7_Multiple_Time_Series_using_Apache_Spark_and_Prophet.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0Z2ttlXkgJD",
        "outputId": "c6342474-e971-4575-a266-d7e184d2ed15"
      },
      "source": [
        "\n",
        "!pip install PyArrow --upgrade\n"
      ],
      "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: PyArrow in /usr/local/lib/python3.7/dist-packages (9.0.0)\n",
            "Collecting PyArrow\n",
            "  Downloading pyarrow-10.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 36.0 MB 255 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.7/dist-packages (from PyArrow) (1.21.6)\n",
            "Installing collected packages: PyArrow\n",
            "  Attempting uninstall: PyArrow\n",
            "    Found existing installation: pyarrow 9.0.0\n",
            "    Uninstalling pyarrow-9.0.0:\n",
            "      Successfully uninstalled pyarrow-9.0.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pandas-gbq 0.17.9 requires pyarrow<10.0dev,>=3.0.0, but you have pyarrow 10.0.1 which is incompatible.\n",
            "db-dtypes 1.0.4 requires pyarrow<10.0dev,>=3.0.0, but you have pyarrow 10.0.1 which is incompatible.\u001b[0m\n",
            "Successfully installed PyArrow-10.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pyspark is like Python binding for Spark. spark is written in scala so Pyspark provides a python binding to work with spark through python scripting.\n",
        "We are also installing pyarrow which is a package to efficiently transfer data across multiple tools and technologies So using it we can easily transfer the data between Python Pandas and Spark in a more efficient way rather than the traditional way of Pickling and Unpickling it.\n",
        "After that, we are installing Facebook Prophet which is used to model the time-series data.\n",
        "\n",
        "Spark is a lazy evaluation framework means until we apply any action it will not print anything only it prepares the DAG(Directed Acyclic Graph) which is a rough plan of execution. So we use an action statement to see the output like the show, count, etc."
      ],
      "metadata": {
        "id": "B6YV615y2eln"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "El3BPSUa5egn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4f14905-3ba1-4d41-a754-1092fb853a2f"
      },
      "source": [
        "!apt-get install -y openjdk-8-jdk-headless -qq > /dev/null \n",
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"  \n",
        "!update-alternatives --set java /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "update-alternatives: using /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java to provide /usr/bin/java (java) in manual mode\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5iNRkmUQmiJo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46d40e05-283a-4686-84da-43cdb5f9add3"
      },
      "source": [
        "!java -version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "openjdk version \"1.8.0_352\"\n",
            "OpenJDK Runtime Environment (build 1.8.0_352-8u352-ga-1~18.04-b08)\n",
            "OpenJDK 64-Bit Server VM (build 25.352-b08, mixed mode)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzyAudcJWcCi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f53d234a-80c0-4e4d-aa44-ca3107b8aaef"
      },
      "source": [
        "!pip install pyspark"
      ],
      "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",
            "Collecting pyspark\n",
            "  Downloading pyspark-3.3.1.tar.gz (281.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 281.4 MB 45 kB/s \n",
            "\u001b[?25hCollecting py4j==0.10.9.5\n",
            "  Downloading py4j-0.10.9.5-py2.py3-none-any.whl (199 kB)\n",
            "\u001b[K     |████████████████████████████████| 199 kB 47.5 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyspark\n",
            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyspark: filename=pyspark-3.3.1-py2.py3-none-any.whl size=281845512 sha256=67cada61cc2d5daaa40740330d2134e94e2925a23b16488ef1c8e8e940e090a4\n",
            "  Stored in directory: /root/.cache/pip/wheels/42/59/f5/79a5bf931714dcd201b26025347785f087370a10a3329a899c\n",
            "Successfully built pyspark\n",
            "Installing collected packages: py4j, pyspark\n",
            "Successfully installed py4j-0.10.9.5 pyspark-3.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-aF9dJFVItfM"
      },
      "source": [
        "# !pip install pyarrow==0.15.1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUe7BW37S6VA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad996fcf-90c8-4e2a-dbe0-f0b92d68c3a9"
      },
      "source": [
        "!pip install pystan==2.19.1.1 prophet\n",
        "!pip install fbprophet\n",
        "from fbprophet import Prophet"
      ],
      "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",
            "Collecting pystan==2.19.1.1\n",
            "  Downloading pystan-2.19.1.1-cp37-cp37m-manylinux1_x86_64.whl (67.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 67.3 MB 102 kB/s \n",
            "\u001b[?25hRequirement already satisfied: prophet in /usr/local/lib/python3.7/dist-packages (1.1.1)\n",
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            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from prophet) (1.3.5)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.7/dist-packages (from prophet) (0.0.9)\n",
