In this article, we will discuss cbind in Python. We have seen cbind() function in R Programming language to combine specified Vector, Matrix, or Data Frame by columns. But in Python, there is concat() function which is equivalent to cbind() function of R.
Create dataframes for demonstration:
Python3
# import pandas module import pandas as pd # create dataframe data1 = pd.DataFrame({'name': ['sravan', 'harsha', 'jyothika'], 'subject1': ['python', 'R', 'php'], 'marks': [96, 89, 90]}) # create dataframe data2 = pd.DataFrame({'name': ['sravan', 'harsha', 'jyothika'], 'subject2': ['html', '.net', 'jsp'], 'marks': [89, 79, 80]}) # display print(data1) print(data2) |
Output:
Example 1: Concat dataframe with equal indices
This will concatenate row-wise data based on the index. here the two dataframe indices are the same.
Python3
# import pandas module import pandas as pd # create dataframe data1 = pd.DataFrame({'name': ['sravan', 'harsha', 'jyothika'], 'subject1': ['python', 'R', 'php'], 'marks': [96, 89, 90]}) # create dataframe data2 = pd.DataFrame({'name': ['sravan', 'harsha', 'jyothika'], 'subject2': ['html', '.net', 'jsp'], 'marks': [89, 79, 80]}) # concat dataframes pd.concat([data1, data2], axis=1) |
Output:
Example 2: Concat dataframe with unequal indices
In this scenario, the two dataframes indices are unequal, when we apply concat() function, this will result into a new dataframe with NaN values
Python3
# import pandas module import pandas as pd # create dataframe data1 = pd.DataFrame({'name': ['sravan', 'harsha', 'jyothika'], 'subject1': ['python', 'R', 'php'], 'marks': [96, 89, 90]}, index=[0, 1, 2]) # create dataframe data2 = pd.DataFrame({'name': ['sravan', 'harsha', 'jyothika'], 'subject2': ['html', '.net', 'jsp'], 'marks': [89, 79, 80]}, index=[3, 4, 5]) # concat dataframes pd.concat([data1, data2], axis=1) |
Output:
In order to remove these NaN rows, we have to drop the index by using reset_index() method
Syntax: dataframe.reset_index(drop=True, inplace=True)
Example:
Python3
# import pandas module import pandas as pd # create dataframe data1 = pd.DataFrame({'name': ['sravan', 'harsha', 'jyothika'], 'subject1': ['python', 'R', 'php'], 'marks': [96, 89, 90]}, index=[0, 1, 2]) # create dataframe data2 = pd.DataFrame({'name': ['sravan', 'harsha', 'jyothika'], 'subject2': ['html', '.net', 'jsp'], 'marks': [89, 79, 80]}, index=[3, 4, 5]) # remove dataframe 1 indices data1.reset_index(drop=True, inplace=True) # remove dataframe 2 indices data2.reset_index(drop=True, inplace=True) # concat dataframes pd.concat([data1, data2], axis=1) |
Output:
