crosstab() for cross-tabulation in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a cross-tabulation table between 'Gender' and 'Preference'.

Pandas

import pandas as pd

data = {'Gender': ['Male', 'Female', 'Female', 'Male'], 'Preference': ['A', 'B', 'A', 'B']}
df = pd.DataFrame(data)

result = pd.crosstab(df['Gender'], df[[1]])
print(result)

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

A'Preference'

B'Gender'

C'Age'

D'Score'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same column for both arguments.

Using a column name that does not exist in the DataFrame.

The crosstab() function is used to compute a simple cross-tabulation of two (or more) factors. Here, we want to cross-tabulate 'Gender' with 'Preference', so the second argument should be df['Preference'].

2fill in blank

medium

Complete the code to add margins (totals) to the cross-tabulation table.

Pandas

import pandas as pd

data = {'Team': ['A', 'B', 'A', 'B'], 'Result': ['Win', 'Lose', 'Lose', 'Win']}
df = pd.DataFrame(data)

ct = pd.crosstab(df['Team'], df['Result'], [1]=True)
print(ct)

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

Amargins

Btotals

Cadd_totals

Dinclude_margins

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like 'totals' or 'include_margins'.

Forgetting to set the parameter to True.

The margins=True parameter adds row and column totals to the cross-tabulation table.

3fill in blank

hard

Fix the error in the code to correctly compute the cross-tabulation of 'City' and 'Category'.

Pandas

import pandas as pd

data = {'City': ['NY', 'LA', 'NY', 'LA'], 'Category': ['Food', 'Food', 'Tech', 'Tech']}
df = pd.DataFrame(data)

ct = pd.crosstab(df.City, df.[1])
print(ct)

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

ACategories

B'Category'

Ccategory

DCategory

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes inside attribute access like df.'Category'.

Using wrong capitalization for column names.

When accessing DataFrame columns as attributes, use the exact column name without quotes. Here, df.Category is correct, not df.'Category'.

4fill in blank

hard

Fill both blanks to create a normalized cross-tabulation table showing row proportions.

Pandas

import pandas as pd

data = {'Group': ['X', 'Y', 'X', 'Y'], 'Outcome': ['Pass', 'Fail', 'Fail', 'Pass']}
df = pd.DataFrame(data)

ct = pd.crosstab(df[[1]], df[[2]], normalize='index')
print(ct)

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

A'Group'

B'Outcome'

C'Pass'

D'Fail'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the row and column variables.

Using values instead of column names.

To normalize by rows, use normalize='index'. The first argument is the row variable ('Group'), the second is the column variable ('Outcome').

5fill in blank

hard

Fill all three blanks to create a cross-tabulation table with margins and specify the aggregation function to count unique 'UserID's.

Pandas

import pandas as pd

data = {'UserID': [1, 2, 1, 3, 2], 'Product': ['A', 'A', 'B', 'B', 'A'], 'Region': ['East', 'West', 'East', 'West', 'East']}
df = pd.DataFrame(data)

ct = pd.crosstab(df[[1]], df[[2]], values=df[[3]], aggfunc='nunique', margins=True)
print(ct)

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

A'Region'

B'Product'

C'UserID'

D'User'

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect column names or values.

Forgetting to set margins=True.

The first two blanks are the row and column variables ('Region' and 'Product'). The third blank is the values to aggregate ('UserID'). Using aggfunc='nunique' counts unique users per group.