How I Used Python to Analyze My Personal Spending Habits (Complete Step-by-Step Guide)

“You can’t change what you can’t measure. Python helped me do both.”

Introduction: The Financial Fog
Why I Wanted to Analyze My Spending
Why I Chose Python Over Excel
Tools and Libraries I Used
Step 1: Gathering My Raw Financial Data
Step 2: Cleaning and Structuring the Data with pandas
Step 3: Categorizing Transactions
Step 4: Creating Monthly Reports and Spending Summaries
Step 5: Visualizing Patterns with matplotlib & seaborn
Step 6: Discovering Hidden Habits and Emotional Spending
Step 7: Automating the Workflow with Python
Step 8: Expanding into Budget Forecasting and Savings Tracking
Unexpected Insights I Found
What Changed in My Behavior
How You Can Do the Same (Beginner-Friendly Walkthrough)
Going Further: Net Worth, Investments, and Passive Income
My Biggest Lessons Learned
Final Thoughts: Code Meets Clarity
FAQ

1. Introduction: The Financial Fog

Before I tracked my spending, everything felt like a black box.
Some months I saved money. Some I didn’t. But I couldn’t explain why.

So I turned to Python — not just as a programming language, but as a lens for financial clarity.

2. Why I Wanted to Analyze My Spending

I didn’t trust budgeting apps
Excel was chaotic
My bank statements were dense and inconsistent
I had no idea how much I was spending on food delivery (spoiler: too much)

3. Why I Chose Python Over Excel

Feature	Excel	Python
Flexibility	Limited	Total control
Repeatability	Manual	Fully automated
Scalability	Struggles with large data	Handles 10,000+ rows easily
Visualization	Decent	Customizable (matplotlib, seaborn)

Also: I just wanted to learn something useful and personal with Python.

4. Tools and Libraries I Used

pandas – for data wrangling
matplotlib – basic graphs
seaborn – nicer visualizations
datetime – parsing and grouping
jupyter notebook – exploration and output
re (regex) – text-based transaction parsing

5. Step 1: Gathering My Raw Financial Data

Exported CSVs from 3 sources:
- Bank account
- Credit card
- PayPal

Each had different structures, formats, encodings, and even date styles (MM/DD/YYYY vs YYYY-MM-DD).

Python to the rescue:

import pandas as pd

df1 = pd.read_csv('bank.csv')
df2 = pd.read_csv('creditcard.csv')
df3 = pd.read_csv('paypal.csv')

data = pd.concat([df1, df2, df3], ignore_index=True)

6. Step 2: Cleaning and Structuring the Data with pandas

Common issues:

Missing values
Duplicates
Strange encodings (à, ™, etc.)
Inconsistent column names

Sample code:

data = data.dropna(subset=['Amount'])
data['Date'] = pd.to_datetime(data['Date'])
data['Amount'] = data['Amount'].astype(float)

7. Step 3: Categorizing Transactions

No app knows your habits better than you.

My Approach:

Manually built a dictionary of keywords → categories

rules = {
    'Grocery': ['walmart', 'whole foods'],
    'Dining': ['uber eats', 'mcdonalds'],
    'Rent': ['property management', 'rent'],
}

Applied it using regex and .apply()

Sample:

import re

def categorize(desc):
    desc = desc.lower()
    for cat, keywords in rules.items():
        if any(k in desc for k in keywords):
            return cat
    return 'Other'

data['Category'] = data['Description'].apply(categorize)

8. Step 4: Creating Monthly Reports and Spending Summaries

Grouped and summarized using groupby:

monthly = data.groupby([data['Date'].dt.to_period('M'), 'Category'])['Amount'].sum().unstack().fillna(0)

Then exported to CSV, sent to my phone weekly.

9. Step 5: Visualizing Patterns with matplotlib & seaborn

Sample graph:

import seaborn as sns
import matplotlib.pyplot as plt

monthly.T.plot(kind='bar', stacked=True, figsize=(12, 6))
plt.title("Monthly Spending by Category")

Visuals helped me:

Spot spikes in certain months
Compare categories side by side
Track declining (or growing) expenses

10. Step 6: Discovering Hidden Habits and Emotional Spending

What I found:

I spent more on food delivery after long workdays
Shopping spiked on Sundays
Some recurring payments (subscriptions!) I had forgotten about

This wasn’t just data.
It was a mirror.

11. Step 7: Automating the Workflow with Python

Wrote a script to:

Ingest all CSVs in /downloads
Clean and merge
Categorize
Generate reports and visualizations
Email results to myself weekly

Scheduled using cron (Mac) and Task Scheduler (Windows)

2. Step 8: Expanding into Budget Forecasting and Savings Tracking

Used historical spending to:

Set monthly budgets
Forecast next month based on moving average
Compare actuals vs budgeted amounts

Plotted savings rate over time:

data['YearMonth'] = data['Date'].dt.to_period('M')
monthly_total = data.groupby('YearMonth')['Amount'].sum()

13. Unexpected Insights I Found

Spending spiked during social events (not dates!)
I underestimated transportation by 40%
The $5–$15 range accounted for most unnecessary expenses
I had 3 duplicate insurance charges for months

14. What Changed in My Behavior

After a month of looking at these visuals weekly:

I stopped ordering food during workdays
Unsubscribed from unused platforms
Combined two insurance plans
Set spending triggers (alerts) in code

15. How You Can Do the Same (Beginner-Friendly Walkthrough)

Even if you don’t code:

Export your bank transactions
Learn pandas basics (Jupyter + 3 tutorials)
Build a simple categorizer
Use matplotlib to chart your data
Repeat monthly

You’ll be amazed.

16. Going Further: Net Worth, Investments, and Passive Income

I now track:

Investment performance (ETF data via API)
Net worth across accounts
Passive income (dividends, blog, etc.)

Even built a simple net worth dashboard with streamlit.

17. My Biggest Lessons Learned

Awareness is the first step to control
Most of my bad habits were emotional, not financial
Automation beats willpower
Code is leverage — and it compounds

18. Final Thoughts: Code Meets Clarity

You don’t need to be a developer.
You just need to be curious and consistent.

Python gave me financial self-awareness.
Not with charts.
But with habits, patterns, and better decisions.

19. FAQ

❓ I’m not a coder. Can I still do this?

Yes. Start with pandas crash courses and YouTube tutorials.

❓ Is this better than budgeting apps?

It’s more personal, flexible, and insightful — but requires setup.

❓ What if I use multiple currencies/accounts?

Use exchange rates and normalize in a separate column.

❓ Can I track subscriptions too?

Yes. Use keyword tagging or regex rules.

📌 Final Thought: Your bank knows how you spend.
Python lets you know — and gives you the power to change it.