Data_Extraction_and_Cleaning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MiguelAmorim-587/ZenPrice/blob/main/Data_Extraction_and_Cleaning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data extration"
      ],
      "metadata": {
        "id": "lU8fOfkUs8Nk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Sa9JH3O-Qi6",
        "outputId": "fd601557-7ef7-46ed-eef4-0fc6c8620916"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive/Tese\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Drive path\n",
        "%cd \"/content/drive/MyDrive/Tese\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4B-me4GPNQYz",
        "outputId": "2d861b90-7664-45bb-c4ff-9305c1d8b136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Oct 27 01:58:33 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   37C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCRMp9UW-ykG",
        "outputId": "c9ed0ae5-f88a-4426-cf68-6e7dd299e3b3"
      },
      "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 currencyconverter\n",
            "  Downloading CurrencyConverter-0.17.2-py3-none-any.whl (557 kB)\n",
            "\u001b[K     |████████████████████████████████| 557 kB 14.7 MB/s \n",
            "\u001b[?25hInstalling collected packages: currencyconverter\n",
            "Successfully installed currencyconverter-0.17.2\n"
          ]
        }
      ],
      "source": [
        "# Base libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.rcParams['figure.figsize'] = (10, 5)\n",
        "\n",
        "# Convert currencies\n",
        "!pip install currencyconverter\n",
        "from currency_converter import CurrencyConverter\n",
        "\n",
        "# Conver strings with commas as thousand separators\n",
        "import locale\n",
        "locale.setlocale(locale.LC_ALL, 'en_US.UTF-8') \n",
        "\n",
        "# Convert datatimes\n",
        "from datetime import datetime\n",
        "\n",
        "# Read zips\n",
        "from zipfile import ZipFile"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "97vr5gR8_ZTu"
      },
      "outputs": [],
      "source": [
        "def zip_2_dataframe(zip_name, file_name, filetype):\n",
        "  zip_file = ZipFile(zip_name, 'r')\n",
        "  data_file = zip_file.open(file_name)\n",
        "\n",
        "  if filetype == \"csv\":\n",
        "    df = pd.read_csv(data_file)\n",
        "  if filetype == \"json\":\n",
        "    df = pd.read_json(data_file, lines=True)\n",
        "\n",
        "  return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n6zig4Ov_by9"
      },
      "outputs": [],
      "source": [
        "zip_name = \"catalog_sample.zip\"\n",
        "file_name = \"catalog_sample.json\"\n",
        "filetype = \"json\"\n",
        "df_og = zip_2_dataframe(zip_name, file_name, filetype)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLP-lJew2sef"
      },
      "source": [
        "## Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oeyp4wHu_o10",
        "outputId": "c45b0f89-4db9-49c8-ceba-4e50f185b269"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       product country    company       price  timestamp  \\\n",
              "0       xiaomi Pocophone 128GB      CL  Falabella    $799,990   20200722   \n",
              "1       xiaomi Pocophone 128GB      CL  Falabella    $799,990   20200721   \n",
              "2       xiaomi Pocophone 128GB      CL  Falabella    $799,990   20200720   \n",
              "3       xiaomi Pocophone 128GB      CL  Falabella    $799,990   20200719   \n",
              "4       xiaomi Pocophone 128GB      CL  Falabella    $799,990   20200718   \n",
              "...                        ...     ...        ...         ...        ...   \n",
              "602051        huawei Y9S 128GB      CL      claro    $229,990   20200621   \n",
              "602052        huawei Y9S 128GB      CO      Claro  $1,168,900   20200621   \n",
              "602053        huawei Y9S 128GB      CO       Tigo  $1,079,900   20200621   \n",
              "602054        huawei Y9S 128GB      PE   Movistar     S/1,519   20200620   \n",
              "602055        huawei Y9S 128GB      PE      Claro     S/1,139   20200620   \n",
              "\n",
              "                  offer_type  \n",
              "0                   unlocked  \n",
              "1                   unlocked  \n",
              "2                   unlocked  \n",
              "3                   unlocked  \n",
              "4                   unlocked  \n",
              "...                      ...  \n",
              "602051              unlocked  \n",
              "602052              unlocked  \n",
              "602053              unlocked  \n",
              "602054  postpaid_portability  \n",
              "602055              unlocked  \n",
              "\n",
              "[602056 rows x 6 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "      <th>product</th>\n",
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              "      <th>0</th>\n",
              "      <td>xiaomi Pocophone 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Falabella</td>\n",
              "      <td>$799,990</td>\n",
              "      <td>20200722</td>\n",
              "      <td>unlocked</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>xiaomi Pocophone 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Falabella</td>\n",
              "      <td>$799,990</td>\n",
              "      <td>20200721</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>xiaomi Pocophone 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Falabella</td>\n",
              "      <td>$799,990</td>\n",
              "      <td>20200720</td>\n",
              "      <td>unlocked</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>xiaomi Pocophone 128GB</td>\n",
              "      <td>CL</td>\n",
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              "      <td>20200719</td>\n",
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              "    <tr>\n",
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              "      <td>CL</td>\n",
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              "      <td>20200718</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>602051</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>claro</td>\n",
              "      <td>$229,990</td>\n",
              "      <td>20200621</td>\n",
              "      <td>unlocked</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602052</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>CO</td>\n",
              "      <td>Claro</td>\n",
              "      <td>$1,168,900</td>\n",
              "      <td>20200621</td>\n",
              "      <td>unlocked</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602053</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>CO</td>\n",
              "      <td>Tigo</td>\n",
              "      <td>$1,079,900</td>\n",
              "      <td>20200621</td>\n",
              "      <td>unlocked</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602054</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>S/1,519</td>\n",
              "      <td>20200620</td>\n",