            "Requirement already satisfied: wheel>=0.37.0 in /usr/local/lib/python3.7/dist-packages (from prophet) (0.38.4)\n",
            "Requirement already satisfied: cmdstanpy>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from prophet) (1.0.8)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from prophet) (3.2.2)\n",
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            "Requirement already satisfied: setuptools>=42 in /usr/local/lib/python3.7/dist-packages (from prophet) (57.4.0)\n",
            "Requirement already satisfied: setuptools-git>=1.2 in /usr/local/lib/python3.7/dist-packages (from prophet) (1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from prophet) (2.8.2)\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.0.0->prophet) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.8.0->prophet) (1.15.0)\n",
            "Installing collected packages: pystan\n",
            "  Attempting uninstall: pystan\n",
            "    Found existing installation: pystan 3.3.0\n",
            "    Uninstalling pystan-3.3.0:\n",
            "      Successfully uninstalled pystan-3.3.0\n",
            "Successfully installed pystan-2.19.1.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting fbprophet\n",
            "  Downloading fbprophet-0.7.1.tar.gz (64 kB)\n",
            "\u001b[K     |████████████████████████████████| 64 kB 1.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: Cython>=0.22 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.29.32)\n",
            "Collecting cmdstanpy==0.9.5\n",
            "  Downloading cmdstanpy-0.9.5-py3-none-any.whl (37 kB)\n",
            "Requirement already satisfied: pystan>=2.14 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.19.1.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.21.6)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.3.5)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (3.2.2)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.0.9)\n",
            "Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.4.0)\n",
            "Requirement already satisfied: holidays>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.17)\n",
            "Requirement already satisfied: setuptools-git>=1.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.8.2)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (4.64.1)\n",
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            "Requirement already satisfied: hijri-converter in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->fbprophet) (2.2.4)\n",
            "Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->fbprophet) (0.3.1)\n",
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            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from LunarCalendar>=0.0.9->fbprophet) (2022.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (1.4.4)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.0.0->fbprophet) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.8.0->fbprophet) (1.15.0)\n",
            "Building wheels for collected packages: fbprophet\n",
            "  Building wheel for fbprophet (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fbprophet: filename=fbprophet-0.7.1-py3-none-any.whl size=6638937 sha256=1ecfc6154ef45037e27d5ee552cc278fa9b781a639aa87e0cd73a878373dc343\n",
            "  Stored in directory: /root/.cache/pip/wheels/cd/a1/12/db63ff624de492fe6cccf676091a0860fdde2ffde4bc3280e2\n",
            "Successfully built fbprophet\n",
            "Installing collected packages: cmdstanpy, fbprophet\n",
            "  Attempting uninstall: cmdstanpy\n",
            "    Found existing installation: cmdstanpy 1.0.8\n",
            "    Uninstalling cmdstanpy-1.0.8:\n",
            "      Successfully uninstalled cmdstanpy-1.0.8\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "prophet 1.1.1 requires cmdstanpy>=1.0.4, but you have cmdstanpy 0.9.5 which is incompatible.\u001b[0m\n",
            "Successfully installed cmdstanpy-0.9.5 fbprophet-0.7.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY5G7SE9PLEB"
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "from fbprophet import Prophet\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (10, 8)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qE2sxBUnXPIP"
      },
      "source": [
        "from pyspark.sql import SparkSession\n",
        "import pyspark\n",
        "spark = SparkSession.builder.master('local').getOrCreate()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4dmzbVV5rrkJ"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/weekly_sales_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwpMqvTdPsEx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "3d6d6a1f-17da-435e-959d-8fcb73b42bd2"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           date  store_id        sales\n",
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              "        \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-f9021ea6-f6e3-4406-a34f-d56e93cd0147 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-f9021ea6-f6e3-4406-a34f-d56e93cd0147');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HS0fi4rUwAzF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b802e1a4-a352-409d-f195-05e61ca56386"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500 entries, 0 to 499\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   date      500 non-null    object \n",
            " 1   store_id  500 non-null    int64  \n",
            " 2   sales     500 non-null    float64\n",
