Analyze and Visualize Travel Expenses using Python

Keeping track of travel expenses is essential for budgeting and financial planning. In this blog post, we'll explore a Python script that helps analyze and visualize travel expenses using the power of data analysis and visualization libraries. This script allows you to gain insights into your spending patterns and make informed decisions for future trips.

Getting Started

To begin, make sure you have Python installed on your system. Additionally, you'll need the following libraries installed:

• pandas
• matplotlib

You can install these libraries using pip, the Python package manager. Open your terminal or command prompt and run the following command:

pip install pandas matplotlib

The Python Script

Let's take a look at the Python script that analyzes and visualizes travel expenses:

import pandas as pd
import matplotlib.pyplot as plt

# Read the travel expenses data from a CSV file
data = pd.read_csv('travel_expenses.csv')

# Print the summary statistics of the data
print(data.describe())

# Calculate the total expenses for each category
expenses_by_category = data.groupby('Category')['Amount'].sum()

# Create a bar chart to visualize the expenses by category
plt.figure(figsize=(10, 6))
expenses_by_category.plot(kind='bar')
plt.xlabel('Category')
plt.ylabel('Total Expenses')
plt.title('Travel Expenses by Category')
plt.xticks(rotation=45)
plt.show()

# Calculate the total expenses by month
data['Date'] = pd.to_datetime(data['Date'])
data['Month'] = data['Date'].dt.month
expenses_by_month = data.groupby('Month')['Amount'].sum()

# Create a line chart to visualize the expenses by month
plt.figure(figsize=(10, 6))
expenses_by_month.plot(kind='line', marker='o')
plt.xlabel('Month')
plt.ylabel('Total Expenses')
plt.title('Travel Expenses by Month')
plt.xticks(range(1, 13))
plt.grid(True)
plt.show()

In this script, we'll assume that you have a CSV file named travel_expenses.csv containing the travel expenses data. Here's an example CSV file travel_expenses.csv that you can use with the Python script:

Category,Amount,Date
Flights,500.50,2023-01-15
Accommodation,800.00,2023-02-10
Meals,200.75,2023-01-20
Transportation,150.25,2023-02-05
Accommodation,600.50,2023-03-12
Meals,180.00,2023-02-25
Flights,350.00,2023-03-05
Meals,120.75,2023-03-18
Transportation,75.50,2023-01-30
Flights,450.25,2023-04-08

Running the Script

To run the script, save your travel expenses data in a CSV file named travel_expenses.csv. Make sure the file is located in the same directory as your Python script. You can use the example data provided or modify it with your own expenses.

Open your terminal or command prompt, navigate to the directory containing the script, and run the following command:

python travel_expenses_analysis.py

The script will load the data, calculate the expenses, and display the summary statistics. It will then show the bar chart visualizing the expenses by category and the line chart visualizing the expenses by month.

Conclusion

Analyzing and visualizing travel expenses can provide valuable insights into your spending habits, helping you plan and manage your finances effectively. With the Python script provided in this blog post, you can effortlessly analyze and visualize your travel expenses based on data stored in a CSV file.

By leveraging the power of pandas and matplotlib, you can gain a deeper understanding of your spending patterns, identify areas where you can cut costs, and make informed decisions for future trips.