Adding new columns to Pandas DataFrames is a fundamental data manipulation task. Frequently, you’ll need to initialize these new columns with a default value. This article explores two efficient methods for achieving this in Pandas: pandas.DataFrame.assign() and pandas.DataFrame.insert(), highlighting their differences and best use cases.
Table of Contents
- Using
pandas.DataFrame.assign()to Add Columns - Adding Columns with Conditional Default Values
- Using
pandas.DataFrame.insert()to Add Columns - Choosing the Right Method
Using pandas.DataFrame.assign() to Add Columns
The assign() method offers a clean and concise way to add new columns. Importantly, it returns a new DataFrame, leaving the original DataFrame unchanged. This functional approach promotes immutability and helps prevent unexpected modifications.
import pandas as pd
# Sample DataFrame
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
print("Original DataFrame:n", df)
# Add a new column 'C' with a default value of 0
df_new = df.assign(C=0)
print("nDataFrame after adding column 'C':n", df_new)
print("nOriginal DataFrame remains unchanged:n", df)
#Adding multiple columns at once
df_new = df.assign(C=0, D='default')
print("nDataFrame after adding multiple columns:n", df_new)
Adding Columns with Conditional Default Values
For more complex scenarios requiring conditional default values based on existing data, assign() can be combined with other Pandas features. This provides greater control and flexibility.
import pandas as pd
import numpy as np
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
# Add a new column 'C' with NaN as a placeholder and then assign values conditionally
df['C'] = np.nan
df.loc[df['A'] > 1, 'C'] = 10
df.loc[df['A'] <= 1, 'C'] = 20
print("nDataFrame after adding and conditionally setting column 'C':n", df)
Using pandas.DataFrame.insert() to Add Columns
The insert() method allows precise control over column placement. Unlike assign(), it modifies the DataFrame in place. This means the original DataFrame is directly altered.
import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
# Add a new column 'C' at position 1 (index 1) with a default value of 0
df.insert(1, 'C', 0)
print("nDataFrame after inserting column 'C':n", df)
Because insert() modifies the DataFrame in place, it’s crucial to create a copy using .copy() if you need to preserve the original DataFrame.
Choosing the Right Method
The choice between assign() and insert() depends on your needs. assign() is generally preferred for its functional, immutable nature, especially when dealing with complex logic or adding multiple columns. insert() is useful when precise column position is critical and in-place modification is acceptable. Always consider the implications of in-place modification to avoid unintended consequences.