02_3_Date_Time_Variables.ipynb

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        "<a href=\"https://colab.research.google.com/github/christophergaughan/ChristopherGaughan.io/blob/master/02_3_Date_Time_Variables.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "## Dates and Times\n",
        "\n",
        "A special type of categorical variable are those that instead of taking traditional labels, like color (blue, red), or city (London, Manchester), take dates and / or time as values. For example, date of birth ('29-08-1987', '12-01-2012'), or date of application ('2016-Dec', '2013-March').\n",
        "\n",
        "Datetime variables can contain dates only, time only, or date and time.\n",
        "\n",
        "We don't usually work with a datetime variable in their raw format because:\n",
        "\n",
        "- Date variables contain a huge number of different categories\n",
        "- We can extract much more information from datetime variables by preprocessing them correctly\n",
        "\n",
        "In addition, often, date variables will contain dates that were not present in the dataset used to train the machine learning model. In fact, date variables will usually contain dates placed in the future, respect to the dates in the training dataset. Therefore, the machine learning model will not know what to do with them, because it never saw them while being trained. \n",
        "\n",
        "**I will cover different was of pre-processing / engineering datetime variables in the section \"Engineering Datetime Variables\". later in this course**\n",
        "\n",
        "=============================================================================\n",
        "\n",
        "## In this demo: Peer to peer lending (Finance)\n",
        "\n",
        "In this demo, we will use a toy data set which simulates data from a peer-o-peer finance company to inspect discrete and continuous numerical variables.\n",
        "\n",
        "- You should have downloaded the **Datasets** together with the Jupyter notebooks in **Section 1**."
      ]
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    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
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        "# let's load the dataset\n",
        "\n",
        "# Variable definitions:\n",
        "#-------------------------\n",
        "# disbursed amount: loan amount lent to the borrower\n",
        "# market: risk band in which borrowers are placed\n",
        "# loan purpose: intended use of the loan\n",
        "# date_issued: date the loan was issued\n",
        "# date_last_payment: date of last payment towards repyaing the loan\n",
        "\n",
        "data = pd.read_csv('../loan.csv')\n",
        "\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>disbursed_amount</th>\n",
              "      <th>interest</th>\n",
              "      <th>market</th>\n",
              "      <th>employment</th>\n",
              "      <th>time_employed</th>\n",
              "      <th>householder</th>\n",
              "      <th>income</th>\n",
              "      <th>date_issued</th>\n",
              "      <th>target</th>\n",
              "      <th>loan_purpose</th>\n",
              "      <th>number_open_accounts</th>\n",
              "      <th>date_last_payment</th>\n",
              "      <th>number_credit_lines_12</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>23201.5</td>\n",
              "      <td>15.4840</td>\n",
              "      <td>C</td>\n",
              "      <td>Teacher</td>\n",
              "      <td>&lt;=5 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>84600.0</td>\n",
              "      <td>2013-06-11</td>\n",
              "      <td>0</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2016-01-14</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7425.0</td>\n",
              "      <td>11.2032</td>\n",
              "      <td>B</td>\n",
              "      <td>Accountant</td>\n",
              "      <td>&lt;=5 years</td>\n",
              "      <td>OWNER</td>\n",
              "      <td>102000.0</td>\n",
              "      <td>2014-05-08</td>\n",
              "      <td>0</td>\n",
              "      <td>Car purchase</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2016-01-25</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>11150.0</td>\n",
              "      <td>8.5100</td>\n",
              "      <td>A</td>\n",
              "      <td>Statistician</td>\n",
              "      <td>&lt;=5 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>69840.0</td>\n",
              "      <td>2013-10-26</td>\n",
              "      <td>0</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2014-09-26</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>7600.0</td>\n",
              "      <td>5.8656</td>\n",
              "      <td>A</td>\n",
              "      <td>Other</td>\n",
              "      <td>&lt;=5 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>100386.0</td>\n",
              "      <td>2015-08-20</td>\n",
              "      <td>0</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2016-01-26</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>31960.0</td>\n",
              "      <td>18.7392</td>\n",
              "      <td>E</td>\n",
              "      <td>Bus driver</td>\n",
              "      <td>&gt;5 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>95040.0</td>\n",
              "      <td>2014-07-22</td>\n",
              "      <td>0</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2016-01-11</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "   customer_id  disbursed_amount  interest market    employment time_employed  \\\n",
              "0            0           23201.5   15.4840      C       Teacher     <=5 years   \n",
              "1            1            7425.0   11.2032      B    Accountant     <=5 years   \n",
              "2            2           11150.0    8.5100      A  Statistician     <=5 years   \n",
              "3            3            7600.0    5.8656      A         Other     <=5 years   \n",
              "4            4           31960.0   18.7392      E    Bus driver      >5 years   \n",
              "\n",
              "  householder    income date_issued  target        loan_purpose  \\\n",
              "0        RENT   84600.0  2013-06-11       0  Debt consolidation   \n",
              "1       OWNER  102000.0  2014-05-08       0        Car purchase   \n",
              "2        RENT   69840.0  2013-10-26       0  Debt consolidation   \n",
              "3        RENT  100386.0  2015-08-20       0  Debt consolidation   \n",
              "4        RENT   95040.0  2014-07-22       0  Debt consolidation   \n",
              "\n",
              "   number_open_accounts date_last_payment  number_credit_lines_12  \n",
              "0                   4.0        2016-01-14                     NaN  \n",
              "1                  13.0        2016-01-25                     NaN  \n",
              "2                   8.0        2014-09-26                     NaN  \n",
              "3                  20.0        2016-01-26                     NaN  \n",
              "4                  14.0        2016-01-11                     NaN  "
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      "source": [
        "# pandas assigns type 'object' when reading dates\n",
        "# and considers them strings.\n",
        "# Let's have a look\n",
        "\n",
        "data[['date_issued', 'date_last_payment']].dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date_issued          object\n",
              "date_last_payment    object\n",
              "dtype: object"
            ]
          },
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            "tags": []
          },
          "execution_count": 3
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      "source": [
        "Both date_issued and date_last_payment are casted as objects. Therefore, pandas will treat them as strings or categorical variables.\n",
        "\n",
        "In order to instruct pandas to treat them as dates, we need to re-cast them into datetime format. See below."
