Karan Sharma

Analyzing credit card transactions with GPT and Python

8 minutes (1895 words)

You know those budget freaks? People who log and categorise every Rupee they’ve spent over the month? The financially sane people? I am definitely not one and I suck at it.

I moved cities a couple of months back and had some big ticket spends off late, mostly financed by credit card. I wanted an easy way to list down where all I’ve spent most money and spot some recurring expenses so I can be better prepared for them from next month. I’ve found that broadly keeping an idea of things where you spend money works for me (v/s the two extremes - completely blind or logging every small transactions). Of course I know people who make budgeting a habit but I only wish I was consistent enough to do that.

Anyway, I downloaded the statement in CSV format from my bank.

Initially I thought I’d use some simple Excel to make sense of this but I realised how bad my Excel skills really are. I got an idea to dump the CSV file to ChatGPT (yay privacy) and ask questions. It kinda sucked at it and gave wrong answers for a lot of questions and also started to hallucinate data which wasn’t even present in the CSV.

The next most obvious step would be to write a simple script and parse it. I wanted to experiment if ChatGPT could do this entire exercise of writing the script and the relevant code for the analysis I wanted to perform.

Here’s the initial prompt I gave:

I am pasting a CSV file which also includes the first row as header. Write Python code to parse this:

,,Transaction Date,Details,,,Amount (INR),,,Reference Number,,
,,27/04/2023,"Blinkit, BANGALORE, IN",,,290.00 Dr.,,,7477468633,,
... other data ...

It returned the following code:

import pandas as pd
from io import StringIO

csv_data = """\
,,Transaction Date,Details,,,Amount (INR),,,Reference Number,,
,,27/04/2023,"Blinkit, BANGALORE, IN",,,290.00 Dr.,,,747746xxxx,,
# ... (the rest of your CSV data)
,,03/04/2023,"DIGITAL LOUNGE, GURGAON, IN",,,799.00 Dr.,,,738201xxx,,"""

# Read the CSV data into a pandas DataFrame
data = StringIO(csv_data)
df = pd.read_csv(data, header=1)

# Clean up the DataFrame (remove unnecessary columns)
df = df.dropna(axis=1, how="all")

# Rename columns
df.columns = [
    "Transaction Date",
    "Amount (INR)",
    "Reference Number"


Looking at this, I was a bit impressed as it figured the CSV contains some useless empty columns and it removed (without me giving any information about it).

I also asked it to modify the code to read the file locally from disk and it swapped StringIO with the path to CSV file:

# Provide the file path to your local CSV file
file_path = "your_file.csv"

# Read the CSV data into a pandas DataFrame
df = pd.read_csv(file_path, header=1)

Next, I prompted to do some analysis on it:

Write some more code to do the analysis to answer these questions:

- How many total debit and credit entries
- What is the total amount of debits and total amount of credits
- Which vendor have I spent the most?
- How much have I spent on `SWIGGY` ?

It returned some one-liners to answer each question:

# Filter debit and credit entries
debit_entries = df[df["Amount (INR)"] > 0]
credit_entries = df[df["Amount (INR)"] < 0]

# Count the total number of debit and credit entries
total_debit_entries = len(debit_entries)
total_credit_entries = len(credit_entries)

# Calculate the total amount of debits and credits
total_debit_amount = debit_entries["Amount (INR)"].sum()
total_credit_amount = credit_entries["Amount (INR)"].sum()

# Find the vendor with the most spending
most_spent_vendor = debit_entries.groupby("Details")["Amount (INR)"].sum().idxmin()

# Calculate the total amount spent on SWIGGY
swiggy_spending = debit_entries[debit_entries["Details"].str.contains("SWIGGY", case=False)]["Amount (INR)"].sum()

# Calculate the total amount spent on AMAZON RECHARGES
amazon_recharges_spending = debit_entries[debit_entries["Details"].str.contains("AMAZON RECHARGES", case=False)]["Amount (INR)"].sum()

