Reporting_Sales.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Playmen998/Data-Analysis/blob/master/Reporting_Sales.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Описание и цели работы"
      ],
      "metadata": {
        "id": "XCLvFKX66aKw"
      },
      "id": "XCLvFKX66aKw"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Вы работаете в бухгалтерии компании, занимающейся продажей запчастей для мотоциклов. Компания управляет тремя складами в большом мегаполисе.\n",
        "\n",
        "Недавно вы научились манипулировать данными и строить графики и предлагаете помочь своему коллеге проанализировать данные о прошлых продажах. Ваш коллега хочет зафиксировать продажи по способу оплаты. Ей также необходимо знать среднюю цену за единицу каждой линейки продуктов."
      ],
      "metadata": {
        "id": "2QdXF9bh6dFP"
      },
      "id": "2QdXF9bh6dFP"
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    {
      "cell_type": "markdown",
      "source": [
        "Данные о продажах имеют следующие поля:  \n",
        "- \"date\" - дата с июня по август 2021 года.\n",
        "- \"warehouse\" - компания управляет тремя складами: North, Central, and West.\n",
        "- \"client_type\" - есть два типа клиентов: Retail and Wholesale.\n",
        "- \"product_line\" - тип приобретаемых товаров.\n",
        "- \"quantity\" - сколько товаров было куплено.\n",
        "- \"unit_price\" - цена проданного товара.\n",
        "- \"total\" - общий объем продаж = quantity * unit_price.\n",
        "- \"payment\" - способ оплаты клиентом:: Cash, Credit card, Transfer."
      ],
      "metadata": {
        "id": "smx5eyfG6mrX"
      },
      "id": "smx5eyfG6mrX"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Создайте отчет, чтобы ответить на вопросы вашего коллеги. Включают:\n",
        "\n",
        "1. Каков общий объем продаж для каждого способа оплаты? + визуализация \n",
        "2. Какова средняя цена за единицу каждой линейки продуктов? + визуализация \n",
        "3. Дальнейшее исследование (например, средняя стоимость покупки по типу клиента, общая стоимость покупки по продуктовой линейке и т. д.)\n",
        "4. Подведите итоги."
      ],
      "metadata": {
        "id": "M7LqEDP57Xnu"
      },
      "id": "M7LqEDP57Xnu"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ссылка на таблицу https://drive.google.com/file/d/1Mb9DH4tjfWK5Cb4-G3le4zly1HyCk4YM/view?usp=share_link"
      ],
      "metadata": {
        "id": "L7RkvUa88hwI"
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      "id": "L7RkvUa88hwI"
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1Mb9DH4tjfWK5Cb4-G3le4zly1HyCk4YM"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1ToFGKF8j5e",
        "outputId": "997d4aa8-8d36-4428-ec4e-079fbf99de1a"
      },
      "id": "i1ToFGKF8j5e",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1Mb9DH4tjfWK5Cb4-G3le4zly1HyCk4YM\n",
            "To: /content/sales_data.csv\n",
            "\r  0% 0.00/67.3k [00:00<?, ?B/s]\r100% 67.3k/67.3k [00:00<00:00, 14.5MB/s]\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "#Разведывательный анализ данных"
      ],
      "metadata": {
        "id": "WzUEviEW8_T3"
      },
      "id": "WzUEviEW8_T3"
    },
    {
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "2af88bc7"
      },
      "id": "2af88bc7",
      "cell_type": "code",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/sales_data.csv')"
      ],
      "metadata": {
        "id": "GmciVVnj8863"
      },
      "id": "GmciVVnj8863",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 206
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        "outputId": "fa2982d5-ede2-4f58-c3a8-98424a6e85c1"
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      "id": "zhXWIMby91dG",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date warehouse client_type           product_line  quantity  \\\n",
              "0  2021-06-01   Central      Retail          Miscellaneous         8   \n",
              "1  2021-06-01     North      Retail        Breaking system         9   \n",
              "2  2021-06-01     North      Retail  Suspension & traction         8   \n",
              "3  2021-06-01     North   Wholesale           Frame & body        16   \n",
              "4  2021-06-01   Central      Retail                 Engine         2   \n",
              "\n",
              "   unit_price   total      payment  \n",
              "0       16.85  134.83  Credit card  \n",
              "1       19.29  173.61         Cash  \n",
              "2       32.93  263.45  Credit card  \n",
              "3       37.84  605.44     Transfer  \n",
              "4       60.48  120.96  Credit card  "
            ],
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              "      <td>North</td>\n",
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              "      <td>North</td>\n",
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              "      <td>Central</td>\n",
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      "cell_type": "code",
      "source": [
        "df.shape"
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        "id": "QvKShmVd9SKW",
        "outputId": "58dab25e-2597-4aae-dc5a-de168df70595"
