DataCampAssignment_1.ipynb

{
 "cells": [
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   "metadata": {
    "dc": {
     "key": "4"
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     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 1. Loading your friend's data into a dictionary\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_1237/img/netflix.jpg\" alt=\"Someone's feet on table facing a television\"></p>\n",
    "<p>Netflix! What started in 1997 as a DVD rental service has since exploded into the largest entertainment/media company by <a href=\"https://www.marketwatch.com/story/netflix-shares-close-up-8-for-yet-another-record-high-2020-07-10\">market capitalization</a>, boasting over 200 million subscribers as of <a href=\"https://www.cbsnews.com/news/netflix-tops-200-million-subscribers-but-faces-growing-challenge-from-disney-plus/\">January 2021</a>.</p>\n",
    "<p>Given the large number of movies and series available on the platform, it is a perfect opportunity to flex our data manipulation skills and dive into the entertainment industry. Our friend has also been brushing up on their Python skills and has taken a first crack at a CSV file containing Netflix data. For their first order of business, they have been performing some analyses, and they believe that the average duration of movies has been declining. </p>\n",
    "<p>As evidence of this, they have provided us with the following information. For the years from 2011 to 2020, the average movie durations are 103, 101, 99, 100, 100, 95, 95, 96, 93, and 90, respectively.</p>\n",
    "<p>If we're going to be working with this data, we know a good place to start would be to probably start working with <code>pandas</code>. But first we'll need to create a DataFrame from scratch. Let's start by creating a Python object covered in <a href=\"https://learn.datacamp.com/courses/intermediate-python\">Intermediate Python</a>: a dictionary!</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d318c45-4645-4331-8adc-845551a62441",
   "metadata": {
    "dc": {
     "key": "4"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'years': [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],\n",
       " 'durations': [103, 101, 99, 100, 100, 95, 95, 96, 93, 90]}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the years and durations lists\n",
    "years = [year for year in range(2011, 2021)]\n",
    "durations = [103, 101, 99, 100, 100, 95, 95, 96, 93, 90]\n",
    "\n",
    "# Create a dictionary with the two lists\n",
    "movie_dict = {'years': years,\n",
    "             'durations': durations}\n",
    "\n",
    "# Print the dictionary\n",
    "movie_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b14d9d-0bf3-412a-84bf-4a4f4476a8ef",
   "metadata": {
    "dc": {
     "key": "11"
    },
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 2. Creating a DataFrame from a dictionary\n",
    "<p>To convert our dictionary <code>movie_dict</code> to a <code>pandas</code> DataFrame, we will first need to import the library under its usual alias. We'll also want to inspect our DataFrame to ensure it was created correctly. Let's perform these steps now.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a7a1062-b85c-4dae-8bb7-025bfc998ddc",
   "metadata": {
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     "key": "11"
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    "tags": [
     "sample_code"
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   "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>years</th>\n",
       "      <th>durations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   years  durations\n",
       "0   2011        103\n",
       "1   2012        101\n",
       "2   2013         99\n",
       "3   2014        100\n",
       "4   2015        100\n",
       "5   2016         95\n",
       "6   2017         95\n",
       "7   2018         96\n",
       "8   2019         93\n",
       "9   2020         90"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pandas under its usual alias\n",
    "import pandas as pd\n",
    "\n",
    "# Create a DataFrame from the dictionary\n",
    "durations_df = pd.DataFrame(movie_dict)\n",
    "\n",
    "# Print the DataFrame\n",
    "durations_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef80795-a3f0-406f-9739-689756cc3238",
   "metadata": {
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    "tags": [
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   "source": [
    "## 3. A visual inspection of our data\n",
    "<p>Alright, we now have a <code>pandas</code> DataFrame, the most common way to work with tabular data in Python. Now back to the task at hand. We want to follow up on our friend's assertion that movie lengths have been decreasing over time. A great place to start will be a visualization of the data.</p>\n",
    "<p>Given that the data is continuous, a line plot would be a good choice, with the dates represented along the x-axis and the average length in minutes along the y-axis. This will allow us to easily spot any trends in movie durations. There are many ways to visualize data in Python, but <code>matploblib.pyplot</code> is one of the most common packages to do so.</p>\n",
    "<p><em>Note: In order for us to correctly test your plot, you will need to initalize a <code>matplotlib.pyplot</code> Figure object, which we have already provided in the cell below. You can continue to create your plot as you have learned in Intermediate Python.</em></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35e31e4d-54eb-4a74-a9ee-d87518b86699",
   "metadata": {
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    "tags": [
     "sample_code"
