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Create a Pandas DataFrame from a Numpy array and specify the index column and column headers

  • Difficulty Level : Medium
  • Last Updated : 21 Aug, 2020

This article demonstrates multiple examples to convert the Numpy arrays into Pandas Dataframe and to specify the index column and column headers for the data frame.

Example 1: In this example, the Pandas dataframe will be generated and proper names of index column and column headers are mentioned in the function. This approach can be used when there is no pattern in naming the index column or column headers. 

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Below is the implementation:



Python3




# Python program to Create a 
# Pandas DataFrame from a Numpy  
# array and specify the index 
# column and column headers
  
# import required libraries
import numpy as np
import pandas as pd
  
# creating a numpy array
numpyArray = np.array([[15, 22, 43], 
                       [33, 24, 56]])
  
# generating the Pandas dataframe
# from the Numpy array and specifying
# name of index and columns
panda_df = pd.DataFrame(data = numpyArray, 
                        index = ["Row_1", "Row_2"], 
                        columns = ["Column_1",
                                   "Column_2", "Column_3"])
  
# printing the dataframe
print(panda_df)

Output: 

Create a Pandas DataFrame from a Numpy array and specify the index column and column headers

Example 2: In this example, the index column and column headers are generated through iteration. The range of iterations for rows and columns are defined by the shape of the Numpy array. With every iteration, a digit will be added to the predefined string and the new index column or column header will generate. Thus, if there is some pattern in naming the labels of the dataframe this approach is suitable. 

Below is the implementation:

Python3




# Python program to Create a 
# Pandas DataFrame from a Numpy  
# array and specify the index column 
# and column headers
  
# import required libraries
import pandas as pd
import numpy as np
  
# creating a numpy array
numpyArray = np.array([[15, 22, 43], 
                       [33, 24, 56]])
  
# generating the Pandas dataframe
# from the Numpy array and specifying
# name of index and columns
panda_df = pd.DataFrame(data = numpyArray[0:, 0:],
                        index = ['Row_' + str(i + 1
                        for i in range(numpyArray.shape[0])],
                        columns = ['Column_' + str(i + 1
                        for i in range(numpyArray.shape[1])])
  
# printing the dataframe
print(panda_df)

Output: 

Create a Pandas DataFrame from a Numpy array and specify the index column and column headers



Example 3: In this example, the index column and column headers are defined before converting the Numpy array into Pandas dataframe. The label names are again generated through iterations but the method is little different. Here, the number of iterations is defined by the length of the sub-array inside the Numpy array. This method can be used if the index column and column header names follow some pattern. 

Below is the implementation:

Python3




# Python program to Create a 
# Pandas DataFrame from a Numpy  
# array and specify the index column 
# and column headers
  
# import required libraries
import pandas as pd
import numpy as np
  
# creating a numpy array
numpyArray = np.array([[15, 22, 43], 
                       [33, 24, 56]])
  
# defining index for the 
# Pandas dataframe
index = ['Row_' + str(i) 
        for i in range(1, len(numpyArray) + 1)]
  
# defining column headers for the 
# Pandas dataframe
columns = ['Column_' + str(i) 
          for i in range(1, len(numpyArray[0]) + 1)]
  
# generating the Pandas dataframe
# from the Numpy array and specifying
# details of index and column headers
panda_df = pd.DataFrame(numpyArray , 
                        index = index,
                        columns = columns)
  
# printing the dataframe
print(panda_df)

Output: 

Create a Pandas DataFrame from a Numpy array and specify the index column and column headers

Example #4: In this approach, the index column and the column headers for the Pandas dataframe will present itself in the Numpy array. During the conversion of the Numpy array into Pandas data frame, proper indexing for the sub-arrays of the Numpy array has to be done in order to get correct sequence of the dataframe labels. 

Below is the implementation:

Python3




# Python program to Create a 
# Pandas DataFrame from a Numpy  
# array and specify the index column 
# and column headers
  
# import required libraries
import pandas as pd
import numpy as np
  
# creating a numpy array and
# specifying the index and 
# column headers along with 
# data stored in the array
numpyArray = np.array([['', 'Column_1', 
                       'Column_2', 'Column_3'],
                       ['Row_1', 15, 22, 43],
                       ['Row_2', 33, 24, 56]])
  
# generating the Pandas dataframe
# from the Numpy array and specifying
# details of index and column headers
panda_df = pd.DataFrame(data = numpyArray[1:, 1:],
                        index = numpyArray[1:, 0],
                        columns = numpyArray[0, 1:])
  
# printing the dataframe
print(panda_df)

Output: 

Create a Pandas DataFrame from a Numpy array and specify the index column and column headers

 




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