Cross-tabulation with crosstab() in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a cross-tabulation table from the DataFrame df using columns 'Gender' and 'Preference'.

Data Analysis Python

import pandas as pd

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

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

A'Income'

B'Preference'

C'City'

D'Age'

Attempts:

3 left

💡 Hint

Common Mistakes

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

Forgetting to put the column name in quotes.

The crosstab function takes two series. Here, we want to cross-tabulate 'Gender' with 'Preference', so the second argument must be df['Preference'].

2fill in blank

medium

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

Data Analysis Python

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

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

Amargins

Bnormalize

Cdropna

Daggfunc

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'normalize' instead of 'margins'.

Forgetting to set the parameter to True.

The 'margins' parameter adds row and column totals to the crosstab output when set to True.

3fill in blank

hard

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

Data Analysis Python

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

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

AFalse

B'columns'

C'index'

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using True or False instead of 'index' or 'columns'.

Using 'columns' when row normalization is needed.

To normalize by row, the 'normalize' parameter should be set to 'index'.

4fill in blank

hard

Fill both blanks to create a cross-tabulation table with counts and rename the index and columns.

Data Analysis Python

ct = pd.crosstab(df[[1]], df[[2]], margins=True)
ct.index.name = 'Gender'
ct.columns.name = 'Preference'
print(ct)

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

A'Gender'

B'Age'

C'Preference'

D'City'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the row and column variables.

Using column names not present in the DataFrame.

The first blank is the row variable 'Gender', and the second blank is the column variable 'Preference'.

5fill in blank

hard

Fill all three blanks to create a normalized cross-tabulation table by columns with margins and rename the index and columns.

Data Analysis Python

ct = pd.crosstab(df[[1]], df[[2]], margins=[3], normalize='columns')
ct.index.name = 'Gender'
ct.columns.name = 'Preference'
print(ct)

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

A'Gender'

B'Preference'

CTrue

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Setting margins to False when totals are needed.

Swapping row and column variables.

Not normalizing by columns correctly.

The first blank is 'Gender' for rows, the second is 'Preference' for columns, and margins should be True to show totals.

Practice

1. What does the pd.crosstab() function do in data analysis?

easy

A. It sorts data in ascending order.

B. It counts how often categories from two variables appear together.

C. It calculates the average of numerical columns.

D. It removes missing values from the dataset.

Solution

1. Step 1: Understand the purpose of crosstab()

   The function pd.crosstab() is used to count occurrences of combinations of categories from two variables.

2. Step 2: Compare with other options

   Sorting, averaging, and removing missing values are different functions, not related to crosstab.

3. Final Answer:

   It counts how often categories from two variables appear together. -> Option B

4. Quick Check:

   Cross-tabulation = counts of category pairs [OK]

Hint: Remember: crosstab counts category pairs, not calculations [OK]

Common Mistakes:

• Thinking crosstab sorts data
• Confusing crosstab with aggregation functions
• Assuming crosstab cleans data

2. Which of the following is the correct syntax to create a cross-tabulation table using pandas?

easy

A. pd.crosstab(index=df['AgeGroup'], columns=df['Gender'])

B. pd.crosstab(df.Gender, columns='AgeGroup')

C. pd.crosstab(columns=df['Gender'], index=df['AgeGroup'])

D. pd.crosstab(df['Gender'], df['AgeGroup'])

Solution

1. Step 1: Recall the correct parameter order for crosstab()

   The function pd.crosstab() takes the first argument as the row variable and the second as the column variable, both as Series or arrays.

2. Step 2: Check each option

   pd.crosstab(df['Gender'], df['AgeGroup']) correctly passes two Series without naming parameters. pd.crosstab(index=df['AgeGroup'], columns=df['Gender']) uses named parameters with row and column variables reversed. pd.crosstab(df.Gender, columns='AgeGroup') passes a string literal instead of a Series for the columns parameter. pd.crosstab(columns=df['Gender'], index=df['AgeGroup']) swaps index and columns incorrectly.

