Pandas insert()

import pandas as pd

# sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}

df = pd.DataFrame(data)

# insert a new column C at index 1
df.insert(1, 'C', [7, 8, 9])

print(df)

'''
Output

   A  C  B
0  1  7  4
1  2  8  5
2  3  9  6
'''

insert() Syntax

The syntax of the insert() method in Pandas is:

DataFrame.insert(loc, column, value, allow_duplicates=False)

insert() Arguments

The insert() method in Pandas has the following arguments:

• loc: the integer index of the column before which the new column is to be inserted
• column: the name to assign to the new column
• value: the data to insert
• allow_duplicates (optional): whether to allow columns with the same name.

insert() Return Value

The insert() method does not return any value. It modifies the DataFrame in-place.

Example 1: Insert a Scalar Value as a Column

import pandas as pd

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

# insert a scalar value at index 0
df.insert(0, 'C', 10)

print(df)

Output

    C  A  B
0  10  1  4
1  10  2  5
2  10  3  6

In this example, we added a new column 'C' to df at index 0 with a constant value of 10.

Example 2: Insert a Column with Duplicate Name

import pandas as pd

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

# insert a column that already exists
# at index 2
df.insert(2, 'B', [7, 8, 9], allow_duplicates=True)

print(df)

Output

   A  B  B
0  1  4  7
1  2  5  8
2  3  6  9

In this example, we set the allow_duplicates flag to True, which allows a new column with the already existing name 'B' to be inserted at specified index without raising an error.