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.

Practice

1. What does the pandas.crosstab() function primarily do?

easy

A. Filters rows based on a condition

B. Sorts data in ascending order

C. Counts how often values from two columns appear together

D. Calculates the mean of numerical columns

Solution

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

   The crosstab() function creates a table showing frequency counts of combinations of values from two or more columns.

2. Step 2: Compare with other options

   Sorting, calculating mean, and filtering are different operations not done by crosstab().

3. Final Answer:

   Counts how often values from two columns appear together -> Option C

4. Quick Check:

   crosstab() = frequency count [OK]

Hint: Remember: crosstab counts pairs of values [OK]

Common Mistakes:

• Confusing crosstab with sorting or filtering
• Thinking crosstab calculates averages
• Assuming crosstab modifies original data

2. Which of the following is the correct syntax to create a crosstab of columns 'A' and 'B' from DataFrame df?

easy

A. pd.crosstab(df.A, df.B, axis=1)

B. pd.crosstab(df['A'], df['B'])

C. df.crosstab('A', 'B')

D. pd.crosstab(df['A', 'B'])

Solution

1. Step 1: Recall correct crosstab syntax

   The correct syntax is pd.crosstab(df['A'], df['B']) where you pass two series as arguments.

2. Step 2: Identify incorrect options

   pd.crosstab(df.A, df.B, axis=1) wrongly uses axis parameter; df.crosstab('A', 'B') tries to call crosstab as a DataFrame method which doesn't exist; pd.crosstab(df['A', 'B']) passes a tuple incorrectly.

3. Final Answer:

   pd.crosstab(df['A'], df['B']) -> Option B

4. Quick Check:

   pd.crosstab(series1, series2) = correct syntax [OK]

Hint: Use pd.crosstab(series1, series2) syntax [OK]

Common Mistakes:

• Calling crosstab as a DataFrame method
• Passing multiple columns as a tuple incorrectly
• Using wrong parameters like axis

3. Given the DataFrame:

import pandas as pd
data = {'Gender': ['M', 'F', 'F', 'M', 'F'], 'Preference': ['Tea', 'Coffee', 'Tea', 'Coffee', 'Tea']}
df = pd.DataFrame(data)

What is the output of pd.crosstab(df['Gender'], df['Preference'])?

medium

A. Preference Coffee Tea
Gender
F 1 2
M 1 1

B. Preference Coffee Tea
Gender
F 2 1
M 1 1

C. Preference Coffee Tea
Gender
F 1 1
M 2 1

D. Preference Coffee Tea
Gender
F 1 2
M 2 0

Solution

1. Step 1: Count occurrences for each Gender and Preference

   For 'F': Coffee appears once, Tea appears twice. For 'M': Coffee appears once, Tea appears once.

2. Step 2: Construct the crosstab table

   Rows are Gender (F, M), columns are Preference (Coffee, Tea), with counts filled accordingly.

3. Final Answer:

   Preference Coffee Tea Gender F 1 2 M 1 1 -> Option A

4. Quick Check:

   Count values by groups = Preference Coffee Tea Gender F 1 2 M 1 1 [OK]

Hint: Count each group pair carefully for crosstab [OK]

Common Mistakes:

• Mixing up row and column labels
• Counting values incorrectly
• Assuming missing pairs have counts > 0

4. What is wrong with this code snippet?

import pandas as pd
data = {'A': ['x', 'y', 'x'], 'B': ['p', 'q', 'p']}
df = pd.DataFrame(data)
result = pd.crosstab(df['A'], df['B'], margins=True, margin_name='Total')
print(result)

It raises an error. Why?

medium

A. The print statement is missing parentheses

B. You cannot use margins=True with crosstab

C. The DataFrame columns must be numeric for crosstab

D. The parameter name should be margins_name, not margin_name

Solution

1. Step 1: Check parameter names for crosstab()

   The correct parameter to name the totals row/column is margins_name, not margin_name.

2. Step 2: Identify the cause of error

   Using margin_name causes a TypeError because it's an unexpected keyword argument.

3. Final Answer:

   The parameter name should be margins_name, not margin_name -> Option D

4. Quick Check:

   Correct parameter is margins_name [OK]

Hint: Use margins_name, not margin_name for totals [OK]

Common Mistakes:

• Misspelling parameter names
• Thinking crosstab requires numeric columns
• Forgetting parentheses in print (Python 3)

5. You have a DataFrame df with columns 'City', 'Product', and 'Sales'. You want to create a crosstab showing total sales for each city-product pair. Which code correctly achieves this?

hard

A. pd.crosstab(df['City'], df['Product'], values=df['Sales'], aggfunc='sum')

B. pd.crosstab(df['City'], df['Product'], values='Sales', aggfunc='sum')

C. pd.crosstab(df['City'], df['Product'], aggfunc='sum')

D. pd.crosstab(df['City'], df['Product'], values=df['Sales'])

Solution

1. Step 1: Understand crosstab with aggregation

   To aggregate values like sales, use values= with the column and aggfunc= with the aggregation function.

2. Step 2: Check each option

   pd.crosstab(df['City'], df['Product'], values=df['Sales'], aggfunc='sum') correctly passes the sales column and sum function. pd.crosstab(df['City'], df['Product'], values='Sales', aggfunc='sum') passes a string instead of a series for values. pd.crosstab(df['City'], df['Product'], aggfunc='sum') misses values parameter. pd.crosstab(df['City'], df['Product'], values=df['Sales']) misses aggfunc, so it defaults to count.

3. Final Answer:

   pd.crosstab(df['City'], df['Product'], values=df['Sales'], aggfunc='sum') -> Option A

4. Quick Check:

   Use values= and aggfunc= for aggregation [OK]

Hint: Use values=column and aggfunc='sum' for totals [OK]

Common Mistakes:

• Passing column name as string instead of series
• Omitting aggfunc for aggregation
• Expecting crosstab to sum without values parameter