2019_income_&_expenses.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/KelvinLam05/personal_finance_tracker/blob/main/2019_income_%26_expenses.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "**Import the package**"
      ],
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      "source": [
        "# Importing library\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load the data**"
      ],
      "metadata": {
        "id": "3JyfVPuqA4zl"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "For the personal finance tracker I’ve used a [personal transaction dataset](https://www.kaggle.com/datasets/bukolafatunde/personal-finance) from Kaggle."
      ],
      "metadata": {
        "id": "whCYcwsMaeVA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset\n",
        "df = pd.read_csv('/content/personal_transactions.csv')"
      ],
      "metadata": {
        "id": "GLHT3xk0-f89"
      },
      "execution_count": 2,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Examine the data\n",
        "df.head()"
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            "text/plain": [
              "         Date          Description   Amount Transaction Type  \\\n",
              "0  01/01/2018               Amazon    11.11            debit   \n",
              "1  01/02/2018     Mortgage Payment  1247.44            debit   \n",
              "2  01/02/2018      Thai Restaurant    24.22            debit   \n",
              "3  01/03/2018  Credit Card Payment  2298.09           credit   \n",
              "4  01/04/2018              Netflix    11.76            debit   \n",
              "\n",
              "              Category   Account Name  \n",
              "0             Shopping  Platinum Card  \n",
              "1      Mortgage & Rent       Checking  \n",
              "2          Restaurants    Silver Card  \n",
              "3  Credit Card Payment  Platinum Card  \n",
              "4        Movies & DVDs  Platinum Card  "
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              "      <th>1</th>\n",
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              "      <td>Mortgage Payment</td>\n",
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      "source": [
        "# Overview of all variables, their datatypes\n",
        "df.info()"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 806 entries, 0 to 805\n",
            "Data columns (total 6 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Date              806 non-null    object \n",
            " 1   Description       806 non-null    object \n",
            " 2   Amount            806 non-null    float64\n",
            " 3   Transaction Type  806 non-null    object \n",
            " 4   Category          806 non-null    object \n",
            " 5   Account Name      806 non-null    object \n",
            "dtypes: float64(1), object(5)\n",
            "memory usage: 37.9+ KB\n"
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    {
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      "source": [
        "**Prepare the data**"
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    {
      "cell_type": "code",
      "source": [
        "# Convert the date to datetime64\n",
        "df['Date'] = pd.to_datetime(df['Date'])"
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      "metadata": {
        "id": "587iEcHH-zy-"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter data for specific year (2019)\n",
        "filtered_df = df.loc[df['Date'].dt.year == 2019]"
      ],
      "metadata": {
        "id": "d2vB41ND0O3u"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the month for each date in our dataframe\n",
        "filtered_df['Month'] = filtered_df['Date'].dt.strftime('%B')"
      ],
      "metadata": {
        "id": "PG8AHWgz_PPk"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "# Convert the column 'Amount' from float to string\n",
        "filtered_df['Amount'] = filtered_df['Amount'].astype(str)"
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      "metadata": {
        "id": "mXTCy7KhAtqq"
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      "source": [
        "# Change decimals to commas \n",
        "filtered_df['Amount'] = filtered_df['Amount'].str.replace('.', ',')"
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      "source": [
        "# Only include the relevant columns\n",
        "filtered_df = filtered_df[['Date', 'Month', 'Category', 'Description', 'Amount']]"
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          "data": {
            "text/plain": [
              "          Date    Month             Category          Description  Amount\n",
              "482 2019-01-01  January             Shopping               Amazon   13,09\n",
              "483 2019-01-02  January  Credit Card Payment  Credit Card Payment   499,6\n",
              "484 2019-01-03  January      Mortgage & Rent     Mortgage Payment  1100,0\n",
              "485 2019-01-03  January             Shopping               Amazon    42,1\n",
              "486 2019-01-04  January             Paycheck    Biweekly Paycheck  2000,0"
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              "      <td>2019-01-01</td>\n",
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              "      <td>13,09</td>\n",
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              "      <td>2019-01-02</td>\n",
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              "      <td>Credit Card Payment</td>\n",
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              "      <td>499,6</td>\n",
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              "      <td>2019-01-03</td>\n",
              "      <td>January</td>\n",
              "      <td>Mortgage &amp; Rent</td>\n",
              "      <td>Mortgage Payment</td>\n",
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              "      <td>2019-01-03</td>\n",
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      "cell_type": "code",
      "source": [
        "# Convert dataframe to csv\n",
        "filtered_df.to_csv('/content/2019_income_&_expenses.csv', index = None, header = True)"
      ],
      "metadata": {
        "id": "o8ogYHxzy5d8"
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      "execution_count": 12,
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}