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-+++
-date = 2022-03-03
-title = "Maintaining a Personal Financial Database"
-description = "An example project showing to build and maintain a simple financial database."
-+++
-
-## Personal Financial Tracking
-
-For the last 6-ish years, I've tracked my finances in a spreadsheet.
-This is common practice in the business world, but any good dev will
-cringe at the thought of storing long-term data in a spreadsheet. A
-spreadsheet is not for long-term storage or as a source of data to pull
-data/reports.
-
-As I wanted to expand the functionality of my financial data (e.g.,
-adding more reports), I decided to migrate the data into a database. To
-run reports, I would query the database and use a language like Python
-or Javascript to process the data, perform calculations, and visualize
-the data.
-
-## SQLite
-
-When choosing the type of database I wanted to use for this project, I
-was split between three options:
-
-1.  MySQL: The database I have the most experience with and have used
-    for years.
-2.  PostgreSQL: A database I'm new to, but want to learn.
-3.  SQLite: A database that I've used for a couple projects and have
-    moderate experience.
-
-I ended up choosing SQLite since it can be maintained within a single
-`.sqlite` file, which allows me more flexibility for storage
-and backup. I keep this file in my cloud storage and pull it up whenever
-needed.
-
-### GUI Editing
-
-Since I didn't want to try and import 1000--1500 records into my new
-database via the command line, I opted to use [DB Browser for SQLite
-(DB4S)](https://sqlitebrowser.org/) as a GUI tool. This application is
-excellent, and I don't see myself going back to the CLI when working in
-this database.
-
-DB4S allows you to copy a range of cells from a spreadsheet and paste it
-straight into the SQL table. I used this process for all 36 accounts,
-1290 account statements, and 126 pay statements. Overall, I'm guessing
-this took anywhere between 4--8 hours. In comparison, it probably took
-me 2-3 days to initially create the spreadsheet.
-
-![DB4S](https://img.cleberg.net/blog/20220303-maintaining-a-personal-financial-database/db4s.png)
-
-### Schema
-
-The schema for this database is actually extremely simple and involves
-only three tables (for now):
-
-1.  Accounts
-2.  Statements
-3.  Payroll
-
-**Accounts**
-
-The Accounts table contains summary information about an account, such
-as a car loan or a credit card. By viewing this table, you can find
-high-level data, such as interest rate, credit line, or owner.
-
-```sql
-CREATE TABLE "Accounts" (
-    "AccountID"     INTEGER NOT NULL UNIQUE,
-    "AccountType"   TEXT,
-    "AccountName"   TEXT,
-    "InterestRate"  NUMERIC,
-    "CreditLine"    NUMERIC,
-    "State"         TEXT,
-    "Owner"         TEXT,
-    "Co-Owner"      TEXT,
-    PRIMARY KEY("AccountID" AUTOINCREMENT)
-)
-```
-
-**Statements**
-
-The Statements table uses the same unique identifier as the Accounts
-table, meaning you can join the tables to find a monthly statement for
-any of the accounts listed in the Accounts table. Each statement has an
-account ID, statement date, and total balance.
-
-```sql
-CREATE TABLE "Statements" (
-    "StatementID"   INTEGER NOT NULL UNIQUE,
-    "AccountID"     INTEGER,
-    "StatementDate" INTEGER,
-    "Balance"       NUMERIC,
-    PRIMARY KEY("StatementID" AUTOINCREMENT),
-    FOREIGN KEY("AccountID") REFERENCES "Accounts"("AccountID")
-)
-```
-
-**Payroll**
-
-The Payroll table is a separate entity, unrelated to the Accounts or
-Statements tables. This table contains all information you would find on
-a pay statement from an employer. As you change employers or obtain new
-perks/benefits, just add new columns to adapt to the new data.
-
-```sql
-CREATE TABLE "Payroll" (
-    "PaycheckID"            INTEGER NOT NULL UNIQUE,
-    "PayDate"               TEXT,
-    "Payee"                 TEXT,
-    "Employer"              TEXT,
-    "JobTitle"              TEXT,
-    "IncomeRegular"         NUMERIC,
-    "IncomePTO"             NUMERIC,
-    "IncomeHoliday"         NUMERIC,
-    "IncomeBonus"           NUMERIC,
-    "IncomePTOPayout"       NUMERIC,
-    "IncomeReimbursements"  NUMERIC,
-    "FringeHSA"             NUMERIC,
-    "FringeStudentLoan"     NUMERIC,
-    "Fringe401k"            NUMERIC,
-    "PreTaxMedical"         NUMERIC,
-    "PreTaxDental"          NUMERIC,
-    "PreTaxVision"          NUMERIC,
-    "PreTaxLifeInsurance"   NUMERIC,
-    "PreTax401k"            NUMERIC,
-    "PreTaxParking"         NUMERIC,
-    "PreTaxStudentLoan"     NUMERIC,
-    "PreTaxOther"           NUMERIC,
-    "TaxFederal"            NUMERIC,
-    "TaxSocial"             NUMERIC,
-    "TaxMedicare"           NUMERIC,
-    "TaxState"              NUMERIC,
-    PRIMARY KEY("PaycheckID" AUTOINCREMENT)
-)
-```
-
-### Python Reporting
-
-Once I created the database tables and imported all my data, the only
-step left was to create a process to report and visualize on various
-aspects of the data.