            "dtypes: float64(1), int64(1), object(1)\n",
            "memory usage: 11.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehFToKs1wAtB"
      },
      "source": [
        "df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FiwFKQyNwApE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd9d162e-b271-4dd4-f8cc-5be84c03577e"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500 entries, 0 to 499\n",
            "Data columns (total 3 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   date      500 non-null    datetime64[ns]\n",
            " 1   store_id  500 non-null    int64         \n",
            " 2   sales     500 non-null    float64       \n",
            "dtypes: datetime64[ns](1), float64(1), int64(1)\n",
            "memory usage: 11.8 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CjGkIT-K1bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38274973-3c15-47e7-cf38-b55ae422c1c1"
      },
      "source": [
        "print (\"\\nFeatures : \\n\" ,df.columns.tolist())\n",
        "print (\"\\nMissing values :  \", df.isnull().any())\n",
        "print (\"\\nUnique values :  \\n\",df.nunique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Features : \n",
            " ['date', 'store_id', 'sales']\n",
            "\n",
            "Missing values :   date        False\n",
            "store_id    False\n",
            "sales       False\n",
            "dtype: bool\n",
            "\n",
            "Unique values :  \n",
            " date         50\n",
            "store_id     10\n",
            "sales       500\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8GK0jnn_1En",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "f3e4913d-083f-4675-cb77-9425011537fe"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          date  store_id        sales\n",
              "0   2019-01-06        25  736657.2267\n",
              "1   2019-01-06        26  233711.0000\n",
              "2   2019-01-06        28  692156.3794\n",
              "3   2019-01-06        29  213706.8000\n",
              "4   2019-01-06        30  266455.0000\n",
              "..         ...       ...          ...\n",
              "495 2019-12-15        31  401803.3590\n",
              "496 2019-12-15        35  135799.1429\n",
              "497 2019-12-15        38  115424.2250\n",
              "498 2019-12-15        41  103375.0000\n",
              "499 2019-12-15        44   51210.0000\n",
              "\n",
              "[500 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae308c0d-3c2b-4868-a556-2367617fee6c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "        text-align: right;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>store_id</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-06</td>\n",
              "      <td>25</td>\n",
              "      <td>736657.2267</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>2019-01-06</td>\n",
              "      <td>26</td>\n",
              "      <td>233711.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-06</td>\n",
              "      <td>28</td>\n",
              "      <td>692156.3794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-06</td>\n",
              "      <td>29</td>\n",
              "      <td>213706.8000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-06</td>\n",
              "      <td>30</td>\n",
              "      <td>266455.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>495</th>\n",
              "      <td>2019-12-15</td>\n",
              "      <td>31</td>\n",
              "      <td>401803.3590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>2019-12-15</td>\n",
              "      <td>35</td>\n",
              "      <td>135799.1429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>2019-12-15</td>\n",
              "      <td>38</td>\n",
              "      <td>115424.2250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>2019-12-15</td>\n",
              "      <td>41</td>\n",
              "      <td>103375.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>2019-12-15</td>\n",
              "      <td>44</td>\n",
              "      <td>51210.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae308c0d-3c2b-4868-a556-2367617fee6c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              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",
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              "  </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",
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              "      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-ae308c0d-3c2b-4868-a556-2367617fee6c 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-ae308c0d-3c2b-4868-a556-2367617fee6c');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeraNov2Cqch"
      },
      "source": [
        "df_base = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8ZiNTvGIBxj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "e8b68ada-b425-4a1b-8ea6-2690ce579a2e"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       store_id         sales\n",
              "count  500.0000  5.000000e+02\n",
              "mean    32.7000  4.470800e+05\n",
              "std      6.1714  2.970660e+05\n",
              "min     25.0000  1.278800e+04\n",
              "25%     28.0000  2.526557e+05\n",
              "50%     30.5000  3.150259e+05\n",
              "75%     38.0000  6.845418e+05\n",
