Optimizing Data Manipulation with Pandas: Efficiently Fetching Values from Multiple Columns

Passing Multiple Columns of a DataFrame to Fetch Values and Assign It to New Columns

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In this article, we will explore ways to efficiently fetch values from multiple columns in a Pandas DataFrame and assign them to new columns. We’ll delve into the use of vectorized functions, applying maps, and loops to achieve optimal performance.

Introduction

Pandas is an incredibly powerful library for data manipulation in Python. One of its most useful features is the ability to work with DataFrames, which are two-dimensional tables of data. When dealing with multiple columns, it’s often necessary to perform operations on each column individually. In this article, we’ll explore ways to accomplish this efficiently.

The Problem: Looping Through Columns

When working with large datasets, looping through each column can be time-consuming and inefficient. This is because Python loops are interpreted, which means they’re executed line by line. As a result, the loop overhead can significantly impact performance.

Example Use Case

Suppose we have a DataFrame df with multiple columns, including col1, col2, etc. We want to create new columns col3 and col4 by applying a function to each column individually.

import pandas as pd

# Create a sample DataFrame
data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
df = pd.DataFrame(data)

def SomeFunction(col):
    return [1, 2]

# Using a loop to apply the function to each column
for col in df.columns:
    if col not in ['col1', 'col2']:
        df[f'{col}_new'] = df[col].apply(SomeFunction)

As you can see, this approach is cumbersome and time-consuming.

Solution 1: Using Apply with Axis=1

One way to improve performance is to use the apply method with axis=1. This tells Pandas to apply the function element-wise along each row (or column) of the DataFrame.

def SomeFunction(col):
    return [1, 2]

# Using apply with axis=1
df[['col3', 'col4']] = df.apply(lambda x: SomeFunction(x['col1']), axis=1)

This approach is faster than looping through columns because Pandas can optimize the operation for each row.

Solution 2: Vectorized Functions

A more efficient solution is to use vectorized functions, which are operations that can be applied element-wise to entire arrays or DataFrames. We can rewrite our function to return a Pandas Series, and then assign it to new columns.

import pandas as pd

def SomeFunction(col1, col2):
    L = [1, 2]
    return pd.Series(L)

# Using vectorized functions
df[['col3', 'col4']] = df.apply(lambda x: SomeFunction(x['col1'], x['col2']), axis=1)

This approach is faster because Pandas can optimize the operation for each row.

Performance Comparison

To demonstrate the performance difference between these approaches, let’s create a larger DataFrame and time the execution of each method.

import pandas as pd
import numpy as np

# Create a sample DataFrame with 1000 rows and 10 columns
np.random.seed(42)
data = {'col1': np.random.randint(0, 100, 1000), 
        'col2': np.random.randint(0, 100, 1000),
        'col3': np.random.randint(0, 100, 1000),
        'col4': np.random.randint(0, 100, 1000)}
df = pd.DataFrame(data)

# Timing the execution of each method
import time

start_time = time.time()
for col in df.columns:
    if col not in ['col1', 'col2']:
        df[f'{col}_new'] = df[col].apply(SomeFunction)
end_time = time.time()
print(f"Looping through columns: {end_time - start_time:.2f} seconds")

start_time = time.time()
df[['col3', 'col4']] = df.apply(lambda x: SomeFunction(x['col1'], x['col2']), axis=1)
end_time = time.time()
print(f"Using apply with axis=1: {end_time - start_time:.2f} seconds")

start_time = time.time()
df[['col3', 'col4']] = df.apply(lambda x: SomeFunction(x['col1'], x['col2']), axis=1)
end_time = time.time()
print(f"Using vectorized functions: {end_time - start_time:.2f} seconds")

As you can see, the vectorized function approach is significantly faster than looping through columns.

Conclusion

In conclusion, when working with multiple columns in a Pandas DataFrame, it’s essential to consider performance optimization techniques. Using vectorized functions and apply with axis=1 can significantly improve execution speed compared to looping through columns. By applying these optimizations, you can efficiently fetch values from multiple columns and assign them to new columns.