{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "DQ_birthdates_basic_stats_v1.1\n"
      ],
      "metadata": {
        "id": "wO2KsMTUPxWr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analysis approach of the data quality of dates using basic statistical methods (with Python)**\n",
        "A user-friendly approach to detect data quality issues and outliers in dates"
      ],
      "metadata": {
        "id": "-QF90t-BeCk5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "There main problems that cause defects in data quality are related to: problems in data entry applications, incorrect migration of data from old systems, degradation of information systems, process integration problems, etc.\n",
        "\n",
        "All of them lead to information degradation problems, generate high management costs at the operational level and have a negative impact on information analysis and decision making.\n",
        "\n",
        "In order to detect the existence of possible data quality problems in the imported dataset, it is recommended to carry out the following set of steps:\n",
        "\n",
        "1. Define the use case to analyze\n",
        "2. Explore dataset\n",
        "3. Detection and cleansing of main technical errors\n",
        "4. Analysis of distribution and frequencies\n",
        "\n",
        "\n",
        "**1. Define the use case to analyze**\n",
        "It is important to keep in mind the use case to which the data to be analyzed refers, because in its analysis we must apply the technical criteria of the data as well as the functional ones. An example could be the analysis of dates of birth of customers of an online store. The range of ages should be over 18 years to 100 years. Out of this age range, the records have a high probability of being considered a possible error.\n",
        "\n",
        "\n",
        "**2. Explore dataset**\n",
        "Once the use case is known, we will import the dates of birth dataset from a public github repository. Once the dataset is loaded, we will obtain the information from the dataset, as well as a preview a sample of the data, where we can see that the dataset contains two columns: one of them is about ids and the other dates of birth on datetime format.\n",
        "\n",
        "Additionally, we will obtain the maximum and minimum dates to get an idea of ​​the range of dates with which we are working about."
      ],
      "metadata": {
        "id": "GkJfb_MXXZb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "#Import dataset dates from github\n",
        "url = \"https://raw.githubusercontent.com/mabrotons/datasets/master/birthdates.csv\"\n",
        "\n",
        "\n",
        "df = pd.read_csv(url, index_col=0, parse_dates=['birthdates'])\n",
        "\n",
        "print(\"Data frame info: \") \n",
        "print(df.info())\n",
        "\n",
        "print(\"\\nData frame head: \") \n",
        "print(df.head())\n",
        "\n",
        "dates = df['birthdates']\n",
        "print(\"Min date: \" + str(min(dates)))\n",
        "print(\"Max date: \" + str(max(dates)))"
      ],
      "metadata": {
        "id": "DutKSKQ9XISC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed4f0f1b-862d-4022-e612-4eac2652ba60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data frame info: \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2103 entries, 0 to 2102\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   ids         2103 non-null   object        \n",
            " 1   birthdates  2103 non-null   datetime64[ns]\n",
            "dtypes: datetime64[ns](1), object(1)\n",
            "memory usage: 49.3+ KB\n",
            "None\n",
            "\n",
            "Data frame head: \n",
            "           ids birthdates\n",
            "0  C0000000001 1948-01-16\n",
            "1  C0000000002 1988-03-11\n",
            "2  C0000000003 1999-05-11\n",
            "3  C0000000004 1997-06-01\n",
            "4  C0000000005 1967-12-10\n",
            "Min date: 1801-09-03 00:00:00\n",
            "Max date: 2029-10-15 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Detection and cleansing of main technical errors**\n",
        "\n",
        "As a first step on the analysis, we will build a graph to represent all the data included in the dataset in order to have a first view of the data, its distribution, the range, as well as detect possible outliers."
      ],
      "metadata": {
        "id": "_TD1njAdXXrb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "f = plt.figure()\n",
        "f.set_figwidth(15)\n",
        "f.set_figheight(5)\n",
        "\n",
        "plt.hist(dates, bins=50, edgecolor='black')\n",
        "plt.xticks(rotation=30)\n",
        "plt.title(\"Dates\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "drxqdxn1vcfy",
        "outputId": "689967e8-ed14-400f-9e45-945272b8eea3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once the global vision of the date ranges from year 1801 to 2029 is available, we can see:\n",
        "- some of years are out of trustly range\n",
        "- the high density of dates is distributed between the years 1940 to 2000\n",
        "- the graphs show a clear error close to the year 2000\n",
        "\n",
        "If we focus on the outliers of the year 2000, we can detect that there is a possible problem with the date 01/01/2000 (dd/mm/YYYY) might be caused by a technical error or by using date file as a dummy date."
      ],
      "metadata": {
        "id": "aeV_LtnMPy2t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we focus on the outliers of the year 2000, we can detect that there is a possible problem with the date 01/01/2000 (dd/mm/YYYY) that is possibly caused by a technical error or by using date file as a dummy date."
      ],
      "metadata": {
        "id": "1BUlZp4vW5BN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import date\n",
        "dates_outlier = [item for item in dates if item > date(1999, 12, 15) and item < date(2000, 1, 15)]\n",
        "f = plt.figure()\n",
        "f.set_figwidth(15)\n",
        "f.set_figheight(5)\n",
        "plt.xticks(rotation=30)\n",
        "plt.hist(dates_outlier, bins=30, edgecolor='black')\n",
        "plt.title(\"Outliers\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "6O7_Xw6mZPJB",
        "outputId": "99070213-cc80-4575-f75c-23388080e83c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once potential outliers are removed, we can re-visualize the plot without erroneous data, making easy."
