Cross-tabulation advanced usage in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple cross-tabulation of 'Gender' and 'Preference'.

Pandas

import pandas as pd

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

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

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

A'Age'

B'Gender'

C'Preference'

D'Score'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same column 'Gender' for both arguments.

Using a column name not in the DataFrame.

The second argument to pd.crosstab should be the column to cross-tabulate against 'Gender'. Here, it is 'Preference'.

2fill in blank

medium

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

Pandas

ct = pd.crosstab(df['Gender'], df['Preference'], [1]=True)
print(ct)

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

Anormalize

Bmargins

Cdropna

Daggfunc

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'normalize' instead of 'margins'.

Using 'aggfunc' without specifying a function.

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

3fill in blank

hard

Fix the error in the code to normalize the cross-tabulation by columns.

Pandas

ct = pd.crosstab(df['Gender'], df['Preference'], normalize=[1])
print(ct)

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

A'columns'

BTrue

C'index'

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using True instead of 'columns' for normalization.

Using 'index' which normalizes by rows.

To normalize by columns, use normalize='columns'. Using True normalizes over all values.

4fill in blank

hard

Fill both blanks to create a cross-tabulation with aggregation of mean 'Score' by 'Gender' and 'Preference'.

Pandas

data = {'Gender': ['Male', 'Female', 'Female', 'Male'], 'Preference': ['A', 'B', 'A', 'B'], 'Score': [10, 20, 15, 25]}
df = pd.DataFrame(data)

ct = pd.crosstab(df['Gender'], df['Preference'], values=df[[1]], aggfunc=[2])
print(ct)

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

A'Score'

B'Gender'

Csum

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-numeric column for values.

Using 'sum' instead of 'mean' for average.

The values argument specifies the column to aggregate ('Score'), and aggfunc specifies the aggregation function ('mean').

5fill in blank

hard

Fill all three blanks to create a normalized cross-tabulation with margins, aggregating the sum of 'Score' by 'Gender' and 'Preference'.

Pandas

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

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

A'Gender'

B'Preference'

Csum

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'mean' instead of 'sum' for aggregation.

Swapping the row and column variables.

The first two blanks are the row and column variables ('Gender' and 'Preference'). The aggregation function is 'sum' to total the scores.