apply() on rows (axis=1) in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply a function to each row of the DataFrame.

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
result = df.apply(lambda row: row['A'] + row['B'], axis=[1])
print(result)

Drag options to blanks, or click blank then click option'

A1

B2

CNone

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 applies the function to columns, not rows.

Forgetting to specify axis parameter.

Setting axis=1 applies the function to each row.

2fill in blank

medium

Complete the code to create a new column 'Sum' by applying a function on each row.

Pandas

import pandas as pd

df = pd.DataFrame({'X': [5, 6], 'Y': [7, 8]})
df['Sum'] = df.apply(lambda row: row['X'] + row['Y'], axis=[1])
print(df)

Drag options to blanks, or click blank then click option'

ANone

B0

C2

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 applies function to columns, causing errors or wrong results.

Not assigning the result back to a new column.

Use axis=1 to apply the function row-wise and create the new column.

3fill in blank

hard

Fix the error in the code to correctly apply a function on rows to get the max value per row.

Pandas

import pandas as pd

df = pd.DataFrame({'P': [10, 20], 'Q': [30, 15]})
max_values = df.apply(lambda row: max(row), axis=[1])
print(max_values)

Drag options to blanks, or click blank then click option'

A2

BNone

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 applies function column-wise, not row-wise.

Forgetting to specify axis parameter.

Using axis=1 applies the function to each row, allowing max(row) to find the max per row.

4fill in blank

hard

Fill both blanks to create a new column 'Difference' that subtracts column 'B' from 'A' for each row.

Pandas

import pandas as pd

df = pd.DataFrame({'A': [9, 4], 'B': [3, 7]})
df['Difference'] = df.apply(lambda [1]: [2]['A'] - [2]['B'], axis=1)
print(df)

Drag options to blanks, or click blank then click option'

Arow

Bdf

Cdata

Dx

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable name that is not the lambda argument to access row data.

Not matching the lambda argument name with its usage inside the function.

The lambda function takes one argument (commonly named row) representing each row. Use this argument to access columns.

5fill in blank

hard

Fill all three blanks to create a dictionary with keys as row indices and values as the row values from column 'b' where the value in 'a' is greater than 2.

Pandas

import pandas as pd

df = pd.DataFrame({'a': [1, 3], 'b': [4, 5]})
result = { [1]: row[[2]] for index, row in df.iterrows() if row[[3]] > 2 }
print(result)

Drag options to blanks, or click blank then click option'

A'A'

B'b'

C'a'

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using column names incorrectly as keys.

Mixing up the filter condition column and value column.

The dictionary uses the row index as key, the value in column 'b' as value, and filters rows where column 'a' is greater than 2.