              "max     44.0000  1.631585e+06"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dcc08480-1b3b-4f47-ab44-3a59978d4c0e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>store_id</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>500.0000</td>\n",
              "      <td>5.000000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>32.7000</td>\n",
              "      <td>4.470800e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.1714</td>\n",
              "      <td>2.970660e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>25.0000</td>\n",
              "      <td>1.278800e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>28.0000</td>\n",
              "      <td>2.526557e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>30.5000</td>\n",
              "      <td>3.150259e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>38.0000</td>\n",
              "      <td>6.845418e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>44.0000</td>\n",
              "      <td>1.631585e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dcc08480-1b3b-4f47-ab44-3a59978d4c0e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </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",
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              "      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-dcc08480-1b3b-4f47-ab44-3a59978d4c0e 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-dcc08480-1b3b-4f47-ab44-3a59978d4c0e');\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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlkocklDcDim",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b9bcb5d-ca4a-4d92-e1c7-ca1f552a6c19"
      },
      "source": [
        "df['store_id'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25    50\n",
              "26    50\n",
              "28    50\n",
              "29    50\n",
              "30    50\n",
              "31    50\n",
              "35    50\n",
              "38    50\n",
              "41    50\n",
              "44    50\n",
              "Name: store_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYAX0wP6K1Mt"
      },
      "source": [
        "item_df = df.set_index('date')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nu4sDoK5K1CT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "3001acef-13d9-4bb0-93ff-78b85582f440"
      },
      "source": [
        "item_df.query(\"store_id==25\")[['sales']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5522bb3310>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wd1j0SMInNkS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "53a8ffe2-3a88-46d2-9a47-a5604732857a"
      },
      "source": [
        "item_df.query(\"store_id==41\")[['sales']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f552257cb90>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXHG7z8-P9T-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "28dc55e4-4452-4625-b394-b552e6f2582b"
      },
      "source": [
        "item_df.query(\"store_id==29\")[['sales']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5522a3f150>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhOZ8G9xP9OJ"
      },
      "source": [
        "sdf = spark.createDataFrame(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PrdXiURiP9Ld",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "788ff891-c356-4525-c328-737be77e3fe3"
      },
      "source": [
        "sdf.show(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------------+--------+-----------+\n",
            "|               date|store_id|      sales|\n",
            "+-------------------+--------+-----------+\n",
            "|2019-01-06 00:00:00|      25|736657.2267|\n",
            "|2019-01-06 00:00:00|      26|   233711.0|\n",
            "|2019-01-06 00:00:00|      28|692156.3794|\n",
            "|2019-01-06 00:00:00|      29|   213706.8|\n",
            "|2019-01-06 00:00:00|      30|   266455.0|\n",
            "+-------------------+--------+-----------+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "If4w2bE7P9Ij",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7316dc4-9489-4d8f-cad0-c3e2c27bd456"
      },
      "source": [
        "sdf.printSchema()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- date: timestamp (nullable = true)\n",
            " |-- store_id: long (nullable = true)\n",
            " |-- sales: double (nullable = true)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6GuZ8PyP9GP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bf1cc8a-af48-457b-c862-e12ca3266f5f"
      },
      "source": [
        "sdf.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "500"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bk4MebeQP9DM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fbd581c-b78c-4267-8e9d-632de5a14a4a"
      },
      "source": [
        "sdf.select(['store_id']).groupBy('store_id').agg({'store_id':'count'}).show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---------------+\n",
            "|store_id|count(store_id)|\n",
            "+--------+---------------+\n",
            "|      26|             50|\n",
            "|      29|             50|\n",
            "|      31|             50|\n",
            "|      25|             50|\n",
            "|      41|             50|\n",
            "|      28|             50|\n",
            "|      44|             50|\n",
            "|      35|             50|\n",
            "|      38|             50|\n",
            "|      30|             50|\n",
            "+--------+---------------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkO5c9JHP9BM"
      },
      "source": [
        "sdf.createOrReplaceTempView(\"sales\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vf5pea8aP8-l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b426a4bb-649b-484c-c7e1-55fb2fb834ee"
      },
      "source": [
        "spark.sql(\"select store_id, count(*) from sales group by store_id order by store_id\").show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+--------+\n",
            "|store_id|count(1)|\n",
            "+--------+--------+\n",
            "|      25|      50|\n",
            "|      26|      50|\n",
            "|      28|      50|\n",
            "|      29|      50|\n",
            "|      30|      50|\n",
            "|      31|      50|\n",
            "|      35|      50|\n",
            "|      38|      50|\n",
            "|      41|      50|\n",
            "|      44|      50|\n",
            "+--------+--------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IB_NkJ-YP866"
      },
      "source": [
        "sql = 'SELECT store_id, date as ds, sum(sales) as y FROM sales GROUP BY store_id, ds ORDER BY store_id, ds'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Yz4zI0qP84H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c80b185a-60c8-4bf7-ab04-40641ad1b746"
      },
      "source": [
        "spark.sql(sql).show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+-------------------+-----------+\n",
            "|store_id|                 ds|          y|\n",
            "+--------+-------------------+-----------+\n",
            "|      25|2019-01-06 00:00:00|736657.2267|\n",
            "|      25|2019-01-13 00:00:00|696457.1717|\n",
            "|      25|2019-01-20 00:00:00| 996876.875|\n",
            "|      25|2019-01-27 00:00:00| 1029737.05|\n",
            "|      25|2019-02-03 00:00:00|1072873.705|\n",
            "|      25|2019-02-10 00:00:00|880462.4681|\n",
            "|      25|2019-02-17 00:00:00| 1088756.78|\n",
            "|      25|2019-02-24 00:00:00|1266928.313|\n",
            "|      25|2019-03-03 00:00:00|907510.1217|\n",
            "|      25|2019-03-10 00:00:00|747920.9824|\n",
            "|      25|2019-03-17 00:00:00|904750.3158|\n",
            "|      25|2019-03-24 00:00:00|1069857.589|\n",
            "|      25|2019-03-31 00:00:00|1195574.314|\n",
            "|      25|2019-04-07 00:00:00|834911.3333|\n",
            "|      25|2019-04-14 00:00:00|788777.4167|\n",
            "|      25|2019-04-21 00:00:00|849603.4333|\n",
            "|      25|2019-04-28 00:00:00| 965521.165|\n",
            "|      25|2019-05-05 00:00:00|1045632.688|\n",
            "|      25|2019-05-12 00:00:00| 587768.245|\n",
            "|      25|2019-05-19 00:00:00|806767.5946|\n",
            "+--------+-------------------+-----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ES28Jos5NfMx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a62e0fb7-fd07-4ec4-f511-7ceae9a02628"
      },
      "source": [
        "sdf.explain()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "== Physical Plan ==\n",
            "*(1) Scan ExistingRDD[date#0,store_id#1L,sales#2]\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CkhDN9CPGU3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc7743b6-fcc6-40bf-9848-ded8ed2776e7"
      },
      "source": [
        "sdf.rdd.getNumPartitions()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DskWUcVlP81s"
      },
      "source": [
        "store_part = (spark.sql( sql ).repartition(spark.sparkContext.defaultParallelism, ['store_id'])).cache()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-mWsPutuxS6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e831b380-28e1-4be7-98fc-afbdcabba7db"
      },
      "source": [
        "store_part"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[store_id: bigint, ds: timestamp, y: double]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BHeebRdNkap",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c562f809-a97f-4260-e2b8-2f01412520a4"
      },
      "source": [
        "store_part.explain()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "== Physical Plan ==\n",
            "InMemoryTableScan [store_id#1L, ds#82, y#83]\n",
            "   +- InMemoryRelation [store_id#1L, ds#82, y#83], StorageLevel(disk, memory, deserialized, 1 replicas)\n",
            "         +- Exchange SinglePartition, REPARTITION_BY_NUM, [plan_id=208]\n",
            "            +- *(2) HashAggregate(keys=[store_id#1L, date#0], functions=[sum(sales#2)])\n",
            "               +- Exchange hashpartitioning(store_id#1L, date#0, 200), ENSURE_REQUIREMENTS, [plan_id=204]\n",
            "                  +- *(1) HashAggregate(keys=[store_id#1L, date#0], functions=[partial_sum(sales#2)])\n",
            "                     +- *(1) Scan ExistingRDD[date#0,store_id#1L,sales#2]\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6E7MgluCuwrL"
      },
      "source": [
        "from pyspark.sql.types import *\n",
        "\n",
        "result_schema =StructType([\n",
        "  StructField('ds',TimestampType()),\n",
        "  StructField('store_id',IntegerType()),\n",
        "  StructField('y',DoubleType()),\n",
        "  StructField('yhat',DoubleType()),\n",
        "  StructField('yhat_upper',DoubleType()),\n",
        "  StructField('yhat_lower',DoubleType())\n",
        "  ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ry6ExeHuwav"