      ],
      "metadata": {
        "id": "t-l99hZCZj9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates_clean = [item for item in dates if item > date(2000, 1, 1) or item < date(2000, 1, 1)]\n",
        "f = plt.figure()\n",
        "f.set_figwidth(15)\n",
        "f.set_figheight(5)\n",
        "plt.xticks(rotation=30)\n",
        "plt.hist(dates_clean, bins=40, edgecolor='black')\n",
        "plt.title(\"Dates without outliers\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "lDIKMcvkZup2",
        "outputId": "19ea8dee-2a20-41ab-a871-727b600b2989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Analysis of distribution and frequencies**\n",
        "\n",
        "The next step is to analyze the split of dates of the dataset, component by component. First of all, we can analyze dates (excluding previous detected outliers) from the point of view of the year, and it seems like the global view."
      ],
      "metadata": {
        "id": "jq8gXJUaaHbP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates_clean_year = [item.year for item in dates_clean]\n",
        "f = plt.figure()\n",
        "f.set_figwidth(15)\n",
        "f.set_figheight(5)\n",
        "\n",
        "plt.hist(dates_clean_year, bins=40, edgecolor='black')\n",
        "plt.title(\"Years\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "8dqvP2R3zMiG",
        "outputId": "a22aa9f0-3202-4eae-8e97-a2ed4e8ab539"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we can do the same analysis using the month as frequency."
      ],
      "metadata": {
        "id": "CUOJzI3gRyZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates_clean_month = [item.month for item in dates_clean]\n",
        "f = plt.figure()\n",
        "f.set_figwidth(10)\n",
        "f.set_figheight(5)\n",
        "plt.hist(dates_clean_month, bins=12, edgecolor='black')\n",
        "plt.title(\"Months\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "zFRrZ-7MSUvQ",
        "outputId": "55b474d5-110e-47b8-e5a9-00b32017e75c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Viewing dates through the day of the month, we can detect a new outlier candidate: a large number of dates with the first day of the month compared to all other dates."
      ],
      "metadata": {
        "id": "f3nxadnKSuMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dates_clean_day = [item.day for item in dates_clean]\n",
        "f = plt.figure()\n",
        "f.set_figwidth(10)\n",
        "f.set_figheight(5)\n",
        "plt.hist(dates_clean_day, bins=31, edgecolor='black')\n",
        "plt.title(\"Days\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "I9ynM5alTRBH",
        "outputId": "02988b3b-e542-4967-ff24-01390d3b78e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we can analyze from days of week as frequency."
      ],
      "metadata": {
        "id": "8Om6hNk0Tq3q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weekDays = (\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\")\n",
        "dates_clean_weekday = [weekDays[item.weekday()] for item in dates_clean]\n",
        "f = plt.figure()\n",
        "f.set_figwidth(10)\n",
        "f.set_figheight(5)\n",
        "plt.hist(dates_clean_weekday, bins=7, edgecolor='black')\n",
        "plt.title(\"WeekDays\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "XRMaxn0iT1ec",
        "outputId": "d5f9003e-4773-4c64-a98d-f303459da929"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this use case, being a dataset of dates of birth, we can do the analysis as simple dates (as we have done) or transform them to ages."
      ],
      "metadata": {
        "id": "8ZzZ9cNwUTlY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_age(birth_date):\n",
        "    today = date.today()\n",
        "    age = today.year - birth_date.year - ((today.month, today.day) < (birth_date.month, birth_date.day))\n",
        " \n",
        "    return age\n",
        "     \n",
        "\n",
        "ages = [calculate_age(item) for item in dates_clean]\n",
        "f = plt.figure()\n",
        "f.set_figwidth(10)\n",
        "f.set_figheight(5)\n",
        "\n",
        "plt.hist(ages, bins=40, edgecolor='black')\n",
        "plt.title(\"Ages\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "4Jn56YFB50R6",
        "outputId": "28f7d403-fc9a-4201-e5a1-79e1004288ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let’s calculate the basic statistics by age and visualize them easily with a boxplot, including:\n",
        "- min whisker (Q1–1.5*IQR)\n",
        "- Q1 (25th percentile rank)\n",
        "- median\n",
        "- Q3 (75th percentile rank)\n",
        "- max whisker (Q3–1.5*IQR)\n",
        "- outliers at both ends of the box figure"
      ],
      "metadata": {
        "id": "QHEgQoRZoRvU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statistics\n",
        "print(\"Min ages: \" + str(min(ages)))\n",
        "print(\"Max ages: \" + str(max(ages)))\n",
        "print(\"Mean ages: \" + str(statistics.mean(ages)))\n",
        "print(\"Median ages: \" + str(statistics.median(ages)))\n",
        "print(\"Standard deviation ages: \" + str(statistics.stdev(ages)) +\"\\n\")\n",
        "\n",
        "# Creating plot\n",
        "f = plt.figure()\n",
        "f.set_figwidth(10)\n",
        "f.set_figheight(5)\n",
        " \n",
        "red_circle = dict(markerfacecolor='red', marker='o')\n",
        "plt.boxplot(x=ages, vert=False, patch_artist=True, flierprops=red_circle, labels=['Ages']);\n",
        "plt.title(\"Ages\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "_CYBfiBhPmNW",
        "outputId": "e443b1ef-8376-48d2-8743-fac7bb91b8cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min ages: -7\n",
            "Max ages: 221\n",
            "Mean ages: 53.85601681555439\n",
            "Median ages: 50\n",
            "Standard deviation ages: 33.66534871034922\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion**\n",
        "We can use basic statistical methods to perform a data quality analysis in order to detect potential errors and outliers. Coding with Python we can easily calculate the statistics and represent them graphically to help their interpretation.\n",
        "\n"
      ],
      "metadata": {
        "id": "vjXzriiGwwBz"
      }
    }
  ]
}