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:
Have another way to solve this solution? Contribute your code (and comments) through Disqus.
Previous: Compare DataFrame element-wise multiplication using for loop vs. * Operator.
Next: Measure concatenation time of DataFrames in Pandas.
What is the difficulty level of this exercise?
Test your Programming skills with w3resource's quiz.
- Weekly Trends and Language Statistics
- Weekly Trends and Language Statistics