Creating New Columns | Pychallenger

The companies dataset has Revenue and Expenses, but no Profit column. Most analysis questions require data that isn’t in the original file. It has to be computed.

Multiply two existing columns to create a new one. Pandas applies the expression row by row automatically:

import pandas as pd
 
df = pd.DataFrame({
    'Price': [10, 20, 30],
    'Quantity': [2, 5, 1]
})
df['Total'] = (
    df['Price'] * df['Quantity']
)
print(df)

Python

Output

The pattern is always df['NewCol'] = expression. Pandas applies it to every row without needing a loop.

What will be the output?

import pandas as pd
df = pd.DataFrame({
    'A': [10, 20],
    'B': [3, 7]
})
df['Sum'] = df['A'] + df['B']
print(df['Sum'].tolist())

Python

Comparisons create True/False columns, which is useful for flagging rows that meet a condition:

import pandas as pd
 
df = pd.DataFrame({
    'Price': [10, 20, 30],
    'Quantity': [2, 5, 1]
})
df['Total'] = (
    df['Price'] * df['Quantity']
)
df['Expensive'] = df['Total'] > 50
print(df['Expensive'])

Python

Output

A boolean column marks each row as passing or failing a test. Later, it can be used to filter the DataFrame.

What will be the output?

import pandas as pd
df = pd.DataFrame({
    'X': [5, 15, 25]
})
df['Big'] = df['X'] > 10
print(df['Big'].tolist())

Python

Chain operations to compute percentages. Here, a profit margin from Revenue and Cost:

import pandas as pd
 
df = pd.DataFrame({
    'Revenue': [200, 500, 100],
    'Cost': [100, 200, 80]
})
df['Profit'] = (
    df['Revenue'] - df['Cost']
)
df['Margin'] = (
    df['Profit'] / df['Revenue'] * 100
)
print(df['Margin'])

Python

Output

Derived columns like profit margins, growth rates, and ratios turn raw numbers into metrics that actually answer questions.

What will be the output?

import pandas as pd
df = pd.DataFrame({
    'Rev': [100, 200],
    'Cost': [80, 50]
})
df['P'] = df['Rev'] - df['Cost']
print(df['P'].tolist())

Python

Existing columns can be overwritten too: df['Price'] = df['Price'] * 1.1 increases every price by 10%.

What will be the output?

import pandas as pd
df = pd.DataFrame({
    'Score': [30, 60]
})
df['F'] = df['Score'] > 50
print(df['F'].dtype)

Python