              "      <td>postpaid_portability</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602055</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Claro</td>\n",
              "      <td>S/1,139</td>\n",
              "      <td>20200620</td>\n",
              "      <td>unlocked</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>602056 rows × 6 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df = df_og.copy()\n",
        "df_og"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0zX5A8pzxsSZ"
      },
      "source": [
        "\n",
        "\n",
        "*   Convert elements of timestamp column to dates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oUfdte1OyAFe"
      },
      "outputs": [],
      "source": [
        "def str_2_date(str_date, date_format):\n",
        "  return datetime.strptime(str(str_date), date_format)\n",
        "\n",
        "df[\"timestamp\"] = df_og[\"timestamp\"].apply(str_2_date, args = ('%Y%m%d',))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgxb03Ghy1im"
      },
      "source": [
        "*   List of different currencies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "llT-HOerzCex"
      },
      "source": [
        "There are three different types of currencies:\n",
        "\n",
        "- Euros\n",
        "- Dollars\n",
        "- Sol (Peru's currency)\n",
        "\n",
        "**Note:** To compare prices of different countries it may be useful to convert all currencies to a base currency (e.g. euro). However, there are fluctuations in the value of each currency, and so it may be better to do the analysis in each currency, but this should be further analysed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oyxz-vfzFY-",
        "outputId": "c31331c0-72ce-45b8-f2f1-48c641e8929b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['$', '€', 'S'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df[\"price\"].str[0].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzVKxjTGzLq3"
      },
      "source": [
        "To compare prices across different countries, a new column will be created with all prices in Euros. A column with the prices withouth commas, dots and currency signs wil also be created.\n",
        "\n",
        "The value of each currency as stated by the Europen Central Bank can be found in this link: https://www.ecb.europa.eu/stats/policy_and_exchange_rates/euro_reference_exchange_rates/html/index.en.html).\n",
        "\n",
        " The Sol (Peru) currency is not present in here."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3QEt0G35zNhb"
      },
      "outputs": [],
      "source": [
        "def replace_last_occurrence(str, old_char, new_char):\n",
        "\n",
        "  # Replace the last ocurrence of a char in a string by another char\n",
        "\n",
        "  last_index = str.rfind(old_char)\n",
        "  if last_index == -1:\n",
        "    return str\n",
        "  else:\n",
        "    return str[:last_index] + new_char + str[last_index+1:]\n",
        "\n",
        "\n",
        "def clean_prices(price):\n",
        "\n",
        "  # Remove commas, dots and currency signs from the prices\n",
        "\n",
        "  # Sol peruano (doesn't have decimal cases - thousands are separated with commas)\n",
        "  if price[0] == \"S\":\n",
        "    price = price.replace(\",\", \"\")                        # remove commas that separate thousands\n",
        "    price = float(price[2:])                              # remove the sign of the currency\n",
        "    return price\n",
        "  \n",
        "  # Dollar (always has decimal cases (i think) - thousands and decimals are both separated by commas)\n",
        "  if price[0] == \"$\":\n",
        "    price = replace_last_occurrence(price, \",\", \".\")      # replace comma that separates decimals by a dot\n",
        "    price = price.replace(\",\", \"\")                        # remove commas that separate thousands\n",
        "    price = float(price[1:])                              # remove the sign of the currency\n",
        "    return price\n",
        "  \n",
        "  # Euro (decimal cases, if there are any, are separated by dots - thousands are separated by commas)\n",
        "  if price[0] == \"€\":\n",
        "    price = price.replace(\",\", \"\")                        # remove commas that separate thousands\n",
        "    price = float(price[1:])                              # remove the sign of the currency\n",
        "    return price                              \n",
        "  \n",
        "  return np.nan\n",
        "\n",
        "def convert_prices_to_euros(row, currency_column, price_column):\n",
        "\n",
        "  # Convert all prices to a base currency (euro)\n",
        "\n",
        "  currency = row[currency_column]\n",
        "  price = row[price_column]\n",
        "\n",
        "  sol = 0.24\n",
        "  dollar = 1/1.0993\n",
        "  \n",
        "  if currency in [\"€\"]:\n",
        "    return price\n",
        "\n",
        "  if currency in [\"$\"]:\n",
        "    return dollar*price \n",
        "  \n",
        "  if currency in [\"S\"]:\n",
        "    return sol*price \n",
        "\n",
        "\n",
        "# Column with type of currency\n",
        "df[\"currency\"] = df_og[\"price\"].str[0]\n",
        "\n",
        "# Clean prices (remove commas, dots, and currency signs)\n",
        "df[\"price_clean\"] = df_og[\"price\"].apply(clean_prices)\n",
        "\n",
        "# Conver prices to euros\n",
        "df[\"price_euro\"] = df.apply(convert_prices_to_euros, args = (\"currency\", \"price_clean\"), axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4SyGioWzUCo",
        "outputId": "b47507e4-ecb8-413a-d889-101f53a6d1ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       product country    company       price  timestamp  \\\n",
              "0       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-22   \n",
              "1       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-21   \n",
              "2       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-20   \n",
              "3       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-19   \n",
              "4       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-18   \n",
              "...                        ...     ...        ...         ...        ...   \n",
              "602051        huawei Y9S 128GB      CL      claro    $229,990 2020-06-21   \n",
              "602052        huawei Y9S 128GB      CO      Claro  $1,168,900 2020-06-21   \n",
              "602053        huawei Y9S 128GB      CO       Tigo  $1,079,900 2020-06-21   \n",
              "602054        huawei Y9S 128GB      PE   Movistar     S/1,519 2020-06-20   \n",
              "602055        huawei Y9S 128GB      PE      Claro     S/1,139 2020-06-20   \n",
              "\n",
              "                  offer_type currency  price_clean   price_euro  \n",
              "0                   unlocked        $       799.99   727.726735  \n",
              "1                   unlocked        $       799.99   727.726735  \n",
              "2                   unlocked        $       799.99   727.726735  \n",
              "3                   unlocked        $       799.99   727.726735  \n",
