Pharma_Sales_Analysis_&_Prediction_stage_1.ipynb

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    "colab": {
      "name": "Pharma Sales Analysis & Prediction - stage 1.ipynb",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Libraries"
      ],
      "metadata": {
        "id": "ZWsehj6Z7AZm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "BVlEsru-6QH-"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import calendar\n",
        "import calendar\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.ensemble import VotingRegressor"
      ],
      "metadata": {
        "id": "tZzc3EWn8bXb"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 12, 8\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "Jnssy5jk8bUf"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.set_printoptions(precision=2)"
      ],
      "metadata": {
        "id": "X8BsDslQ8bRm"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uFvWh0V8o9c",
        "outputId": "e1f6f174-acfd-4540-8346-62a2ba865ecb"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load data"
      ],
      "metadata": {
        "id": "YWkrKY2R_1D9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_daily = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Projects/Pharma Sales/salesdaily.csv')"
      ],
      "metadata": {
        "id": "sVMlmT9F8rnI"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_monthly = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Projects/Pharma Sales/salesmonthly.csv')"
      ],
      "metadata": {
        "id": "ppQnh_AY_2Ya"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_hourly = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Projects/Pharma Sales/saleshourly.csv')"
      ],
      "metadata": {
        "id": "KcYUg2m__2TL"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_daily.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cKUqbaLO-ePk",
        "outputId": "40b55a89-9a80-48a6-c31a-9e67d3c1d7bc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      datum  M01AB  M01AE  N02BA  N02BE  N05B  N05C   R03  R06  Year  Month  \\\n",
              "0  1/2/2014    0.0   3.67    3.4  32.40   7.0   0.0   0.0  2.0  2014      1   \n",
              "1  1/3/2014    8.0   4.00    4.4  50.60  16.0   0.0  20.0  4.0  2014      1   \n",
              "2  1/4/2014    2.0   1.00    6.5  61.85  10.0   0.0   9.0  1.0  2014      1   \n",
              "3  1/5/2014    4.0   3.00    7.0  41.10   8.0   0.0   3.0  0.0  2014      1   \n",
              "4  1/6/2014    5.0   1.00    4.5  21.70  16.0   2.0   6.0  2.0  2014      1   \n",
              "\n",
              "   Hour Weekday Name  \n",
              "0   248     Thursday  \n",
              "1   276       Friday  \n",
              "2   276     Saturday  \n",
              "3   276       Sunday  \n",
              "4   276       Monday  "
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              "      <th>datum</th>\n",
              "      <th>M01AB</th>\n",
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              "      <th>N02BA</th>\n",
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              "      <th>0</th>\n",
              "      <td>1/2/2014</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.67</td>\n",
              "      <td>3.4</td>\n",
              "      <td>32.40</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1/3/2014</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.4</td>\n",
              "      <td>50.60</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>276</td>\n",
              "      <td>Friday</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1/4/2014</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>6.5</td>\n",
              "      <td>61.85</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>276</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/5/2014</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>7.0</td>\n",
              "      <td>41.10</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>276</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1/6/2014</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>4.5</td>\n",
              "      <td>21.70</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>276</td>\n",
              "      <td>Monday</td>\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Group by the 2nd drug by Weekday"
      ],
      "metadata": {
        "id": "a-nCqoYL_pGA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_daily = df_sales_daily[['M01AE', 'Weekday Name']]\n",
        "result = df_sales_daily.groupby(['Weekday Name'], as_index=False).sum().sort_values('M01AE', ascending=False)"
      ],
      "metadata": {
        "id": "3BwQppR--hIB"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_day = result.iloc[0,0]"
      ],
      "metadata": {
        "id": "p4zO4USQ-9At"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_value = round(result.iloc[0,1], 2)"
      ],
      "metadata": {
        "id": "zLxy5DHv_LKV"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('The second drug, M01AE, was most frequently sold on ' + str(result_day) \n",