# Print the results
print(f"Total debit entries: {total_debit_entries}")
print(f"Total credit entries: {total_credit_entries}")

print(f"Total debit amount: {total_debit_amount}")
print(f"Total credit amount: {total_credit_amount}")

print(f"Vendor with the most spending: {most_spent_vendor}")

print(f"Total amount spent on SWIGGY: {swiggy_spending}")

At this point, I know that this will fail because we’ve not cleaned up the data. The column Amount (INR) needs cleaning up. I prompted ChatGPT to write a function to clean this column:

The amount column as `21,620.54 Dr.` to represent that Rs 21,620.54 is debited. And `73,500.00 Cr.` to represent that Rs 73,500.00 is credited. Can you write a function to apply this transformation on the `Amount (INR)` column?

It responded with:

# Clean up the data
def clean_amount(value):
    if ' Cr.' in value:
        value = value.replace(' Cr.', '')
        return -float(value.replace(',', ''))
    elif ' Dr.' in value:
        value = value.replace(' Dr.', '')
        return float(value.replace(',', ''))

df["Amount (INR)"] = df["Amount (INR)"].apply(clean_amount)

# Now proceed with the analysis code

Perfect! After transforming the amounts, I ran the analysis code:

The next prompt I gave was to analyse the spending in various categories.

In the above CSV file can you guess the categories of each merchant based on the `Details`? And write the code for grouping the spends on various categories. If you're unable to figure out the category for the transaction, you can use a "Misc" column to bucket them.

It actually did an okayish job at this and ignored a lot of vendors which I think it could have guessed easily:

def categorize(details):
    details = details.lower()
    if "swiggy" in details:
        return "Food Delivery"
    elif "amazon recharges" in details:
        return "Recharge"
    elif "uber" in details or "ola" in details:
        return "Cab"
    elif "zomato" in details:
        return "Food Delivery"
    elif "netflix" in details or "prime video" in details:
        return "Streaming"
        return "Misc"

# Create a new column called "Category" by applying the categorize function to the "Details" column
df["Category"] = df["Details"].apply(categorize)

# Group the transactions by category and calculate the total amount spent in each category
grouped = df[df["Amount (INR)"] > 0].groupby("Category")["Amount (INR)"].sum()


I decided to give some manual inputs to it to refine the function.

I can help you with categorising it further.
`Blink` -  Grocery
`Indigo` - Travel
`PEGS` - Alcohol
`Nike` - Shopping

Also note that the cateorgy name matching should be case-insensitive.

And it modified the Python snippet to add these rules.

Mixed reactions looking at this. Happy that I could practically get exactly the result I had in mind in just 10 minutes without writing any code. Sad because damn I need to limit those empty calories from next month (famous last words).

Next, I wanted to see if my spends on weekends are higher or not. (I don’t expect them to be, but you never know).


Can you also write the code to figure how much spends I've done grouped by the day of week?


# Convert the 'Transaction Date' column to a datetime format
df['Transaction Date'] = pd.to_datetime(df['Transaction Date'])

# Create a new column called "Day of Week" containing the day of the week for each transaction
df['Day of Week'] = df['Transaction Date'].dt.day_name()

# Group the transactions by the "Day of Week" and calculate the total amount spent on each day of the week
grouped_by_day = df[df["Amount (INR)"] > 0].groupby('Day of Week')['Amount (INR)'].sum().abs()

# Sort the grouped data by the total amount spent in descending order
sorted_grouped_by_day = grouped_by_day.sort_values(ascending=False)


It was a fun 10-15 min exercise to figure out my spending habits based on the last month’s statement. I intend to do this for the next couple of months and then it would make sense to write more queries which would show trend-lines of spends in various categories over time.

Honestly, I just loved how ChatGPT made this task so seemingly simple. It’s not that I can’t myself write the code for these kind of simple analysis. It’s the sheer power at your hand to go from ideation phase to answer within seconds. And I think that’s why I love it so much. I didn’t have to go through Pandas docs (because I don’t use that in my day job so it’s quite normal to not know various syntax/functions that I could use) and I’d grok through different StackOverflow questions to achieve what I wanted to. And maybe imagining all this resistance on a Sunday morning would have meant that I never got to write the script in the first place.


Tags: #gpt #python