eda_preprocess_visualize.ipynb

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      "source": [
        "# Task 1 - Exploratory Data Analysis\n",
        "\n",
        "This notebook will walk you through this task interactively, meaning that once you've imported this notebook into `Google Colab`, you'll be able to run individual cells of code independantly, and see the results as you go.\n",
        "\n",
        "This notebooks is designed for users that have an understanding of Python and data analysis. There will be some helper functions and initial setup code provided, but it will be up to you to perform the analysis and to draw insights!\n",
        "\n",
        "---\n",
        "\n",
        "## Section 1 - Setup\n",
        "\n",
        "First, we need to mount this notebook to our Google Drive folder, in order to access the CSV data file. If you haven't already, watch this video https://www.youtube.com/watch?v=woHxvbBLarQ to help you mount your Google Drive folder."
      ],
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to view, analyse and manipulate the dataset, we must load it into something called a `dataframe`, which is a way of storing tabulated data in a virtual table. This dataframe will allow us to analyse the data freely. To load it into a dataframe, we will need a package called `Pandas`. We can install pandas with this command:"
      ],
      "metadata": {
        "id": "rgaQP4eos9jS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas"
      ],
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        "outputId": "ac8cc4bb-0462-4986-cf26-7fe2f6e46ff7"
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2022.7.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.22.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And now we can import this package like so:"
      ],
      "metadata": {
        "id": "Ur2OdJMttaGP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "_SP0zwPYq-ef"
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      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## Section 2 - Data loading\n",
        "\n",
        "Now that Google Drive is mounted, you can store the CSV file anywhere in your Drive and update the `path` variable below to access it within this notebook. Once we've updated the `path`, let's read this CSV file into a pandas dataframe and see what it looks like"
      ],
      "metadata": {
        "id": "M5nmOA2Rtd2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/sample_sales_data.csv\"\n",
        "df = pd.read_csv(path)\n",
        "df.drop(columns=[\"Unnamed: 0\"], inplace=True, errors='ignore')\n",
        "df.head()"
      ],
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              "                         transaction_id            timestamp  \\\n",
              "0  a1c82654-c52c-45b3-8ce8-4c2a1efe63ed  2022-03-02 09:51:38   \n",
              "1  931ad550-09e8-4da6-beaa-8c9d17be9c60  2022-03-06 10:33:59   \n",
              "2  ae133534-6f61-4cd6-b6b8-d1c1d8d90aea  2022-03-04 17:20:21   \n",
              "3  157cebd9-aaf0-475d-8a11-7c8e0f5b76e4  2022-03-02 17:23:58   \n",
              "4  a81a6cd3-5e0c-44a2-826c-aea43e46c514  2022-03-05 14:32:43   \n",
              "\n",
              "                             product_id category customer_type  unit_price  \\\n",
              "0  3bc6c1ea-0198-46de-9ffd-514ae3338713    fruit          gold        3.99   \n",
              "1  ad81b46c-bf38-41cf-9b54-5fe7f5eba93e    fruit      standard        3.99   \n",
              "2  7c55cbd4-f306-4c04-a030-628cbe7867c1    fruit       premium        0.19   \n",
              "3  80da8348-1707-403f-8be7-9e6deeccc883    fruit          gold        0.19   \n",
              "4  7f5e86e6-f06f-45f6-bf44-27b095c9ad1d    fruit         basic        4.49   \n",
              "\n",
              "   quantity  total payment_type  \n",
              "0         2   7.98     e-wallet  \n",
              "1         1   3.99     e-wallet  \n",
              "2         2   0.38     e-wallet  \n",
              "3         4   0.76     e-wallet  \n",
              "4         2   8.98   debit card  "
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              "      <th>0</th>\n",
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              "      <td>2022-03-02 09:51:38</td>\n",
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              "      <td>e-wallet</td>\n",
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              "      <th>1</th>\n",
              "      <td>931ad550-09e8-4da6-beaa-8c9d17be9c60</td>\n",
              "      <td>2022-03-06 10:33:59</td>\n",
              "      <td>ad81b46c-bf38-41cf-9b54-5fe7f5eba93e</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2022-03-04 17:20:21</td>\n",
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              "      <td>0.38</td>\n",
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              "      <th>3</th>\n",
              "      <td>157cebd9-aaf0-475d-8a11-7c8e0f5b76e4</td>\n",
              "      <td>2022-03-02 17:23:58</td>\n",
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              "      <td>0.76</td>\n",
              "      <td>e-wallet</td>\n",
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              "      <th>4</th>\n",
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              "      <td>2022-03-05 14:32:43</td>\n",
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              "      <td>fruit</td>\n",
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              "      <td>4.49</td>\n",
              "      <td>2</td>\n",
              "      <td>8.98</td>\n",
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      "cell_type": "markdown",
      "source": [
        "Using the `.head()` method allows us to see the top 5 (5 by default) rows within the dataframe. We can use `.tail()` to see the bottom 5. If you want to see more than 5 rows, simply enter a number into the parentheses, e.g. `head(10)` or `tail(10)`."