              "4                   unlocked        $       799.99   727.726735  \n",
              "...                      ...      ...          ...          ...  \n",
              "602051              unlocked        $       229.99   209.214955  \n",
              "602052              unlocked        $      1168.90  1063.313017  \n",
              "602053              unlocked        $      1079.90   982.352406  \n",
              "602054  postpaid_portability        S      1519.00   364.560000  \n",
              "602055              unlocked        S      1139.00   273.360000  \n",
              "\n",
              "[602056 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c9ad8577-00c7-4e24-aa09-e329342ba4d3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product</th>\n",
              "      <th>country</th>\n",
              "      <th>company</th>\n",
              "      <th>price</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>offer_type</th>\n",
              "      <th>currency</th>\n",
              "      <th>price_clean</th>\n",
              "      <th>price_euro</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>xiaomi Pocophone 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Falabella</td>\n",
              "      <td>$799,990</td>\n",
              "      <td>2020-07-22</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>$</td>\n",
              "      <td>799.99</td>\n",
              "      <td>727.726735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>xiaomi Pocophone 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Falabella</td>\n",
              "      <td>$799,990</td>\n",
              "      <td>2020-07-21</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>$</td>\n",
              "      <td>799.99</td>\n",
              "      <td>727.726735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>xiaomi Pocophone 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Falabella</td>\n",
              "      <td>$799,990</td>\n",
              "      <td>2020-07-20</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>$</td>\n",
              "      <td>799.99</td>\n",
              "      <td>727.726735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>xiaomi Pocophone 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Falabella</td>\n",
              "      <td>$799,990</td>\n",
              "      <td>2020-07-19</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>$</td>\n",
              "      <td>799.99</td>\n",
              "      <td>727.726735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>xiaomi Pocophone 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Falabella</td>\n",
              "      <td>$799,990</td>\n",
              "      <td>2020-07-18</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>$</td>\n",
              "      <td>799.99</td>\n",
              "      <td>727.726735</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602051</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>claro</td>\n",
              "      <td>$229,990</td>\n",
              "      <td>2020-06-21</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>$</td>\n",
              "      <td>229.99</td>\n",
              "      <td>209.214955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602052</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>CO</td>\n",
              "      <td>Claro</td>\n",
              "      <td>$1,168,900</td>\n",
              "      <td>2020-06-21</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>$</td>\n",
              "      <td>1168.90</td>\n",
              "      <td>1063.313017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602053</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>CO</td>\n",
              "      <td>Tigo</td>\n",
              "      <td>$1,079,900</td>\n",
              "      <td>2020-06-21</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>$</td>\n",
              "      <td>1079.90</td>\n",
              "      <td>982.352406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602054</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>S/1,519</td>\n",
              "      <td>2020-06-20</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>S</td>\n",
              "      <td>1519.00</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602055</th>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Claro</td>\n",
              "      <td>S/1,139</td>\n",
              "      <td>2020-06-20</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>S</td>\n",
              "      <td>1139.00</td>\n",
              "      <td>273.360000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>602056 rows × 9 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Replace Company"
      ],
      "metadata": {
        "id": "6NQF8jlsFXgd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.replace(to_replace=\"claro\",value=\"Claro\")"
      ],
      "metadata": {
        "id": "flMMHLUUahpz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.replace(to_replace=\"movistar\",value=\"Movistar\")"
      ],
      "metadata": {
        "id": "RoB72vrDamhh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "coJDtG1w2wXi"
      },
      "source": [
        "## Analysis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_samples_per_product = df.groupby(['product']).size()\n",
        "n_samples_per_product"
      ],
      "metadata": {
        "id": "_CI7Q4jTvmZI",
        "outputId": "791bf9ad-93f7-4e6e-8b87-8239802c8a0c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product\n",
              "alcatel 1 32GB             192\n",
              "alcatel 1 8GB              476\n",
              "alcatel 1S 32GB (2020)    2456\n",
              "alcatel 1V 16GB (2019)      70\n",
              "alcatel 2051               673\n",
              "                          ... \n",
              "zte Blade V9               733\n",
              "zte R340                   854\n",
              "zte R341                   288\n",
              "zte R550                   477\n",
              "zte Z2371                  191\n",
              "Length: 707, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_samples_per_product = df.groupby(['offer_type']).size()\n",
        "n_samples_per_product"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22AkjjJNqTqA",
        "outputId": "89858498-1302-4cc8-fd6e-a5c5b267a65e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "offer_type\n",
              "postpaid_new_line        25470\n",
              "postpaid_portability     11380\n",
              "postpaid_renew             462\n",
              "unlocked                564744\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_samples_per_product = df.groupby(['company']).size()\n",
        "n_samples_per_product"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCXa__EsCW66",
        "outputId": "2000ec90-7c68-4d24-b80f-eb075070d94e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "company\n",
              "Abcdin            29127\n",
              "Avantel            2982\n",
              "Bitel              3236\n",
              "Claro             96348\n",
              "Falabella         45516\n",
              "Lider             12163\n",
