LS_DS_111_Exploratory_Data_Analysis_Assignment.ipynb

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      "name": "LS_DS_111_Exploratory_Data_Analysis_Assignment.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/TheTechSurgeon/DS-Unit-1-Sprint-1-Data-Wrangling-and-Storytelling/blob/master/LS_DS_111_Exploratory_Data_Analysis_Assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MSnsTgZLKO72"
      },
      "source": [
        "# Practice Loading and Exploring Datasets\n",
        "\n",
        "This assignment is purposely open-ended. You will be asked to load datasets from the [UC-Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php). \n",
        "\n",
        "Even though you maybe using different datasets than your fellow classmates, try and be supportive and assist each other in the challenges that you are facing. You will only deepend your understanding of these topics as you work to assist one another. Many popular UCI datasets face similar data loading challenges.\n",
        "\n",
        "Remember that the UCI datasets do not necessarily have a file type of `.csv` so it's important that you learn as much as you can about the dataset before you try and load it. See if you can look at the raw text of the file either locally or using the `!curl` shell command -or in some other way before you try and read it in as a dataframe. This will help you catch what would otherwise be unforseen problems.\n",
        "\n",
        "Feel free to embellish this notebook with additional markdown cells,code cells, comments, graphs, etc. Whatever you think helps adequately address the questions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "156P6ndeKojO",
        "colab_type": "text"
      },
      "source": [
        "## 1) Load a dataset from UCI (via its URL)\n",
        "\n",
        "Please navigate to the home page and choose a dataset (other than the Adult dataset) from the \"Most Popular\" section on the right-hand side of the home page. Load the dataset via its URL and check the following (show your work):\n",
        "\n",
        "- Are the headers showing up properly?\n",
        "- Look at the first 5 and the last 5 rows, do they seem to be in order?\n",
        "- Does the dataset have the correct number of rows and columns as described in the UCI page? \n",
        " - Remember, that UCI does not count the y variable (column of values that we might want to predict via a machine learning model) as an \"attribute\" but rather as a \"class attribute\" so you may end up seeing a number of columns that is one greater than the number listed on the UCI website.\n",
        "- Does UCI list this dataset as having missing values? Check for missing values and see if your analysis corroborates what UCI reports?\n",
        "- if `NaN` values or other missing value indicators are not being detected by `df.isnull().sum()` find a way to replace whatever is indicating the missing values with `np.NaN`.\n",
        "- Use the .describe() function in order to see the summary statistics of both the numeric and non-numeric columns. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJdISe69ZT7E",
        "colab_type": "code",
        "outputId": "a4d61dab-da28-4bae-bd7a-0acb071b36f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "#imported new dataset. Number 12 on the UCI most popular list:poker\n",
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/poker/poker-hand-testing.data\")\n",
        "#the header is not showing up correctly. I'll need to feed it in manually\n",
        "print(\"HEAD\")\n",
        "print(df.head())\n",
        "\n",
        "print(\"TAIL\")\n",
        "print(df.tail())\n",
        "#Are there any null values before we place the header? doesnt appear to be any. 1Mil instances so not too keen on checking it physically\n",
        "print(\"checking for NULL data\")\n",
        "print(df.isnull().sum())\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "HEAD\n",
            "   1  1.1  1.2  13  2   4  2.1   3  1.3  12  0\n",
            "0  3   12    3   2  3  11    4   5    2   5  1\n",
            "1  1    9    4   6  1   4    3   2    3   9  1\n",
            "2  1    4    3  13  2  13    2   1    3   6  1\n",
            "3  3   10    2   7  1   2    2  11    4   9  0\n",
            "4  1    3    4   5  3   4    1  12    4   6  0\n",
            "TAIL\n",
            "        1  1.1  1.2  13  2  4  2.1   3  1.3  12  0\n",
            "999994  3    1    1  12  2  9    4   9    2   6  1\n",
            "999995  3    3    4   5  2  7    1   4    4   3  1\n",
            "999996  1   11    4   7  3  9    1  13    2   7  1\n",
            "999997  3   11    1   8  1  1    3  13    2   8  1\n",
            "999998  2    5    2   9  4  9    2   3    3   3  2\n",
            "checking for NULL data\n",
            "1      0\n",
            "1.1    0\n",
            "1.2    0\n",
            "13     0\n",
            "2      0\n",
            "4      0\n",
            "2.1    0\n",
            "3      0\n",
            "1.3    0\n",
            "12     0\n",
            "0      0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gFnZR6iLLPY",
        "colab_type": "text"
      },
      "source": [
        "## 2) Load a dataset from your local machine.\n",
        "Choose a second dataset from the \"Popular Datasets\" listing on UCI, but this time download it to your local machine instead of reading it in via the URL. Upload the file to Google Colab using the files tab in the left-hand sidebar or by importing `files` from `google.colab` The following link will be a useful resource if you can't remember the syntax: <https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92>\n",
        "\n",
        "- Answer all of the same bullet point questions from part 1 again on this new dataset. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qUmwX-ZoM9cq",
        "colab_type": "code",
        "outputId": "05fc2b06-1392-4417-aa9e-adcf98914150",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5b6fc5b7-3e9b-4489-9478-e94e6be94043\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-5b6fc5b7-3e9b-4489-9478-e94e6be94043\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving winequality-red.csv to winequality-red.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjmZm1KX2_P7",
