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We can use the add_columns() assign()and add_columns() insert()methods of the DataFrame object to add new columns to an existing DataFrame with default values. We can also assign default values ​​directly to the DataFrame columns to be created.

In the following sections, we will use the following DataFrameas an example.

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()Adding New Columns to a Pandas DataFrame

We can use pandas.DataFrame.assign() method to add new columns to an existing DataFrame and DataFrameassign default values ​​to the newly created columns.

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

This code creates a new column in the DataFrame Profitand sets the value of the entire column to 6.

Access the new column to set it to the default value

We can use DataFrame indexing to create new columns in a DataFrame and set them to default values.

grammar:

df[col_name] = value

It dfcreates a new column in the DataFrame col_nameand sets the default value for the entire column 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()Adding New Columns to a Pandas DataFrame

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

grammar:

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

It loccreates a columnnew column named at position with a default value of value. allow_duplicates=FalseMake sure that there is only one columncolumn named 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, profita column named is inserted into the index 2with a default value of 4.