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   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import matplotlib.pyplot under its usual alias and create a figure\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "\n",
    "# Draw a line plot of release_years and durations\n",
    "plt.plot(durations_df['years'], durations_df['durations'])\n",
    "\n",
    "# Create a title\n",
    "plt.title('Netflix Movie Durations 2011-2020')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245af3b6-953e-46cd-8f9d-d461a512f7b1",
   "metadata": {
    "dc": {
     "key": "25"
    },
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 4. Loading the rest of the data from a CSV\n",
    "<p>Well, it looks like there is something to the idea that movie lengths have decreased over the past ten years! But equipped only with our friend's aggregations, we're limited in the further explorations we can perform. There are a few questions about this trend that we are currently unable to answer, including:</p>\n",
    "<ol>\n",
    "<li>What does this trend look like over a longer period of time?</li>\n",
    "<li>Is this explainable by something like the genre of entertainment?</li>\n",
    "</ol>\n",
    "<p>Upon asking our friend for the original CSV they used to perform their analyses, they gladly oblige and send it. We now have access to the CSV file, available at the path <code>\"datasets/netflix_data.csv\"</code>. Let's create another DataFrame, this time with all of the data. Given the length of our friend's data, printing the whole DataFrame is probably not a good idea, so we will inspect it by printing only the first five rows.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9dbd3c5c-fc5d-4d24-9c68-43260ed8a0c5",
   "metadata": {
    "dc": {
     "key": "25"
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    "tags": [
     "sample_code"
    ]
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <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>duration</th>\n",
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       "      <th>0</th>\n",
       "      <td>s1</td>\n",
       "      <td>TV Show</td>\n",
       "      <td>3%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>João Miguel, Bianca Comparato, Michel Gomes, R...</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>August 14, 2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>In a future where the elite inhabit an island ...</td>\n",
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       "      <td>Jorge Michel Grau</td>\n",
       "      <td>Demián Bichir, Héctor Bonilla, Oscar Serrano, ...</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>December 23, 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>93</td>\n",
       "      <td>After a devastating earthquake hits Mexico Cit...</td>\n",
       "      <td>Dramas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s3</td>\n",
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       "      <td>23:59</td>\n",
       "      <td>Gilbert Chan</td>\n",
       "      <td>Tedd Chan, Stella Chung, Henley Hii, Lawrence ...</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>2011</td>\n",
       "      <td>78</td>\n",
       "      <td>When an army recruit is found dead, his fellow...</td>\n",
       "      <td>Horror Movies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s4</td>\n",
       "      <td>Movie</td>\n",
       "      <td>9</td>\n",
       "      <td>Shane Acker</td>\n",
       "      <td>Elijah Wood, John C. Reilly, Jennifer Connelly...</td>\n",
       "      <td>United States</td>\n",
       "      <td>November 16, 2017</td>\n",
       "      <td>2009</td>\n",
       "      <td>80</td>\n",
       "      <td>In a postapocalyptic world, rag-doll robots hi...</td>\n",
       "      <td>Action</td>\n",
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       "      <td>January 1, 2020</td>\n",
       "      <td>2008</td>\n",
       "      <td>123</td>\n",
       "      <td>A brilliant group of students become card-coun...</td>\n",
       "      <td>Dramas</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "  show_id     type  title           director  \\\n",
       "0      s1  TV Show     3%                NaN   \n",
       "1      s2    Movie   7:19  Jorge Michel Grau   \n",
       "2      s3    Movie  23:59       Gilbert Chan   \n",
       "3      s4    Movie      9        Shane Acker   \n",
       "4      s5    Movie     21     Robert Luketic   \n",
       "\n",
       "                                                cast        country  \\\n",
       "0  João Miguel, Bianca Comparato, Michel Gomes, R...         Brazil   \n",
       "1  Demián Bichir, Héctor Bonilla, Oscar Serrano, ...         Mexico   \n",
       "2  Tedd Chan, Stella Chung, Henley Hii, Lawrence ...      Singapore   \n",
       "3  Elijah Wood, John C. Reilly, Jennifer Connelly...  United States   \n",
       "4  Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...  United States   \n",
       "\n",
       "          date_added  release_year  duration  \\\n",
       "0    August 14, 2020          2020         4   \n",
       "1  December 23, 2016          2016        93   \n",
       "2  December 20, 2018          2011        78   \n",
       "3  November 16, 2017          2009        80   \n",
       "4    January 1, 2020          2008       123   \n",
       "\n",
       "                                         description             genre  \n",
       "0  In a future where the elite inhabit an island ...  International TV  \n",
       "1  After a devastating earthquake hits Mexico Cit...            Dramas  \n",
       "2  When an army recruit is found dead, his fellow...     Horror Movies  \n",
       "3  In a postapocalyptic world, rag-doll robots hi...            Action  \n",
       "4  A brilliant group of students become card-coun...            Dramas  "
