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+#+date: <2022-03-03>
+#+title: Maintaining a Personal Financial Database
+#+description: 
+
+
+* Personal Financial Tracking
+:PROPERTIES:
+:CUSTOM_ID: personal-financial-tracking
+:END:
+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
+:PROPERTIES:
+:CUSTOM_ID: sqlite
+:END:
+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
+:PROPERTIES:
+:CUSTOM_ID: gui-editing
+:END:
+Since I didn't want to try and import 1000--1500 records into my new
+database via the command line, I opted to use
+[[https://sqlitebrowser.org/][DB Browser for SQLite (DB4S)]] 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.
+
+** Schema
+:PROPERTIES:
+:CUSTOM_ID: schema
+:END:
+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.
+
+#+begin_src 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)
+)
+#+end_src
+
+*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.
+
+#+begin_src 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")
+)
+#+end_src
+
+*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.
+
+#+begin_src 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)
+)
+#+end_src
+
+** Python Reporting
+:PROPERTIES:
+:CUSTOM_ID: python-reporting
+:END:
+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.
+
+*** Step 1: Jupyter Notebooks
+:PROPERTIES:
+:CUSTOM_ID: step-1-jupyter-notebooks
+:END:
+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:
+
+#+begin_src 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%%')
+#+end_src
+
+*** Step 2: Python Scripts
+:PROPERTIES:
+:CUSTOM_ID: step-2-python-scripts
+:END:
+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:
+
+#+begin_src 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
+#+end_src
+
+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=:
+
+#+begin_src 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)
+#+end_src
+
+The result? A high-quality pie chart that is read directly by the
+=public/index.html= template I use.
+
+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?