              "MEO               14134\n",
              "Media Markt       14796\n",
              "Movistar         159778\n",
              "NOS               13695\n",
              "Paris             28707\n",
              "Radio Popular     19813\n",
              "Ripley            32374\n",
              "Tigo              17091\n",
              "Vodafone          12834\n",
              "Worten            16195\n",
              "entel             57436\n",
              "macOnline          2146\n",
              "vtr                5533\n",
              "wom               18152\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_samples_per_product = df.groupby(['country']).size()\n",
        "n_samples_per_product"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RG07I9p8C4_8",
        "outputId": "9a3fef3e-3695-4188-c712-0d84847e53d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country\n",
              "CL    354257\n",
              "CO     78038\n",
              "PE     78294\n",
              "PT     91467\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKYa1hWP3Cja",
        "outputId": "909460d2-d024-456a-9618-effa88665930"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "company  country  product                                  \n",
              "Abcdin   CL       alcatel 1S 32GB (2020)                       190\n",
              "                  alcatel 1V 16GB (2019)                        70\n",
              "                  alcatel 3X (2019) 64GB                       133\n",
              "                  alcatel Tiger V Senior                        51\n",
              "                  apple  iPhone 8 Plus 64GB Reacondicionado    393\n",
              "                                                              ... \n",
              "wom      CO       vivo Y11s 32GB                               126\n",
              "                  vivo Y20 64GB                                  6\n",
              "                  xiaomi Redmi Note 8 128GB                     56\n",
              "                  xiaomi Redmi Note 9 128GB                     56\n",
              "                  zte Blade A3 Lite 32GB                       157\n",
              "Length: 2920, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "n_samples_per_product = df.groupby(['company', 'country', 'product']).size()\n",
        "n_samples_per_product"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N9MKvkRbYikv",
        "outputId": "62617180-e216-4173-de64-e7c88095a493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       product country    company       price  timestamp  \\\n",
              "0       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-22   \n",
              "1       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-21   \n",
              "2       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-20   \n",
              "3       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-19   \n",
              "4       xiaomi Pocophone 128GB      CL  Falabella    $799,990 2020-07-18   \n",
              "...                        ...     ...        ...         ...        ...   \n",
              "602051        huawei Y9S 128GB      CL      Claro    $229,990 2020-06-21   \n",
              "602052        huawei Y9S 128GB      CO      Claro  $1,168,900 2020-06-21   \n",
              "602053        huawei Y9S 128GB      CO       Tigo  $1,079,900 2020-06-21   \n",
              "602054        huawei Y9S 128GB      PE   Movistar     S/1,519 2020-06-20   \n",
              "602055        huawei Y9S 128GB      PE      Claro     S/1,139 2020-06-20   \n",
              "\n",
              "                  offer_type currency  price_clean   price_euro  \n",
              "0                   unlocked        $       799.99   727.726735  \n",
              "1                   unlocked        $       799.99   727.726735  \n",
              "2                   unlocked        $       799.99   727.726735  \n",
              "3                   unlocked        $       799.99   727.726735  \n",
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          "metadata": {},
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    {
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      "source": [
        "df[\"currency\"]"
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              "0         $\n",
              "1         $\n",
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              "         ..\n",
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              "602054    S\n",
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              "Name: currency, Length: 602056, dtype: object"
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        "new_df=df.drop(columns=['price','currency','price_clean'])\n",
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              "                       product country    company  timestamp  \\\n",
              "0       xiaomi Pocophone 128GB      CL  Falabella 2020-07-22   \n",
              "1       xiaomi Pocophone 128GB      CL  Falabella 2020-07-21   \n",
              "2       xiaomi Pocophone 128GB      CL  Falabella 2020-07-20   \n",
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              "4       xiaomi Pocophone 128GB      CL  Falabella 2020-07-18   \n",
              "...                        ...     ...        ...        ...   \n",
              "602051        huawei Y9S 128GB      CL      Claro 2020-06-21   \n",
              "602052        huawei Y9S 128GB      CO      Claro 2020-06-21   \n",
              "602053        huawei Y9S 128GB      CO       Tigo 2020-06-21   \n",
              "602054        huawei Y9S 128GB      PE   Movistar 2020-06-20   \n",
              "602055        huawei Y9S 128GB      PE      Claro 2020-06-20   \n",
              "\n",
              "                  offer_type   price_euro  \n",
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              "3                   unlocked   727.726735  \n",
              "4                   unlocked   727.726735  \n",
              "...                      ...          ...  \n",
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              "\n",
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              "          const element = document.querySelector('#df-4020c8b6-2f6a-4e72-b97c-68eb0c53ef4c');\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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "3kpSdXKa0GBC",
        "outputId": "6e4ff576-c142-4602-cb60-10e284491148"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "        product_id                 product country    company  timestamp  \\\n",
              "0                1  xiaomi Pocophone 128GB      CL  Falabella 2020-07-22   \n",
              "1                1  xiaomi Pocophone 128GB      CL  Falabella 2020-07-21   \n",
              "2                1  xiaomi Pocophone 128GB      CL  Falabella 2020-07-20   \n",
              "3                1  xiaomi Pocophone 128GB      CL  Falabella 2020-07-19   \n",
              "4                1  xiaomi Pocophone 128GB      CL  Falabella 2020-07-18   \n",
              "...            ...                     ...     ...        ...        ...   \n",
