7a-exploring-plotting-data-lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring and Plotting Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the last lesson, we saw how we can use pandas to explore our data, both with sample statistics, and plotting different features of our data.  In this lesson, we'll practice this with our imdb dataset to explore various movie data.  Let's get started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's get a better sense of the data in our 538 moves dataset.  The first thing we'll do is load up the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "url = \"./imdb_movies.csv\"\n",
    "movies_df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title', 'genre', 'budget', 'runtime', 'year', 'month', 'revenue'], dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It may be difficult to understand what information is in these columns so perhaps we want to look at some of the initial values, which we can do with the `head` or `tail` methods.\n",
    "\n",
    "> First use the `head` method to select the first five rows of 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>title</th>\n",
       "      <th>genre</th>\n",
       "      <th>budget</th>\n",
       "      <th>runtime</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>Action</td>\n",
       "      <td>237000000</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>12</td>\n",
       "      <td>2787965087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>300000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2007</td>\n",
       "      <td>5</td>\n",
       "      <td>961000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>Action</td>\n",
       "      <td>245000000</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>880674609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Action</td>\n",
       "      <td>250000000</td>\n",
       "      <td>165.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>1084939099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>John Carter</td>\n",
       "      <td>Action</td>\n",
       "      <td>260000000</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>284139100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      title      genre     budget  runtime  \\\n",
       "0                                    Avatar     Action  237000000    162.0   \n",
       "1  Pirates of the Caribbean: At World's End  Adventure  300000000    169.0   \n",
       "2                                   Spectre     Action  245000000    148.0   \n",
       "3                     The Dark Knight Rises     Action  250000000    165.0   \n",
       "4                               John Carter     Action  260000000    132.0   \n",
       "\n",
       "   year  month     revenue  \n",
       "0  2009     12  2787965087  \n",
       "1  2007      5   961000000  \n",
       "2  2015     10   880674609  \n",
       "3  2012      7  1084939099  \n",
       "4  2012      3   284139100  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df.head()\n",
    "\n",
    "\n",
    "# title\tgenre\tbudget\truntime\tyear\tmonth\trevenue\n",
    "# 0\tAvatar\tAction\t237000000\t162.0\t2009\t12\t2787965087\n",
    "# 1\tPirates of the Caribbean: At World's End\tAdventure\t300000000\t169.0\t2007\t5\t961000000\n",
    "# 2\tSpectre\tAction\t245000000\t148.0\t2015\t10\t880674609\n",
    "# 3\tThe Dark Knight Rises\tAction\t250000000\t165.0\t2012\t7\t1084939099\n",
    "# 4\tJohn Carter\tAction\t260000000\t132.0\t2012\t3\t284139100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's use the `describe` method to get an overview of the data in the various columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>budget</th>\n",
       "      <th>runtime</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.00000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.043686e+07</td>\n",
       "      <td>113.267500</td>\n",
       "      <td>2004.42650</td>\n",
       "      <td>7.044000</td>\n",
       "      <td>1.643506e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.692045e+07</td>\n",
       "      <td>21.073728</td>\n",
       "      <td>7.95319</td>\n",
       "      <td>3.398947</td>\n",
       "      <td>2.186816e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1940.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000e+07</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2000.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.103408e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000e+07</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>2005.00000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.400000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.500000e+07</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>2011.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.026695e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.800000e+08</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>2016.00000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.787965e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             budget      runtime        year        month       revenue\n",
       "count  2.000000e+03  2000.000000  2000.00000  2000.000000  2.000000e+03\n",
       "mean   6.043686e+07   113.267500  2004.42650     7.044000  1.643506e+08\n",
       "std    4.692045e+07    21.073728     7.95319     3.398947  2.186816e+08\n",
       "min    0.000000e+00     0.000000  1940.00000     1.000000  0.000000e+00\n",
       "25%    3.000000e+07    98.000000  2000.00000     4.000000  3.103408e+07\n",
       "50%    4.800000e+07   110.000000  2005.00000     7.000000  9.400000e+07\n",
       "75%    7.500000e+07   125.000000  2011.00000    10.000000  2.026695e+08\n",
       "max    3.800000e+08   254.000000  2016.00000    12.000000  2.787965e+09"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df.describe()\n",
    "\n",
    "\n",
    "# budget\truntime\tyear\tmonth\trevenue\n",
    "# count\t2.000000e+03\t2000.000000\t2000.00000\t2000.000000\t2.000000e+03\n",
    "# mean\t6.043686e+07\t113.267500\t2004.42650\t7.044000\t1.643506e+08\n",
