Python

Pandas insert() Column

Pandas simplify many tedious, time-consuming tasks associated with working with the data. The columns in the DataFrame can also be adjusted, along with the data source. There are four ways to add a column to a DataFrame in Pandas, but in this article, we use the Pandas column “insert()” function.

DataFrame.insert()

By utilizing the DataFrame “insert()” method, you can add columns between the current columns rather than adding them at the bottom of the Pandas DataFrame. It allows us to add a column anywhere we choose rather than simply at the conclusion. Additionally, it offers many ways to add the values for the columns. When you add a column at a specified position or index, the Pandas “insert()” function is useful.

Syntax

pandas.DataFrame.insert(position,’column’,[values…])

Parameters

  1. “position” is the first parameter that refers to the column index position where the column has to be inserted.
  2. “column” is the new column name
  3. Values are placed in a list and inserted into the column.

Example 1

In this example, we have a DataFrame named “things” that holds the “Name” and “Purchased Status” columns.

Let’s add a new column named “Cost” with values.

import pandas

things=pandas.DataFrame({'Name':['Solar dish','glasses','oil'],
                               'Purchased Status':[1,0,0]})

print(things)

# Insert 'Cost' column to the above dataframe at index-2

things.insert(2, "Cost", [890.0,98.0,67.91])

print()

print(things)

Output

         Name  Purchased Status

0  Solar dish                 1

1     glasses                 0

2         oil                 0

 

         Name  Purchased Status    Cost

0  Solar dish                 1  890.00

1     glasses                 0   98.00

2         oil                 0   67.91

Explanation

We specified the position as 2 in the insert() function. So the column “Cost” is inserted in the third position (index – 2). Finally, the columns are [‘Name’,‘Purchased Status’,‘Cost’].

Example 2

Let’s add a new column named “review” with string-type values at position-1.

import pandas

things=pandas.DataFrame({'Name':['Solar dish','glasses','oil'],
                        'Purchased Status':[1,0,0]})

print(things)

# Insert 'review' column to the above dataframe at index-1

things.insert(1, "review",["good","bad","good"])

print()

print(things)

Output

         Name  Purchased Status

0  Solar dish                 1

1     glasses                 0

2         oil                 0

 

         Name review  Purchased Status

0  Solar dish   good                 1

1     glasses    bad                 0

2         oil   good                 0

Explanation

We specified the position as 1 in the insert() function. So the column “review” is inserted in the second position (index-1). Finally, the columns are [‘Name’,‘review’,‘Purchased Status’].

Example 3

Create a DataFrame named orders with 2 columns and insert 2 columns at index-1 one by one.

import pandas

orders=pandas.DataFrame({'id':[1,2,3,4,5],
                           'name':['o1','o2','o3','o4','o5']})

print(orders)

# Insert 'Company' column to the above dataframe at index-1.

orders.insert(1, "Company", ["comp-1","comp-2","comp-3","comp-4","comp-5"])

print()

print(orders)

# Insert 'Sales' column to the above dataframe at index-1.

orders.insert(1, "Sales", [10,20,30,56,78])

print()

print(orders)

Output

    id name

0   1   o1

1   2   o2

2   3   o3

3   4   o4

4   5   o5

 

    id Company name

0   1  comp-1   o1

1   2  comp-2   o2

2   3  comp-3   o3

3   4  comp-4   o4

4   5  comp-5   o5

 

    id  Sales Company name

0   1     10  comp-1   o1

1   2     20  comp-2   o2

2   3     30  comp-3   o3

3   4     56  comp-4   o4

4   5     78  comp-5   o5

Explanation

First, the order of the columns is [id,name].

After adding ‘Company’ at position 1, the columns are [id.Company,name].

After adding ‘Sales’ at position 1, the columns are [id.Sales,Company,name].

Conclusion

A commonly used data analysis and update operation is adding columns to DataFrame. Pandas gives you numerous options for completing the task by offering four different methods. However, in our article, we only utilize one technique, which is the Pandas “insert()” column. We discussed three different examples of inserting the column in an existing DataFrame.

About the author

Gottumukkala Sravan Kumar

B tech-hon's in Information Technology; Known programming languages - Python, R , PHP MySQL; Published 500+ articles on computer science domain