              "602051        3222        huawei Y9S 128GB      CL      Claro 2020-06-21   \n",
              "602052        3234        huawei Y9S 128GB      CO      Claro 2020-06-21   \n",
              "602053        3228        huawei Y9S 128GB      CO       Tigo 2020-06-21   \n",
              "602054        3236        huawei Y9S 128GB      PE   Movistar 2020-06-20   \n",
              "602055        3233        huawei Y9S 128GB      PE      Claro 2020-06-20   \n",
              "\n",
              "                  offer_type   price_euro  \n",
              "0                   unlocked   727.726735  \n",
              "1                   unlocked   727.726735  \n",
              "2                   unlocked   727.726735  \n",
              "3                   unlocked   727.726735  \n",
              "4                   unlocked   727.726735  \n",
              "...                      ...          ...  \n",
              "602051              unlocked   209.214955  \n",
              "602052              unlocked  1063.313017  \n",
              "602053              unlocked   982.352406  \n",
              "602054  postpaid_portability   364.560000  \n",
              "602055              unlocked   273.360000  \n",
              "\n",
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              "      <td>3234</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>CO</td>\n",
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              "      <td>unlocked</td>\n",
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              "      <td>3228</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
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              "      <td>Tigo</td>\n",
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              "      <th>602054</th>\n",
              "      <td>3236</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
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              "      <td>Movistar</td>\n",
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              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
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              "      <th>602055</th>\n",
              "      <td>3233</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
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              "      <td>Claro</td>\n",
              "      <td>2020-06-20</td>\n",
              "      <td>unlocked</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\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": 22
        }
      ],
      "source": [
        "new_df.insert(loc=0, column='product_id', value=df.set_index(['company','country', 'product','offer_type']).index.factorize()[0]+1)\n",
        "new_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfIyFa873vNJ",
        "outputId": "05c1acae-6df6-4438-c133-1566a935a7d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3236\n"
          ]
        }
      ],
      "source": [
        "print(max(new_df[\"product_id\"]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3x03rNoK6y5A",
        "outputId": "088785da-9045-4632-d19d-3097c90209e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_id\n",
              "1        81\n",
              "2       281\n",
              "3       119\n",
              "4        56\n",
              "5        23\n",
              "       ... \n",
              "3232     48\n",
              "3233    147\n",
              "3234    204\n",
              "3235     65\n",
              "3236     32\n",
              "Length: 3236, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "n_samples = new_df.groupby(by = [\"product_id\"]).size()\n",
        "n_samples"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df2 = new_df.sort_values(by=['product_id','timestamp'], ascending=True)\n",
        "new_df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "baK3q_DNvVnk",
        "outputId": "65578b91-0b39-4399-b1dd-184a542b09aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        product_id                 product country    company  timestamp  \\\n",
              "80               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-14   \n",
              "79               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-15   \n",
              "78               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-16   \n",
              "77               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-17   \n",
              "76               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-18   \n",
              "...            ...                     ...     ...        ...        ...   \n",
              "601733        3236        huawei Y9S 128GB      PE   Movistar 2020-07-17   \n",
              "601720        3236        huawei Y9S 128GB      PE   Movistar 2020-07-18   \n",
              "601707        3236        huawei Y9S 128GB      PE   Movistar 2020-07-19   \n",
              "601693        3236        huawei Y9S 128GB      PE   Movistar 2020-07-20   \n",
              "601678        3236        huawei Y9S 128GB      PE   Movistar 2020-07-21   \n",
              "\n",
              "                  offer_type  price_euro  \n",
              "80                  unlocked  727.726735  \n",
              "79                  unlocked  727.726735  \n",
              "78                  unlocked  727.726735  \n",
              "77                  unlocked  727.726735  \n",
              "76                  unlocked  727.726735  \n",
              "...                      ...         ...  \n",
              "601733  postpaid_portability  364.560000  \n",
              "601720  postpaid_portability  364.560000  \n",
              "601707  postpaid_portability  364.560000  \n",
              "601693  postpaid_portability  364.560000  \n",
              "601678  postpaid_portability  364.560000  \n",
              "\n",
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              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601720</th>\n",
              "      <td>3236</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>2020-07-18</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601707</th>\n",
              "      <td>3236</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>2020-07-19</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601693</th>\n",
              "      <td>3236</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>2020-07-20</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601678</th>\n",
              "      <td>3236</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>2020-07-21</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>602056 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc0e2ad9-aa79-4ae7-9a16-299ae333cdcd')\"\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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dc0e2ad9-aa79-4ae7-9a16-299ae333cdcd 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-dc0e2ad9-aa79-4ae7-9a16-299ae333cdcd');\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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kyEvmmtD79V7",
