Add Column to Pandas DataFrame with a Default Value

Let's discuss how to add column to Pandas DataFrame with a default value using assign(), the [] operator, and insert().

Add Column with a Default Value using assign()

The assign() method is used to add new columns to a DataFrame and returns a new object with all existing columns and the new ones. Existing columns that are re-assigned will be overwritten. This method allows you to set the default value for the new column as well.

DataFrame.assign(**kwargs)

Let's consider an example:

# importing pandas as pd
import pandas as pd

# creating the dataframe
df = pd.DataFrame({"Name": ['Anurag', 'Manjeet', 'Shubham','Saurabh', 'Ujjawal'],
                   "Address": ['Patna', 'Delhi', 'Coimbatore','Greater noida', 'Patna'],
                   "ID": [20123, 20124, 20145, 20146, 20147],
                   "Sell": [140000, 300000, 600000, 200000, 600000]})

print("Original DataFrame :")
display(df)

Output:

Add a new column using assign()

# Using assign() to add a 'profit' column
new_df = df.assign(profit=[40000, 20000, 30000, 60000, 200000])
print("\nDataFrame after using assign() to add 'profit' column:")
print(new_df)

Output:

DataFrame after using assign() to add 'profit' column:
      Name        Address     ID    Sell  profit
0   Anurag          Patna  20123  140000   40000
1  Manjeet          Delhi  20124  300000   20000
2  Shubham     Coimbatore  20145  600000   30000
3  Saurabh  Greater noida  20146  200000   60000
4  Ujjawal          Patna  20147  600000  200000

Add a new column with Default Value:

# Using assign() to add a 'loss' column with a default value 'NAN'
new_df = df.assign(loss='NAN')
print("\nDataFrame after using assign() to add 'loss' column with default value:")
print(new_df)

Output:

Adding column to Pandas Dataframe using [] operator

The [] operator is the most straightforward method for adding a new column. You simply reference the column name and assign a value. This method allows you to assign both lists or scalar values.

df[col_name]=value

Let's use [] operator to add 'loss' column to the existing dataframe:

# Using the [] operator to add a 'loss' column
df['loss'] = [40000, 20000, 30000, 60000, 200000]
print("\nDataFrame after using [] operator to add 'loss' column:")
print(df)

Output:

Adding a New Column with a Default Value:

# Using the [] operator to add a 'loss' column with a default value 'NAN'
df['loss'] = 'NAN'
print("\nDataFrame after using [] operator to add 'loss' column with default value:")
print(df)

Output:

DataFrame after using [] operator to add 'loss' column with default value:
      Name        Address     ID    Sell loss
0   Anurag          Patna  20123  140000  NAN
1  Manjeet          Delhi  20124  300000  NAN
2  Shubham     Coimbatore  20145  600000  NAN
3  Saurabh  Greater noida  20146  200000  NAN
4  Ujjawal          Patna  20147  600000  NAN

Using DataFrame.insert()

The insert() method adds a new column into a DataFrame at a specified location. This can be helpful if you need to control where the new column appears in the DataFrame.

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

Let's understand with example:

# Inserting a new column 'expenditure' at position 2
df.insert(2, "expenditure", 4500, allow_duplicates=False)
print("\nDataFrame after using insert() to add 'expenditure' column at position 2:")
print(df)

Output:

DataFrame after using insert() to add 'expenditure' column at position 2:
      Name        Address  expenditure     ID    Sell loss
0   Anurag          Patna         4500  20123  140000  NAN
1  Manjeet          Delhi         4500  20124  300000  NAN
2  Shubham     Coimbatore         4500  20145  600000  NAN
3  Saurabh  Greater noida         4500  20146  200000  NAN
4  Ujjawal          Patna         4500  20147  600000  NAN

Each of the methods discussed has its unique advantages:

• assign(): Ideal for adding new columns with a functional approach, especially when you want to return a modified copy of the DataFrame.
• [] operator: The simplest and most common method for quick modifications when you're working with DataFrames.
• insert(): Useful when you need to control the position of the new column.