Cross-tabulation advanced usage in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is cross-tabulation in pandas used for?

Cross-tabulation is used to compute a simple frequency table of two or more factors. It helps to understand the relationship between categorical variables by showing counts or other statistics in a matrix format.

beginner

How can you add margins (totals) to a pandas crosstab?

Use the parameter margins=True in pd.crosstab(). This adds row and column totals to the table, helping to see overall counts.

intermediate

What does the normalize parameter do in pd.crosstab()?

The normalize parameter converts counts into proportions. You can normalize by 'index', 'columns', or 'all' to get relative frequencies instead of raw counts.

advanced

How do you include multiple aggregation functions in a pandas crosstab?

Use the aggfunc parameter with a list of functions (e.g., [np.sum, np.mean]) and provide a values argument to specify the data to aggregate.

advanced

Explain how to create a multi-index crosstab with more than two categorical variables.

Pass multiple arrays or columns to the index and/or columns parameters as lists. This creates a multi-level index in the resulting table, showing detailed breakdowns.

Which parameter in pd.crosstab() adds row and column totals?

A normalize='all'

B margins=True

C aggfunc='sum'

D dropna=False

The margins=True parameter adds totals for rows and columns in the crosstab.

What does normalize='index' do in a crosstab?

A Shows proportions across each row

B Shows proportions across each column

C Shows overall proportions

D Removes missing values

Normalizing by 'index' converts counts into proportions across each row.

How do you specify multiple aggregation functions in a crosstab?

A aggfunc=None

B aggfunc='sum,mean'

C aggfunc='multiple'

D aggfunc=[np.sum, np.mean]

You pass a list of functions like [np.sum, np.mean] to the aggfunc parameter.

To create a multi-level index in a crosstab, you should:

A Use margins=True

B Set normalize='columns'

C Pass lists of columns to index and columns parameters

D Use dropna=True

Passing multiple columns as lists to index or columns creates multi-level indexes.

Which of these is NOT a valid use of pd.crosstab()?

A Creating a scatter plot

B Aggregating numerical data with multiple functions

C Calculating frequency counts between two variables

D Normalizing counts to proportions

Cross-tabulation does not create scatter plots; it summarizes categorical data.

Describe how to use pandas crosstab to analyze the relationship between three categorical variables with totals and normalized proportions.

Think about multi-index and normalization options in crosstab.

Explain how to apply multiple aggregation functions on numerical data within a pandas crosstab.

Consider how aggfunc and values work together.