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.

Practice

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

easy

A. It sorts a DataFrame by multiple columns.

B. It computes a frequency table of two or more factors.

C. It merges two DataFrames based on a key column.

D. It filters rows based on a condition.

Solution

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

   pd.crosstab() is used to compute a cross-tabulation, which is a frequency table showing counts of combinations of factors.

2. Step 2: Compare with other pandas functions

   Sorting, merging, and filtering are done by other functions like sort_values(), merge(), and boolean indexing, not crosstab.

3. Final Answer:

   It computes a frequency table of two or more factors. -> Option B

4. Quick Check:

   Cross-tabulation = frequency table [OK]

Hint: Remember: crosstab counts combinations of categories [OK]

Common Mistakes:

• Confusing crosstab with merge or join operations
• Thinking crosstab sorts data
• Assuming crosstab filters rows

2. Which of the following is the correct syntax to create a cross-tabulation with totals (margins) included?

easy

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

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

C. pd.crosstab(df['A'], df['B'], add_totals=True)

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

Solution

1. Step 1: Recall the parameter for totals in pd.crosstab()

   The correct parameter to add row and column totals is margins=True.

2. Step 2: Check other options for correctness

   Parameters like total, add_totals, or include_margins do not exist in pd.crosstab.

3. Final Answer:

   pd.crosstab(df['A'], df['B'], margins=True) -> Option D

4. Quick Check:

   margins=True adds totals [OK]

Hint: Use margins=True to add totals in crosstab [OK]

Common Mistakes:

• Using incorrect parameter names for totals
• Forgetting to set margins=True
• Confusing margins with normalization

3. Given the DataFrame:

import pandas as pd
data = {'City': ['NY', 'LA', 'NY', 'LA', 'NY'], 'Category': ['A', 'A', 'B', 'B', 'A'], 'Sales': [100, 200, 150, 300, 250]}
df = pd.DataFrame(data)

What is the output of:

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

medium

A. {'A': {'LA': 200, 'NY': 350}, 'B': {'LA': 300, 'NY': 150}}

B. {'A': {'LA': 100, 'NY': 250}, 'B': {'LA': 150, 'NY': 300}}

C. {'A': {'LA': 300, 'NY': 350}, 'B': {'LA': 200, 'NY': 150}}

D. {'A': {'LA': 200, 'NY': 250}, 'B': {'LA': 300, 'NY': 150}}

Solution

1. Step 1: Group sales by City and Category with sum aggregation

   For LA and A: sales = 200; LA and B: 300; NY and A: 100 + 250 = 350; NY and B: 150.

2. Step 2: Check the options carefully

   {'A': {'LA': 200, 'NY': 250}, 'B': {'LA': 300, 'NY': 150}} matches the sums except NY and A is 250 in D, but actual sum is 100 + 250 = 350, so re-check.

3. Step 3: Recalculate sums

   NY & A: rows 0 and 4 have sales 100 and 250, total 350. So correct sums are: LA A=200, LA B=300, NY A=350, NY B=150.

4. Step 4: Match with options

   {'A': {'LA': 200, 'NY': 350}, 'B': {'LA': 300, 'NY': 150}} matches these sums exactly.

5. Final Answer:

   {'A': {'LA': 200, 'NY': 350}, 'B': {'LA': 300, 'NY': 150}} -> Option A

6. Quick Check:

   Sum sales by City and Category = {'A': {'LA': 200, 'NY': 350}, 'B': {'LA': 300, 'NY': 150}} [OK]

Hint: Sum sales by groups to match crosstab output [OK]

Common Mistakes:

• Adding wrong sales values for groups
• Confusing rows for LA and NY
• Ignoring aggfunc='sum' effect

4. Consider this code snippet:

pd.crosstab(index=df['City'], columns=df['Category'], values=df['Sales'], aggfunc='mean', margins=True)

It raises a TypeError. What is the likely cause?

medium

A. The 'values' parameter must be numeric, but 'Sales' contains strings.

B. The 'aggfunc' parameter must be a string, not a function.

C. The 'margins' parameter cannot be used with 'aggfunc'.

D. The DataFrame 'df' is missing the 'Category' column.

Solution

1. Step 1: Understand the interaction of margins and aggfunc

   Using margins=True with aggfunc other than 'count' or 'sum' can cause errors because totals for means are ambiguous.

2. Step 2: Check other options for errors

   Sales is numeric, so A is wrong; aggfunc='mean' is a valid string, so B is wrong; if 'Category' was missing, it would raise KeyError, not TypeError, so D is wrong.

3. Final Answer:

   The 'margins' parameter cannot be used with 'aggfunc'. -> Option C

4. Quick Check:

   margins with aggfunc='mean' causes error [OK]

Hint: Avoid margins=True with aggfunc='mean' to prevent errors [OK]

Common Mistakes:

• Assuming margins always works with aggfunc
• Confusing TypeError with KeyError
• Thinking 'values' must be strings

5. You have a DataFrame with columns Region, Product, and Profit. You want to create a cross-tabulation that shows the average profit per product in each region, including overall averages per region and product. Which code snippet correctly achieves this?

hard

A. pd.crosstab(df['Region'], df['Product'], values=df['Profit'], aggfunc='mean', margins=True)

B. pd.crosstab(df['Region'], df['Product'], values=df['Profit'], aggfunc='sum', margins=True)

C. pd.crosstab(df['Region'], df['Product'], values=df['Profit'], aggfunc='mean')

D. pd.crosstab(df['Region'], df['Product'], aggfunc='mean', margins=True)

Solution

1. Step 1: Identify the need for average profit with totals

   We want average profit per product per region, plus overall averages (totals), so aggfunc='mean' and margins=True are needed.

2. Step 2: Check the use of values parameter

   To aggregate profit, values=df['Profit'] must be specified; otherwise, crosstab counts occurrences.

3. Step 3: Evaluate options

   pd.crosstab(df['Region'], df['Product'], values=df['Profit'], aggfunc='mean', margins=True) uses mean, values, and margins correctly. pd.crosstab(df['Region'], df['Product'], values=df['Profit'], aggfunc='sum', margins=True) uses sum instead of mean. pd.crosstab(df['Region'], df['Product'], values=df['Profit'], aggfunc='mean') lacks margins. pd.crosstab(df['Region'], df['Product'], aggfunc='mean', margins=True) misses values parameter.

4. Final Answer:

   pd.crosstab(df['Region'], df['Product'], values=df['Profit'], aggfunc='mean', margins=True) -> Option A

5. Quick Check:

   Mean profit with totals = pd.crosstab(df['Region'], df['Product'], values=df['Profit'], aggfunc='mean', margins=True) [OK]

Hint: Use values + aggfunc='mean' + margins=True for averages with totals [OK]

Common Mistakes:

• Omitting values parameter
• Using sum instead of mean for average
• Forgetting margins=True for totals