      },
      "source": [
        "from pyspark.sql.functions import pandas_udf, PandasUDFType\n",
        "\n",
        "@pandas_udf( result_schema, PandasUDFType.GROUPED_MAP )\n",
        "def forecast_sales( store_pd ):\n",
        "\n",
        "  model = Prophet(interval_width=0.95,seasonality_mode = 'multiplicative', weekly_seasonality=True, yearly_seasonality=True) #IMP\n",
        "\n",
        "  model.fit( store_pd )\n",
        "\n",
        "  future_pd = model.make_future_dataframe(\n",
        "    periods=5, #prediction for 5 weeks\n",
        "    freq='w'\n",
        "    )\n",
        "  \n",
        "  forecast_pd = model.predict( future_pd )  \n",
        "  \n",
        "  f_pd = forecast_pd[ ['ds','yhat', 'yhat_upper', 'yhat_lower'] ].set_index('ds')\n",
        "  \n",
        "  st_pd = store_pd[['ds','store_id','y']].set_index('ds')\n",
        "  \n",
        "  results_pd = f_pd.join( st_pd, how='left' )\n",
        "  results_pd.reset_index(level=0, inplace=True)\n",
        "  \n",
        "  results_pd['store_id'] = store_pd['store_id'].iloc[0]\n",
        "\n",
        "  return results_pd[ ['ds', 'store_id','y', 'yhat', 'yhat_upper', 'yhat_lower'] ]  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpie_8DUPdIT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e6cf67c-5903-451a-fe09-7f8d6abffe0d"
      },
      "source": [
        "from pyspark.sql.functions import current_date\n",
        "\n",
        "results = (\n",
        "  store_part\n",
        "    .groupBy('store_id')\n",
        "    .apply(forecast_sales)\n",
        "    .withColumn('training_date', current_date() )\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyspark/sql/pandas/group_ops.py:102: UserWarning: It is preferred to use 'applyInPandas' over this API. This API will be deprecated in the future releases. See SPARK-28264 for more details.\n",
            "  UserWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6pAQ0nYnS_Sz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28b57bf2-aa4b-4b48-9685-d322170c13ec"
      },
      "source": [
        "results.cache()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[ds: timestamp, store_id: int, y: double, yhat: double, yhat_upper: double, yhat_lower: double, training_date: date]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S974tPXcPc-v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57507e83-c211-4106-8ee9-be18cdff85f1"
      },
      "source": [
        "results.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------------+--------+-----------+------------------+------------------+------------------+-------------+\n",
            "|                 ds|store_id|          y|              yhat|        yhat_upper|        yhat_lower|training_date|\n",
            "+-------------------+--------+-----------+------------------+------------------+------------------+-------------+\n",
            "|2019-01-06 00:00:00|      25|736657.2267|  712575.069403434| 934954.6172883983|468719.88759553415|   2022-11-29|\n",
            "|2019-01-13 00:00:00|      25|696457.1717| 743751.7568928566| 989736.3770845457|509776.22713284264|   2022-11-29|\n",
            "|2019-01-20 00:00:00|      25| 996876.875| 972909.8778010785|1212296.7010900895| 747903.2295921228|   2022-11-29|\n",
            "|2019-01-27 00:00:00|      25| 1029737.05|1034447.5557582716|1270545.3787073335| 801949.2427059528|   2022-11-29|\n",
            "|2019-02-03 00:00:00|      25|1072873.705| 986365.7266742919|1211839.5694439004| 746162.5024418719|   2022-11-29|\n",
            "|2019-02-10 00:00:00|      25|880462.4681|1016291.0609313073|1227756.3321758218| 786141.0425344213|   2022-11-29|\n",
            "|2019-02-17 00:00:00|      25| 1088756.78|1108793.2648313895|1358132.0129700892| 863243.4217319676|   2022-11-29|\n",
            "|2019-02-24 00:00:00|      25|1266928.313|1099825.7781849585|1341506.4150957796|  864950.136891664|   2022-11-29|\n",
            "|2019-03-03 00:00:00|      25|907510.1217| 954930.7385013084|1179246.4572363275|  730515.381203573|   2022-11-29|\n",
            "|2019-03-10 00:00:00|      25|747920.9824| 840307.5564991488|1080066.0265025862| 622411.5797713908|   2022-11-29|\n",
            "|2019-03-17 00:00:00|      25|904750.3158| 895698.5219441094| 1129003.981908936| 640756.2335078288|   2022-11-29|\n",
            "|2019-03-24 00:00:00|      25|1069857.589|1040022.5385686739|1306418.9184375517| 804675.6473598764|   2022-11-29|\n",
            "|2019-03-31 00:00:00|      25|1195574.314|1078329.0328769777|1315635.1402282498| 832826.5172477145|   2022-11-29|\n",
            "|2019-04-07 00:00:00|      25|834911.3333| 957531.8584494952|1192083.3068523319| 715715.8130314954|   2022-11-29|\n",
            "|2019-04-14 00:00:00|      25|788777.4167| 825594.4716836136| 1078335.284496608| 591291.5169634096|   2022-11-29|\n",
            "|2019-04-21 00:00:00|      25|849603.4333| 826255.1077112334| 1079998.400592648| 605579.5635872582|   2022-11-29|\n",
            "|2019-04-28 00:00:00|      25| 965521.165| 913980.6218878272|1144208.0391937352| 685239.3605939106|   2022-11-29|\n",
            "|2019-05-05 00:00:00|      25|1045632.688| 931383.5223415782|1162667.0757193372| 706176.2608415595|   2022-11-29|\n",
            "|2019-05-12 00:00:00|      25| 587768.245|  831326.984993252|1060418.0364006094| 607822.4781302895|   2022-11-29|\n",
            "|2019-05-19 00:00:00|      25|806767.5946| 740232.6751845299| 975061.7131885365| 499789.5730007758|   2022-11-29|\n",
