basics/Basics_Graphing.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.0"
    },
    "colab": {
      "name": "Basics_Graphing.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/krmiddlebrook/intro_to_deep_learning/blob/master/basics/Basics_Graphing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlwNEKumaMWk",
        "colab_type": "text"
      },
      "source": [
        "# Visualizing Popular Games on Steam"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_Hlw99VaMWl",
        "colab_type": "text"
      },
      "source": [
        "Generally, the first code cell is where you import all the libraries your program needs. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGHok3_FaMWl",
        "colab_type": "text"
      },
      "source": [
        "If you need to install a package you can do so inside your notebook! This is a very convenient feature of notebooks. The below cell demonstrates how to install pandas into your conda envirnment. Since this is a demo, I commented out the statement that would install pandas, just in case you don't want to install it locally right now. Simply uncomment the statement to install pandas if desired. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlIbU6g2aMWp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ovx9lhyJaMWs",
        "colab_type": "code",
        "colab": {},
        "outputId": "e3a69a26-ff0c-4309-f975-b7221baa43eb"
      },
      "source": [
        "# load the Steam Video Games dataset into a pandas dataframe\n",
        "path_steam200K = './datasets/steam-200k.csv'\n",
        "columns = ['user_id', 'game', 'behavior', 'hours', '0']\n",
        "steam200k = pd.read_csv(path_steam200K, names=columns)\n",
        "steam200k.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>user_id</th>\n",
              "      <th>game</th>\n",
              "      <th>behavior</th>\n",
              "      <th>hours</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>151603712</td>\n",
              "      <td>The Elder Scrolls V Skyrim</td>\n",
              "      <td>purchase</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>151603712</td>\n",
              "      <td>The Elder Scrolls V Skyrim</td>\n",
              "      <td>play</td>\n",
              "      <td>273.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>151603712</td>\n",
              "      <td>Fallout 4</td>\n",
              "      <td>purchase</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151603712</td>\n",
              "      <td>Fallout 4</td>\n",
              "      <td>play</td>\n",
              "      <td>87.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>151603712</td>\n",
              "      <td>Spore</td>\n",
              "      <td>purchase</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     user_id                        game  behavior  hours  0\n",
              "0  151603712  The Elder Scrolls V Skyrim  purchase    1.0  0\n",
              "1  151603712  The Elder Scrolls V Skyrim      play  273.0  0\n",
              "2  151603712                   Fallout 4  purchase    1.0  0\n",
              "3  151603712                   Fallout 4      play   87.0  0\n",
              "4  151603712                       Spore  purchase    1.0  0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kAW2PYwaMWv",
        "colab_type": "text"
      },
      "source": [
        "Great, let's visualize the number of rows associated with a behavior, which is can be either purchase or play."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bCZ8ImcaMWv",
        "colab_type": "code",
        "colab": {},
        "outputId": "a510bbac-ffff-4a70-9fc1-8d4c16f4d755"
      },
      "source": [
        "# quickly check the unique value counts in the behavior column\n",
        "steam200k['behavior'].value_counts() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "purchase    129511\n",
              "play         70489\n",
              "Name: behavior, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nl_Khfv8aMWy",
        "colab_type": "code",
        "colab": {},
        "outputId": "06f9ad54-93e5-404f-d79c-eb7cbeb717b7"
      },
      "source": [
        "# make a bar plot\n",
        "steam200k['behavior'].value_counts().plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x121759340>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EjdLlRa3aMW0",
        "colab_type": "text"
      },
      "source": [
        "This isn't a very pretty bar chart, but it was very easy to create and allows us to learn a lot about the data without much effort on our end. However, if we want to make a nice visualization we can! There are a number of graphing libraries we could choose from (`plotly`, `seaborn`, `ggplot`, etc.), each one has it's pros and cons. Plotly allows you to make interactive embedded visualizations, but it isn't easy to set up. For this tutorial, I'll use the awesome `seaborn` library to spruce up our visualizations. Note, if you wanted to use `plotly` and are using the newer `jupyter lab` command to start your local notebook server (hopefully you are) instead of the traditional `jupyter notebook` command, you may need to add a few jupyter lab extension before plotly can be displayed in your notebooks. The command you need is in the following cell. Warning, the code may yell at you if you haven't installed node.js yet. If this is the case, follow the link they provide in the error message to install node.js locally. P.S. I know getting your environment set up can be frustrating, but hang in there, this is basically as bad as it gets. It's smooth sailing afterwards.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "afV_-xAlaMW1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !jupyter labextension install jupyterlab-plotly"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIgmUWbBaMW4",
