Mastering Reindexing in Pandas: Enhancing Dataframe flexibility

In the versatile world of data manipulation with Pandas, reindexing is a fundamental technique to rearrange the data according to a new set of indices. This article explores the concept of reindexing in Pandas, its parameters, and demonstrates its application with real-world examples.

Reindexing in Pandas

Reindexing in Pandas involves altering the row labels and column labels of a DataFrame. This process is essential for reordering existing data, aligning data from different sources, and handling missing values.

Key Parameters of Reindexing

The reindexing function comes with several parameters, each serving a specific purpose:

1. labels: New labels to index along the axis.
2. index / columns: Alternatives to labels for row or column reindexing.
3. method: Method to fill in missing values (‘ffill’, ‘bfill’, etc.).
4. fill_value: Value to use for missing values introduced by the reindexing.
5. limit: Maximum number of consecutive missing values to fill.
6. tolerance: Maximum distance between original and new label for filling.
7. level: Match simple Index levels with MultiIndex levels.
8. copy: Copy underlying data if new labels are equivalent to old labels.

Implementing Reindexing in Pandas

Creating a Sample DataFrame

Let’s begin with a DataFrame representing different individuals and their attributes.

import pandas as pd
# Your original data and index : Learning @ Freshers.in 
data = {'Age': [32, 29, 35, 40, 28, 33],
        'City': ['Mumbai', 'Bangalore', 'Chennai', 'Delhi', 'New York', 'San Francisco']}
index = ['Sachin', 'Manju', 'Ram', 'Raju', 'David', 'Wilson']
df = pd.DataFrame(data, index=index)
print(df)

        Age           City
Sachin   32         Mumbai
Manju    29      Bangalore
Ram      35        Chennai
Raju     40          Delhi
David    28       New York
Wilson   33  San Francisco

# New index
new_index = ['Manju', 'Ram', 'Sachin', 'Raju', 'David', 'Wilson', 'Michael']
# Reindexing
df_reindexed = df.reindex(new_index)
# Handling missing values
df_reindexed['Age'].fillna(0, inplace=True)
df_reindexed['City'].fillna('Unknown', inplace=True)
# Display the updated DataFrame
print(df_reindexed)

          Age           City
Manju    29.0      Bangalore
Ram      35.0        Chennai
Sachin   32.0         Mumbai
Raju     40.0          Delhi
David    28.0       New York
Wilson   33.0  San Francisco
Michael   0.0        Unknown