            "+-------------------+--------+-----------+------------------+------------------+------------------+-------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pm9V9lRFM_Ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22acea69-a41f-4353-97b2-04d3d34b16a4"
      },
      "source": [
        "results.explain()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "== Physical Plan ==\n",
            "InMemoryTableScan [ds#157, store_id#158, y#159, yhat#160, yhat_upper#161, yhat_lower#162, training_date#169]\n",
            "   +- InMemoryRelation [ds#157, store_id#158, y#159, yhat#160, yhat_upper#161, yhat_lower#162, training_date#169], StorageLevel(disk, memory, deserialized, 1 replicas)\n",
            "         +- *(2) Project [ds#157, store_id#158, y#159, yhat#160, yhat_upper#161, yhat_lower#162, 2022-11-29 AS training_date#169]\n",
            "            +- FlatMapGroupsInPandas [store_id#1L], forecast_sales(store_id#1L, ds#82, y#83)#156, [ds#157, store_id#158, y#159, yhat#160, yhat_upper#161, yhat_lower#162]\n",
            "               +- *(1) Sort [store_id#1L ASC NULLS FIRST], false, 0\n",
            "                  +- InMemoryTableScan [store_id#1L, store_id#1L, ds#82, y#83]\n",
            "                        +- InMemoryRelation [store_id#1L, ds#82, y#83], StorageLevel(disk, memory, deserialized, 1 replicas)\n",
            "                              +- Exchange SinglePartition, REPARTITION_BY_NUM, [plan_id=208]\n",
            "                                 +- *(2) HashAggregate(keys=[store_id#1L, date#0], functions=[sum(sales#2)])\n",
            "                                    +- Exchange hashpartitioning(store_id#1L, date#0, 200), ENSURE_REQUIREMENTS, [plan_id=204]\n",
            "                                       +- *(1) HashAggregate(keys=[store_id#1L, date#0], functions=[partial_sum(sales#2)])\n",
            "                                          +- *(1) Scan ExistingRDD[date#0,store_id#1L,sales#2]\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXL4kmexSH0D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3feeceda-8015-45a5-fe67-2c7800e606eb"
      },
      "source": [
        "results.coalesce(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[ds: timestamp, store_id: int, y: double, yhat: double, yhat_upper: double, yhat_lower: double, training_date: date]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nj260kgSMa7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35f8acce-d8f4-4d02-ebec-3a4b19386844"
      },
      "source": [
        "results.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "550"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4Zj9bWYPnhD"
      },
      "source": [
        "results.createOrReplaceTempView('forecasted')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLQys5g8CUzz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0710358-1c73-4216-8af0-704d62d68fc3"
      },
      "source": [
        "spark.sql(\"select store_id, count(*) from forecasted group by store_id\").show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+--------+\n",
            "|store_id|count(1)|\n",
            "+--------+--------+\n",
            "|      25|      55|\n",
            "|      26|      55|\n",
            "|      28|      55|\n",
            "|      29|      55|\n",
            "|      30|      55|\n",
            "|      31|      55|\n",
            "|      35|      55|\n",
            "|      38|      55|\n",
            "|      41|      55|\n",
            "|      44|      55|\n",
            "+--------+--------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhCPlXG_PczA"
      },
      "source": [
        "final_df = results.toPandas()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPX69_FBKBeh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "a5a916a5-edb2-42b0-dcf9-7dce118faa28"
      },
      "source": [
        "final_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ds  store_id             y          yhat    yhat_upper  \\\n",
              "0   2019-01-06        25  7.366572e+05  7.125751e+05  9.349546e+05   \n",
              "1   2019-01-13        25  6.964572e+05  7.437518e+05  9.897364e+05   \n",
              "2   2019-01-20        25  9.968769e+05  9.729099e+05  1.212297e+06   \n",
              "3   2019-01-27        25  1.029737e+06  1.034448e+06  1.270545e+06   \n",
              "4   2019-02-03        25  1.072874e+06  9.863657e+05  1.211840e+06   \n",
              "..         ...       ...           ...           ...           ...   \n",
              "545 2019-12-22        44           NaN  6.265474e+04  1.245438e+05   \n",
              "546 2019-12-29        44           NaN  2.657442e+04  9.165139e+04   \n",
              "547 2020-01-05        44           NaN  8.801264e+03  7.810892e+04   \n",
              "548 2020-01-12        44           NaN  4.665900e+03  6.433131e+04   \n",
              "549 2020-01-19        44           NaN  2.708830e+03  5.959441e+04   \n",
              "\n",
              "        yhat_lower training_date  \n",
              "0    468719.887596    2022-11-29  \n",
              "1    509776.227133    2022-11-29  \n",
              "2    747903.229592    2022-11-29  \n",
              "3    801949.242706    2022-11-29  \n",
              "4    746162.502442    2022-11-29  \n",
              "..             ...           ...  \n",
              "545     840.548687    2022-11-29  \n",