-
-In order to explore and create the reports I'm interested in, I
-utilized a two-part process involving Jupyter Notebooks and Python
-scripts.
-
-1.  Step 1: Jupyter Notebooks
-
-    When I need to explore data, try different things, and re-run my
-    code cell-by-cell, I use Jupyter Notebooks. For example, I explored
-    the `Accounts` table until I found the following useful
-    information:
-
-    ```python
-    import sqlite3
-    import pandas as pd
-    import matplotlib
-
-    # Set up database filename and connect
-    db = "finances.sqlite"
-    connection = sqlite3.connect(db)
-    df = pd.read_sql_query("SELECT * FROM Accounts", connection)
-
-    # Set global matplotlib variables
-    %matplotlib inline
-    matplotlib.rcParams['text.color'] = 'white'
-    matplotlib.rcParams['axes.labelcolor'] = 'white'
-    matplotlib.rcParams['xtick.color'] = 'white'
-    matplotlib.rcParams['ytick.color'] = 'white'
-    matplotlib.rcParams['legend.labelcolor'] = 'black'
-
-    # Display graph
-    df.groupby(['AccountType']).sum().plot.pie(title='Credit Line by Account Type', y='CreditLine', figsize=(5,5), autopct='%1.1f%%')
-    ```
-
-2.  Step 2: Python Scripts
-
-    Once I explored enough through the notebooks and had a list of
-    reports I wanted, I moved on to create a Python project with the
-    following structure:
-
-    ```txt
-    finance/
-    ├── notebooks/
-    │   │   ├── account_summary.ipynb
-    │   │   ├── account_details.ipynb
-    │   │   └── payroll.ipynb
-    ├── public/
-    │   │   ├── image-01.png
-    │   │   └── image-0X.png
-    ├── src/
-    │   └── finance.sqlite
-    ├── venv/
-    ├── _init.py
-    ├── database.py
-    ├── process.py
-    ├── requirements.txt
-    └── README.md
-    ```
-
-    This structure allows me to:
-
-    1.  Compile all required python packages into
-        `requirements.txt` for easy installation if I move to
-        a new machine.
-    2.  Activate a virtual environment in `venv/` so I don't
-        need to maintain a system-wide Python environment just for this
-        project.
-    3.  Keep my `notebooks/` folder to continuously explore
-        the data as I see fit.
-    4.  Maintain a local copy of the database in `src/` for
-        easy access.
-    5.  Export reports, images, HTML files, etc. to
-        `public/`.
-
-    Now, onto the differences between the code in a Jupyter Notebook and
-    the actual Python files. To create the report in the Notebook
-    snippet above, I created the following function inside
-    `process.py`:
-
-    ```python
-    # Create summary pie chart
-    def summary_data(accounts: pandas.DataFrame) -> None:
-        accounts_01 = accounts[accounts["Owner"] == "Person01"]
-        accounts_02 = accounts[accounts["Owner"] == "Person02"]
-        for x in range(1, 4):
-            if x == 1:
-                df = accounts
-                account_string = "All Accounts"
-            elif x == 2:
-                df = accounts_01
-                account_string = "Person01's Accounts"
-            elif x == 3:
-                df = accounts_02
-                account_string = "Person02's Accounts"
-            print(f"Generating pie chart summary image for {account_string}...")
-            summary_chart = (
-                df.groupby(["AccountType"])
-                .sum()
-                .plot.pie(
-                    title=f"Credit Line by Type for {account_string}",
-                    y="CreditLine",
-                    autopct="%1.1f%%",
-                )
-            )
-            summary_chart.figure.savefig(f"public/summary_chart_{x}.png", dpi=1200)
-    ```
-
-    The result? A high-quality pie chart that is read directly by the
-    `public/index.html` template I use.
-
-    ![Summary Pie
-    Chart](https://img.cleberg.net/blog/20220303-maintaining-a-personal-financial-database/summary_chart.png)
-
-    Other charts generated by this project include:
-
-    -   Charts of account balances over time.
-    -   Line chart of effective tax rate (taxes divided by taxable
-        income).
-    -   Salary projections and error limits using past income and
-        inflation rates.
-    -   Multi-line chart of gross income, taxable income, and net
-        income.
-
-    The best thing about this project? I can improve it at any given
-    time, shaping it into whatever helps me the most for that time. I
-    imagine that I will be introducing an asset tracking table soon to
-    track the depreciating value of cars, houses, etc. Who knows what's
-    next?