Performance comparison of cumulative Sum calculation in Pandas

Pandas: Performance Optimization Exercise-14 with Solution

Write a Pandas program to compare the performance of calculating the cumulative sum of a column using the “cumsum” method vs. using a "for" loop.

Sample Solution :

Python Code :

# Import necessary libraries
import pandas as pd
import numpy as np
import time

# Create a sample DataFrame
num_rows = 1000000
df = pd.DataFrame({'value': np.random.randn(num_rows)})

# Measure time for cumsum method
start_time = time.time()
cumsum_result = df['value'].cumsum()
end_time = time.time()
cumsum_time = end_time - start_time

# Measure time for for loop method
start_time = time.time()
cumsum_for_loop = np.zeros(num_rows)
cumsum_for_loop[0] = df['value'].iloc[0]
for i in range(1, num_rows):
    cumsum_for_loop[i] = cumsum_for_loop[i-1] + df['value'].iloc[i]
end_time = time.time()
for_loop_time = end_time - start_time

# Print the time taken for each method
print(f"Time taken using cumsum method: {cumsum_time:.6f} seconds")
print(f"Time taken using for loop: {for_loop_time:.6f} seconds")

Output:

Time taken using cumsum method: 0.006079 seconds
Time taken using for loop: 8.145854 seconds

Explanation:

• Import Libraries:
• Import pandas, numpy, and time.
• Create DataFrame:
• Generate a sample DataFrame with 1,000,000 rows.
• Time Measurement for cumsum Method:
• Measure the time taken to calculate the cumulative sum using the cumsum method.
• Time Measurement for for Loop:
• Measure the time taken to calculate the cumulative sum using a for loop.
• Print Results:
• Print the time taken for each method.

Python-Pandas Code Editor:

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