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      "outputs": [
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          "data": {
            "text/plain": [
              "(1000, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "В таблице 1000 строк и 8 столбцов"
      ],
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        "id": "2g3nmLnU9Yz1"
      },
      "id": "2g3nmLnU9Yz1"
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      "cell_type": "code",
      "source": [
        "df.dtypes"
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        "id": "SkIXpG5s9bj2",
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      "id": "SkIXpG5s9bj2",
      "execution_count": 6,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date             object\n",
              "warehouse        object\n",
              "client_type      object\n",
              "product_line     object\n",
              "quantity          int64\n",
              "unit_price      float64\n",
              "total           float64\n",
              "payment          object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Колонка с датами представлена в типе данных object переведем в timestamp. Остальные колонки имеют правильный тип данных"
      ],
      "metadata": {
        "id": "59pkPuxa95eR"
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      "id": "59pkPuxa95eR"
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    {
      "cell_type": "code",
      "source": [
        "df['date'] = pd.to_datetime(df['date'], format = '%Y-%m-%d')"
      ],
      "metadata": {
        "id": "1zL4959t-TIG"
      },
      "id": "1zL4959t-TIG",
      "execution_count": 7,
      "outputs": []
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      "cell_type": "code",
      "source": [
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      "id": "b9QDlGN7-lHO",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date            0.0\n",
              "warehouse       0.0\n",
              "client_type     0.0\n",
              "product_line    0.0\n",
              "quantity        0.0\n",
              "unit_price      0.0\n",
              "total           0.0\n",
              "payment         0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
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      "cell_type": "markdown",
      "source": [
        "В данных нет пропусков, отлично!"
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      "id": "dQqm0SVt_al2",
      "execution_count": 9,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          quantity   unit_price        total\n",
              "count  1000.000000  1000.000000  1000.000000\n",
              "mean      9.395000    30.322040   289.113000\n",
              "std       9.659207    12.256488   345.227596\n",
              "min       1.000000    10.030000    10.350000\n",
              "25%       4.000000    21.085000    93.687500\n",
              "50%       6.500000    28.570000   178.360000\n",
              "75%      10.000000    37.917500   321.690000\n",
              "max      40.000000    66.620000  2546.330000"
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              "      <th>mean</th>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c101ea33-c1da-447c-91b2-ec5ab2614335')\"\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",
              "    <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-c101ea33-c1da-447c-91b2-ec5ab2614335 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-c101ea33-c1da-447c-91b2-ec5ab2614335');\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": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Достаточно большие отклонения в столбце \"total\" max значения от 75% процентиля, скорее всего это из-за выбросов"
      ],
      "metadata": {
        "id": "YJWRkK_N_wwG"
      },
      "id": "YJWRkK_N_wwG"
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(2, 4)\n",
        "\n",
        "fig.set_figheight(8)\n",
        "fig.set_figwidth(15)\n",
        "\n",
        "x = 0\n",
        "y = 0\n",
        "for i in range(df.shape[1]):\n",
        "  if i < 4:\n",
        "    if (i == 0) | (i == 3):\n",
        "      axes[x][y].get_xaxis ().set_visible( False )\n",
        "    axes[x][y].hist(df.iloc[: , i], bins = 5, rwidth = 0.6)\n",
        "    axes[x][y].set_title(df.columns[i])\n",
        "    y += 1\n",
        "  else:\n",
        "    y -= 1\n",
        "    x = 1\n",
        "    axes[x][y].hist(df.iloc[: , i], bins = 5, rwidth = 0.6)\n",
        "    axes[x][y].set_title(df.columns[i])\n",
        "    \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "j_yqIOBNlVz2",
        "outputId": "cf5dbc0d-a406-4b38-90ea-88a98e81b799"
      },
      "id": "j_yqIOBNlVz2",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Проанализируем распределения каждого столбца:\n",
        "+ В \"date\" распределение напоминает равномерное, тем неменее некоторые bins отклоняются\n",
        "+ В \"warehouse\" присутствует больше всего значений \"Central\" дальше идут \"North\" и \"West\" соответственно\n",
        "+ В \"client_type\" значение \"Retail\" имеет гораздо больше наблюдений, чем в \"Wholesale\" \n",
