Add Column to Pandas DataFrame with a Default Value

The three ways to add a column to Pandas DataFrame with Default Value.

• Using pandas.DataFrame.assign(**kwargs)
• Using [] operator
• Using pandas.DataFrame.insert()

Using Pandas.DataFrame.assign(**kwargs)

It Assigns new columns to a DataFrame and returns a new object with all existing columns to new ones. Existing columns that are re-assigned will be overwritten.

Syntax: Pandas.DataFrame.assign(**kwargs)

Parameters : **kwargsdict of {str: callable or Series}

Returns : DataFrame

Let’s understand with examples:

First, create a simple DataFrame.

Output:

Add a new column:

Output:

Add a new column with Default Value:

Output:

Using [] operator to add a new column

We can use DataFrame indexing to create a new column in DataFrame and set it to default values.

Syntax:

df[col_name]=value

Let’s understand with an example:

Output:

Add new column in Dataframe:

Output:

Add a new column with default values:

Output:

Using pandas.DataFrame.insert()

Add new column into DataFrame at specified location.

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

Parameters

loc : int Insertion index. Must verify 0 <= loc <= len(columns).

column : str, number, or hashable object Label of the inserted column.

value : int, Series, or array-like

allow_duplicates : bool, optional

Let’s understand with examples:

Output:

Add a new column with default value:

Output: