Reindexing in Pandas DataFrame

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Reindexing in Pandas can be used to change the index of rows and columns in a DataFrame. Indexes can be used with reference to multiple DataStructure indexes associated with multiple panda series or panda DataFrame. Let’s see how we can reindex columns and rows in a Pandas DataFrame.

Reindex Rows

You can reindex one or more rows using reindex () . The default values ‚Äã‚Äãin the new index that are not in the data frame are assigned NaN.

Example # 1:

# import numpy and pandas module

import pandas as pd

import numpy as np

column = [ ’a’ , ’ b’ , ’c’ , ’ d’ , ’e’ ]

index = [ ’A’ , ’ B’ , ’C’ , ’ D’ , ’E’ ]


# create a data frame of random array values ‚Äã‚Äã

df1 = pd.DataFrame (np.random.rand ( 5 , 5 ),

columns = column, index = index)

print (df1)

print ( ’Dataframe after reindexing rows:’ ,

df1.reindex ([ ’B’ , ’ D’ , ’ A’ , ’C’ , ’E’ ]))

Output:

Example # 2:

# import numpy and pandas modules

import pandas as pd

import numpy as np

column = [ ’a’ , ’ b’ , ’c’ , ’ d’ , ’e’ ]

index = [ ’A’ , ’B’ , ’ C’ , ’D’ , ’ E ’ ]


# create data frame of random array values ‚Äã‚Äã

df1 = pd.DataFrame (np.random.rand ( 5 , 5 ),

columns = column, index = index )


# create a new index for rows

new_index = [ ’U’ , ’ A’ , ’B’ , ’ C’ , ’Z’ ]

print (df1.reindex (new_index))

Output:

Re-indexing columns using keyword axis

It is possible to reindex one column or multiple columns using reindex () and specifying the axis we want to reindex. The default values ‚Äã‚Äãin the new index that are not in the data frame are assigned NaN.

Example # 1:

# import numpy and pandas module

import pandas as pd

import numpy as np

column = [ ’a’ , ’ b’ , ’c’ , ’ d’ , ’e’ ]

index = [ ’A’ , ’ B’ , ’C’ , ’ D’ , ’E’ ]


# create a data frame of random array values ‚Äã‚Äã

df1 = pd.DataFrame (np.random.rand ( 5 , 5 ),

columns = column, index = index)

colum = [ ’e’ , ’ a’ , ’b’ , ’c’ , ’ d’ ]


# create a new index for columns

print (df1.reindex (colum, axis = ’ columns’ ))

Output:

Example # 2:

# import numpy and pandas module

import pandas as pd

import numpy as np

column = [ ’a’ , ’b’ , ’ c’ , ’ d’ , ’e’ ]

index = [ ’A’ , ’ B’ , ’C’ , ’ D’ , ’E’ ]


# create a data frame of random array values ‚Äã‚Äã

df1 = pd.DataFrame (np.random.rand ( 5 , 5 ),

columns = column, index = index)

colum = [ ’a’ , ’ b’ , ’c’ , ’ g’ , ’ h’ ]


# create a new index for columns

print (df1.reindex (colum, axis = ’columns’ ))

Output:

Replacing missing values ‚Äã‚Äã

Code # 1: You can fill in missing values ‚Äã‚Äãfrom a data frame by passing a value to the keyword fill_value . This keyword replaces NaN values.

# import numpy and pandas module

import pandas as pd

import numpy as np

column = [ ’ a’ , ’b’ , ’ c’ , ’d’ , ’ e’ ]

index = [ ’ A’ , ’B’ , ’ C’ , ’D’ , ’ E’ ]


# create a data frame of random array values ‚Äã‚Äã

df1 = pd.DataFrame (np.random.rand ( 5 , 5 ) ,

columns = column, index = index)

colum = [ ’a’ , ’ b’ , ’c’ , ’g’ , ’h’ ]


# create a new index for columns

print (df1.reindex (colum, axis = ’columns’ , fill_value = 1.5 ))

Output:

Code # 2: Replace missing data with a string.

# import numpy and pandas module

import pandas as pd

import numpy as np

column = [ ’a’ , ’ b’ , ’ c’ , ’d’ , ’e’ ]

index = [ ’A’ , ’B’ , ’ C’ , ’D’ , ’E’ ]


# create a data frame of random array values ‚Äã‚Äã

df1 = pd.DataFrame (np.random.rand ( 5 , 5 ),

columns = column, index = index)

colum = [ ’a’ , ’b’ , ’ c’ , ’ g’ , ’h’ ]


# create a new index for columns

print (df1.reindex (colum, axis = ’columns’ , fill_value = ’ data missing’ ))

Output:

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We hope this article has helped you to resolve the problem. Apart from Reindexing in Pandas DataFrame, check other array Python module-related topics.

Want to excel in Python? See our review of the best Python online courses 2023. If you are interested in Data Science, check also how to learn programming in R.

By the way, this material is also available in other languages:



Chen Richtgofen

San Francisco | 2023-02-01

Thanks for explaining! I was stuck with Reindexing in Pandas DataFrame for some hours, finally got it done 🤗. I am just not quite sure it is the best method

Javier Williams

Milan | 2023-02-01

Thanks for explaining! I was stuck with Reindexing in Pandas DataFrame for some hours, finally got it done 🤗. Checked yesterday, it works!

Cornwall Ungerschaft

Prague | 2023-02-01

string Python module is always a bit confusing 😭 Reindexing in Pandas DataFrame is not the only problem I encountered. I am just not quite sure it is the best method

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