    "# std\t4.692045e+07\t21.073728\t7.95319\t3.398947\t2.186816e+08\n",
    "# min\t0.000000e+00\t0.000000\t1940.00000\t1.000000\t0.000000e+00\n",
    "# 25%\t3.000000e+07\t98.000000\t2000.00000\t4.000000\t3.103408e+07\n",
    "# 50%\t4.800000e+07\t110.000000\t2005.00000\t7.000000\t9.400000e+07\n",
    "# 75%\t7.500000e+07\t125.000000\t2011.00000\t10.000000\t2.026695e+08\n",
    "# max\t3.800000e+08\t254.000000\t2016.00000\t12.000000\t2.787965e+09"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see that the average year is 2004, with a minimum year of 1940, and a maximum year of 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auditing our dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before moving forward with our data, it's good to get a sense of how representative that data is.  For example, do we have an expected spread of data across years, how about across genres?  Let's take a look.  \n",
    "\n",
    "Let's started by getting a count of each of the genres in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Action             483\n",
       "Drama              365\n",
       "Comedy             359\n",
       "Adventure          236\n",
       "Animation           93\n",
       "Fantasy             80\n",
       "Crime               76\n",
       "Thriller            73\n",
       "Horror              59\n",
       "Science Fiction     52\n",
       "Romance             40\n",
       "Name: genre, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df['genre'].value_counts()\n",
    "\n",
    "# Action             483\n",
    "# Drama              365\n",
    "# Comedy             359\n",
    "# Adventure          236\n",
    "# Animation           93\n",
    "# Fantasy             80\n",
    "# Crime               76\n",
    "# Thriller            73\n",
    "# Horror              59\n",
    "# Science Fiction     52\n",
    "# Romance             40\n",
    "# Name: genre, dtype: int64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does look we more action movies than any other category, which seems to go with our intuition.  Or it may indicate a dataset that leans on more recent data.  Let's plot a histogram that displays the number of movies for each year represented in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e987fa0>"
      ]
     },
     "execution_count": 9,
     "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": [
    "movies_df['year'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like the vast majority of our data is between 2000, and perhaps 2016.  Use the `value_counts` method to confirm this.  \n",
    "\n",
    "Assign the result to `movies_count_by_year`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005    105\n",
       "2011    105\n",
       "2004    104\n",
       "2009    100\n",
       "2008     98\n",
       "2010     95\n",
       "2006     95\n",
       "2013     94\n",
       "2001     92\n",
       "2012     91\n",
       "2002     91\n",
       "2014     87\n",
       "2015     84\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_count_by_year = movies_df['year'].value_counts()\n",
    "\n",
    "movies_count_by_year[:13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like most of our movies are from the year 2001 or greater.  So let's scope our data down to only have movies from the year 2001 or later.  Return a dataframe of those movies below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_post_2000 = movies_df[movies_df['year'] > 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1445, 7)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_post_2000.shape\n",
    "\n",
    "# (1445, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have scoped down our data to only have include movies from 2001 to 2016, let's plot some of the relationships between data.\n",
    "\n",
    "Begin by using the `plot` method to create scatter plot with budget on the x axis and revenue on the y axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ea76460>"
      ]
     },
     "execution_count": 20,
     "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": [
    "movies_post_2000.plot(kind = 'scatter', x = 'budget', y = 'revenue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to limit our data so that we can get a better picture of it.  Call describe on our movies_post_2000 dataset, so we can learn how to scope our budget and revenue data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `xlim` and `ylim` to only plot movies between 0 and 300,000,000 and movies with revenue between 0 and 1 billion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ffca5b0>"
      ]
     },
     "execution_count": 47,
     "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": [
    "movies_post_2000.plot(kind = 'scatter', x = 'budget', y = 'revenue', \n",
    "                      xlim = [0, 300_000_000], ylim = [0, 1000_000_000], title = 'budget vs revenue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it appears that while there is a trend between budget and revenue, that the spread in the data also tends to increase as more is spent on the movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.plotting.scatter_matrix(movies_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lesson, we practiced various methods to get an understanding of our data.  For a numerical overview of our data, we used the `describe` method to get a sense of the data across our numerical columns, and the `value_counts` method to get a better sense of our categorical data.  \n",
    "\n",
    "We also plotted data to get a better understand our data.  We used the `hist` method to see the distribution of a single column, and the `plot` method with `kind = 'scatter'` to create a scatter that shows the relationship between a movie's budget and revenue.  We zoomed in on a subset of the data with the `xlim` and `ylim` data.  To go further in our analysis, in seeing relationships between data, it may be best to lean on statistical or machine learning algorithms.  "
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}