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        "---\n",
        "\n",
        "## Section 3 - Descriptive statistics\n",
        "\n",
        "In this section, you should try to gain a description of the data, that is: what columns are present, how many null values exist and what data types exists within each column.\n",
        "\n",
        "To get you started an explanation of what the column names mean are provided below:\n",
        "\n",
        "- transaction_id = this is a unique ID that is assigned to each transaction\n",
        "- timestamp = this is the datetime at which the transaction was made\n",
        "- product_id = this is an ID that is assigned to the product that was sold. Each product has a unique ID\n",
        "- category = this is the category that the product is contained within\n",
        "- customer_type = this is the type of customer that made the transaction\n",
        "- unit_price = the price that 1 unit of this item sells for\n",
        "- quantity = the number of units sold for this product within this transaction\n",
        "- total = the total amount payable by the customer\n",
        "- payment_type = the payment method used by the customer\n",
        "\n",
        "After this, you should try to compute some descriptive statistics of the numerical columns within the dataset, such as:\n",
        "\n",
        "- mean\n",
        "- median\n",
        "- count\n",
        "- etc..."
      ],
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7829 entries, 0 to 7828\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   transaction_id  7829 non-null   object \n",
            " 1   timestamp       7829 non-null   object \n",
            " 2   product_id      7829 non-null   object \n",
            " 3   category        7829 non-null   object \n",
            " 4   customer_type   7829 non-null   object \n",
            " 5   unit_price      7829 non-null   float64\n",
            " 6   quantity        7829 non-null   int64  \n",
            " 7   total           7829 non-null   float64\n",
            " 8   payment_type    7829 non-null   object \n",
            "dtypes: float64(2), int64(1), object(6)\n",
            "memory usage: 550.6+ KB\n"
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              "        unit_price     quantity        total\n",
              "count  7829.000000  7829.000000  7829.000000\n",
              "mean      7.819480     2.501597    19.709905\n",
              "std       5.388088     1.122722    17.446680\n",
              "min       0.190000     1.000000     0.190000\n",
              "25%       3.990000     1.000000     6.570000\n",
              "50%       7.190000     3.000000    14.970000\n",
              "75%      11.190000     4.000000    28.470000\n",
              "max      23.990000     4.000000    95.960000"
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              "      <th>mean</th>\n",
              "      <td>7.819480</td>\n",
              "      <td>2.501597</td>\n",
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              "      <th>std</th>\n",
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              "      <th>25%</th>\n",
              "      <td>3.990000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <th>75%</th>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-16cfd356-2f90-4b18-a644-a8d1dd2bf500 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-16cfd356-2f90-4b18-a644-a8d1dd2bf500');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Box plot\n",
        "the median is line in the box, and the mean is a point"
      ],
      "metadata": {
        "id": "KQixIc9Y_vWP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(kind = 'box')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "Jfre-wMW_x_2",
        "outputId": "a7132f1d-7352-45a3-e8b4-31c317a1f39a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histogram"
      ],
      "metadata": {
        "id": "adDg7cfQ_-Al"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(kind='hist', bins=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "nrjIyHTlACak",
        "outputId": "6a5dbd28-0b0b-48de-e99d-758633a66fdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AxesSubplot:ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## Section 4 - Visualisation\n",
        "\n",
        "Now that you've computed some descriptive statistics of the dataset, let's create some visualisations. You may use any package that you wish for visualisation, however, some helper functions have been provided that make use of the `seaborn` package. If you wish to use these helper functions, ensure to run the below cells that install and import `seaborn`."