3. Final Answer:

   pd.crosstab(df['Gender'], df['AgeGroup']) -> Option D

4. Quick Check:

   Use positional args: rows, columns [OK]

Hint: Use positional args: first is rows, second is columns [OK]

Common Mistakes:

• Swapping index and columns parameters
• Mixing positional and named arguments incorrectly
• Passing DataFrame instead of Series

3. Given the DataFrame:

import pandas as pd
data = {'Color': ['Red', 'Blue', 'Red', 'Green', 'Blue', 'Green'],
        'Shape': ['Circle', 'Square', 'Square', 'Circle', 'Circle', 'Square']}
df = pd.DataFrame(data)
print(pd.crosstab(df['Color'], df['Shape']))

What is the output?

medium

A. Shape Circle Square
Color
Blue 1 1
Green 1 1
Red 1 1

B. Shape Circle Square
Color
Blue 2 0
Green 0 2
Red 1 1

C. Shape Circle Square
Color
Blue 1 0
Green 1 1
Red 1 1

D. Shape Circle Square
Color
Blue 1 1
Green 1 0
Red 1 1

Solution

1. Step 1: Count occurrences of each Color-Shape pair

   From the data:
   - Blue: Circle(1), Square(1)
   - Green: Circle(1), Square(1)
   - Red: Circle(1), Square(1)

2. Step 2: Construct the crosstab table

   Each color has one Circle and one Square, so the table shows 1 for each pair.

3. Final Answer:

   Shape Circle Square Color Blue 1 1 Green 1 1 Red 1 1 -> Option A

4. Quick Check:

   Count pairs correctly = Shape Circle Square Color Blue 1 1 Green 1 1 Red 1 1 [OK]

Hint: Count each category pair carefully from data [OK]

Common Mistakes:

• Miscounting pairs
• Swapping rows and columns
• Ignoring repeated categories

4. What is wrong with this code snippet?

import pandas as pd
data = {'Team': ['A', 'B', 'A', 'B'], 'Result': ['Win', 'Lose', 'Lose', 'Win']}
df = pd.DataFrame(data)
ct = pd.crosstab(index=df['Team'], columns='Result')
print(ct)

medium

A. The DataFrame is missing a column named 'Result'.

B. The index parameter should be a list, not a Series.

C. The columns parameter should be a Series, not a string.

D. The crosstab function requires numeric data only.

Solution

1. Step 1: Check the columns argument in crosstab()

   The columns argument must be a Series or array representing categories, not a string literal.

2. Step 2: Identify the error in the code

   Here, columns='Result' is a string, not df['Result']. This causes crosstab to treat it incorrectly.

3. Final Answer:

   The columns parameter should be a Series, not a string. -> Option C

4. Quick Check:

   Columns must be Series, not string [OK]

Hint: Pass Series for index and columns, not strings [OK]

Common Mistakes:

• Passing column name as string instead of Series
• Assuming crosstab needs numeric data
• Confusing index and columns parameters

5. You have a DataFrame with columns 'Department' and 'Satisfaction' (values: 'High', 'Medium', 'Low'). You want to find how many employees in each department have each satisfaction level, but only show departments with at least 3 employees total. Which code correctly does this?

hard

A. ct = pd.crosstab(df['Department'], df['Satisfaction'])
ct = ct[ct.sum(axis=1) >= 3]

B. ct = pd.crosstab(index=df['Satisfaction'], columns=df['Department'])
ct = ct[ct.sum(axis=0) >= 3]

C. ct = pd.crosstab(df['Department'], df['Satisfaction'])
ct = ct[ct.sum(axis=0) >= 3]

D. ct = pd.crosstab(index=df['Department'], columns=df['Satisfaction'])
ct = ct[ct.sum(axis=1) < 3]

Solution

1. Step 1: Create crosstab with Department as rows and Satisfaction as columns

   Use pd.crosstab(df['Department'], df['Satisfaction']) to count satisfaction levels per department.

2. Step 2: Filter departments with total employees >= 3

   Sum counts across columns (axis=1) for each department, then keep rows where sum >= 3.

3. Final Answer:

   ct = pd.crosstab(df['Department'], df['Satisfaction']) ct = ct[ct.sum(axis=1) >= 3] -> Option A

4. Quick Check:

   Filter rows by sum(axis=1) >= 3 [OK]

Hint: Sum rows (axis=1) to filter departments by total count [OK]

Common Mistakes:

• Filtering columns instead of rows
• Swapping index and columns in crosstab
• Using wrong axis in sum()