loc for label-based selection in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the row with label 'b' using loc.

Pandas

selected_row = df.loc[[1]]

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

A'a'

B'b'

C'c'

D'd'

Attempts:

3 left

💡 Hint

Common Mistakes

Using integer index instead of label.

Using iloc instead of loc.

Using df.loc['b'] selects the row labeled 'b' from the DataFrame.

2fill in blank

medium

Complete the code to select the 'Age' column for the row labeled 'c'.

Pandas

age_value = df.loc[[1], 'Age']

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

A'c'

B'b'

C'a'

D'd'

Attempts:

3 left

💡 Hint

Common Mistakes

Using column name as first argument.

Using integer index instead of label.

df.loc['c', 'Age'] selects the value in the 'Age' column for the row labeled 'c'.

3fill in blank

hard

Fix the error in the code to select rows labeled 'a' to 'c' inclusive.

Pandas

subset = df.loc[[1]]

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

A'a','c'

B['a':'c']

C'a':'c'

D['a', 'b', 'c']

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string slice inside a list.

Using incorrect syntax for slicing.

Using df.loc['a':'c'] selects rows with labels from 'a' to 'c' inclusive.

Note: ['a', 'b', 'c'] also works but is a list of labels, while 'a':'c' is a slice.

4fill in blank

hard

Fill both blanks to select the 'Name' and 'Age' columns for rows labeled from 'b' to 'd' inclusive.

Pandas

subset = df.loc[[1], [2]]

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

A'b':'d'

B['Name', 'Age']

C'Name'

D'Age'

Attempts:

3 left

💡 Hint

Common Mistakes

Using single column names instead of a list for multiple columns.

Using incorrect slicing syntax.

df.loc['b':'d', ['Name', 'Age']] selects rows from 'b' to 'd' and columns 'Name' and 'Age'.

5fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

Pandas

lengths = {word: {BLANK_2}} for word in words if {{BLANK_2}}

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

A:

Blen(word)

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Putting a colon inside quotes in the first blank.

Using the wrong condition in the if statement.

Confusing key and value positions.

The dictionary comprehension syntax is {key: value for item in iterable if condition}.

Here, key is word, value is len(word), and condition is len(word) > 3.