Python | Pandas Index.append() (original) (raw)

Last Updated : 24 Aug, 2023

Python is an excellent language for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. **Pandas are one of those packages, making importing and analyzing data much easier.

PandasIndex.append() The function is used to append a single or a collection of indices together. In the case of a collection of indices, all of them get appended to the original index in the same order as they are passed to the Index.append() function. The function returns an appended index.

**Syntax:
Index.append(other)

**Parameters :
**other : Index or list/tuple of indices

**Returns : Index

**Example 1: Use Index.append() function to append a single index to the given index.

Python3

import pandas as pd

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

df2 = pd.Index([ 11 , 16 , 54 , 58 ])

print (df1, "\n" , df2)

**Output :

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

Let’s append the df2 index at the end of df1.

Python3

**Output :

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

As we can see in the output, the second index i.e. _df2 has been appended at the end of _df1 .

**Example 2: Use Index.append() function to append a collection of indexes at the end of the given index.

Python3

import pandas as pd

df1 = pd.Index([ 'Jan' , 'Feb' , 'Mar' , 'Apr' ])

df2 = pd.Index([ 'May' , 'Jun' , 'Jul' , 'Aug' ])

df3 = pd.Index([ 'Sep' , 'Oct' , 'Nov' , 'Dec' ])

print (df1, "\n" , df2, "\n" , df3)

**Output :

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

Let’s append both the indexes _df2 and _df3 at the end of _df1.

Python3

**Output :

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