Performance comparison of DataFrame filtering in Pandas

Last update on December 21 2024 07:41:48 (UTC/GMT +8 hours)

Pandas: Performance Optimization Exercise-12 with Solution

Write a Pandas program that uses the query method to filter rows of a DataFrame based on a condition. Compare the performance with boolean indexing.

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({
    'A': np.random.randint(0, 100, size=num_rows),
    'B': np.random.randn(num_rows),
    'C': np.random.rand(num_rows)
})

# Define the condition
condition = 'A > 50 and B < 0'

# Measure time for query method
start_time = time.time()
result_query = df.query(condition)
end_time = time.time()
query_time = end_time - start_time

# Measure time for boolean indexing
start_time = time.time()
result_boolean_indexing = df[(df['A'] > 50) & (df['B'] < 0)]
end_time = time.time()
boolean_indexing_time = end_time - start_time

# Print the time taken for each method
print(f"Time taken using query method: {query_time:.6f} seconds")
print(f"Time taken using boolean indexing: {boolean_indexing_time:.6f} seconds")

Output:

Time taken using query method: 0.021941 seconds
Time taken using boolean indexing: 0.008976 seconds

Explanation:

• Import Libraries:
• Import pandas, numpy, and time.
• Create DataFrame:
• Generate a sample DataFrame with 1,000,000 rows.
• Define Condition:
• Set a condition for filtering rows.
• Time Measurement for query Method:
• Measure the time taken to filter rows using the query method.
• Time Measurement for Boolean Indexing:
• Measure the time taken to filter rows using boolean indexing.
• Print Results:
• Print the time taken for each method.

Python-Pandas Code Editor:

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