        "      + ' with the volume of ' + str(result_value))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVe0SZHQ_RlJ",
        "outputId": "ca1d045b-fab6-436f-994c-e92cb4b7ce94"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The second drug, M01AE, was most frequently sold on Sunday with the volume of 1384.94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Top 3 drugs gaving the largest sales in January 2015, July 2016, September 2017"
      ],
      "metadata": {
        "id": "70ifrnu5_9w5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_monthly.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "yR0507Kl_mBU",
        "outputId": "3831ae50-62e6-4baa-85cf-40977c875e0e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        datum   M01AB    M01AE    N02BA     N02BE   N05B  N05C    R03    R06\n",
              "0  2014-01-31  127.69   99.090  152.100   878.030  354.0  50.0  112.0   48.2\n",
              "1  2014-02-28  133.32  126.050  177.000  1001.900  347.0  31.0  122.0   36.2\n",
              "2  2014-03-31  137.44   92.950  147.655   779.275  232.0  20.0  112.0   85.4\n",
              "3  2014-04-30  113.10   89.475  130.900   698.500  209.0  18.0   97.0   73.7\n",
              "4  2014-05-31  101.79  119.933  132.100   628.780  270.0  23.0  107.0  123.7"
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              "      <th></th>\n",
              "      <th>datum</th>\n",
              "      <th>M01AB</th>\n",
              "      <th>M01AE</th>\n",
              "      <th>N02BA</th>\n",
              "      <th>N02BE</th>\n",
              "      <th>N05B</th>\n",
              "      <th>N05C</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-01-31</td>\n",
              "      <td>127.69</td>\n",
              "      <td>99.090</td>\n",
              "      <td>152.100</td>\n",
              "      <td>878.030</td>\n",
              "      <td>354.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>48.2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-02-28</td>\n",
              "      <td>133.32</td>\n",
              "      <td>126.050</td>\n",
              "      <td>177.000</td>\n",
              "      <td>1001.900</td>\n",
              "      <td>347.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>36.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-03-31</td>\n",
              "      <td>137.44</td>\n",
              "      <td>92.950</td>\n",
              "      <td>147.655</td>\n",
              "      <td>779.275</td>\n",
              "      <td>232.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>85.4</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-04-30</td>\n",
              "      <td>113.10</td>\n",
              "      <td>89.475</td>\n",
              "      <td>130.900</td>\n",
              "      <td>698.500</td>\n",
              "      <td>209.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>73.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-05-31</td>\n",
              "      <td>101.79</td>\n",
              "      <td>119.933</td>\n",
              "      <td>132.100</td>\n",
              "      <td>628.780</td>\n",
              "      <td>270.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>123.7</td>\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def top_3_drugs_by_month(month, year):\n",
        "    month = str(month) if (month > 9) else '0' + str(month)\n",
        "    year = str(year)\n",
        "\n",
        "    # Filter by Date\n",
        "    sales = df_sales_monthly.loc[df_sales_monthly['datum'].str.contains('^' + year + '\\-' + month + '' , flags=re.I, regex=True)]\n",
        "    \n",
        "    # Reset index\n",
        "    sales = sales.reset_index()\n",
        "    \n",
        "    # Filter chosen drugs\n",
        "    top_Sales_by_Product = sales[['M01AB', 'M01AE', 'N02BA', 'N02BE', 'N05B', 'N05C', 'R03', 'R06']]\n",
        "    \n",
        "    # Sort values horizontally in descending order\n",
        "    top_Sales_by_Product = top_Sales_by_Product.sort_values(by=0, ascending=False, axis=1)\n",
        "    \n",
        "    # Print results\n",
        "    print('Top 3 Drugs by Sale in ' + calendar.month_name[int(month)] + ' ' + year)\n",
        "    for drug in top_Sales_by_Product.columns.values[0:3]:\n",
        "        print(' - Product: ' + str(drug) + ', Volume sold: ' + str(round(top_Sales_by_Product[drug].iloc[0], 2)))\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "id": "1PueuyiIAcD6"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Top 3 Drugs by Sale in January 2015\n",
        "top_3_drugs_by_month(1, 2015)\n",
        "\n",
        "# Top 3 Drugs by Sale in July 2016\n",
        "top_3_drugs_by_month(7, 2016)\n",
        "\n",
        "# Top 3 Drugs by Sale in September 2017\n",
        "top_3_drugs_by_month(9, 2017)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VME9cFUeEGiv",
        "outputId": "3b467586-0d13-4f84-ad48-b57beaade696"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 3 Drugs by Sale in January 2015\n",
