crosstab() for cross-tabulation in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the crosstab() function in pandas?

The crosstab() function creates a table that shows the frequency (count) of combinations between two or more categorical variables. It helps to summarize and compare data easily.

beginner

How do you create a simple cross-tabulation between two columns col1 and col2 in a DataFrame df?

Use pd.crosstab(df['col1'], df['col2']). This counts how many times each combination of values from col1 and col2 appears.

intermediate

What does the normalize parameter do in crosstab()?

The normalize parameter changes counts into proportions or percentages. For example, normalize='index' shows row-wise proportions, and normalize='columns' shows column-wise proportions.

intermediate

Can crosstab() handle more than two variables? How?

Yes, by passing multiple arrays or columns as arguments. For example, pd.crosstab([df['col1'], df['col2']], df['col3']) creates a multi-index table showing counts for combinations of col1 and col2 against col3.

beginner

What is a real-life example where crosstab() is useful?

Imagine a survey with answers about gender and favorite fruit. crosstab() can show how many males and females prefer each fruit, helping to understand preferences by group.

What does pd.crosstab(df['A'], df['B']) return?

A A table counting occurrences of each combination of values in columns A and B

B A table showing the sum of values in columns A and B

C A table with the mean of columns A and B

D A table with unique values from columns A and B

crosstab() counts how often each pair of values appears in the two columns.

How do you get proportions instead of counts in crosstab()?

A Set <code>normalize='index'</code> or <code>normalize='columns'</code>

B Set <code>normalize='all'</code>

C Set <code>normalize=True</code>

D Set <code>normalize=False</code>

Use normalize='index' for row proportions or normalize='columns' for column proportions.

Which of these is a valid way to use crosstab() with three variables?

A <code>pd.crosstab(df['A'], df['B'], df['C'])</code>

B <code>pd.crosstab(df['A'])</code>

C <code>pd.crosstab(df['A'] + df['B'], df['C'])</code>

D <code>pd.crosstab([df['A'], df['B']], df['C'])</code>

Passing a list of columns as the first argument allows cross-tabulation on multiple variables.

What type of data is best suited for crosstab()?

A Numerical continuous data

B Categorical data

C Time series data

D Text data

crosstab() works best with categorical data to count combinations.

If you want to see how many customers bought product A or B by region, which function helps?

A groupby()

B pivot_table()

C crosstab()

D merge()

crosstab() quickly shows counts of combinations like product and region.

Explain how to use crosstab() to analyze the relationship between two categorical columns in a DataFrame.

Think about how to count how often each pair of categories appears.

Describe how the normalize parameter changes the output of crosstab() and why it might be useful.

Consider when percentages are easier to understand than raw counts.