        "+ В \"payment\" значение \"Credit card\" имеет больше наблюдений, чем \"Cash\" и \"transfer\"\n",
        "+ Столбцы \"total\" , \"unit_price\", \"quantity\" имеют распределения с жирным хвостом"
      ],
      "metadata": {
        "id": "7FzytcA5yO86"
      },
      "id": "7FzytcA5yO86"
    },
    {
      "cell_type": "code",
      "source": [
        "df['product_line'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1Y0pSAqyMs0",
        "outputId": "b61cd157-71fe-48c8-e652-5c13bdd5ec98"
      },
      "id": "Z1Y0pSAqyMs0",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Breaking system          230\n",
              "Suspension & traction    228\n",
              "Electrical system        193\n",
              "Frame & body             166\n",
              "Miscellaneous            122\n",
              "Engine                    61\n",
              "Name: product_line, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Самые большие значения в столбце \"product_line\": \"Breaking system\", \"Suspension & traction\", \"Electrical system\""
      ],
      "metadata": {
        "id": "YvV2iYcJ0JGi"
      },
      "id": "YvV2iYcJ0JGi"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Анализ данных"
      ],
      "metadata": {
        "id": "KZbInQ780ihC"
      },
      "id": "KZbInQ780ihC"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Общий объем продаж для каждого способа оплаты"
      ],
      "metadata": {
        "id": "6ri7HdAQ0r8a"
      },
      "id": "6ri7HdAQ0r8a"
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "L0EVfFVr0xF6",
        "outputId": "8db27add-bd1f-4cf9-893b-3cc2ee43203a"
      },
      "id": "L0EVfFVr0xF6",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        date warehouse client_type           product_line  quantity  \\\n",
              "0 2021-06-01   Central      Retail          Miscellaneous         8   \n",
              "1 2021-06-01     North      Retail        Breaking system         9   \n",
              "2 2021-06-01     North      Retail  Suspension & traction         8   \n",
              "3 2021-06-01     North   Wholesale           Frame & body        16   \n",
              "4 2021-06-01   Central      Retail                 Engine         2   \n",
              "\n",
              "   unit_price   total      payment  \n",
              "0       16.85  134.83  Credit card  \n",
              "1       19.29  173.61         Cash  \n",
              "2       32.93  263.45  Credit card  \n",
              "3       37.84  605.44     Transfer  \n",
              "4       60.48  120.96  Credit card  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2345731-aa86-47c1-9a81-928e321f930c\">\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>date</th>\n",
              "      <th>warehouse</th>\n",
              "      <th>client_type</th>\n",
              "      <th>product_line</th>\n",
              "      <th>quantity</th>\n",
              "      <th>unit_price</th>\n",
              "      <th>total</th>\n",
              "      <th>payment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>Central</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Miscellaneous</td>\n",
              "      <td>8</td>\n",
              "      <td>16.85</td>\n",
              "      <td>134.83</td>\n",
              "      <td>Credit card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>North</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Breaking system</td>\n",
              "      <td>9</td>\n",
              "      <td>19.29</td>\n",
              "      <td>173.61</td>\n",
              "      <td>Cash</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>North</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Suspension &amp; traction</td>\n",
              "      <td>8</td>\n",
              "      <td>32.93</td>\n",
              "      <td>263.45</td>\n",
              "      <td>Credit card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>North</td>\n",
              "      <td>Wholesale</td>\n",
              "      <td>Frame &amp; body</td>\n",
              "      <td>16</td>\n",
              "      <td>37.84</td>\n",
              "      <td>605.44</td>\n",
              "      <td>Transfer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>Central</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Engine</td>\n",
              "      <td>2</td>\n",
              "      <td>60.48</td>\n",
              "      <td>120.96</td>\n",
              "      <td>Credit card</td>\n",
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          "metadata": {},
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        "df.groupby('payment')['total'].agg(['sum','count','median'])"
      ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   sum  count   median\n",
              "payment                               \n",
              "Cash          19199.10    116  139.135\n",
              "Credit card  110271.57    659  143.580\n",
              "Transfer     159642.33    225  599.270"
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Больше всего получено дохода через Transfer, медианное значение оплаты в несколько раз выше, чем через оплату Credit card и Cash, однако кол-во переводов небольшое, возможно это коммерческие компании, которые совершают оптовые покупки  \n",
