Understanding Dataframe Modifications in Pandas

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When working with dataframes in pandas, it’s not uncommon to encounter unexpected behavior where the original dataframe changes. In this post, we’ll delve into the world of pandas and explore why this happens, along with some practical examples and explanations.

Introduction to Dataframes

A pandas dataframe is a two-dimensional table of data with rows and columns. It’s a fundamental data structure in python for handling tabular data. Dataframes are similar to spreadsheets or SQL tables but offer more features and flexibility.

When working with dataframes, it’s essential to understand the following key concepts:

• Index: The index is a label assigned to each row in the dataframe.
• Columns: Columns are vertical labels that represent the different variables in the dataset.
• Rows: Rows are horizontal labels that represent individual observations or records.

Creating and Modifying Dataframes

To create a new dataframe, you can use the pd.DataFrame() function. To modify an existing dataframe, you can use various methods such as filtering, selecting, or modifying columns.

Example Code

import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    'Respondent': ['John', 'Anna', 'Peter', 'Linda'],
    'ExpectedSalary': [50000.0, 60000.0, 70000.0, 80000.0],
    'HoursPerWeek': [40.0, 35.0, 45.0, 30.0]
})

# Print the original dataframe
print("Original DataFrame:")
print(df)

Dropna and Selecting Dataframes

When dealing with missing data, pandas provides a convenient way to remove rows or columns containing null values.

Example Code

# Remove rows containing missing salary values
df = df.dropna(subset=['Salary'], axis=0)

# Print the updated dataframe
print("\nDataFrame after removing rows with missing salary:")
print(df)

Selecting Specific Columns

To select specific columns, you can use the select_dtypes() function or index them directly.

Example Code

# Select only numerical columns
df_numeric = df.select_dtypes(include=['float'])

# Print the selected dataframe
print("\nDataFrame with only numerical columns:")
print(df_numeric)

Filling Missing Values

When dealing with missing values, you can use various methods to fill them. The fillna() function is one of the most convenient ways to do this.

Example Code

# Replace missing hours per week values with the mean value
df['HoursPerWeek'] = df['HoursPerWeek'].fillna(df['HoursPerWeek'].mean())

# Print the updated dataframe
print("\nDataFrame after filling missing hours per week:")
print(df)

Copying Dataframes

When you want to perform operations on a copy of the original dataframe, it’s essential to use the copy() function.

Example Code

# Create a copy of the original dataframe
df_copy = df.copy()

# Modify the copied dataframe
df_copy['HoursPerWeek'] *= 2

# Print the updated copied dataframe
print("\nCopied DataFrame after modifying hours per week:")
print(df_copy)

# Verify that the original dataframe remains unchanged
print("\nOriginal DataFrame:")
print(df)

Understanding Why Original Dataframe Changes

Now, let’s dive deeper into why the original dataframe changes when you create a copy and perform operations on it.

Explanation

When you use the copy() function to create a new dataframe, pandas creates an independent copy of the original dataframe. However, when you modify columns using methods like select_dtypes() or index them directly, you’re modifying the original dataframe because the modified column is being added back into the original dataframe.

This can lead to unexpected behavior where the original dataframe changes unexpectedly.

To avoid this issue, it’s essential to use the copy() function to create a new dataframe when performing operations that may modify columns.

Best Practices for Working with Dataframes

Here are some best practices to keep in mind when working with dataframes:

• Always use the copy() function to create a new dataframe when performing operations that may modify columns.
• Be mindful of index and column modifications, as these can affect the original dataframe unexpectedly.
• Use methods like dropna() and select_dtypes() judiciously to avoid modifying the original dataframe.

By following these best practices and understanding how dataframes work in pandas, you’ll be able to work efficiently with your datasets and avoid common pitfalls.

Last modified on 2023-07-02