Visualize expenses by category from bank CSVs

To visualize expenses by category from bank CSVs, follow these steps:

1. Prepare the CSV File

Ensure your CSV file has columns like:

• Date

• Description

• Amount (negative for expenses, positive for income)

• Category (optional – you can manually or programmatically assign these)

2. Load and Clean Data (Python Example)

python
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import pandas as pd

# Load CSV
df = pd.read_csv('bank_statement.csv')

# Optional: Convert date
df['Date'] = pd.to_datetime(df['Date'])

# Ensure 'Amount' is numeric
df['Amount'] = pd.to_numeric(df['Amount'], errors='coerce')

# Filter only expenses (negative amounts)
expenses = df[df['Amount'] < 0]

3. Categorize Transactions (If Needed)

If no Category column exists:

python
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def categorize(description):
    if 'grocery' in description.lower():
        return 'Groceries'
    elif 'uber' in description.lower():
        return 'Transport'
    elif 'netflix' in description.lower():
        return 'Entertainment'
    else:
        return 'Other'

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

4. Group and Summarize Expenses

python
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category_summary = expenses.groupby('Category')['Amount'].sum().abs().sort_values(ascending=False)
print(category_summary)

5. Visualize with Pie or Bar Chart

python
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import matplotlib.pyplot as plt

# Pie Chart
category_summary.plot(kind='pie', autopct='%1.1f%%', figsize=(8, 8), title='Expenses by Category')
plt.ylabel('')
plt.tight_layout()
plt.show()

# Or Bar Chart
category_summary.plot(kind='bar', figsize=(10, 6), title='Expenses by Category')
plt.xlabel('Category')
plt.ylabel('Total Spent')
plt.tight_layout()
plt.show()

Optional: Interactive Visualization

For more interactive visuals, consider using Plotly:

python
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import plotly.express as px

fig = px.pie(values=category_summary.values, names=category_summary.index, title='Expenses by Category')
fig.show()

Would you like to upload a CSV so I can generate the chart for you?