            " - Product: N02BE, Volume sold: 1044.24\n",
            " - Product: N05B, Volume sold: 463.0\n",
            " - Product: R03, Volume sold: 177.25\n",
            "\n",
            "\n",
            "Top 3 Drugs by Sale in July 2016\n",
            " - Product: N02BE, Volume sold: 652.36\n",
            " - Product: N05B, Volume sold: 240.0\n",
            " - Product: M01AB, Volume sold: 203.97\n",
            "\n",
            "\n",
            "Top 3 Drugs by Sale in September 2017\n",
            " - Product: N02BE, Volume sold: 863.75\n",
            " - Product: N05B, Volume sold: 223.0\n",
            " - Product: R03, Volume sold: 139.0\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Top 1 Drug sold on Mondays in 2017"
      ],
      "metadata": {
        "id": "oxMhSdmBFS-e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_daily = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Projects/Pharma Sales/salesdaily.csv')"
      ],
      "metadata": {
        "id": "dsEa8UjaEIOc"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_daily.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UD3UeKb1Hk0U",
        "outputId": "85236774-7158-4b19-e032-b6baf5e2f040"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      datum  M01AB  M01AE  N02BA  N02BE  N05B  N05C   R03  R06  Year  Month  \\\n",
              "0  1/2/2014    0.0   3.67    3.4  32.40   7.0   0.0   0.0  2.0  2014      1   \n",
              "1  1/3/2014    8.0   4.00    4.4  50.60  16.0   0.0  20.0  4.0  2014      1   \n",
              "2  1/4/2014    2.0   1.00    6.5  61.85  10.0   0.0   9.0  1.0  2014      1   \n",
              "3  1/5/2014    4.0   3.00    7.0  41.10   8.0   0.0   3.0  0.0  2014      1   \n",
              "4  1/6/2014    5.0   1.00    4.5  21.70  16.0   2.0   6.0  2.0  2014      1   \n",
              "\n",
              "   Hour Weekday Name  \n",
              "0   248     Thursday  \n",
              "1   276       Friday  \n",
              "2   276     Saturday  \n",
              "3   276       Sunday  \n",
              "4   276       Monday  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>datum</th>\n",
              "      <th>M01AB</th>\n",
              "      <th>M01AE</th>\n",
              "      <th>N02BA</th>\n",
              "      <th>N02BE</th>\n",
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              "      <td>1/2/2014</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.67</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>6.5</td>\n",
              "      <td>61.85</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>276</td>\n",
              "      <td>Saturday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1/5/2014</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>7.0</td>\n",
              "      <td>41.10</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>276</td>\n",
              "      <td>Sunday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1/6/2014</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>4.5</td>\n",
              "      <td>21.70</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2014</td>\n",
              "      <td>1</td>\n",
              "      <td>276</td>\n",
              "      <td>Monday</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Filter out all except Mondays in 2017"
      ],
      "metadata": {
        "id": "Lm_mP0G_G7tH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_daily = df_sales_daily.loc[df_sales_daily['datum'].str.contains('2017', flags=re.I, regex=True) & (df_sales_daily['Weekday Name'] == 'Monday')]"
      ],
      "metadata": {
        "id": "bWJc00DEGykE"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Group by Weekday and Sum up"
      ],
      "metadata": {
        "id": "nEX8-3TDHrwA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_daily = df_sales_daily.groupby(['Weekday Name'], as_index=False).sum()"
      ],
      "metadata": {
        "id": "SZ4CcPV-Hd3n"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Filter by the chosen Drugs and Sort by descending order"
      ],
      "metadata": {
        "id": "Bev0ZfLoH1vu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_daily = df_sales_daily[['M01AB', 'M01AE', 'N02BA', 'N02BE', 'N05B', 'N05C', 'R03', 'R06']]\n",
        "result = df_sales_daily.sort_values(by=0, ascending=False, axis=1)"
      ],
      "metadata": {
        "id": "dLbx4eJbHxHe"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for field in result.columns.values[0:1]:\n",
        "    print('The most frequently sold Drug on Mondays in 2017 is ' + str(field) + ' with the Volume of ' + str(round(result[field].iloc[0], 2)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvQV3pnGH0-e",
        "outputId": "c7388ebe-4ca9-449d-b351-5df881d618fa"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The most frequently sold Drug on Mondays in 2017 is N02BE with the Volume of 1160.56\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ohFefAtmIFJ_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}