Pandas Index.append()

Pandas Index.append() Method

The Index.append() method in pandas is used to concatenate two or more Index objects, returning a new Index object. This method is particularly useful when you need to combine multiple Index objects or add new labels to an existing Index.

Syntax

Index.append(other)

Parameters:

• other: Index or list/tuple of Index objects. The Index or collection of Index objects to append to the original Index.

Example 1: Appending One Index to Another

Consider two Index objects:

import pandas as pd

# Creating the first Index
df1 = pd.Index([17, 69, 33, 5, 0, 74, 0])

# Creating the second Index
df2 = pd.Index([11, 16, 54, 58])

# Appending df2 to df1
result = df1.append(df2)

print(result)

Output:

Int64Index([17, 69, 33, 5, 0, 74, 0, 11, 16, 54, 58], dtype='int64')

In this example, df2 is appended to df1, resulting in a new Index object.

Example 2: Appending Multiple Indexes

You can also append multiple Index objects:

import pandas as pd

# Creating the first Index
df1 = pd.Index(['Jan', 'Feb', 'Mar', 'Apr'])

# Creating the second Index
df2 = pd.Index(['May', 'Jun', 'Jul', 'Aug'])

# Creating the third Index
df3 = pd.Index(['Sep', 'Oct', 'Nov', 'Dec'])

# Appending df2 and df3 to df1
result = df1.append([df2, df3])

print(result)

Output:

Index(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
       'Nov', 'Dec'],
      dtype='object')

Here, both df2 and df3 are appended to df1, demonstrating how to append multiple Index objects simultaneously.

Important Notes

• The Index.append() method does not modify the original Index; it returns a new Index object with the appended data.
• As of pandas version 2.0, the append() method has been removed. It is recommended to use pd.concat() for appending Index objects.

Conclusion

The Index.append() method is a convenient way to add labels to an Index. However, with its removal in pandas 2.0, transitioning to pd.concat() is advisable for future-proofing your code. Understanding these methods enhances your ability to manipulate and analyze data efficiently in pandas.