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In this tutorial, you will learn to add specific columns to a Pandas DataFrame.

Before we start, let's create a dummy DataFrame to work with. Here, we make two DataFrames, namely dat1and dat2, and some entries.

import pandas as pd

dat1 = pd.DataFrame({"dat1": [9, 5]})
print(dat1)

Output:

   dat1
0     9
1     5

Now, let’s create another dat2DataFrame called . We can do this using the following code.

dat2 = pd.DataFrame({"dat2": [7, 6]})
print(dat2)

Output:

   dat2
0     7
1     6

As we can see in dat1, dat2we have 2 columns and 2 rows, one of which represents the index and the second one represents the values ​​in our DataFrame.

concat()Appending a column in Pandas using

We can use the merge or concatenate function in Pandas concatto merge or join multiple DataFrames into one with the help of a single parameter which is passed as an array with all the DataFrames to be combined.

We need to specify the axis to which we want to add the DataFrame to alter the DataFrame based on columns or rows.

Now, let's try to dat2merge into dat1a DataFrame. We use the following code:

dat1 = pd.concat([dat1, dat2], axis=1)

Output:

   dat1  dat2
0     9     7
1     5     6

As is evident from the code, we are using the axis parameter with a value of 1. The axis parameter indicates that we want to add a column to the array DataFrame assigned in the first parameter.

In the output, dat1has been changed so that an additional column is added in the first axis.

join()Appending a column in Pandas using

Pandas joinhelps us with this using another function called the join function. This function helps in joining two different DataFrames, thus helping us to add a specific column to a specific DataFrame.

dat1We can combine and with the help of this function dat2.

val = dat1.join(dat2)
print(val)

Output:

   dat1  dat2
0     9     7
1     5     6

As we can see, we get the expected result. It is worth noting that joinwe dat1added a new column to our DataFrame with the help of the function in Pandas.

joinWith the help of the and functions in Pandas concat, we can effectively filter the data as and when required and add a specific column or a set of columns to a particular dataset.