How to Use cbind in Python?

In Python, the equivalent function cbind in R is typically achieved using the concat function from the Pandas library. In this article, we will discuss cbind in Python. We have seen cbind() function in R Programming language to combine specified Vector, Matrix, or DataFrame by columns. But in Python, there is concat() function which is equivalent to cbind() function of R.

Creating DataFrame for Demonstration

We will create a sample Pandas DataFrame that we will use in our article.

# 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:

      name subject1  marks
0   sravan   python     96
1   harsha        R     89
2 jyothika      php     90
      name subject2  marks
0   sravan     html     89
1   harsha     .net     79
2 jyothika      jsp     80

Using cbind in Python

Below are some examples by which we will see how to cbind DataFrames Pandas Python and also combining DataFrames using cbind in Pandas Python:

Concat Dataframe with Equal Indices Using cbind

This will concatenate row-wise data based on the index. here the two dataframe indices are the same. In this example, two Pandas DataFrames, 'data1' and 'data2', containing information about students, subjects, and marks, are created. The `pd.concat()` function is then utilized with `axis=1` to perform a column-wise concatenation, effectively combining the information from both DataFrames into a single DataFrame based on the shared 'name' column.

# 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:

      name subject1  marks     name subject2  marks
0   sravan   python     96   sravan     html     89
1   harsha        R     89   harsha     .net     79
2 jyothika      php     90 jyothika      jsp     80

Concat DataFrame with Unequal Indices Using cbind

In this scenario, the two dataframes indices are unequal, when we apply concat() function, this will result into a new dataframe with NaN values.

# 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:

      name subject1  marks     name subject2  marks
0   sravan   python     96      NaN      NaN    NaN
1   harsha        R     89      NaN      NaN    NaN
2 jyothika      php     90      NaN      NaN    NaN
3   sravan      NaN    NaN   sravan     html   89.0
4   harsha      NaN    NaN   harsha     .net   79.0
5 jyothika      NaN    NaN jyothika      jsp   80.0

In order to remove these NaN rows, we have to drop the index by using reset_index() method.

Example:

In this example, two Pandas DataFrames, 'data1' and 'data2', are created with overlapping indices. To perform a column-wise concatenation using `pd.concat`, the indices of both DataFrames are reset using `reset_index(drop=True, inplace=True)` to avoid conflicts.

# 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:

      name subject1  marks     name subject2  marks
0   sravan   python     96   sravan     html     89
1   harsha        R     89   harsha     .net     79
2 jyothika      php     90 jyothika      jsp     80

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Article Tags :

• Python
• Python-pandas
• Python pandas-dataFrame
• Pandas-DataFrame-Methods

Practice Tags :

• python