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# now let's parse the dates, currently coded as strings, into datetime format\n",
        "# this will allow us to make some analysis afterwards\n",
        "\n",
        "data['date_issued_dt'] = pd.to_datetime(data['date_issued'])\n",
        "data['date_last_payment_dt'] = pd.to_datetime(data['date_last_payment'])\n",
        "\n",
        "data[['date_issued', 'date_issued_dt', 'date_last_payment', 'date_last_payment_dt']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date_issued</th>\n",
              "      <th>date_issued_dt</th>\n",
              "      <th>date_last_payment</th>\n",
              "      <th>date_last_payment_dt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2013-06-11</td>\n",
              "      <td>2013-06-11</td>\n",
              "      <td>2016-01-14</td>\n",
              "      <td>2016-01-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-05-08</td>\n",
              "      <td>2014-05-08</td>\n",
              "      <td>2016-01-25</td>\n",
              "      <td>2016-01-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-10-26</td>\n",
              "      <td>2013-10-26</td>\n",
              "      <td>2014-09-26</td>\n",
              "      <td>2014-09-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-08-20</td>\n",
              "      <td>2015-08-20</td>\n",
              "      <td>2016-01-26</td>\n",
              "      <td>2016-01-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-07-22</td>\n",
              "      <td>2014-07-22</td>\n",
              "      <td>2016-01-11</td>\n",
              "      <td>2016-01-11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "  date_issued date_issued_dt date_last_payment date_last_payment_dt\n",
              "0  2013-06-11     2013-06-11        2016-01-14           2016-01-14\n",
              "1  2014-05-08     2014-05-08        2016-01-25           2016-01-25\n",
              "2  2013-10-26     2013-10-26        2014-09-26           2014-09-26\n",
              "3  2015-08-20     2015-08-20        2016-01-26           2016-01-26\n",
              "4  2014-07-22     2014-07-22        2016-01-11           2016-01-11"
            ]
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      "source": [
        "# let's extract the month and the year from the variable date\n",
        "# to make nicer plots\n",
        "\n",
        "# more on this in section 12 of the course\n",
        "\n",
        "data['month'] = data['date_issued_dt'].dt.month\n",
        "data['year'] = data['date_issued_dt'].dt.year"
      ],
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
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      "source": [
        "# let's see how much money Lending Club has disbursed\n",
        "# (i.e., lent) over the years to the different risk\n",
        "# markets (grade variable)\n",
        "\n",
        "fig = data.groupby(['year','month', 'market'])['disbursed_amount'].sum().unstack().plot(\n",
        "    figsize=(14, 8), linewidth=2)\n",
        "\n",
        "fig.set_title('Disbursed amount in time')\n",
        "fig.set_ylabel('Disbursed Amount')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Disbursed Amount')"
            ]
          },
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          "execution_count": 6
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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2vy6m6bAoS20"
      },
      "source": [
        "This toy finance company seems to have increased the amount of money lent from 2012 onwards. The tendency indicates that they continue to grow. In addition, we can see that their major business comes from lending money to C and B grades.\n",
        "\n",
        "'A' grades are the lower risk borrowers, borrowers that most likely will be able to repay their loans, as they are typically in a better financial situation. Borrowers within this grade are charged lower interest rates.\n",
        "\n",
        "D and E grades represent the riskier borrowers. Usually borrowers in somewhat tighter financial situations, or for whom there is not sufficient financial history to make a reliable credit assessment. They are typically charged higher rates, as the business, and therefore the investors, take a higher risk when lending them money."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "VjgUpLP5oS20"
      },
      "source": [
        "**That is all for this demonstration. I hope you enjoyed the notebook, and see you in the next one.**"
      ]
    }
  ]
}