value_counts() for frequency in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the value_counts() function do in pandas?

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

beginner

How do you use value_counts() to find the frequency of values in a DataFrame column named 'color'?

Use df['color'].value_counts() to get the count of each unique color in the 'color' column.

intermediate

What parameter would you use with value_counts() to include missing values in the count?

Use dropna=False to include missing (NaN) values in the frequency count.

intermediate

How can you get the relative frequency (percentage) of values using value_counts()?

Set normalize=True in value_counts() to get the relative frequency as proportions.

beginner

What type of object does value_counts() return?

It returns a pandas Series where the index is the unique values and the values are their counts.

What does df['A'].value_counts() return?

A Counts of unique values in column 'A'

B Sum of all values in column 'A'

C Mean of values in column 'A'

D Sorted values of column 'A'

value_counts() counts how many times each unique value appears in the specified column.

How to include NaN values in the count using value_counts()?

A Use <code>dropna=True</code>

B Use <code>dropna=False</code>

C Use <code>include_nan=True</code>

D NaN values are always included

Setting dropna=False includes NaN values in the frequency count.

What does value_counts(normalize=True) return?

A Counts of unique values

B Sum of values

C Sorted unique values

D Relative frequencies (proportions) of unique values

Using normalize=True returns the relative frequency as proportions instead of counts.

Which pandas object type is returned by value_counts()?

A List

B DataFrame

C Series

D Dictionary

value_counts() returns a pandas Series with unique values as index and counts as values.

If you want the top 3 most frequent values, which parameter do you use with value_counts()?

A <code>head(3)</code> after <code>value_counts()</code>

B <code>top=3</code>

C <code>n=3</code>

D <code>count=3</code>

Use value_counts() then chain .head(3) to get the top 3 frequent values.

Explain how to use value_counts() to find the frequency of values in a pandas Series and how to include missing values in the count.

Think about counting unique values and how to include NaN.

Describe how to get relative frequencies (percentages) of unique values using value_counts() and what type of object it returns.

Focus on the parameter that changes counts to proportions.