Compare performance of eval method vs. standard operations in Pandas

Pandas: Performance Optimization Exercise-10 with Solution

Write a Pandas program that uses the "eval" method to perform multiple arithmetic operations on DataFrame columns and compare performance with standard operations.

Sample Solution :

Python Code :

import pandas as pd  # Import the Pandas library
import numpy as np  # Import the NumPy library
import time  # Import the time module to measure execution time

# Create a sample DataFrame
np.random.seed(0)  # Set seed for reproducibility
data = {
    'A': np.random.randint(1, 100, size=1000000),
    'B': np.random.randint(1, 100, size=1000000),
    'C': np.random.randint(1, 100, size=1000000),
    'D': np.random.randint(1, 100, size=1000000)
}
df = pd.DataFrame(data)

# Perform arithmetic operations using standard operations
start_time = time.time()  # Record the start time
df['Result_standard'] = df['A'] + df['B'] - df['C'] * df['D'] / df['A']
time_standard = time.time() - start_time  # Calculate the time taken

# Perform arithmetic operations using the eval method
start_time = time.time()  # Record the start time
df['Result_eval'] = df.eval('A + B - C * D / A')
time_eval = time.time() - start_time  # Calculate the time taken

# Print the time taken for both methods
print("Time taken using standard operations:", time_standard, "seconds")
print("Time taken using eval method:", time_eval, "seconds")

Output:

Time taken using standard operations: 0.010995149612426758 seconds
Time taken using eval method: 0.02275371551513672 seconds

Explanation:

• Import Libraries:
• Import the Pandas library for data manipulation.
• Import the NumPy library for generating random data.
• Import the time module to measure execution time.
• Create a Sample DataFrame:
• Set a seed for reproducibility using np.random.seed(0).
• Create a dictionary data with columns 'A', 'B', 'C', and 'D' containing random integers.
• Generate a DataFrame df using the dictionary.
• Perform Arithmetic Operations Using Standard Operations:
• Record the start time using time.time().
• Perform multiple arithmetic operations on the DataFrame columns and store the result in a new column 'Result_standard'.
• Calculate the time taken by subtracting the start time from the current time.
• Perform Arithmetic Operations Using eval Method:
• Record the start time using time.time().
• Use the "eval" method to perform the same arithmetic operations on the DataFrame columns and store the result in a new column 'Result_eval'.
• Calculate the time taken by subtracting the start time from the current time.
• Print Results:
• Display the time taken for both the standard operations method and the "eval" method.

Python-Pandas Code Editor:

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