.00     1\n",
       "2.50     1\n",
       "2.85     1\n",
       "4.05     1\n",
       "4.15     1\n",
       "2.20     1\n",
       "4.30     1\n",
       "2.55     1\n",
       "2.15     1\n",
       "3.40     1\n",
       "2.90     1\n",
       "2.10     1\n",
       "Name: IMDB_norm, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_distribution = pd.Index(norm_reviews[\"IMDB_norm\"],sort=True)\n",
    "imdb_distribution.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [ Matplotlib ] ##[Continues] The histograms with Matplotlib ##\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a subgraph and assign the objects Figure to the variable fig and Axes to the variable ax.\n",
    "- Generate a histogram of the values in the column 'Fandango_Ratingvalue' using a range from 0 to 5.\n",
    "- Use plt.show() to display the graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax) = plt.subplots(nrows=1,ncols=1)\n",
    "ax.hist(norm_reviews[\"Fandango_Ratingvalue\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of histograms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw a graph with 4 subgraphs:\n",
    "- For the subgraph associated with the variable ax1:\n",
    ">- Generate a histogram of the values in the column 'Fandango_Ratingvalue' using 20 classes (bins) and an interval from 0 to 5.\n",
    "- For the subgraph associated with the variable ax2:\n",
    ">- Generate a histogram of the values of the column 'RT_user_norm' using 20 classes (bins) and an interval from 0 to 5\n",
    "- For the subgraph associated with the variable ax3:\n",
    ">- Generate a histogram of the values of the column 'Metacritic_user_name' using 20 classes (bins) and an interval from 0 to 5\n",
    "- For the subgraph associated with the variable ax4:\n",
    ">- Generate a histogram of the values of the column 'IMDB_norm' using 20 classes (bins) and an interval from 0 to 5\n",
    "- For all graphs set the range on the y-axis from 0 to 50 using Axes.set_ylim() and choose a title.\n",
    "- Display the histograms.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2,ax3,ax4) = plt.subplots(nrows=4,ncols=1,figsize =(10,10))\n",
    "ax1.hist(norm_reviews[\"Fandango_Ratingvalue\"],bins=20)\n",
    "ax1.set_xlim(0,5)\n",
    "ax1.set_ylim(0,50) \n",
    "ax2.hist(norm_reviews[\"RT_user_norm\"],bins=20)\n",
    "ax2.set_xlim(0,5)\n",
    "ax2.set_ylim(0,50)\n",
    "ax3.hist(norm_reviews[\"Metacritic_User\"],bins=20)\n",
    "ax3.set_xlim(0,5)\n",
    "ax3.set_ylim(0,50)\n",
    "ax4.hist(norm_reviews[\"IMDB_norm\"],bins=20)\n",
    "ax4.set_xlim(0,5)\n",
    "ax4.set_ylim(0,50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple box diagrams ##\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a subgraph and assign the objects Figure to the variable fig and Axes to the variable ax.\n",
    "- Generate a box diagram for each column of num_cols = ['RT_user_norm', 'Metacritic_user_name', 'IMDB_norm', 'Fandango_Ratingvalue'].\n",
    "- Define for each diagram a heading corresponding to the name of the column num_cols and rotate this heading by 90 degrees.\n",
    "- Define the y-axis interval from 0 to 5.\n",
    "- Display the graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2,ax3,ax4) = plt.subplots(nrows=4,ncols=1,figsize =(10,6))\n",
    "ax1.boxplot(norm_reviews[\"RT_user_norm\"])\n",
    "ax1.set_ylim(0,5)\n",
    "ax1.set_title('RT_user_norm')\n",
    "ax2.boxplot(norm_reviews[\"Metacritic_User\"])\n",
    "#ax2.set_ylim(0,5)\n",
    "ax2.set_title('Metacritic_user_name')\n",
    "ax3.boxplot(norm_reviews[\"IMDB_norm\"])\n",
    "ax3.set_ylim(0,5)\n",
    "ax3.set_title('IMDB_norm')\n",
    "ax4.boxplot(norm_reviews[\"Fandango_Ratingvalue\"])\n",
    "ax4.set_ylim(0,5)\n",
    "ax4.set_title('Fandango_Ratingvalue')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}