      ]
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   ],
   "source": [
    "# Read in the CSV as a DataFrame\n",
    "netflix_df = pd.read_csv('datasets/netflix_data.csv')\n",
    "\n",
    "# Print the first five rows of the DataFrame\n",
    "netflix_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1c5c243-bb3a-4f10-8e84-a444eeff8dfe",
   "metadata": {
    "dc": {
     "key": "32"
    },
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 5. Filtering for movies!\n",
    "<p>Okay, we have our data! Now we can dive in and start looking at movie lengths. </p>\n",
    "<p>Or can we? Looking at the first five rows of our new DataFrame, we notice a column <code>type</code>. Scanning the column, it's clear there are also TV shows in the dataset! Moreover, the <code>duration</code> column we planned to use seems to represent different values depending on whether the row is a movie or a show (perhaps the number of minutes versus the number of seasons)?</p>\n",
    "<p>Fortunately, a DataFrame allows us to filter data quickly, and we can select rows where <code>type</code> is <code>Movie</code>. While we're at it, we don't need information from all of the columns, so let's create a new DataFrame <code>netflix_movies</code> containing only <code>title</code>, <code>country</code>, <code>genre</code>, <code>release_year</code>, and <code>duration</code>.</p>\n",
    "<p>Let's put our data subsetting skills to work!</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ecc6cfc9-68aa-4d45-9298-9797a6f78dd0",
   "metadata": {
    "dc": {
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    {
     "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>country</th>\n",
       "      <th>genre</th>\n",
       "      <th>release_year</th>\n",
       "      <th>duration</th>\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>7:19</td>\n",
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       "      <td>2016</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>23:59</td>\n",
       "      <td>Singapore</td>\n",
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       "      <td>2011</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
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       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>United States</td>\n",
       "      <td>Dramas</td>\n",
       "      <td>2008</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>122</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Horror Movies</td>\n",
       "      <td>2019</td>\n",
       "      <td>95</td>\n",
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      "text/plain": [
       "   title        country          genre  release_year  duration\n",
       "1   7:19         Mexico         Dramas          2016        93\n",
       "2  23:59      Singapore  Horror Movies          2011        78\n",
       "3      9  United States         Action          2009        80\n",
       "4     21  United States         Dramas          2008       123\n",
       "6    122          Egypt  Horror Movies          2019        95"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subset the DataFrame for type \"Movie\"\n",
    "netflix_df_movies_only = netflix_df.loc[netflix_df['type'] == 'Movie']\n",
    "\n",
    "# Select only the columns of interest\n",
    "netflix_movies_col_subset = netflix_df_movies_only[['title', 'country', 'genre', 'release_year', 'duration']]\n",
    "\n",
    "# Print the first five rows of the new DataFrame\n",
    "netflix_movies_col_subset.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4721fa54-054e-4a2a-8600-d3810cd11d59",
   "metadata": {
    "dc": {
     "key": "39"
    },
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 6. Creating a scatter plot\n",
    "<p>Okay, now we're getting somewhere. We've read in the raw data, selected rows of movies, and have limited our DataFrame to our columns of interest. Let's try visualizing the data again to inspect the data over a longer range of time.</p>\n",
    "<p>This time, we are no longer working with aggregates but instead with individual movies. A line plot is no longer a good choice for our data, so let's try a scatter plot instead. We will again plot the year of release on the x-axis and the movie duration on the y-axis.</p>\n",
    "<p><em>Note: Although not taught in Intermediate Python, we have provided you the code <code>fig = plt.figure(figsize=(12,8))</code> to increase the size of the plot (to help you see the results), as well as to assist with testing. For more information on how to create or work with a <code>matplotlib</code> <code>figure</code>, refer to the <a href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html\">documentation</a>.</em></p>"
   ]
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and increase the figure size\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "\n",
    "# Create a scatter plot of duration versus year\n",
    "plt.scatter(netflix_movies_col_subset['duration'], netflix_movies_col_subset['release_year'])\n",
    "\n",
    "# Create a title\n",
    "plt.title('Movie Duration by Year of Releas')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "473b8f18-7c8b-43da-8473-13b848b158a9",
   "metadata": {
    "dc": {
     "key": "46"
    },
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 7. Digging deeper\n",
    "<p>This is already much more informative than the simple plot we created when our friend first gave us some data. We can also see that, while newer movies are overrepresented on the platform, many short movies have been released in the past two decades.</p>\n",
    "<p>Upon further inspection, something else is going on. Some of these films are under an hour long! Let's filter our DataFrame for movies with a <code>duration</code> under 60 minutes and look at the genres. This might give us some insight into what is dragging down the average.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24bdffe3-34df-4a02-8e6a-c9e825813d12",
   "metadata": {
    "dc": {
     "key": "46"
    },
    "tags": [
     "sample_code"
    ]
   },
   "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>country</th>\n",
       "      <th>genre</th>\n",
       "      <th>release_year</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>#Rucker50</td>\n",
       "      <td>United States</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>2016</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>100 Things to do Before High School</td>\n",
       "      <td>United States</td>\n",
       "      <td>Uncategorized</td>\n",
       "      <td>2014</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>13TH: A Conversation with Oprah Winfrey &amp; Ava ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Uncategorized</td>\n",
       "      <td>2017</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>3 Seconds Divorce</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>2018</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>A 3 Minute Hug</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>A Christmas Special: Miraculous: Tales of Lady...</td>\n",
       "      <td>France</td>\n",
       "      <td>Uncategorized</td>\n",
       "      <td>2016</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>A Family Reunion Christmas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Uncategorized</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>A Go! Go! Cory Carson Christmas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Children</td>\n",
       "      <td>2020</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>A Go! Go! Cory Carson Halloween</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Children</td>\n",
       "      <td>2020</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>A Go! Go! Cory Carson Summer Camp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Children</td>\n",
       "      <td>2020</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>A Grand Night In: The Story of Aardman</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>2015</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>A Love Song for Latasha</td>\n",
       "      <td>United States</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>2020</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>A Russell Peters Christmas</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Stand-Up</td>\n",
       "      <td>2011</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>A StoryBots Christmas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Children</td>\n",
       "      <td>2017</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>A Tale of Two Kitchens</td>\n",
       "      <td>United States</td>\n",
       "      <td>Documentaries</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>A Trash Truck Christmas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Children</td>\n",
       "      <td>2020</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>A Very Murray Christmas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Comedies</td>\n",
       "      <td>2015</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Abominable Christmas</td>\n",
       "      <td>United States</td>\n",
       "      <td>Children</td>\n",
       "      <td>2012</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Across Grace Alley</td>\n",
       "      <td>United States</td>\n",
       "      <td>Dramas</td>\n",
       "      <td>2013</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>Adam Devine: Best Time of Our Lives</td>\n",
       "      <td>United States</td>\n",
       "      <td>Stand-Up</td>\n",
       "      <td>2019</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 title         country  \\\n",
       "35                                           #Rucker50   United States   \n",
       "55                 100 Things to do Before High School   United States   \n",
       "67   13TH: A Conversation with Oprah Winfrey & Ava ...             NaN   \n",
       "101                                  3 Seconds Divorce          Canada   \n",
       "146                                     A 3 Minute Hug          Mexico   \n",
       "162  A Christmas Special: Miraculous: Tales of Lady...          France   \n",
       "171                         A Family Reunion Christmas   United States   \n",
       "177                    A Go! Go! Cory Carson Christmas   United States   \n",
       "178                    A Go! Go! Cory Carson Halloween             NaN   \n",
       "179                  A Go! Go! Cory Carson Summer Camp             NaN   \n",
       "181             A Grand Night In: The Story of Aardman  United Kingdom   \n",
       "200                            A Love Song for Latasha   United States   \n",
       "220                         A Russell Peters Christmas          Canada   \n",
       "233                              A StoryBots Christmas   United States   \n",
       "237                             A Tale of Two Kitchens   United States   \n",
       "242                            A Trash Truck Christmas             NaN   \n",
       "247                            A Very Murray Christmas   United States   \n",
       "285                               Abominable Christmas   United States   \n",
       "295                                 Across Grace Alley   United States   \n",
       "305                Adam Devine: Best Time of Our Lives   United States   \n",
       "\n",
       "             genre  release_year  duration  \n",
