Add a column with incremental Numbers to a Pandas DataFrame

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# Add a column with incremental Numbers to a Pandas DataFrame

To add a column with incremental numbers to a Pandas DataFrame:

Use the DataFrame.insert() method to insert a column into the DataFrame at a specific index.
Specify the name of the column as the second parameter.
Use the range() class to add a column with incremental numbers.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', None, None],
    'experience': [None, 5, None, None],
    'salary': [None, 180.2, 190.3, 205.4],
})

#     name  experience  salary
# 0  Alice         NaN     NaN
# 1  Bobby         5.0   180.2
# 2   None         NaN   190.3
# 3   None         NaN   205.4
print(df)

df.insert(0, 'ID', range(0, 0 + len(df)))

#    ID   name  experience  salary
# 0   0  Alice         NaN     NaN
# 1   1  Bobby         5.0   180.2
# 2   2   None         NaN   190.3
# 3   3   None         NaN   205.4
print(df)

add column with incremental numbers to pandas dataframe

The code for this article is available on GitHub

The DataFrame.insert() method inserts a column into a DataFrame at a specified location.

Note that the column raises a ValueError if a column with the specified name is already contained in the DataFrame unless the allow_duplicates parameter is set to `True`.

We passed the following 3 parameters to the DataFrame.insert() method:

The insertion index. Indices are zero-based, so an index of 0 inserts the ID column as the first in the DataFrame.

Note that the insertion index has to be greater than or equal to 0 and less than or equal to len(df).

The label of the inserted column (ID in the example).
The values the column should contain. Can be scalar, Series or array-like.

main.py

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df.insert(0, 'ID', range(0, 0 + len(df)))

#    ID   name  experience  salary
# 0   0  Alice         NaN     NaN
# 1   1  Bobby         5.0   180.2
# 2   2   None         NaN   190.3
# 3   3   None         NaN   205.4
print(df)

We used the range() class to get an object that contains the values of the column.

The incremental numbers in the example start from 0, however, you can specify any other starting value.

Here is an example that uses the number 5 as the starting value of the incremental column.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', None, None],
    'experience': [None, 5, None, None],
    'salary': [None, 180.2, 190.3, 205.4],
})

#     name  experience  salary
# 0  Alice         NaN     NaN
# 1  Bobby         5.0   180.2
# 2   None         NaN   190.3
# 3   None         NaN   205.4
print(df)

df.insert(0, 'ID', range(5, 5 + len(df)))

#    ID   name  experience  salary
# 0   5  Alice         NaN     NaN
# 1   6  Bobby         5.0   180.2
# 2   7   None         NaN   190.3
# 3   8   None         NaN   205.4
print(df)

add column with incremental numbers to dataframe starting from specific number

The code for this article is available on GitHub

The range() class is commonly used for looping a specific number of times in for loops and takes the following arguments:

Name	Description
`start`	An integer representing the start of the range (defaults to `0`)
`stop`	Go up to, but not including the provided integer
`step`	Range will consist of every N numbers from `start` to `stop` (defaults to `1`)

The first argument you pass to the range() class is going to be the number you want to start incrementing from.

The second argument is the stop value (exclusive) and is determined by adding the start value to the length of the DataFrame.

Note that the DataFrame.insert() method modifies the DataFrame in place and returns None.

If you don't need to add the new column at a specific index, you can shorten this a bit.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', None, None],
    'experience': [None, 5, None, None],
    'salary': [None, 180.2, 190.3, 205.4],
})

#     name  experience  salary
# 0  Alice         NaN     NaN
# 1  Bobby         5.0   180.2
# 2   None         NaN   190.3
# 3   None         NaN   205.4
print(df)

df['ID'] = range(5, 5 + len(df))

print('-' * 50)

#     name  experience  salary  ID
# 0  Alice         NaN     NaN   5
# 1  Bobby         5.0   180.2   6
# 2   None         NaN   190.3   7
# 3   None         NaN   205.4   8
print(df)

The code for this article is available on GitHub

We directly added a column with incremental numbers to the DataFrame without using insert().

However, notice that the column is added at the end of the DataFrame.

# Adding a column with incremental numbers to a DataFrame by using `rename()`

You can also use the DataFrame.rename() method to add a column with incremental numbers to a DataFrame.

main.py

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import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', None, None],
    'experience': [None, 5, None, None],
    'salary': [None, 180.2, 190.3, 205.4],
})

#     name  experience  salary
# 0  Alice         NaN     NaN
# 1  Bobby         5.0   180.2
# 2   None         NaN   190.3
# 3   None         NaN   205.4
print(df)

df = df.reset_index()

df = df.rename(columns={'index': 'ID'})

df['ID'] = df.index + 5

print('-' * 50)

#    ID   name  experience  salary
# 0   5  Alice         NaN     NaN
# 1   6  Bobby         5.0   180.2
# 2   7   None         NaN   190.3
# 3   8   None         NaN   205.4
print(df)

add column with incremental numbers using rename

The code for this article is available on GitHub

We used the DataFrame.reset_index() method to reset the index of the DataFrame.

The next step is to use the DataFrame.rename() method to rename the index column to ID (or any other name).

Lastly, we set the starting value of the ID column to 5.

This can be any other value you want to start incrementing from.

The ID column is an index column, so it automatically increments the value with each row.

# Add a column with incremental numbers to a DataFrame using `assign()`

You can also use the pandas.assign() method to add a column with incremental numbers to a DataFrame.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'name': ['Alice', 'Bobby', None, None],
    'experience': [None, 5, None, None],
    'salary': [None, 180.2, 190.3, 205.4],
})

#     name  experience  salary
# 0  Alice         NaN     NaN
# 1  Bobby         5.0   180.2
# 2   None         NaN   190.3
# 3   None         NaN   205.4
print(df)

df = df.assign(ID=lambda x: range(5, 5 + len(x)))

print('-' * 50)

#     name  experience  salary  ID
# 0  Alice         NaN     NaN   5
# 1  Bobby         5.0   180.2   6
# 2   None         NaN   190.3   7
# 3   None         NaN   205.4   8
print(df)

add column with incremental numbers to dataframe using assign

The code for this article is available on GitHub

The DataFrame.assign() method assigns a new column to a DataFrame.

The method returns a new object with all the existing DataFrame columns and the new columns.

Columns whose names already exist in the DataFrame, get overwritten.

We passed the name of the column as the name of the keyword argument and used a lambda function to get the value for each row.

We start incrementing from 5 in the example, however, you could use any other value.

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Add a column with incremental Numbers to a Pandas DataFrame

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