Pandas DataFrame Insert Method

Introduction

In this lab, we will learn how to use the DataFrame.insert() method in Python's pandas library. This method allows us to insert a column into a DataFrame at a specified location. We can also choose whether to allow duplicate columns or not.

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Skills Graph

Import the pandas library

We'll start by importing the pandas library, which will allow us to work with DataFrames.

import pandas as pd

Create a DataFrame

Next, let's create a DataFrame to work with. For this example, we'll create a DataFrame with two columns, 'A' and 'B', and four rows.

df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]})

Insert a new column into the DataFrame

Now, let's insert a new column called 'C' at index 2 in the DataFrame. We'll set the value of this column to 1 for all rows.

df.insert(2, 'C', 1)

Print the DataFrame

To see the updated DataFrame, let's print it using the print() function.

print(df)

Insert a Series as a column

Alternatively, we can insert a Series object as a column in the DataFrame. Let's create a new Series object with values [1, 2, 3, 4] and insert it at index 0 in the DataFrame.

series = pd.Series([1, 2, 3, 4])
df.insert(0, 'C', series)

Print the DataFrame

Once again, let's print the DataFrame to see the changes.

print(df)

Handle duplicate columns

By default, the DataFrame.insert() method raises a ValueError if we try to insert a column with a label that already exists in the DataFrame. However, we can override this behavior by setting allow_duplicates to True. Let's try inserting a column with a duplicate label and see the result.

df.insert(2, 'A', 1, allow_duplicates = True)

Print the DataFrame

After attempting to insert the duplicate column, let's print the DataFrame to see the error message.

print(df)

Summary

In this lab, we learned how to use the DataFrame.insert() method in pandas to insert new columns into a DataFrame at specific locations. We learned how to insert columns with constant values and with Series objects. We also saw how to handle duplicates when inserting columns. This method is useful when we need to add new features or modify the structure of our DataFrame.