        "Платежи через Cash имеют самое маленькое число по доходу, кол-во сделок,медианного значения оплаты и сильно отстает от Credit card и Transfer"
      ],
      "metadata": {
        "id": "fxv5Q1i52LYb"
      },
      "id": "fxv5Q1i52LYb"
    },
    {
      "cell_type": "code",
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        "df.groupby(['payment','client_type'])['total'].agg(['sum','count','median'])"
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      "id": "-hf7Iw714O0Q",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               sum  count   median\n",
              "payment     client_type                           \n",
              "Cash        Retail        19199.10    116  139.135\n",
              "Credit card Retail       110271.57    659  143.580\n",
              "Transfer    Wholesale    159642.33    225  599.270"
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              "      <td>116</td>\n",
              "      <td>139.135</td>\n",
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              "    <tr>\n",
              "      <th>Credit card</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<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",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Наша гипотеза подтвердилась: через платеж Transfer совершаются оптовые покупки, через Cash и Credit card совершаются покупки в розницу"
      ],
      "metadata": {
        "id": "N6RRwiuw6FNg"
      },
      "id": "N6RRwiuw6FNg"
    },
    {
      "cell_type": "code",
      "source": [
        "payment_total = df.groupby('payment')['total'].agg(['sum','count','median']).reset_index()"
      ],
      "metadata": {
        "id": "j5CYIvtO5zfh"
      },
      "id": "j5CYIvtO5zfh",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Построим визуализацию"
      ],
      "metadata": {
        "id": "rPQhRBqj7yr5"
      },
      "id": "rPQhRBqj7yr5"
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(ncols=3)\n",
        "fig.set_figheight(5)\n",
        "fig.set_figwidth(20)\n",
        "sns.barplot(x='payment', y='sum', data=payment_total, ax=axs[0])\n",
        "axs[0].set_title(\"Общий объем продаж\")\n",
        "\n",
        "sns.barplot(x='payment', y='median', data=payment_total, ax=axs[1])\n",
        "axs[1].set_title(\"Медианное значение сделки\")\n",
        "\n",
        "sns.barplot(x='payment',y='count', data=payment_total, ax=axs[2])\n",
        "axs[2].set_title(\"Кол-во сделок\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "k1xKUb6C-2y3",
        "outputId": "359727fe-9b59-4ea8-8646-277cbece4e3d"
      },
      "id": "k1xKUb6C-2y3",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Кол-во сделок')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cредняя цена за единицу каждой линейки продуктов"
      ],
      "metadata": {
        "id": "8BUtLbYQA2cv"
      },
      "id": "8BUtLbYQA2cv"
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9nIXzDrGA6T2",
        "outputId": "9e8e38d8-78ce-4eda-cbf1-84fe876cafde"
      },
      "id": "9nIXzDrGA6T2",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        date warehouse client_type           product_line  quantity  \\\n",
              "0 2021-06-01   Central      Retail          Miscellaneous         8   \n",
              "1 2021-06-01     North      Retail        Breaking system         9   \n",
              "2 2021-06-01     North      Retail  Suspension & traction         8   \n",
              "3 2021-06-01     North   Wholesale           Frame & body        16   \n",
              "4 2021-06-01   Central      Retail                 Engine         2   \n",
              "\n",
              "   unit_price   total      payment  \n",
              "0       16.85  134.83  Credit card  \n",
              "1       19.29  173.61         Cash  \n",
              "2       32.93  263.45  Credit card  \n",
              "3       37.84  605.44     Transfer  \n",
              "4       60.48  120.96  Credit card  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3818be40-6c44-40a3-9be3-01a8c520ece5\">\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>date</th>\n",
              "      <th>warehouse</th>\n",
              "      <th>client_type</th>\n",
              "      <th>product_line</th>\n",
              "      <th>quantity</th>\n",
              "      <th>unit_price</th>\n",
              "      <th>total</th>\n",
              "      <th>payment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>Central</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Miscellaneous</td>\n",
              "      <td>8</td>\n",
              "      <td>16.85</td>\n",
              "      <td>134.83</td>\n",
              "      <td>Credit card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>North</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Breaking system</td>\n",
              "      <td>9</td>\n",
              "      <td>19.29</td>\n",
              "      <td>173.61</td>\n",
              "      <td>Cash</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>North</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Suspension &amp; traction</td>\n",