      ],
      "metadata": {
        "id": "N-krPtHdHNrh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install seaborn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fT9DrY9RHMrd",
        "outputId": "7f6e617b-4a80-4265-d85b-1088160dab93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.8/dist-packages (0.11.2)\n",
            "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.8/dist-packages (from seaborn) (3.5.3)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.22.4)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.3.5)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.8/dist-packages (from seaborn) (1.10.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (23.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (3.0.9)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (4.38.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.2->seaborn) (1.4.4)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=0.23->seaborn) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "32RDb2C7KOpN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To analyse the dataset, below are snippets of code that you can use as helper functions to visualise different columns within the dataset. They include:\n",
        "\n",
        "- plot_continuous_distribution = this is to visualise the distribution of numeric columns\n",
        "- get_unique_values = this is to show how many unique values are present within a column\n",
        "- plot_categorical_distribution = this is to visualise the distribution of categorical columns\n",
        "- correlation_plot = this is to plot the correlations between the numeric columns within the data"
      ],
      "metadata": {
        "id": "rNvl5rGeKv-h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_continuous_distribution(data: pd.DataFrame = None, column: list = None, height: int = 5):\n",
        "  for i in column:\n",
        "    sns.displot(data, x=i, kde=True, height=height, aspect=height/5).set(title=f'Distribution of {column}');\n",
        "\n",
        "def get_unique_values(data, column):\n",
        "  num_unique_values = len(data[column].unique())\n",
        "  value_counts = data[column].value_counts()\n",
        "  print(f\"Column: {column} has {num_unique_values} unique values\\n\")\n",
        "  print(value_counts)\n",
        "\n",
        "def plot_categorical_distribution(data: pd.DataFrame = None, column: str = None, height: int = 8, aspect: int = 2):\n",
        "  _ = sns.catplot(data=data, x=column, kind='count', height=height, aspect=aspect).set(title=f'Distribution of {column}');\n",
        "\n",
        "def correlation_plot(data: pd.DataFrame = None):\n",
        "  corr = data.corr()\n",
        "  sns.heatmap(corr, cmap='coolwarm', annot=True, square=True)"
      ],
      "metadata": {
        "id": "AyUPE4QRKcYQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_continuous_distribution(df, ['unit_price', 'quantity', 'total'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EUWvmHGEBKdS",
        "outputId": "75580c3a-13e7-4802-ac16-0b980f46a251"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation plot\n",
        "correlation_plot(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "ECeuTXz1CnHM",
        "outputId": "62855472-35bf-4312-be3a-3af4a726b847"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now it is your chance to visualise the columns, give it your best shot! As well as simply visualising the columns, try to interpret what the results mean in the context of the client."
      ],
      "metadata": {
        "id": "Atm2hxjv70Wr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## Section 5 - Summary\n",
        "\n",
        "We have completed an initial exploratory data analysis on the sample of data provided. We should now have a solid understanding of the data. \n",
        "\n",
        "The client wants to know\n",
        "\n",
        "```\n",
        "\"How to better stock the items that they sell\"\n",
        "```\n",
        "\n",
        "From this dataset, it is impossible to answer that question. In order to make the next step on this project with the client, it is clear that:\n",
        "\n",
        "- We need more rows of data. The current sample is only from 1 store and 1 week worth of data\n",
        "- We need to frame the specific problem statement that we want to solve. The current business problem is too broad, we should narrow down the focus in order to deliver a valuable end product\n",
        "- We need more features. Based on the problem statement that we move forward with, we need more columns (features) that may help us to understand the outcome that we're solving for\n",
        "\n"
      ],
      "metadata": {
        "id": "oBin5kdG4iS0"
      }
    }
  ]
}