Compare performance of apply vs. Vectorized operations in Pandas

Pandas: Performance Optimization Exercise-2 with Solution

Write a Pandas program to compare the performance of applying a custom function to a column using apply vs. using vectorized 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 large DataFrame with random integers
np.random.seed(0)  # Set seed for reproducibility
data = np.random.randint(1, 100, size=(1000000, 1))  # Generate random data
df = pd.DataFrame(data, columns=['Values'])  # Create a DataFrame

# Define a custom function to apply
def custom_function(x):
    return x * 2 + 3

# Measure the time taken to apply the custom function using apply
start_time = time.time()  # Record the start time
df['Apply_Result'] = df['Values'].apply(custom_function)  # Apply the custom function using apply
time_apply = time.time() - start_time  # Calculate the time taken

# Measure the time taken to apply the custom function using vectorized operations
start_time = time.time()  # Record the start time
df['Vectorized_Result'] = custom_function(df['Values'])  # Apply the custom function using vectorized operations
time_vectorized = time.time() - start_time  # Calculate the time taken

# Print the time taken for both methods
print("Time taken using apply:", time_apply, "seconds")
print("Time taken using vectorized operations:", time_vectorized, "seconds")

Output:

Time taken using apply: 0.25844264030456543 seconds
Time taken using vectorized operations: 0.0029630661010742188 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 Large DataFrame:
• Set a seed for reproducibility using np.random.seed(0).
• Generate random integers with np.random.randint and create a large DataFrame with 1,000,000 rows and one column named 'Values'.
• Define a Custom Function:
• Create a custom function custom_function(x) that performs a simple operation on the input x (e.g., x * 2 + 3).
• Measure Time Using apply:
• Record the start time using time.time().
• Apply the custom function to the 'Values' column using the Pandas apply method and store the result in a new column 'Apply_Result'.
• Calculate the time taken by subtracting the start time from the current time.
• Measure Time Using Vectorized Operations:
• Record the start time using time.time().
• Apply the custom function to the 'Values' column using vectorized operations and store the result in a new column 'Vectorized_Result'.
• Calculate the time taken by subtracting the start time from the current time.
• Finally display the time taken for both the "apply()" method and the vectorized operations.

Python-Pandas Code Editor:

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