              "      <td>8</td>\n",
              "      <td>32.93</td>\n",
              "      <td>263.45</td>\n",
              "      <td>Credit card</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>North</td>\n",
              "      <td>Wholesale</td>\n",
              "      <td>Frame &amp; body</td>\n",
              "      <td>16</td>\n",
              "      <td>37.84</td>\n",
              "      <td>605.44</td>\n",
              "      <td>Transfer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>Central</td>\n",
              "      <td>Retail</td>\n",
              "      <td>Engine</td>\n",
              "      <td>2</td>\n",
              "      <td>60.48</td>\n",
              "      <td>120.96</td>\n",
              "      <td>Credit card</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3818be40-6c44-40a3-9be3-01a8c520ece5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.groupby(\"product_line\")['unit_price'].agg(['median','mean']).sort_values(by = 'mean', ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
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        "id": "Q5CnYIpv0M5O",
        "outputId": "ee6a2220-d6d6-447b-adb2-7c941fd36feb"
      },
      "id": "Q5CnYIpv0M5O",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       median       mean\n",
              "product_line                            \n",
              "Engine                 59.820  60.091803\n",
              "Frame & body           43.105  42.832229\n",
              "Suspension & traction  33.730  33.969868\n",
              "Electrical system      25.530  25.585130\n",
              "Miscellaneous          22.480  22.810738\n",
              "Breaking system        17.380  17.740522"
            ],
            "text/html": [
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              "      <th>median</th>\n",
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              "    <tr>\n",
              "      <th>product_line</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Engine</th>\n",
              "      <td>59.820</td>\n",
              "      <td>60.091803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Frame &amp; body</th>\n",
              "      <td>43.105</td>\n",
              "      <td>42.832229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suspension &amp; traction</th>\n",
              "      <td>33.730</td>\n",
              "      <td>33.969868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Electrical system</th>\n",
              "      <td>25.530</td>\n",
              "      <td>25.585130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miscellaneous</th>\n",
              "      <td>22.480</td>\n",
              "      <td>22.810738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Breaking system</th>\n",
              "      <td>17.380</td>\n",
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              "  </style>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Самые дорогие товары находятся в категориях Engine и Frame & body, также стоит сказать о том, что среднее значение не сильно отличается от медианного, значит среднее значение можно объективно использовать, как метрику для анализа  "
      ],
      "metadata": {
        "id": "SSCbuM6gHRy9"
      },
      "id": "SSCbuM6gHRy9"
    },
    {
      "cell_type": "code",
      "source": [
        "product_price = df.groupby(\"product_line\")['unit_price'].agg(['median','mean']).sort_values(by = 'mean', ascending = False).reset_index()"
      ],
      "metadata": {
        "id": "XP5BvDNWIIuF"
      },
      "id": "XP5BvDNWIIuF",
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Построим визуализацию"
      ],
      "metadata": {
        "id": "HuqMzDsJI7NO"
      },
      "id": "HuqMzDsJI7NO"
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots()\n",
        "fig.set_figheight(6)\n",
        "fig.set_figwidth(12)\n",
        "sns.barplot(x='product_line',y='mean', data=product_price)\n",
        "axs.set_title(\"Средняя стоимость товара\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "RTNE9nZ40Mzx",
        "outputId": "c0c5a703-45a2-4dcc-c2ea-f017ebc4f377"
      },
      "id": "RTNE9nZ40Mzx",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Средняя стоимость товара')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('product_line')[['quantity','total']].agg({'total' : 'median', 'quantity' : 'sum'}).sort_values(by = 'total', ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "OiLZeXEYNiG1",
        "outputId": "3ae021b7-6097-4eb0-d8b2-21f2d2e864ea"
      },
      "id": "OiLZeXEYNiG1",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         total  quantity\n",
              "product_line                            \n",
              "Engine                 414.330       627\n",
              "Frame & body           272.605      1619\n",
              "Suspension & traction  228.715      2145\n",
              "Electrical system      153.830      1698\n",
              "Miscellaneous          147.140      1176\n",
              "Breaking system         95.770      2130"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ba8f145-c176-424e-b4d0-2adc7f6df946\">\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>total</th>\n",
