2018_income_&_expenses.ipynb

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        "<a href=\"https://colab.research.google.com/github/KelvinLam05/cash_book/blob/main/2018_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": "1V4hNkLcEet5"
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    {
      "cell_type": "markdown",
      "source": [
        "For the cash book I’ve used a [personal transaction dataset](https://www.kaggle.com/datasets/bukolafatunde/personal-finance) from Kaggle."
      ],
      "metadata": {
        "id": "kTypXmYBNKuc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset\n",
        "df = pd.read_csv('/content/personal_transactions.csv')"
      ],
      "metadata": {
        "id": "6l0dTBEfK_JB"
      },
      "execution_count": 2,
      "outputs": []
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      "source": [
        "# Examine the data\n",
        "df.head()"
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              "         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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            "<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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      "source": [
        "**Prepare the data**"
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      "cell_type": "code",
      "source": [
        "df[['Category', 'Transaction Type']].value_counts()"
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      "execution_count": 5,
      "outputs": [
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          "data": {
            "text/plain": [
              "Category                Transaction Type\n",
              "Groceries               debit               105\n",
              "Restaurants             debit                81\n",
              "Credit Card Payment     credit               72\n",
              "                        debit                71\n",
              "Utilities               debit                63\n",
              "Shopping                debit                60\n",
              "Gas & Fuel              debit                52\n",
              "Paycheck                credit               46\n",
              "Home Improvement        debit                36\n",
              "Coffee Shops            debit                31\n",
              "Alcohol & Bars          debit                25\n",
              "Internet                debit                21\n",
              "Mobile Phone            debit                21\n",
              "Mortgage & Rent         debit                21\n",
              "Music                   debit                21\n",
              "Auto Insurance          debit                18\n",
              "Movies & DVDs           debit                18\n",
              "Fast Food               debit                16\n",
              "Haircut                 debit                13\n",
              "Television              debit                 8\n",
              "Electronics & Software  debit                 4\n",
              "Food & Dining           debit                 2\n",
              "Entertainment           debit                 1\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 5
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      "cell_type": "code",
      "source": [
        "# Change credit card payment type to debit\n",
        "df.loc[df['Category'] == 'Credit Card Payment', 'Transaction Type'] = 'debit'"
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      "outputs": []
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      "source": [
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        "df['Date'] = pd.to_datetime(df['Date'])"
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      "execution_count": 7,
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      "source": [
        "# Filter data for specific year (2018)\n",
        "filtered_df = df.loc[df['Date'].dt.year == 2018]"
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        "filtered_df['Transaction Type'] = filtered_df['Transaction Type'].str.capitalize()"
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      "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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        "# Change decimals to commas \n",
        "filtered_df['Amount'] = filtered_df['Amount'].str.replace('.', ',')"
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      "outputs": []
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        "# Only include the relevant columns\n",
        "filtered_df = filtered_df[['Date', 'Category', 'Description', 'Transaction Type', 'Amount']]"
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            "text/plain": [
              "        Date             Category          Description Transaction Type  \\\n",
              "0 2018-01-01             Shopping               Amazon            Debit   \n",
              "1 2018-01-02      Mortgage & Rent     Mortgage Payment            Debit   \n",
              "2 2018-01-02          Restaurants      Thai Restaurant            Debit   \n",
              "3 2018-01-03  Credit Card Payment  Credit Card Payment            Debit   \n",
              "4 2018-01-04        Movies & DVDs              Netflix            Debit   \n",
              "\n",
              "    Amount  \n",
              "0    11,11  \n",
              "1  1247,44  \n",
              "2    24,22  \n",
              "3  2298,09  \n",
              "4    11,76  "
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              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-435b50ff-1ca6-44c2-a15d-27e0f5e220f2 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-435b50ff-1ca6-44c2-a15d-27e0f5e220f2');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert dataframe to csv\n",
        "filtered_df.to_csv('/content/2018_income_&_expenses.csv', index = None, header = True)"
      ],
      "metadata": {
        "id": "iptO3Q_5NCFi"
      },
      "execution_count": 14,
      "outputs": []
    }
  ]
}