Efficiently apply multiple Aggregation functions in Pandas

Pandas: Performance Optimization Exercise-19 with Solution

Write a Python program that uses the agg method to apply multiple aggregation functions to a DataFrame and compares the performance with applying each function individually.

Sample Solution :

Python Code :

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

# Create a sample DataFrame
np.random.seed(0)
df = pd.DataFrame({
    'A': np.random.randint(1, 100, 1000),
    'B': np.random.rand(1000),
    'C': np.random.randint(1, 100, 1000)
})

# Define aggregation functions
aggregations = {
    'A': ['sum', 'mean', 'std'],
    'B': ['sum', 'mean', 'std'],
    'C': ['sum', 'mean', 'std']
}

# Timing the agg method
start_time_agg = time.time()
df_agg = df.agg(aggregations)
time_agg = time.time() - start_time_agg

# Timing the individual application of functions
start_time_individual = time.time()
results_individual = {
    'A_sum': df['A'].sum(),
    'A_mean': df['A'].mean(),
    'A_std': df['A'].std(),
    'B_sum': df['B'].sum(),
    'B_mean': df['B'].mean(),
    'B_std': df['B'].std(),
    'C_sum': df['C'].sum(),
    'C_mean': df['C'].mean(),
    'C_std': df['C'].std()
}
time_individual = time.time() - start_time_individual

# Print results
print(f"Time using agg method: {time_agg:.6f} seconds")
print(f"Time applying functions individually: {time_individual:.6f} seconds")
print("Aggregated results using agg method:")
print(df_agg)
print("Results applying functions individually:")
print(results_individual)

Output:

Time using agg method: 0.001994 seconds
Time applying functions individually: 0.000000 seconds
Aggregated results using agg method:
                 A           B             C
sum   49723.000000  509.199400  48276.000000
mean     49.723000    0.509199     48.276000
std      28.857183    0.296208     28.470799
Results applying functions individually:
{'A_sum': 49723, 'A_mean': 49.723, 'A_std': 28.857182953434812, 'B_sum': 509.19940043113445, 'B_mean': 0.5091994004311344, 'B_std': 0.2962083809189193, 'C_sum': 48276, 'C_mean': 48.276, 'C_std': 28.47079925837016}

Explanation:

• Import necessary libraries:
• Import pandas, numpy, and time.
• Create a sample DataFrame:
• Random data is generated for columns 'A', 'B', and 'C'.
• Define aggregation functions:
• A dictionary specifying the functions to be applied to each column.
• Timing the agg method:
• Measure the time taken to apply multiple aggregations using the agg method.
• Timing the individual application of functions:
• Measure the time taken to apply each function individually.
• Finally compare the times and show the results from both methods

Python-Pandas Code Editor:

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