              "      <th>quantity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_line</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Engine</th>\n",
              "      <td>414.330</td>\n",
              "      <td>627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Frame &amp; body</th>\n",
              "      <td>272.605</td>\n",
              "      <td>1619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suspension &amp; traction</th>\n",
              "      <td>228.715</td>\n",
              "      <td>2145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Electrical system</th>\n",
              "      <td>153.830</td>\n",
              "      <td>1698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miscellaneous</th>\n",
              "      <td>147.140</td>\n",
              "      <td>1176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Breaking system</th>\n",
              "      <td>95.770</td>\n",
              "      <td>2130</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Самый большой доход приносит категория товаров Engine   \n",
        "Чаще всего покупают товары из категорий: Suspension, Breaking system, Frame & body, Electrical system\t"
      ],
      "metadata": {
        "id": "Jv9RzR1HN86c"
      },
      "id": "Jv9RzR1HN86c"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Посчитаем процентное соотношения товаров на каждом складу. Эти выводы помогут понять, какие категории товаров чаще покупают, что позволит понять сколько товаров, каких категорий стоит отгружать на склад"
      ],
      "metadata": {
        "id": "431JFGF2Ysks"
      },
      "id": "431JFGF2Ysks"
    },
    {
      "cell_type": "code",
      "source": [
        "place_product = df.groupby(['warehouse','product_line'])['quantity'].sum().reset_index()"
      ],
      "metadata": {
        "id": "ot1BvVDLQRSG"
      },
      "id": "ot1BvVDLQRSG",
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "place_total_sum = place_product.groupby('warehouse')['quantity'].sum().reset_index().rename(columns = {'quantity':'sum_quantity'})"
      ],
      "metadata": {
        "id": "NiIGX4laQuC0"
      },
      "id": "NiIGX4laQuC0",
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "place_product = place_product.merge(place_total_sum, on = 'warehouse', how = 'left', )"
      ],
      "metadata": {
        "id": "CzEaUJrwQ6qD"
      },
      "id": "CzEaUJrwQ6qD",
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "place_product['pct_total'] = ((place_product['quantity'] * 100 ) / place_product['sum_quantity']).round(2)"
      ],
      "metadata": {
        "id": "cIf1cpefUBRM"
      },
      "id": "cIf1cpefUBRM",
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "place_product = place_product.sort_values(by=['warehouse', 'pct_total'], ascending = False)"
      ],
      "metadata": {
        "id": "IlkFzkSCYGbT"
      },
      "id": "IlkFzkSCYGbT",
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "place_product = place_product.drop(['quantity','sum_quantity'], axis = 1)"
      ],
      "metadata": {
        "id": "YxnSXcY4VACz"
      },
      "id": "YxnSXcY4VACz",
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warehouses = place_product['warehouse'].unique()"
      ],
      "metadata": {
        "id": "sSMJsMGQWHsT"
      },
      "id": "sSMJsMGQWHsT",
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "cm = sns.dark_palette(\"green\", as_cmap=True)"
      ],
      "metadata": {
        "id": "EAepKW-tV5Rj"
      },
      "id": "EAepKW-tV5Rj",
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def palette(warehouses):\n",
        "    return place_product[place_product['warehouse'] == warehouses].style.background_gradient(cmap=cm, subset=['pct_total'])"
      ],
      "metadata": {
        "id": "Em4ON_AtW-oz"
      },
      "id": "Em4ON_AtW-oz",
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "palette(warehouses[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "7_sz8ihXX2bk",
        "outputId": "f31224cc-2fe8-486d-dffd-6907433460df"
      },
      "id": "7_sz8ihXX2bk",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<pandas.io.formats.style.Styler at 0x7f6cc9c81bb0>"
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              "<style type=\"text/css\">\n",
              "#T_219ff_row0_col2 {\n",
              "  background-color: #008000;\n",
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              "}\n",
              "#T_219ff_row1_col2 {\n",
              "  background-color: #0f580f;\n",
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              "#T_219ff_row2_col2 {\n",
              "  background-color: #125012;\n",
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              "#T_219ff_row3_col2 {\n",
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              "  background-color: #222722;\n",
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              "</style>\n",
              "<table id=\"T_219ff_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >warehouse</th>\n",
              "      <th class=\"col_heading level0 col1\" >product_line</th>\n",
