In this post, you will learn different techniques to append or add one column or multiple columns to Pandas Dataframe (Python). There are different scenarios where this could come very handy. For example, when there are two or more data frames created using different data sources, and you want to select a specific set of columns from different data frames to create one single data frame, the methods given below can be used to append or add one or more columns to create one single data frame. It will be good to know these methods as it helps in data preprocessing stage of building machine learning models.
In this post, we will work the following two Pandas data frames. The requirement is to append second data frame (df2) to first dataframe (df1)
import pandas as pd
#
# Dataframe 1 having 3 columns
#
df1 = pd.DataFrame([
['Ajitesh', 'M', 'Software'],
['Sumit', 'M', 'Software'],
['Sarojini', 'M', 'Business'],
['Amitabh', 'M', 'Digital Marketing']
])
df1.columns = ['name', 'gender', 'profession']
#
# Dataframe 2 having 2 columns
#
df2 = pd.DataFrame([
['tall', 84],
['tall', 88],
['short', 62],
['very tall', 85]
])
df2.columns = ['height', 'weight']
This is how the above data frames look like.
Technique 1: Use Join
Use Dataframe Join method to append one or more columns to existing data frame. Note that columns of df2 is appended to df1. The following code will work:
df1 = df1.join(df2)
Technique 2: Use Concat
Use Pandas concat method to append one or more columns to existing data frame. The way this is different from join method is that concat method (static method) is invoked on pandas class while join method is invoked on an instance of data frame. Note that columns of df2 is appended to df1. The following code will work:
df1 = pd.concat([df1, df2], axis=1)
Conclusion
In this post, you learned about how to append or add one column or multiple columns to the Pandas data frame. Here are two commands which can be used:
- Use Dataframe join command to append the columns
- Use Pandas concat command to append the columns
- Both methods can be used to join multiple columns from different data frames and create one data frame.
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I found it very helpful. However the differences are not too understandable for me