Investigating Netflix Movies and Guest Stars in The Office.ipynb

{
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  {
   "cell_type": "markdown",
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    "## 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": 86,
   "id": "7cf4e82d",
   "metadata": {
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    "tags": [
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   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the years and durations lists\n",
    "years = [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]\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,'durations':durations}\n",
    "\n",
    "# Print the dictionary\n",
    "movie_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b697a7",
   "metadata": {
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   "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": 88,
   "id": "2594f08b",
   "metadata": {
    "dc": {
     "key": "11"
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    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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\n"
     ]
    }
   ],
   "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",
    "print(durations_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0305c959",
   "metadata": {
    "dc": {
     "key": "18"
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    "deletable": false,
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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": 90,
   "id": "9859ef31",
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   "outputs": [
    {
     "data": {
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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(years,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": "958d720e",
   "metadata": {
    "dc": {
     "key": "25"
    },
    "deletable": false,
    "editable": false,
    "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": 92,
   "id": "0db9c32f",
   "metadata": {
    "dc": {
     "key": "25"
    },
    "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>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",
       "      <th>description</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "      <td>International TV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s2</td>\n",
       "      <td>Movie</td>\n",
       "      <td>7:19</td>\n",
       "      <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",
       "      <td>Movie</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s5</td>\n",
       "      <td>Movie</td>\n",
       "      <td>21</td>\n",
       "      <td>Robert Luketic</td>\n",
       "      <td>Jim Sturgess, Kevin Spacey, Kate Bosworth, Aar...</td>\n",
       "      <td>United States</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d7cc0a",
   "metadata": {
    "dc": {
     "key": "32"
    },
    "deletable": false,
    "editable": false,
    "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": 94,
   "id": "5f67358b",
   "metadata": {
    "dc": {
     "key": "32"
    },
    "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>1</th>\n",
       "      <td>7:19</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Dramas</td>\n",
       "      <td>2016</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23:59</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>Horror Movies</td>\n",
       "      <td>2011</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>United States</td>\n",
       "      <td>Action</td>\n",
       "      <td>2009</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subset the DataFrame for type \"Movie\"\n",
    "netflix_df_movies_only = netflix_df[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[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d433db7",
   "metadata": {
    "dc": {
     "key": "39"
    },
    "deletable": false,
    "editable": false,
    "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>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "250c93ac",
   "metadata": {
    "dc": {
     "key": "39"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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['release_year'],netflix_movies_col_subset['duration'])\n",
    "\n",
    "# Create a title\n",
    "plt.title('Movie Duration by Year of Release')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef25ee6c",
   "metadata": {
    "dc": {
     "key": "46"
    },
    "deletable": false,
    "editable": false,
    "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": 98,
   "id": "c1c2816e",
   "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": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter for durations shorter than 60 minutes\n",
    "short_movies = netflix_movies_col_subset[netflix_movies_col_subset['duration'] < 60]\n",
    "\n",
    "# Print the first 20 rows of short_movies\n",
    "short_movies[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc73f7a6",
   "metadata": {
    "dc": {
     "key": "53"
    },
    "deletable": false,
    "editable": false,
    "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": 100,
   "id": "69f5ab1e",
   "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": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define an empty list\n",
    "colors = []\n",
    "\n",
    "# Iterate over rows of netflix_movies_col_subset\n",
    "for lab, 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": "03f7e61f",
   "metadata": {
    "dc": {
     "key": "60"
    },
    "deletable": false,
    "editable": false,
    "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": 102,
   "id": "f3f53b72",
   "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[\"release_year\"], netflix_movies_col_subset[\"duration\"], 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()"
   ]
  },
  {
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    "## 10. What next?\n",
    "<p>Well, as we suspected, non-typical genres such as children's movies and documentaries are all clustered around the bottom half of the plot. But we can't know for certain until we perform additional analyses. </p>\n",
    "<p>Congratulations, you've performed an exploratory analysis of some entertainment data, and there are lots of fun ways to develop your skills as a Pythonic data scientist. These include learning how to analyze data further with statistics, creating more advanced visualizations, and perhaps most importantly, learning more advanced ways of working with data in <code>pandas</code>. This latter skill is covered in our fantastic course <a href=\"www.datacamp.com/courses/data-manipulation-with-pandas\">Data Manipulation with pandas</a>.</p>\n",
    "<p>We hope you enjoyed this application of the skills learned in Intermediate Python, and wish you all the best on the rest of your journey!</p>"
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    "# Are we certain that movies are getting shorter?\n",
    "are_movies_getting_shorter = \"maybe\""
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