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]()

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

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])

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

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]()

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

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}

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

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

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

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.

Practice

1. What does the value_counts() function do when applied to a pandas Series?

easy

A. Calculates the average of all values in the Series

B. Counts how many times each unique value appears in the Series

C. Sorts the Series in ascending order

D. Removes duplicate values from the Series

Solution

1. Step 1: Understand the purpose of value_counts()

   This function counts each unique value's frequency in a pandas Series.

2. Step 2: Compare with other options

   Sorting, averaging, or removing duplicates are different operations not done by value_counts().

3. Final Answer:

   Counts how many times each unique value appears in the Series -> Option B

4. Quick Check:

   value_counts() = counts unique values [OK]

Hint: Remember: value_counts counts unique values frequency [OK]

Common Mistakes:

• Thinking it sorts values instead of counting
• Confusing it with mean or average calculation
• Assuming it removes duplicates instead of counting

2. Which of the following is the correct syntax to get the percentage distribution of values in a pandas Series data?

easy

A. data.value_counts(percent=True)

B. data.value_counts(normalize=False)

C. data.value_counts(normalize=True)

D. data.value_counts(percentage=True)

Solution

1. Step 1: Recall the parameter for percentage in value_counts()

   The correct parameter is normalize=True to get proportions.

2. Step 2: Check other options for correctness

   Options with percent or percentage are invalid parameters and cause errors.

3. Final Answer:

   data.value_counts(normalize=True) -> Option C

4. Quick Check:

   Use normalize=True for percentages [OK]

Hint: Use normalize=True to get percentages, not percent or percentage [OK]

Common Mistakes:

• Using wrong parameter names like percent or percentage
• Forgetting to set normalize=True for percentages
• Confusing normalize=False with percentage output

3. Given the pandas Series colors = pd.Series(['red', 'blue', 'red', 'green', 'blue', 'blue']), what is the output of colors.value_counts()?

medium

A. {'blue': 3, 'red': 2, 'green': 1}

B. {'red': 3, 'blue': 2, 'green': 1}

C. {'green': 1, 'red': 2, 'blue': 3}

D. {'red': 2, 'blue': 3, 'green': 1}

Solution

1. Step 1: Count each color's occurrences

   'red' appears 2 times, 'blue' 3 times, 'green' 1 time.

2. Step 2: Understand value_counts() output order

   It sorts counts descending by default, so 'blue' (3), 'red' (2), 'green' (1).

3. Final Answer:

   {'blue': 3, 'red': 2, 'green': 1} -> Option A

4. Quick Check:

   Counts sorted descending = {'blue':3,'red':2,'green':1} [OK]

Hint: value_counts() sorts by count descending automatically [OK]

Common Mistakes:

• Ignoring the default descending sort order
• Mixing up counts of 'red' and 'blue'
• Assuming output is unordered or sorted alphabetically

4. What is wrong with this code snippet if df['Category'] is a pandas Series?

df.value_counts(normalize=True)

medium

A. It should be df['Category'].value_counts(normalize=True) to count values in the column

B. The parameter normalize is not valid for value_counts()

C. You cannot use value_counts() on a pandas Series

D. The code will count values but not normalize them

Solution

1. Step 1: Identify the object value_counts() is called on

   df is a DataFrame, but value_counts() works on Series, not DataFrame directly.

2. Step 2: Correct usage for counting a column

   Use df['Category'].value_counts(normalize=True) to count values in that column.

3. Final Answer:

   It should be df['Category'].value_counts(normalize=True) to count values in the column -> Option A

4. Quick Check:

   value_counts() needs a Series, not DataFrame [OK]

Hint: Call value_counts() on a Series, not on the whole DataFrame [OK]

Common Mistakes:

• Calling value_counts() directly on DataFrame
• Confusing DataFrame and Series methods
• Thinking normalize is invalid parameter

5. You have a pandas Series responses with values: ['yes', 'no', 'yes', 'maybe', 'no', 'yes', 'no', 'maybe', 'yes', 'no'].
How do you get the percentage distribution of each response rounded to 2 decimals?

hard

A. responses.value_counts().round(2)

B. responses.value_counts().normalize().round(2)

C. responses.value_counts(percent=True).round(2)

D. responses.value_counts(normalize=True).round(2)

Solution

1. Step 1: Use value_counts(normalize=True) to get percentages

   This returns the relative frequencies as decimals.

2. Step 2: Apply round(2) to round percentages to 2 decimals

   Chaining .round(2) rounds the decimal values properly.

3. Final Answer:

   responses.value_counts(normalize=True).round(2) -> Option D

4. Quick Check:

   Normalize then round decimals = responses.value_counts(normalize=True).round(2) [OK]

Hint: Chain normalize=True with round(2) to get rounded percentages [OK]

Common Mistakes:

• Using invalid parameter percent=True
• Calling normalize() as a method instead of parameter
• Rounding counts instead of percentages