Building a Personal Finance Tracker with Python and Pandas

Introduction

Welcome to PythonSage! In this blog post, we'll guide you step-by-step to create a simple personal finance tracker using Python and Pandas. This project will help you manage your finances by tracking your income and expenses, categorizing transactions, and visualizing your spending habits. By the end, you'll have a functional personal finance tracker that you can customize and expand.

Getting Started

Before we dive into the code, make sure you have Python and Pandas installed. You can install Pandas using pip:

pip install pandas

Step 1: Setting Up the Data

First, we create a CSV file named “transactions.csv” to store our financial transactions. This file contains columns for the date, description, category (either 'Income' or 'Expense'), and amount of each transaction.

Date,Description,Category,Amount

2024-07-01,Salary,Income,5000

2024-07-02,Rent,Expense,1500

2024-07-03,Groceries,Expense,200

2024-07-04,Utilities,Expense,100

2024-07-05,Entertainment,Expense,50

Step 2: Loading the Data

We load the data from the CSV file into a Pandas DataFrame. Pandas is a powerful library for data manipulation and analysis in Python.

import pandas as pd

 

# Load the data

df = pd.read_csv('transactions.csv')

 

# Display the data

print(df)

Here, pd.read_csv('transactions.csv') reads the CSV file and stores the data in the DataFrame df. print(df) displays the contents of the DataFrame.

Step 3: Analyzing the Data

Next, we calculate the total income, total expenses, and the balance.

# Calculate total income and expenses

total_income = df[df['Category'] == 'Income']['Amount'].sum()

total_expenses = df[df['Category'] == 'Expense']['Amount'].sum()

 

# Calculate balance

balance = total_income - total_expenses

 

# Display the results

print(f"Total Income: SAR{total_income}")

print(f"Total Expenses: SAR{total_expenses}")

print(f"Balance: SAR{balance}")

Here, df[df['Category'] == 'Income']['Amount'].sum() filters the DataFrame to include only rows where the category is 'Income' and then sums the amounts. The same is done for expenses. The balance is calculated by subtracting total expenses from total income. Finally, we print the results.

Step 4: Visualizing the Data

We use Matplotlib to create a bar chart that shows expenses by category.

import matplotlib.pyplot as plt

 

# 1. Filter for expenses

expenses = df[df['Category'] == 'Expense']

 

# 2. Group expenses by description

grouped_expenses = expenses.groupby('Description')

 

# 3. Sum the amounts for each group

expenses_by_category = grouped_expenses['Amount'].sum()

 

# Plot the data

expenses_by_category.plot(kind='bar')

plt.title('Expenses by Category')

plt.xlabel('Category')

plt.ylabel('Amount')

plt.show()

Here, df[df['Category'] == 'Expense'].groupby('Description')['Amount'].sum() groups the expense transactions by their description and sums the amounts. expenses_by_category.plot(kind='bar') creates a bar chart, and plt.show() displays it.

Full Code of Personal Finance Tracker 

Here is the complete code for our personal finance tracker:

import pandas as pd

import matplotlib.pyplot as plt

 

# Load the data

df = pd.read_csv('transactions.csv')

 

# Display the data

print(df)

 

# Calculate total income and expenses

total_income = df[df['Category'] == 'Income']['Amount'].sum()

total_expenses = df[df['Category'] == 'Expense']['Amount'].sum()

 

# Calculate balance

balance = total_income - total_expenses

 

# Display the results

print(f"Total Income: ${total_income}")

print(f"Total Expenses: ${total_expenses}")

print(f"Balance: ${balance}")

 

# Step-by-step simplification

# 1. Filter for expenses

expenses = df[df['Category'] == 'Expense']

 

# 2. Group expenses by description

grouped_expenses = expenses.groupby('Description')

 

# 3. Sum the amounts for each group

expenses_by_category = grouped_expenses['Amount'].sum()

 

# Plot the data

expenses_by_category.plot(kind='bar')

plt.title('Expenses by Category')

plt.xlabel('Category')

plt.ylabel('Amount')

plt.show()

Conclusion

Congratulations! You've created a simple personal finance tracker using Python and Pandas. This project is a great starting point for managing your finances and can be customized to fit your needs. Experiment with different features and visualizations to make it even more powerful.

External Links to Learn More

Pandas Documentation

Python Official Website

MatplotlibDocumentation

By following this guide, you'll gain valuable skills in data analysis and visualization, which are crucial for managing personal finances effectively.

Happy coding from PythonSage!