value_counts() for distributions in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the value_counts() function do in data analysis?

value_counts() counts how many times each unique value appears in a column or series. It helps us see the distribution of data.

beginner

How can value_counts() help in understanding categorical data?

It shows the frequency of each category, making it easy to spot the most common or rare categories.

intermediate

What parameter would you use with value_counts() to get the relative frequencies (percentages) instead of counts?

Use normalize=True to get the relative frequencies as proportions of the total.

intermediate

How do you sort the results of value_counts() in ascending order?

Use sort_values() on the result of value_counts() with ascending=True.

beginner

Can value_counts() be used on numerical data? What does it show?

Yes, it counts how many times each unique number appears, showing the distribution of values.

What does value_counts() return when used on a pandas Series?

A Mean of the values in the Series

B Sum of all values in the Series

C Counts of unique values in the Series

D Sorted list of values

value_counts() returns the count of each unique value in the Series.

Which parameter makes value_counts() return percentages instead of counts?

A percent=True

B normalize=True

C relative=True

D as_percent=True

The normalize=True parameter returns relative frequencies as proportions.

How do you get the top 3 most common values using value_counts()?

A <code>value_counts().top(3)</code>

B <code>value_counts().tail(3)</code>

C <code>value_counts().sort_values(3)</code>

D <code>value_counts().head(3)</code>

head(3) returns the first 3 rows, which are the top counts by default.

If you want to include missing values (NaN) in the counts, which parameter do you use?

A dropna=False

B include_nan=True

C nan=True

D keep_nan=True

Use dropna=False to include NaN values in the counts.

What type of object does value_counts() return?

A pandas Series

B pandas DataFrame

C list

D dictionary

value_counts() returns a pandas Series with counts indexed by unique values.

Explain how value_counts() helps you understand the distribution of data in a column.

Think about how counting helps you see what values appear most.

Describe how to get relative frequencies (percentages) of values using value_counts().

Look for a parameter that changes counts to proportions.