value_counts() for frequency in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to count the frequency of each fruit in the list.

Pandas

import pandas as pd
fruits = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']
counts = pd.Series(fruits).[1]()
print(counts)

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Aunique

Bcount

Cvalue_counts

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

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

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

The value_counts() method counts the frequency of each unique value in a Series.

2fill in blank

medium

Complete the code to get the frequency of each color in the DataFrame column 'color'.

Pandas

import pandas as pd
data = {'color': ['red', 'blue', 'red', 'green', 'blue', 'blue']}
df = pd.DataFrame(data)
color_counts = df['color'].[1]()
print(color_counts)

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Avalue_counts

Bunique

Ccount

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

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

Trying to use value_counts() on the whole DataFrame instead of a column.

The value_counts() method on a DataFrame column counts the frequency of each unique value.

3fill in blank

hard

Fix the error in the code to correctly count the frequency of each animal in the list.

Pandas

import pandas as pd
animals = ['cat', 'dog', 'cat', 'bird', 'dog', 'dog']
counts = pd.Series(animals).[1]()
print(counts)

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Aunique

Bcount

Ccount_values

Dvalue_counts

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent method count_values().

Using count() which does not count frequencies.

The correct method is value_counts(). The option count_values is incorrect and causes an error.

4fill in blank

hard

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

Pandas

words = ['data', 'science', 'ai', 'ml', 'python']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using < instead of > in the condition.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their counts for words with count greater than 1.

Pandas

words = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']
counts = pd.Series(words).value_counts()
result = [1]: [2] for [3], [2] in counts.items() if [2] > 1}
print(result)

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Aword.upper()

Bcount

Cword

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using item instead of word as the loop variable.

Using the count variable name inconsistently.

The dictionary comprehension uses word.upper() as the key, count as the value, and iterates over word, count pairs from counts.items(). It filters counts greater than 1.