       "35   Documentaries          2016        56  \n",
       "55   Uncategorized          2014        44  \n",
       "67   Uncategorized          2017        37  \n",
       "101  Documentaries          2018        53  \n",
       "146  Documentaries          2019        28  \n",
       "162  Uncategorized          2016        22  \n",
       "171  Uncategorized          2019        29  \n",
       "177       Children          2020        22  \n",
       "178       Children          2020        22  \n",
       "179       Children          2020        21  \n",
       "181  Documentaries          2015        59  \n",
       "200  Documentaries          2020        20  \n",
       "220       Stand-Up          2011        44  \n",
       "233       Children          2017        26  \n",
       "237  Documentaries          2019        30  \n",
       "242       Children          2020        28  \n",
       "247       Comedies          2015        57  \n",
       "285       Children          2012        44  \n",
       "295         Dramas          2013        24  \n",
       "305       Stand-Up          2019        59  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter for durations shorter than 60 minutes\n",
    "short_movies = netflix_movies_col_subset.loc[netflix_movies_col_subset['duration'] < 60]\n",
    "# Print the first 20 rows of short_movies\n",
    "short_movies.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca84f2a-7eff-421c-acdc-f59cadb0535a",
   "metadata": {
    "dc": {
     "key": "53"
    },
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 8. Marking non-feature films\n",
    "<p>Interesting! It looks as though many of the films that are under 60 minutes fall into genres such as \"Children\", \"Stand-Up\", and \"Documentaries\". This is a logical result, as these types of films are probably often shorter than 90 minute Hollywood blockbuster. </p>\n",
    "<p>We could eliminate these rows from our DataFrame and plot the values again. But another interesting way to explore the effect of these genres on our data would be to plot them, but mark them with a different color.</p>\n",
    "<p>In Python, there are many ways to do this, but one fun way might be to use a loop to generate a list of colors based on the contents of the <code>genre</code> column. Much as we did in Intermediate Python, we can then pass this list to our plotting function in a later step to color all non-typical genres in a different color!</p>\n",
    "<p><em>Note: Although we are using the basic colors of red, blue, green, and black, <code>matplotlib</code> has many named colors you can use when creating plots. For more information, you can refer to the documentation <a href=\"https://matplotlib.org/stable/gallery/color/named_colors.html\">here</a>!</em></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "391bf43e-e8d1-41e7-a85d-7b5e4bdbfbd2",
   "metadata": {
    "dc": {
     "key": "53"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['black',\n",
       " 'black',\n",
       " 'black',\n",
       " 'black',\n",
       " 'black',\n",
       " 'black',\n",
       " 'black',\n",
       " 'black',\n",
       " 'black',\n",
       " 'blue']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define an empty list\n",
    "colors = []\n",
    "\n",
    "# Iterate over rows of netflix_movies_col_subset\n",
    "for label, row in netflix_movies_col_subset.iterrows():\n",
    "    if row['genre'] == 'Children' :\n",
    "        colors.append('red')\n",
    "    elif row['genre'] == 'Documentaries':\n",
    "        colors.append('blue')\n",
    "    elif row['genre'] == 'Stand-Up' :\n",
    "        colors.append('green')\n",
    "    else:\n",
    "        colors.append('black')\n",
    "        \n",
    "# Inspect the first 10 values in your list        \n",
    "colors[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de308fc-13e8-4ab1-9456-175ea33fd944",
   "metadata": {
    "dc": {
     "key": "60"
    },
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 9. Plotting with color!\n",
    "<p>Lovely looping! We now have a <code>colors</code> list that we can pass to our scatter plot, which should allow us to visually inspect whether these genres might be responsible for the decline in the average duration of movies.</p>\n",
    "<p>This time, we'll also spruce up our plot with some additional axis labels and a new theme with <code>plt.style.use()</code>. The latter isn't taught in Intermediate Python, but can be a fun way to add some visual flair to a basic <code>matplotlib</code> plot. You can find more information on customizing the style of your plot <a href=\"https://matplotlib.org/stable/tutorials/introductory/customizing.html\">here</a>!</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc23184e-f8ec-4d52-ab88-be032cfab923",
   "metadata": {
    "dc": {
     "key": "60"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure style and initalize a new figure\n",
    "plt.style.use('fivethirtyeight')\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "\n",
    "# Create a scatter plot of duration versus release_year\n",
    "plt.scatter(netflix_movies_col_subset['duration'], netflix_movies_col_subset['release_year'], c=colors)\n",
    "\n",
    "# Create a title and axis labels\n",
    "plt.title(\"Movie duration by year of release\")\n",
    "plt.xlabel(\"Release year\")\n",
    "plt.ylabel(\"Duration (min)\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f3afed8a-8310-4470-aeed-0f3988136838",
   "metadata": {
    "dc": {
     "key": "67"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "# Are we certain that movies are getting shorter?\n",
    "are_movies_getting_shorter = ..."
   ]
  }
 ],
 "metadata": {
  "editor": "DataCamp Workspace",
  "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}