              "546  -30769.235768    2022-11-29  \n",
              "547  -56640.932177    2022-11-29  \n",
              "548  -60566.592030    2022-11-29  \n",
              "549  -55768.297130    2022-11-29  \n",
              "\n",
              "[550 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d848b1ab-55bb-455a-9771-36a88e3eed08\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "        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>ds</th>\n",
              "      <th>store_id</th>\n",
              "      <th>y</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>training_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-06</td>\n",
              "      <td>25</td>\n",
              "      <td>7.366572e+05</td>\n",
              "      <td>7.125751e+05</td>\n",
              "      <td>9.349546e+05</td>\n",
              "      <td>468719.887596</td>\n",
              "      <td>2022-11-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-13</td>\n",
              "      <td>25</td>\n",
              "      <td>6.964572e+05</td>\n",
              "      <td>7.437518e+05</td>\n",
              "      <td>9.897364e+05</td>\n",
              "      <td>509776.227133</td>\n",
              "      <td>2022-11-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-20</td>\n",
              "      <td>25</td>\n",
              "      <td>9.968769e+05</td>\n",
              "      <td>9.729099e+05</td>\n",
              "      <td>1.212297e+06</td>\n",
              "      <td>747903.229592</td>\n",
              "      <td>2022-11-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-27</td>\n",
              "      <td>25</td>\n",
              "      <td>1.029737e+06</td>\n",
              "      <td>1.034448e+06</td>\n",
              "      <td>1.270545e+06</td>\n",
              "      <td>801949.242706</td>\n",
              "      <td>2022-11-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-02-03</td>\n",
              "      <td>25</td>\n",
              "      <td>1.072874e+06</td>\n",
              "      <td>9.863657e+05</td>\n",
              "      <td>1.211840e+06</td>\n",
              "      <td>746162.502442</td>\n",
              "      <td>2022-11-29</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>545</th>\n",
              "      <td>2019-12-22</td>\n",
              "      <td>44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.265474e+04</td>\n",
              "      <td>1.245438e+05</td>\n",
              "      <td>840.548687</td>\n",
              "      <td>2022-11-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>2019-12-29</td>\n",
              "      <td>44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.657442e+04</td>\n",
              "      <td>9.165139e+04</td>\n",
              "      <td>-30769.235768</td>\n",
              "      <td>2022-11-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>547</th>\n",
              "      <td>2020-01-05</td>\n",
              "      <td>44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.801264e+03</td>\n",
              "      <td>7.810892e+04</td>\n",
              "      <td>-56640.932177</td>\n",
              "      <td>2022-11-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>548</th>\n",
              "      <td>2020-01-12</td>\n",
              "      <td>44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.665900e+03</td>\n",
              "      <td>6.433131e+04</td>\n",
              "      <td>-60566.592030</td>\n",
              "      <td>2022-11-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>2020-01-19</td>\n",
              "      <td>44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.708830e+03</td>\n",
              "      <td>5.959441e+04</td>\n",
              "      <td>-55768.297130</td>\n",
              "      <td>2022-11-29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>550 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d848b1ab-55bb-455a-9771-36a88e3eed08')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d848b1ab-55bb-455a-9771-36a88e3eed08 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-d848b1ab-55bb-455a-9771-36a88e3eed08');\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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtc2_p12KBbO"
      },
      "source": [
        "final_df=final_df.set_index('ds')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CKgGuh_KBYX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "5ec43935-063f-41d1-b05f-68db0871f846"
      },
      "source": [
        "final_df.query('store_id==44')[['y','yhat']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f552238ad90>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r11ePm2TvAfz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "8c7b8142-b6bf-4889-e173-501bb71301f6"
      },
      "source": [
        "final_df.query('store_id==26')[['y','yhat']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f55221dc3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnvd3OdXKBU1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "43038af7-5193-4c73-ce10-7b0e95113546"
      },
      "source": [
        "final_df.query('store_id==31')[['y','yhat']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f55220e2e90>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZlPHeOSTKBRd"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejHQin5pKBLw"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3hfLt3ZP8yv"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}