value_counts() for distributions in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to count the frequency of each unique value in the 'color' column.

Data Analysis Python

counts = df['color'].[1]()

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Avalue_counts

Bunique

Ccount

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which counts non-null values but not unique frequencies.

Using unique() which returns unique values but not counts.

The value_counts() method counts how many times each unique value appears in a column.

2fill in blank

medium

Complete the code to get the relative frequency (proportion) of each unique value in the 'fruit' column.

Data Analysis Python

relative_freq = df['fruit'].value_counts(normalize=[1])

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AFalse

B0

CTrue

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Leaving normalize as False returns counts, not proportions.

Passing None or 0 does not activate normalization.

Setting normalize=True returns the relative frequencies as proportions.

3fill in blank

hard

Fix the error in the code to count values in the 'city' column of DataFrame df.

Data Analysis Python

counts = df.city.[1]()

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Avalue_counts

Bcount_values

Cvalues_counts

Dvaluecount

Attempts:

3 left

💡 Hint

Common Mistakes

Misspelling the method name causes AttributeError.

Using count_values or similar incorrect names.

The correct method name is value_counts(). Other options are misspelled and cause errors.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 4 letters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 4}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > changes the filter condition.

Using word instead of len(word) as dictionary value.

The dictionary comprehension maps each word to its length if the word length is greater than 4.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their counts if count is more than 1.

Data Analysis Python

result = [1]: [2] for [3] in counts if counts[[3]] > 1

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Aword.upper()

Bcounts[word]

Cword

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of uppercase keys.

Using counts[word] incorrectly or missing the filter condition.

This dictionary comprehension creates keys as uppercase words and values as their counts, filtering counts greater than 1.