Pandas DataFrame DataFrame.transpose() Function

  1. Syntax of pandas.DataFrame.transpose()
  2. Example Codes: DataFrame.transpose()
  3. Example Codes: DataFrame.transpose() to Transpose DataFrame With Homogeneous Data Types
  4. Example Codes: DataFrame.transpose() to Transpose DataFrame With Mixed Data Types

Python Pandas DataFrame.transpose() function changes the rows of the DataFrame to columns, and columns to rows. In other words, it generates a new DataFrame which is the transpose of the original DataFrame.

Syntax of pandas.DataFrame.transpose()

DataFrame.transpose(*args,
                    copy= False) 

Parameters

*args These are the additional keyword arguments for compatibility with numPy.
copy It is a Boolean value. It decides whether the values of the DataFrame will be copied or not after taking the transpose. By default, its value is False.

Return

It returns a transposed DataFrame. The rows of the original DataFrame are columns in the returned DataFrame and vice versa.

Example Codes: DataFrame.transpose()

We will implement this function in the next few codes.

import pandas as pd

dataframe=pd.DataFrame({
                        'Attendance': 
                            {0: 60, 
                            1: 100, 
                            2: 80,
                            3: 78,
                            4: 95},
                        'Name': 
                            {0: 'Olivia', 
                            1: 'John', 
                            2: 'Laura',
                            3: 'Ben',
                            4: 'Kevin'},
                        'Obtained Marks': 
                            {0: 90, 
                            1: 75, 
                            2: 82, 
                            3: 64, 
                            4: 45}
                        })

print(dataframe)

The example DataFrame is,

   Attendance    Name  Obtained Marks
0          60  Olivia              90
1         100    John              75
2          80   Laura              82
3          78     Ben              64
4          95   Kevin              45

All the parameters of this function are optional. If we execute this function without passing any parameter then it produces the following output.

import pandas as pd

dataframe=pd.DataFrame({
                        'Attendance': 
                            {0: 60, 
                            1: 100, 
                            2: 80,
                            3: 78,
                            4: 95},
                        'Name': 
                            {0: 'Olivia', 
                            1: 'John', 
                            2: 'Laura',
                            3: 'Ben',
                            4: 'Kevin'},
                        'Obtained Marks': 
                            {0: 90, 
                            1: 75, 
                            2: 82, 
                            3: 64, 
                            4: 45}
                        })

dataframe1 = dataframe.transpose()
print(dataframe1)

Output:

                     0     1      2    3      4
Attendance          60   100     80   78     95
Name            Olivia  John  Laura  Ben  Kevin
Obtained Marks      90    75     82   64     45

Example Codes: DataFrame.transpose() to Transpose DataFrame With Homogeneous Data Types

The behavior of this function is different for homogeneous and mixed data types. We will analyze it one by one. If we have a homogeneous type DataFrame then the data types of the original and the transposed Dataframes are the same.

The DataFrame with the homogeneous data types is as follows

import pandas as pd

dataframe = pd.DataFrame({'A': {0: 6, 1: 20, 2: 80,3: 78,4: 95}, 
                          'B': {0: 60, 1: 50, 2: 7,3: 67,4: 54}})

print(dataframe)

Our DataFrame is,

    A   B
0   6  60
1  20  50
2  80   7
3  78  67
4  95  54
5  98  34

To get the transpose of this DataFrame,

import pandas as pd

dataframe = pd.DataFrame({'A': {0: 6, 1: 20, 2: 80,3: 78,4: 95}, 
                          'B': {0: 60, 1: 50, 2: 7,3: 67,4: 54}})

dataframe1 = dataframe.transpose()
print(dataframe1)

Output:

    0   1   2   3   4
A   6  20  80  78  95
B  60  50   7  67  54

Now, let’s analyze the data types of the original DataFrame and the returned DataFrame.

import pandas as pd

dataframe = pd.DataFrame({'A': {0: 6, 1: 20, 2: 80,3: 78,4: 95}, 
                          'B': {0: 60, 1: 50, 2: 7,3: 67,4: 54}})

dataframe1 = dataframe.transpose()

print(dataframe.dtypes)
print(dataframe1.dtypes)

Output:

A    int64
B    int64
dtype: object
0    int64
1    int64
2    int64
3    int64
4    int64
dtype: object

Note that the data types of the original and the transposed DataFrames are the same.

Example Codes: DataFrame.transpose() to Transpose DataFrame With Mixed Data Types

If we have a mixed type DataFrame then the data types of the original and the transposed Dataframes are different. The transposed DataFrame has object data types. The DataFrame with the mixed data types is as follows

import pandas as pd

dataframe=pd.DataFrame({
                        'Attendance': 
                            {0: 60, 
                            1: 100, 
                            2: 80,
                            3: 78,
                            4: 95},
                        'Name': 
                            {0: 'Olivia', 
                            1: 'John', 
                            2: 'Laura',
                            3: 'Ben',
                            4: 'Kevin'},
                        'Obtained Marks': 
                            {0: 90, 
                            1: 75, 
                            2: 82, 
                            3: 64, 
                            4: 45}
                        })

print(dataframe)

Our DataFrame is,

   Attendance    Name  Obtained Marks
0          60  Olivia              90
1         100    John              75
2          80   Laura              82
3          78     Ben              64
4          95   Kevin              45

To get the transpose of this DataFrame,

import pandas as pd

dataframe=pd.DataFrame({
                        'Attendance': 
                            {0: 60, 
                            1: 100, 
                            2: 80,
                            3: 78,
                            4: 95},
                        'Name': 
                            {0: 'Olivia', 
                            1: 'John', 
                            2: 'Laura',
                            3: 'Ben',
                            4: 'Kevin'},
                        'Obtained Marks': 
                            {0: 90, 
                            1: 75, 
                            2: 82, 
                            3: 64, 
                            4: 45}
                        })

dataframe1 = dataframe.transpose()
print(dataframe1)

Output:

                     0     1      2    3      4
Attendance          60   100     80   78     95
Name            Olivia  John  Laura  Ben  Kevin
Obtained Marks      90    75     82   64     45

Now, let’s analyze the data types of the original DataFrame and the returned DataFrame.

import pandas as pd

dataframe=pd.DataFrame({
                        'Attendance': 
                            {0: 60, 
                            1: 100, 
                            2: 80,
                            3: 78,
                            4: 95},
                        'Name': 
                            {0: 'Olivia', 
                            1: 'John', 
                            2: 'Laura',
                            3: 'Ben',
                            4: 'Kevin'},
                        'Obtained Marks': 
                            {0: 90, 
                            1: 75, 
                            2: 82, 
                            3: 64, 
                            4: 45}
                        })

dataframe1 = dataframe.transpose()
print(dataframe.dtypes)
print(dataframe1.dtypes)

Output:

Attendance         int64
Name              object
Obtained Marks     int64
dtype: object
0    object
1    object
2    object
3    object
4    object
dtype: object

Note that the data types of the transposed DataFrame are of the object data type.

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