        "colab_type": "code",
        "colab": {},
        "outputId": "8cb0adec-43ca-44d5-ba87-3c4112e7a14b"
      },
      "source": [
        "# remember we imported seaborn as sns at the top of our notebook\n",
        "ax = sns.countplot(x=\"behavior\", data=steam200k)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0Z-EvCLaMW6",
        "colab_type": "text"
      },
      "source": [
        "Now lets visualize the game counts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kePhkwLNaMW7",
        "colab_type": "code",
        "colab": {},
        "outputId": "5aa8b181-ac7b-4ce7-c09a-cdc876c9ff1b"
      },
      "source": [
        "# quickly check the value counts and number of unique games\n",
        "steam200k['game'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dota 2                                   9682\n",
              "Team Fortress 2                          4646\n",
              "Counter-Strike Global Offensive          2789\n",
              "Unturned                                 2632\n",
              "Left 4 Dead 2                            1752\n",
              "                                         ... \n",
              "Pirates vs Corsairs Davy Jones's Gold       1\n",
              "The Talos Principle - Soundtrack            1\n",
              "Siralim                                     1\n",
              "In Exilium                                  1\n",
              "Ironclads High Seas                         1\n",
              "Name: game, Length: 5155, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IfPhtwroaMW9",
        "colab_type": "text"
      },
      "source": [
        "5155 is a lot of unique games! It doesn't make much sense to plot all of them (most of the games probably have fewer than 10 entries in our data). One solution is to visualize only the top 20 games rather than all 5155. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rm7FmJnpaMW9",
        "colab_type": "code",
        "colab": {},
        "outputId": "6cafd33b-6bb1-4f4f-e28e-e453f5013612"
      },
      "source": [
        "# need to get the names of the top 20 games so we can filter our dataframe\n",
        "top20game_names = steam200k['game'].value_counts().nlargest(20).index\n",
        "\n",
        "# filter the dataframe\n",
        "top20 = steam200k.loc[steam200k['game'].isin(top20game_names), :].reset_index(drop=True)\n",
        "top20.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>user_id</th>\n",
              "      <th>game</th>\n",
              "      <th>behavior</th>\n",
              "      <th>hours</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>151603712</td>\n",
              "      <td>The Elder Scrolls V Skyrim</td>\n",
              "      <td>purchase</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>151603712</td>\n",
              "      <td>The Elder Scrolls V Skyrim</td>\n",
              "      <td>play</td>\n",
              "      <td>273.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>151603712</td>\n",
              "      <td>Left 4 Dead 2</td>\n",
              "      <td>purchase</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>151603712</td>\n",
              "      <td>Left 4 Dead 2</td>\n",
              "      <td>play</td>\n",
              "      <td>8.9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>151603712</td>\n",
              "      <td>Team Fortress 2</td>\n",
              "      <td>purchase</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     user_id                        game  behavior  hours  0\n",
              "0  151603712  The Elder Scrolls V Skyrim  purchase    1.0  0\n",
              "1  151603712  The Elder Scrolls V Skyrim      play  273.0  0\n",
              "2  151603712               Left 4 Dead 2  purchase    1.0  0\n",
              "3  151603712               Left 4 Dead 2      play    8.9  0\n",
              "4  151603712             Team Fortress 2  purchase    1.0  0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EP0aMcuPaMXA",
        "colab_type": "code",
        "colab": {},
        "outputId": "0d392ace-b85c-4fdc-f738-3ff46e284fa1"
      },
      "source": [
        "# now we can make our bar chart\n",
        "top20.game.value_counts().plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x12a8b0d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6WpbKB38aMXC",
        "colab_type": "text"
      },
      "source": [
        "Cool...but I have to tilt my head sideways to read the game titles easily. Let's make our bar chart easier to read by turning it into a horizontal bar chart. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZR1yDFraMXD",
        "colab_type": "code",
        "colab": {},
        "outputId": "703e8d43-f92d-40b6-d1c4-6ab7431ec058"
      },
      "source": [
        "top20.game.value_counts().plot(kind='barh')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x12d1dba60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3Y0l11caMXF",
        "colab_type": "text"
      },
      "source": [
        "Easy right! Thanks to `pandas` and `matplotlib` we can quickly and easily visualize our data! Below is a nicer version of the previous horizontal bar chart using seaborn. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4hemrPcaMXF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}