How to add a new column to existing DataFrame with default value in Pandas

1. pandas.DataFrame.assign() to add a new column in Pandas DataFrame
2. Access the new column to set it with a default value
3. pandas.DataFrame.insert() to add a new column in Pandas DataFrame

We could use assign() and insert() methods of DataFrame objects to add a new column to the existing DataFrame with default values. We can also directly assign a default value to the column of DataFrame to be created.

We will use the below dataframe as an example in the following sections.

import pandas as pd

dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]

df = pd.DataFrame({'Date':dates ,
                   'Fruit':fruits ,
                   'Price': prices})

print(df)

Output:

       Date   Fruit  Price
0  April-10   Apple      3
1  April-11  Papaya      1
2  April-12  Banana      2
3  April-13   Mango      4

pandas.DataFrame.assign() to add a new column in Pandas DataFrame

We can use pandas.DataFrame.assign() method to add a new column to the existing DataFrame and assign the newly created DataFrame column with default values.

import pandas as pd

dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]

df = pd.DataFrame({'Date':dates ,
                   'Fruit':fruits ,
                   'Price': prices})

new_df=df.assign(Profit=6)
print(new_df)

Output:

       Date   Fruit  Price  Profit
0  April-10   Apple      3       6
1  April-11  Papaya      1       6
2  April-12  Banana      2       6
3  April-13   Mango      4       6

The code creates a new column Profit in the DataFrame and sets the entire column’s values to be 6.

Access the new column to set it with a default value

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

Syntax:

df[col_name]=value

It creates a new column col_name in DataFrame df and sets the default value for the entire column to value.

import pandas as pd

dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]

df = pd.DataFrame({'Date':dates ,
                   'Fruit':fruits ,
                   'Price': prices})

df['Profit']=5
print(df)

Output:

       Date   Fruit  Price  Profit
0  April-10   Apple      3       5
1  April-11  Papaya      1       5
2  April-12  Banana      2       5
3  April-13   Mango      4       5

pandas.DataFrame.insert() to add a new column in Pandas DataFrame

pandas.DataFrame.insert() allows us to insert a column in a DataFrame at specified location.

Syntax:

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

It creates a new column with the name column at location loc with default value value. allow_duplicates=False ensures there is only one column with the name column in the dataFrame.

import pandas as pd

dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]

df = pd.DataFrame({'Date':dates ,
                   'Fruit':fruits ,
                   'Price': prices})

df.insert(2, "profit", 4, allow_duplicates=False)
print(df

Output:

       Date   Fruit  profit  Price
0  April-10   Apple       4      3
1  April-11  Papaya       4      1
2  April-12  Banana       4      2
3  April-13   Mango       4      4

Here, a column with the name profit is inserted at index 2 with the default value 4.

Related Article - Pandas DataFrame