              "      <th class=\"col_heading level0 col2\" >pct_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_219ff_level0_row0\" class=\"row_heading level0 row0\" >12</th>\n",
              "      <td id=\"T_219ff_row0_col0\" class=\"data row0 col0\" >West</td>\n",
              "      <td id=\"T_219ff_row0_col1\" class=\"data row0 col1\" >Breaking system</td>\n",
              "      <td id=\"T_219ff_row0_col2\" class=\"data row0 col2\" >31.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_219ff_level0_row1\" class=\"row_heading level0 row1\" >17</th>\n",
              "      <td id=\"T_219ff_row1_col0\" class=\"data row1 col0\" >West</td>\n",
              "      <td id=\"T_219ff_row1_col1\" class=\"data row1 col1\" >Suspension & traction</td>\n",
              "      <td id=\"T_219ff_row1_col2\" class=\"data row1 col2\" >18.530000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_219ff_level0_row2\" class=\"row_heading level0 row2\" >16</th>\n",
              "      <td id=\"T_219ff_row2_col0\" class=\"data row2 col0\" >West</td>\n",
              "      <td id=\"T_219ff_row2_col1\" class=\"data row2 col1\" >Miscellaneous</td>\n",
              "      <td id=\"T_219ff_row2_col2\" class=\"data row2 col2\" >16.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_219ff_level0_row3\" class=\"row_heading level0 row3\" >13</th>\n",
              "      <td id=\"T_219ff_row3_col0\" class=\"data row3 col0\" >West</td>\n",
              "      <td id=\"T_219ff_row3_col1\" class=\"data row3 col1\" >Electrical system</td>\n",
              "      <td id=\"T_219ff_row3_col2\" class=\"data row3 col2\" >15.680000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_219ff_level0_row4\" class=\"row_heading level0 row4\" >15</th>\n",
              "      <td id=\"T_219ff_row4_col0\" class=\"data row4 col0\" >West</td>\n",
              "      <td id=\"T_219ff_row4_col1\" class=\"data row4 col1\" >Frame & body</td>\n",
              "      <td id=\"T_219ff_row4_col2\" class=\"data row4 col2\" >15.240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_219ff_level0_row5\" class=\"row_heading level0 row5\" >14</th>\n",
              "      <td id=\"T_219ff_row5_col0\" class=\"data row5 col0\" >West</td>\n",
              "      <td id=\"T_219ff_row5_col1\" class=\"data row5 col1\" >Engine</td>\n",
              "      <td id=\"T_219ff_row5_col2\" class=\"data row5 col2\" >3.470000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "В районе склада Central больше всего было совершенно покупок товаров из категорий Breaking system, Suspension & traction, Electrical system\t  "
      ],
      "metadata": {
        "id": "sQ8yeaMhY6Ub"
      },
      "id": "sQ8yeaMhY6Ub"
    },
    {
      "cell_type": "code",
      "source": [
        "palette(warehouses[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "1G6-LiEIX2Yb",
        "outputId": "123fed02-fa51-41e6-c559-d23170fb5040"
      },
      "id": "1G6-LiEIX2Yb",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  background-color: #164616;\n",
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              "}\n",
              "#T_0adaf_row5_col2 {\n",
              "  background-color: #222722;\n",
              "  color: #f1f1f1;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_0adaf_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >warehouse</th>\n",
              "      <th class=\"col_heading level0 col1\" >product_line</th>\n",
              "      <th class=\"col_heading level0 col2\" >pct_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_0adaf_level0_row0\" class=\"row_heading level0 row0\" >11</th>\n",
              "      <td id=\"T_0adaf_row0_col0\" class=\"data row0 col0\" >North</td>\n",
              "      <td id=\"T_0adaf_row0_col1\" class=\"data row0 col1\" >Suspension & traction</td>\n",
              "      <td id=\"T_0adaf_row0_col2\" class=\"data row0 col2\" >26.610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0adaf_level0_row1\" class=\"row_heading level0 row1\" >9</th>\n",
              "      <td id=\"T_0adaf_row1_col0\" class=\"data row1 col0\" >North</td>\n",
              "      <td id=\"T_0adaf_row1_col1\" class=\"data row1 col1\" >Frame & body</td>\n",
              "      <td id=\"T_0adaf_row1_col2\" class=\"data row1 col2\" >20.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0adaf_level0_row2\" class=\"row_heading level0 row2\" >7</th>\n",
              "      <td id=\"T_0adaf_row2_col0\" class=\"data row2 col0\" >North</td>\n",
              "      <td id=\"T_0adaf_row2_col1\" class=\"data row2 col1\" >Electrical system</td>\n",
              "      <td id=\"T_0adaf_row2_col2\" class=\"data row2 col2\" >18.650000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0adaf_level0_row3\" class=\"row_heading level0 row3\" >6</th>\n",
              "      <td id=\"T_0adaf_row3_col0\" class=\"data row3 col0\" >North</td>\n",
              "      <td id=\"T_0adaf_row3_col1\" class=\"data row3 col1\" >Breaking system</td>\n",
              "      <td id=\"T_0adaf_row3_col2\" class=\"data row3 col2\" >18.590000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0adaf_level0_row4\" class=\"row_heading level0 row4\" >10</th>\n",
              "      <td id=\"T_0adaf_row4_col0\" class=\"data row4 col0\" >North</td>\n",
              "      <td id=\"T_0adaf_row4_col1\" class=\"data row4 col1\" >Miscellaneous</td>\n",
              "      <td id=\"T_0adaf_row4_col2\" class=\"data row4 col2\" >11.650000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_0adaf_level0_row5\" class=\"row_heading level0 row5\" >8</th>\n",
              "      <td id=\"T_0adaf_row5_col0\" class=\"data row5 col0\" >North</td>\n",
              "      <td id=\"T_0adaf_row5_col1\" class=\"data row5 col1\" >Engine</td>\n",
              "      <td id=\"T_0adaf_row5_col2\" class=\"data row5 col2\" >3.750000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "В районе склада North больше всего было совершенно покупок товаров из категорий Suspension & traction, Frame & body, Electrical system"
      ],
      "metadata": {
        "id": "5qhZupHTZUUa"
      },
      "id": "5qhZupHTZUUa"
    },
    {
      "cell_type": "code",
      "source": [
        "palette(warehouses[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "ac2FGoalYZdi",
        "outputId": "870d5277-c0eb-4ced-bbf1-3f7608ebdaac"
      },
      "id": "ac2FGoalYZdi",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >warehouse</th>\n",
              "      <th class=\"col_heading level0 col1\" >product_line</th>\n",
              "      <th class=\"col_heading level0 col2\" >pct_total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th id=\"T_c14af_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_c14af_row0_col0\" class=\"data row0 col0\" >Central</td>\n",
              "      <td id=\"T_c14af_row0_col1\" class=\"data row0 col1\" >Breaking system</td>\n",
              "      <td id=\"T_c14af_row0_col2\" class=\"data row0 col2\" >22.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c14af_level0_row1\" class=\"row_heading level0 row1\" >5</th>\n",
              "      <td id=\"T_c14af_row1_col0\" class=\"data row1 col0\" >Central</td>\n",
              "      <td id=\"T_c14af_row1_col1\" class=\"data row1 col1\" >Suspension & traction</td>\n",
              "      <td id=\"T_c14af_row1_col2\" class=\"data row1 col2\" >21.650000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c14af_level0_row2\" class=\"row_heading level0 row2\" >1</th>\n",
              "      <td id=\"T_c14af_row2_col0\" class=\"data row2 col0\" >Central</td>\n",
              "      <td id=\"T_c14af_row2_col1\" class=\"data row2 col1\" >Electrical system</td>\n",
              "      <td id=\"T_c14af_row2_col2\" class=\"data row2 col2\" >18.510000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c14af_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_c14af_row3_col0\" class=\"data row3 col0\" >Central</td>\n",
              "      <td id=\"T_c14af_row3_col1\" class=\"data row3 col1\" >Frame & body</td>\n",
              "      <td id=\"T_c14af_row3_col2\" class=\"data row3 col2\" >15.420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c14af_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_c14af_row4_col0\" class=\"data row4 col0\" >Central</td>\n",
              "      <td id=\"T_c14af_row4_col1\" class=\"data row4 col1\" >Miscellaneous</td>\n",
              "      <td id=\"T_c14af_row4_col2\" class=\"data row4 col2\" >11.880000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c14af_level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "      <td id=\"T_c14af_row5_col0\" class=\"data row5 col0\" >Central</td>\n",
              "      <td id=\"T_c14af_row5_col1\" class=\"data row5 col1\" >Engine</td>\n",
              "      <td id=\"T_c14af_row5_col2\" class=\"data row5 col2\" >9.920000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "В районе склада West больше всего было совершенно покупок товаров из категорий Breaking system, Suspension & traction, Miscellaneous"
      ],
      "metadata": {
        "id": "1SkYVyTCZc7K"
      },
      "id": "1SkYVyTCZc7K"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Выводы"
      ],
      "metadata": {
        "id": "ej9i6RJOI_W-"
      },
      "id": "ej9i6RJOI_W-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Наш магазин по продажи запчастей для мотоциклов получает больше всего дохода через платежи Transfer, т.к. это оптовые продажи.  \n",
        "Через платежи Cash магазин получает наименьшее кол-во дохода, также суммарно Cash сильно отстает от Transfer и Credit  \n",
        "Через платежи Credit и Cash совершаются покупки в розницу\n",
        "2. Самые дорогие по цене товары находятся в категориях Engine и Frame & body\n",
        "3. Самый большой доход приносит категория товаров Engine\n",
        "4. Чаще всего покупают товары из категорий: Suspension, Breaking system, Frame & body, Electrical system\n",
        "5. Процентное соотношения товаров на каждом складе:\n",
        "  + Склад Central имеет больше всего покупок товаров из категорий Breaking system, Suspension & traction, Electrical system\n",
        "  + Склад North имеет больше всего покупок товаров из категорий Suspension & traction, Frame & body, Electrical system\n",
        "  + Склад West имеет больше всего покупок товаров из категорий Breaking system, Suspension & traction, Miscellaneous"
      ],
      "metadata": {
        "id": "eddRCu5Ccs5h"
      },
      "id": "eddRCu5Ccs5h"
    }
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