How to Add a New Column to Existing DataFrame With Default Value in Pandas

Suraj Joshi Feb 02, 2024 Pandas Pandas DataFrame Column Pandas DataFrame
  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
How to Add a New Column to Existing DataFrame With Default Value in Pandas

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.

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Author: Suraj Joshi
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Suraj Joshi is a backend software engineer at Matrice.ai.

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