        "colab_type": "code",
        "outputId": "5da4cd90-bc5c-4448-850c-9e9b80b11a48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "source": [
        "#Eww what is that disgusting looking format? it looks like a bunch of semicolons which apparently Collab doesn't recognize. Googling solution\n",
        "#after using the wordpad to replace the semicolons the data looks a lot cleaner\n",
        "import io\n",
        "df2 = pd.read_csv(io.BytesIO(uploaded[\"winequality-red.csv\"]))\n",
        "print(df2)\n",
        "print(\"HEAD\")\n",
        "print(df2.head())\n",
        "\n",
        "print(\"TAIL\")\n",
        "print(df2.tail())\n",
        "#no null values\n",
        "print(\"checking for NULL data\")\n",
        "print(df2.isnull().sum())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
            "0               7.4             0.700         0.00  ...       0.56      9.4        5\n",
            "1               7.8             0.880         0.00  ...       0.68      9.8        5\n",
            "2               7.8             0.760         0.04  ...       0.65      9.8        5\n",
            "3              11.2             0.280         0.56  ...       0.58      9.8        6\n",
            "4               7.4             0.700         0.00  ...       0.56      9.4        5\n",
            "...             ...               ...          ...  ...        ...      ...      ...\n",
            "1594            6.2             0.600         0.08  ...       0.58     10.5        5\n",
            "1595            5.9             0.550         0.10  ...       0.76     11.2        6\n",
            "1596            6.3             0.510         0.13  ...       0.75     11.0        6\n",
            "1597            5.9             0.645         0.12  ...       0.71     10.2        5\n",
            "1598            6.0             0.310         0.47  ...       0.66     11.0        6\n",
            "\n",
            "[1599 rows x 12 columns]\n",
            "HEAD\n",
            "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
            "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
            "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
            "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
            "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
            "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
            "\n",
            "[5 rows x 12 columns]\n",
            "TAIL\n",
            "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
            "1594            6.2             0.600         0.08  ...       0.58     10.5        5\n",
            "1595            5.9             0.550         0.10  ...       0.76     11.2        6\n",
            "1596            6.3             0.510         0.13  ...       0.75     11.0        6\n",
            "1597            5.9             0.645         0.12  ...       0.71     10.2        5\n",
            "1598            6.0             0.310         0.47  ...       0.66     11.0        6\n",
            "\n",
            "[5 rows x 12 columns]\n",
            "checking for NULL data\n",
            "fixed acidity           0\n",
            "volatile acidity        0\n",
            "citric acid             0\n",
            "residual sugar          0\n",
            "chlorides               0\n",
            "free sulfur dioxide     0\n",
            "total sulfur dioxide    0\n",
            "density                 0\n",
            "pH                      0\n",
            "sulphates               0\n",
            "alcohol                 0\n",
            "quality                 0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mq_aQjxlM-u5",
        "colab_type": "text"
      },
      "source": [
        "## 3) Make Crosstabs of the Categorical Variables\n",
        "\n",
        "Take whichever of the above datasets has more categorical variables and use crosstabs to tabulate the different instances of the categorical variables.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-n9-LTM-18p",
        "colab_type": "code",
        "outputId": "c4659355-e34d-4022-f060-c98ffcd417e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "# I was curious to see kow alkalinity impacted quality. On initial inspection there appears to be no correlation\n",
        "pd.crosstab(df2.pH, df2.quality)"
      ],
      "execution_count": 6,
      "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>quality</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2.74</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.86</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.87</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.88</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.89</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.75</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.78</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.85</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.90</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.01</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>89 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "quality  3  4  5  6  7  8\n",
              "pH                       \n",
              "2.74     0  1  0  0  0  0\n",
              "2.86     0  0  0  1  0  0\n",
              "2.87     0  0  0  1  0  0\n",
              "2.88     0  0  1  0  0  1\n",
              "2.89     0  0  2  2  0  0\n",
              "...     .. .. .. .. .. ..\n",
              "3.75     0  1  0  0  0  0\n",
              "3.78     0  0  0  1  1  0\n",
              "3.85     0  0  0  1  0  0\n",
              "3.90     0  1  0  1  0  0\n",
              "4.01     0  0  0  2  0  0\n",
              "\n",
              "[89 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJ4t2IAk-6Vg",
        "colab_type": "text"
      },
      "source": [
        "## 4) Explore the distributions of the variables of the dataset using:\n",
        "- Histograms\n",
        "- Scatterplots\n",
        "- Density Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-_A3sF-uNHz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "57d01892-119d-486c-f653-55946052ce4d"
      },
      "source": [
        "#this one I didn't quite understand. I know that we should have the same number of bins as categories. yet this graph isnt weirdly disjointed\n",
        "df2[\"quality\"].hist(bins=10) "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7bac31ecf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RuPacttuAw1W1Czjc1gGuA3a1xz7gzsGUKUlaixXDvar+GHj+nOY9wMG2fBC4YUn7l2rR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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhES5WmH_NFt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e0e6dad6-422e-4e8a-b0a7-b3a3faae367b"
      },
      "source": [
        "#this graph shows the distribution of pH values\n",
        "df2[\"pH\"].value_counts().sort_index().plot()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7baca1c080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VvGtU8gXn2jIvdWUeXrW6PuX4zZc1cbpvlC1Hu+dtjGJhyul3bqVUM7AB2Ao0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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NClbe8Z8vDAH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "601c2f49-45b9-4194-89e0-13d6770beece"
      },
      "source": [
        "df2.plot.scatter(\"quality\",\"alcohol\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7bac0ee978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PKk9Y6rEESz1uuGLxRfjFofF/zK9YfFEIowlG6mo1ANzAlQec8VvizDnvNrWm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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXXTzpgp_N0q",
        "colab_type": "text"
      },
      "source": [
        "## 5) Create at least one visualization from a crosstab:\n",
        "\n",
        "Remember that a crosstab is just a dataframe and can be manipulated in the same way by row index, column, index, or column/row/cell position.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "0ba1edbe-add5-4052-e112-9a2e5a00bd2c",
        "id": "V_dtAq44tieo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "#\n",
        "pd.crosstab(df2.pH, df2.quality).plot()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7bac4d8048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ao6MkinUSQj2/N97LaF79xRA69lDYvUAN44jq2JuVOI2EjV3t/DDyshPR99qx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\nL11NTqqXFtAS3Yc+ZuKq0msACNTXY5x9FghBuL0jWU4gaNK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HYQRF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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZCxTwKuReV9",
        "colab_type": "text"
      },
      "source": [
        "## Stretch Goals \n",
        "\n",
        "The following additional study tasks are optional, they are intended to give you an opportunity to stretch yourself beyond the main requirements of the assignment. You can pick and choose from the below, you do not need to complete them in any particular order.\n",
        "\n",
        "### - Practice Exploring other Datasets\n",
        "\n",
        "### -  Try using the Seaborn plotting library's \"Pairplot\" functionality in order to explore all of the possible histograms and scatterplots of your dataset all at once:\n",
        "\n",
        "[Seaborn Pairplot](https://seaborn.pydata.org/generated/seaborn.pairplot.html)\n",
        "\n",
        "### - Turn some of the continuous variables into categorical variables by binning the values using:\n",
        "- [pd.cut()](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.cut.html)\n",
        "- [pd.qcut()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html)\n",
        "- <https://stackoverflow.com/questions/30211923/what-is-the-difference-between-pandas-qcut-and-pandas-cut>\n",
        "\n",
        "And then use crosstabs to compare/visualize these binned variables against the other variables.\n",
        "\n",
        "\n",
        "### - Other types and sources of data\n",
        "Not all data comes in a nice single file - for example, image classification involves handling lots of image files. You still will probably want labels for them, so you may have tabular data in addition to the image blobs - and the images may be reduced in resolution and even fit in a regular csv as a bunch of numbers.\n",
        "\n",
        "If you're interested in natural language processing and analyzing text, that is another example where, while it can be put in a csv, you may end up loading much larger raw data and generating features that can then be thought of in a more standard tabular fashion.\n",
        "\n",
        "Overall you will in the course of learning data science deal with loading data in a variety of ways. Another common way to get data is from a database - most modern applications are backed by one or more databases, which you can query to get data to analyze. We'll cover this more in our data engineering unit.\n",
        "\n",
        "How does data get in the database? Most applications generate logs - text files with lots and lots of records of each use of the application. Databases are often populated based on these files, but in some situations you may directly analyze log files. The usual way to do this is with command line (Unix) tools - command lines are intimidating, so don't expect to learn them all at once, but depending on your interests it can be useful to practice.\n",
        "\n",
        "One last major source of data is APIs: https://github.com/toddmotto/public-apis\n",
        "\n",
        "API stands for Application Programming Interface, and while originally meant e.g. the way an application interfaced with the GUI or other aspects of an operating system, now it largely refers to online services that let you query and retrieve data. You can essentially think of most of them as \"somebody else's database\" - you have (usually limited) access.\n",
        "\n",
        "*Stretch goal* - research one of the above extended forms of data/data loading. See if you can get a basic example working in a notebook. Image, text, or (public) APIs are probably more tractable - databases are interesting, but there aren't many publicly accessible and they require a great deal of setup."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4QP6--JBXNK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}