        "outputId": "0ed0638d-af9c-4a03-c097-a249eb61a3cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2020-04-14 00:00:00\n",
            "2020-07-22 00:00:00\n",
            "2020-04-14 00:00:00\n",
            "2020-07-22 00:00:00\n"
          ]
        }
      ],
      "source": [
        "##print(max(df[\"timestamp\"]))\n",
        "df_prod0 = new_df.loc[new_df['product']=='xiaomi Pocophone 128GB']\n",
        "print(min(df_prod0[\"timestamp\"]))\n",
        "print(max(df_prod0[\"timestamp\"]))\n",
        "\n",
        "\n",
        "df_prod1 = df.loc[df['product']=='xiaomi Pocophone 128GB']\n",
        "print(min(df_prod1[\"timestamp\"]))\n",
        "print(max(df_prod1[\"timestamp\"]))\n",
        "##print('Maximum value:', max_value)\n",
        "##df_prod0 = df[\"product\"]\n",
        "##print(max(df[\"timestamp\"][]))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Obter produtos com 1 única amostra\n",
        "\n"
      ],
      "metadata": {
        "id": "VSvT7ht24lnb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3=new_df2[new_df2.groupby(['product_id']).product_id.transform('count')<2]\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJSJYT-H2AAg",
        "outputId": "256090f0-e133-4079-cb70-c9cf661bb040"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        product_id                        product country      company  \\\n",
              "427              6      xiaomi Redmi Note 8 128GB      CO     Movistar   \n",
              "6925            45       samsung Galaxy A31 128GB      PE        Claro   \n",
              "7543            46       samsung Galaxy A31 128GB      PE        entel   \n",
              "13385           81     motorola Moto G8 Play 32GB      PE        Claro   \n",
              "19951          116       samsung Galaxy A51 128GB      PE        entel   \n",
              "...            ...                            ...     ...          ...   \n",
              "593672        3197  samsung Galaxy A5 (2016) 16Gb      PT  Media Markt   \n",
              "593673        3198  samsung Galaxy A5 (2017) 32GB      PT  Media Markt   \n",
              "595665        3206    motorola Moto G7 Power 64GB      CL        Paris   \n",
              "596812        3214                 nokia 6.1 32GB      CL    Falabella   \n",
              "596982        3221               huawei Y9S 128GB      PE        Claro   \n",
              "\n",
              "        timestamp            offer_type  price_euro  \n",
              "427    2021-06-01     postpaid_new_line  627.626672  \n",
              "6925   2020-11-08     postpaid_new_line  282.960000  \n",
              "7543   2020-09-29  postpaid_portability  278.160000  \n",
              "13385  2020-11-08     postpaid_new_line  138.960000  \n",
              "19951  2021-03-08     postpaid_new_line  318.960000  \n",
              "...           ...                   ...         ...  \n",
              "593672 2021-03-20              unlocked  249.000000  \n",
              "593673 2021-03-20              unlocked  259.000000  \n",
              "595665 2021-05-27              unlocked  109.151278  \n",
              "596812 2020-08-21              unlocked  181.924861  \n",
              "596982 2021-10-29     postpaid_new_line  153.360000  \n",
              "\n",
              "[127 rows x 7 columns]"
            ],
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              "\n",
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              "      <td>45</td>\n",
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              "      <td>Claro</td>\n",
              "      <td>2020-11-08</td>\n",
              "      <td>postpaid_new_line</td>\n",
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              "      <th>7543</th>\n",
              "      <td>46</td>\n",
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              "      <td>entel</td>\n",
              "      <td>2021-03-08</td>\n",
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              "      <td>318.960000</td>\n",
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              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>593673</th>\n",
              "      <td>3198</td>\n",
              "      <td>samsung Galaxy A5 (2017) 32GB</td>\n",
              "      <td>PT</td>\n",
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              "    <tr>\n",
              "      <th>595665</th>\n",
              "      <td>3206</td>\n",
              "      <td>motorola Moto G7 Power 64GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Paris</td>\n",
              "      <td>2021-05-27</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>109.151278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596812</th>\n",
              "      <td>3214</td>\n",
              "      <td>nokia 6.1 32GB</td>\n",
              "      <td>CL</td>\n",
              "      <td>Falabella</td>\n",
              "      <td>2020-08-21</td>\n",
              "      <td>unlocked</td>\n",
              "      <td>181.924861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596982</th>\n",
              "      <td>3221</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Claro</td>\n",
              "      <td>2021-10-29</td>\n",
              "      <td>postpaid_new_line</td>\n",
              "      <td>153.360000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>127 rows × 7 columns</p>\n",
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              "      </button>\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fa5b2e1d-5139-43e7-92a8-379926c22a58');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Séires temporais com mais do que 15 amostras que será utilizado mais à frente\n",
        "\n"
      ],
      "metadata": {
        "id": "Je27AkIQ4rM1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4=new_df2[new_df2.groupby(['product_id']).product_id.transform('count')>15]\n",
        "df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMyfglll4yaK",
        "outputId": "6f1a850f-9d3e-45b6-b4c0-d0ea35686ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        product_id                 product country    company  timestamp  \\\n",
              "80               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-14   \n",
              "79               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-15   \n",
              "78               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-16   \n",
              "77               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-17   \n",
              "76               1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-18   \n",
              "...            ...                     ...     ...        ...        ...   \n",
              "601733        3236        huawei Y9S 128GB      PE   Movistar 2020-07-17   \n",
              "601720        3236        huawei Y9S 128GB      PE   Movistar 2020-07-18   \n",
              "601707        3236        huawei Y9S 128GB      PE   Movistar 2020-07-19   \n",
              "601693        3236        huawei Y9S 128GB      PE   Movistar 2020-07-20   \n",
              "601678        3236        huawei Y9S 128GB      PE   Movistar 2020-07-21   \n",
              "\n",
              "                  offer_type  price_euro  \n",
              "80                  unlocked  727.726735  \n",
              "79                  unlocked  727.726735  \n",
              "78                  unlocked  727.726735  \n",
              "77                  unlocked  727.726735  \n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df6=new_df2[new_df2.groupby(['product_id']).product_id.transform('count')<16]\n",
        "df6"
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        "id": "w0YhWAabDcIb",
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      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "        product_id                      product country    company  timestamp  \\\n",
              "427              6    xiaomi Redmi Note 8 128GB      CO   Movistar 2021-06-01   \n",
              "878             11    xiaomi Redmi Note 8 128GB      CL      Lider 2020-07-16   \n",
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              "...            ...                          ...     ...        ...        ...   \n",
              "595665        3206  motorola Moto G7 Power 64GB      CL      Paris 2021-05-27   \n",
              "596812        3214               nokia 6.1 32GB      CL  Falabella 2020-08-21   \n",
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              "597944        3227             huawei Y9S 128GB      CO   Movistar 2021-05-31   \n",
              "597938        3227             huawei Y9S 128GB      CO   Movistar 2021-06-01   \n",
              "\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",
              "          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",
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              "        }\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df7 = df6.drop_duplicates(subset=['product_id'])\n",
        "df7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ID1uInBS0cx",
        "outputId": "700fb50e-5e07-428b-f6d9-0132153a84ef"
      },
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "        product_id                          product country    company  \\\n",
              "427              6        xiaomi Redmi Note 8 128GB      CO   Movistar   \n",
              "878             11        xiaomi Redmi Note 8 128GB      CL      Lider   \n",
              "1205            17         blu Joy Adulto Mayor Sos      CL  Falabella   \n",
              "6925            45         samsung Galaxy A31 128GB      PE      Claro   \n",
              "7543            46         samsung Galaxy A31 128GB      PE      entel   \n",
              "...            ...                              ...     ...        ...   \n",
              "595477        3204  huawei P30 128GB + P Smart 2019      CO      Claro   \n",
              "595665        3206      motorola Moto G7 Power 64GB      CL      Paris   \n",
              "596812        3214                   nokia 6.1 32GB      CL  Falabella   \n",
              "596982        3221                 huawei Y9S 128GB      PE      Claro   \n",
              "597944        3227                 huawei Y9S 128GB      CO   Movistar   \n",
              "\n",
              "        timestamp            offer_type   price_euro  \n",
              "427    2021-06-01     postpaid_new_line   627.626672  \n",
              "878    2020-07-16              unlocked   218.311653  \n",
              "1205   2020-04-14              unlocked    36.377695  \n",
              "6925   2020-11-08     postpaid_new_line   282.960000  \n",
              "7543   2020-09-29  postpaid_portability   278.160000  \n",
              "...           ...                   ...          ...  \n",
              "595477 2020-03-31              unlocked  1673.701446  \n",
              "595665 2021-05-27              unlocked   109.151278  \n",
              "596812 2020-08-21              unlocked   181.924861  \n",
              "596982 2021-10-29     postpaid_new_line   153.360000  \n",
              "597944 2021-05-31     postpaid_new_line  1182.497044  \n",
              "\n",
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              "      <td>2021-10-29</td>\n",
              "      <td>postpaid_new_line</td>\n",
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              "          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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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df8=new_df2[new_df2.groupby(['product_id']).product_id.transform('count')>1]\n",
        "df8=df8[df8.groupby(['product_id']).product_id.transform('count')<16]\n",
        "df8.insert(loc=0, column='ID', value=df8.set_index(['company','country', 'product', 'offer_type']).index.factorize()[0]+1)\n",
        "df8= df8.drop_duplicates(subset=['ID'])\n",
        "df8"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSEj84WcUh9Q",
        "outputId": "ac89fc55-eb68-4834-e82b-7844864ca6ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         ID  product_id                          product country    company  \\\n",
              "878       1          11        xiaomi Redmi Note 8 128GB      CL      Lider   \n",
              "1205      2          17         blu Joy Adulto Mayor Sos      CL  Falabella   \n",
              "8935      3          49                      blu G9 64GB      CL  Falabella   \n",
              "8937      4          50                        nokia 3.1      CL      Paris   \n",
              "10341     5          59        motorola One Action 128GB      CL      Paris   \n",
              "...     ...         ...                              ...     ...        ...   \n",
              "586755  343        3165        samsung Galaxy S10+ 512GB      CL   Movistar   \n",
              "593417  344        3189             apple iPhone 6S 64GB      CL     Abcdin   \n",
              "595476  345        3203  huawei P30 128GB + P Smart 2019      CL      entel   \n",
              "595477  346        3204  huawei P30 128GB + P Smart 2019      CO      Claro   \n",
              "597944  347        3227                 huawei Y9S 128GB      CO   Movistar   \n",
              "\n",
              "        timestamp         offer_type   price_euro  \n",
              "878    2020-07-16           unlocked   218.311653  \n",
              "1205   2020-04-14           unlocked    36.377695  \n",
              "8935   2020-04-14           unlocked   209.214955  \n",
              "8937   2021-03-24           unlocked    72.764486  \n",
              "10341  2020-08-01           unlocked   145.538070  \n",
              "...           ...                ...          ...  \n",
              "586755 2020-08-18  postpaid_new_line   727.728555  \n",
              "593417 2021-08-30           unlocked   136.441372  \n",
              "595476 2020-03-31           unlocked   398.326208  \n",
              "595477 2020-03-31           unlocked  1673.701446  \n",
              "597944 2021-05-31  postpaid_new_line  1182.497044  \n",
              "\n",
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        "df4.insert(loc=0, column='ID', value=df4.set_index(['company','country', 'product', 'offer_type']).index.factorize()[0]+1)\n",
        "df4"
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      "execution_count": null,
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              "          ID  product_id                 product country    company  \\\n",
              "80         1           1  xiaomi Pocophone 128GB      CL  Falabella   \n",
              "79         1           1  xiaomi Pocophone 128GB      CL  Falabella   \n",
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    },
    {
      "cell_type": "code",
      "source": [
        "del df4['product_id']\n",
        "df4"
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        "id": "FULUitkl5UmR",
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      "execution_count": null,
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            "text/plain": [
              "          ID                 product country    company  timestamp  \\\n",
              "80         1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-14   \n",
              "79         1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-15   \n",
              "78         1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-16   \n",
              "77         1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-17   \n",
              "76         1  xiaomi Pocophone 128GB      CL  Falabella 2020-04-18   \n",
              "...      ...                     ...     ...        ...        ...   \n",
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              "601678  2762        huawei Y9S 128GB      PE   Movistar 2020-07-21   \n",
              "\n",
              "                  offer_type  price_euro  \n",
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              "79                  unlocked  727.726735  \n",
              "78                  unlocked  727.726735  \n",
              "77                  unlocked  727.726735  \n",
              "76                  unlocked  727.726735  \n",
              "...                      ...         ...  \n",
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              "601720  postpaid_portability  364.560000  \n",
              "601707  postpaid_portability  364.560000  \n",
              "601693  postpaid_portability  364.560000  \n",
              "601678  postpaid_portability  364.560000  \n",
              "\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601733</th>\n",
              "      <td>2762</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>2020-07-17</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601720</th>\n",
              "      <td>2762</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>2020-07-18</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601707</th>\n",
              "      <td>2762</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>2020-07-19</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601693</th>\n",
              "      <td>2762</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>2020-07-20</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601678</th>\n",
              "      <td>2762</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
              "      <td>PE</td>\n",
              "      <td>Movistar</td>\n",
              "      <td>2020-07-21</td>\n",
              "      <td>postpaid_portability</td>\n",
              "      <td>364.560000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>599388 rows × 7 columns</p>\n",
              "</div>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(df4['ID'])"
      ],
      "metadata": {
        "id": "hAwUJp7g5ci0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbe376d9-a342-44c0-8867-eafd5735e0f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2762"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df5 = df4.drop_duplicates(subset=['ID'])\n",
        "df5"
      ],
      "metadata": {
        "id": "jP1fCWeA8M0b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e2fcd13-6d43-4e30-e620-12ced6d4b4f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ID                    product country    company  timestamp  \\\n",
              "80         1     xiaomi Pocophone 128GB      CL  Falabella 2020-04-14   \n",
              "781        2  xiaomi Redmi Note 8 128GB      CO   Movistar 2021-01-22   \n",
              "432        3  xiaomi Redmi Note 8 128GB      CO       Tigo 2021-05-31   \n",
              "429        4  xiaomi Redmi Note 8 128GB      CO        wom 2021-06-01   \n",
              "337        5  xiaomi Redmi Note 8 128GB      CO      Claro 2021-07-03   \n",
              "...      ...                        ...     ...        ...        ...   \n",
              "599597  2758           huawei Y9S 128GB      CL        wom 2020-12-22   \n",
              "602055  2759           huawei Y9S 128GB      PE      Claro 2020-06-20   \n",
              "602052  2760           huawei Y9S 128GB      CO      Claro 2020-06-21   \n",
              "602046  2761           huawei Y9S 128GB      PE  Falabella 2020-06-21   \n",
              "602054  2762           huawei Y9S 128GB      PE   Movistar 2020-06-20   \n",
              "\n",
              "                  offer_type   price_euro  \n",
              "80                  unlocked   727.726735  \n",
              "781                 unlocked   636.723369  \n",
              "432        postpaid_new_line   600.291094  \n",
              "429                 unlocked   636.677886  \n",
              "337                 unlocked   684.890385  \n",
              "...                      ...          ...  \n",
              "599597     postpaid_new_line   212.862731  \n",
              "602055              unlocked   273.360000  \n",
              "602052              unlocked  1063.313017  \n",
              "602046              unlocked   335.760000  \n",
              "602054  postpaid_portability   364.560000  \n",
              "\n",
              "[2762 rows x 7 columns]"
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              "      <td>2759</td>\n",
              "      <td>huawei Y9S 128GB</td>\n",
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              "      <td>2760</td>\n",
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              "      <th>602046</th>\n",
              "      <td>2761</td>\n",
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              "      <td>Falabella</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
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        "## Texto info"
      ],
      "metadata": {
        "id": "fG2Nohr06eru"
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    {
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        "*   df -> matriz obtida da base de dados com a coluna do preço em euros \n",
        "*   new_df -> df com os product IDs e com o preço em euros\n",
        "*   new_df2 -> mesma matriz mas ordenada\n",
        "*   df3 -> produtos com apenas 1 amostra\n",
        "*   df4 -> produtos com mais do que 15 amostras ordenado\n",
        "*   df5 -> Apenas os produtos sem duplicados e ordenados por ID\n",
        "*   df6 -> produtos com menos do que 16 amostras"
      ],
      "metadata": {
        "id": "nXzsExSs6iNi"
      }
    }
  ],
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