apply() function for custom logic in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply a function that doubles each value in the 'score' column.

Data Analysis Python

df['double_score'] = df['score'].[1](lambda x: x * 2)

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

Agroupby

Bmap

Cfilter

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using map instead of apply, which works differently on Series.

Trying to use filter which is for filtering rows, not transforming values.

The apply() function lets you run a custom function on each element of the column. Here, it doubles each score.

2fill in blank

medium

Complete the code to apply a function that returns 'Pass' if score is 50 or more, else 'Fail'.

Data Analysis Python

df['result'] = df['score'].[1](lambda x: 'Pass' if x >= 50 else 'Fail')

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

Aapply

Bsort_values

Cgroupby

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using filter which is for filtering rows, not transforming values.

Trying to use groupby which is for grouping data, not element-wise operations.

Using apply() lets us run a custom function that checks each score and returns 'Pass' or 'Fail'.

3fill in blank

hard

Fix the error in applying a function that adds 10 to each value in the 'score' column.

Data Analysis Python

df['new_score'] = df['score'].[1](lambda x: x + 10)

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

Amap

Bfilter

Capply

Dreduce

Attempts:

3 left

💡 Hint

Common Mistakes

Using filter which filters rows instead of transforming values.

Using reduce which aggregates values instead of element-wise operation.

apply() is the correct method to apply a function to each element. map() works differently and filter() is for filtering rows.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in the condition.

Using word instead of len(word) for the value.

The dictionary comprehension uses len(word) to get length and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values only if value is positive.

Data Analysis Python

result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original key instead of uppercase key.

Using <= or < instead of > for filtering positive values.

The dictionary comprehension uses k.upper() for keys, v for values, and filters values greater than 0 with >.