Reshaping and transposing in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of reshaping a NumPy array

What is the output of this code snippet that reshapes a NumPy array?

Data Analysis Python

import numpy as np
arr = np.arange(6)
reshaped = arr.reshape(2, 3)
print(reshaped)

A

[[0 1 2 3]
 [4 5 6 7]]

B

[[0 1]
 [2 3]
 [4 5]]

C

[[0 1 2]
 [3 4 5]]

D [0 1 2 3 4 5]

💡 Hint

Remember reshape changes the shape but keeps all elements in order.

The array arr has 6 elements from 0 to 5. Reshaping it to (2,3) creates 2 rows and 3 columns, filling rows first.

❓ data_output

intermediate

Result of pandas DataFrame transpose

Given this DataFrame, what is the result of transposing it?

Data Analysis Python

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
result = df.T
print(result)

A

   0  1
A  1  2
B  3  4

B

   A  B
0  1  3
1  2  4

C

   1  2
A  0  1
B  3  4

D

   0  1
A  3  4
B  1  2

💡 Hint

Transpose swaps rows and columns.

Original DataFrame has columns A and B with rows 0 and 1. Transpose swaps rows and columns, so columns become rows and vice versa.

🔧 Debug

advanced

Identify the error in reshaping a pandas DataFrame

What error does this code raise and why? import pandas as pd df = pd.DataFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]}) df_reshaped = df.values.reshape(2, 4)

Data Analysis Python

import pandas as pd
df = pd.DataFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]})
df_reshaped = df.values.reshape(2, 4)

A No error, reshaping works fine

B TypeError: 'DataFrame' object is not callable

C AttributeError: 'DataFrame' object has no attribute 'values'

D ValueError: cannot reshape array of size 6 into shape (2,4)

💡 Hint

Check if the total number of elements matches the new shape.

The DataFrame has 6 elements (3 rows * 2 columns). Reshaping to (2,4) requires 8 elements, which is not possible.

❓ visualization

advanced

Visualize the effect of pivoting a DataFrame

What does the pivot operation produce from this DataFrame? import pandas as pd df = pd.DataFrame({ 'Date': ['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-02'], 'City': ['NY', 'LA', 'NY', 'LA'], 'Temperature': [30, 60, 28, 65] }) pivoted = df.pivot(index='Date', columns='City', values='Temperature') print(pivoted)

Data Analysis Python

import pandas as pd
df = pd.DataFrame({
  'Date': ['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-02'],
  'City': ['NY', 'LA', 'NY', 'LA'],
  'Temperature': [30, 60, 28, 65]
})
pivoted = df.pivot(index='Date', columns='City', values='Temperature')
print(pivoted)

A

Date        LA  NY
City               
2023-01-01  30  60
2023-01-02  28  65

B

City        LA  NY
Date               
2023-01-01  60  30
2023-01-02  65  28

C

City        NY  LA
Date               
2023-01-01  30  60
2023-01-02  28  65

D

Date        NY  LA
City               
2023-01-01  60  30
2023-01-02  65  28

💡 Hint

Pivot uses the index and columns parameters to rearrange data.

Pivot sets 'Date' as index, 'City' as columns, and fills values with 'Temperature'. So each date row shows temperatures for each city column.

🚀 Application

expert

Transform and summarize data with melt and groupby

Given this DataFrame, which option correctly melts it and then groups by 'variable' to find the mean value? import pandas as pd df = pd.DataFrame({ 'ID': [1, 2], 'Math': [90, 80], 'Science': [85, 95] }) melted = pd.melt(df, id_vars=['ID'], value_vars=['Math', 'Science']) result = melted.groupby('variable')['value'].mean() print(result)

Data Analysis Python

import pandas as pd
df = pd.DataFrame({
  'ID': [1, 2],
  'Math': [90, 80],
  'Science': [85, 95]
})
melted = pd.melt(df, id_vars=['ID'], value_vars=['Math', 'Science'])
result = melted.groupby('variable')['value'].mean()
print(result)

A

Math       85.0
Science    90.0
Name: value, dtype: float64

B

ID
1    87.5
2    87.5
Name: value, dtype: float64

C

Math       90
Science    95
Name: value, dtype: int64

D

variable
Math       85
Science    90
Name: value, dtype: int64

💡 Hint

Melt converts columns to rows, then groupby calculates mean per variable.

Melt creates rows for Math and Science scores per ID. Grouping by 'variable' and averaging 'value' gives mean scores for Math and Science.

Practice

1. What does the transpose() function do to a DataFrame in pandas?

easy

A. It swaps the rows and columns of the DataFrame.

B. It sorts the DataFrame by index.

C. It removes duplicate rows from the DataFrame.

D. It fills missing values with zeros.

Solution

1. Step 1: Understand transpose operation

   The transpose() function flips the DataFrame so rows become columns and columns become rows.

2. Step 2: Compare with other options

   Sorting, removing duplicates, or filling missing values are different operations not related to transposing.

3. Final Answer:

   It swaps the rows and columns of the DataFrame. -> Option A

4. Quick Check:

   transpose() = swap rows and columns [OK]

Hint: Remember transpose flips rows and columns [OK]

Common Mistakes:

• Confusing transpose with sorting
• Thinking transpose removes duplicates
• Assuming transpose fills missing values

2. Which of the following is the correct syntax to reshape a DataFrame df from wide to long format using pandas?

easy

A. df.melt(id_vars=['id'], var_name='variable', value_name='value')

B. df.stack(level=0)

C. df.transpose()

D. df.pivot(index='id', columns='variable', values='value')

Solution

1. Step 1: Identify function for wide to long

   The melt() function converts wide format to long format by unpivoting columns.

2. Step 2: Check syntax correctness

   df.melt(id_vars=['id'], var_name='variable', value_name='value') is the correct syntax to keep 'id' fixed and melt other columns.

3. Final Answer:

   df.melt(id_vars=['id'], var_name='variable', value_name='value') -> Option A

4. Quick Check:

   melt() = wide to long reshape [OK]

Hint: Use melt() to go from wide to long format [OK]

Common Mistakes:

• Using pivot() instead of melt() for wide to long
• Confusing transpose() with reshaping
• Using stack() without understanding its effect

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({
  'A': [1, 2],
  'B': [3, 4]
})

What is the output of df.T?

medium

A. An error because T is not a valid attribute.

B. A DataFrame sorted by column 'A'.

C. A DataFrame with rows and columns swapped, showing columns as rows.

D. A DataFrame with duplicated rows removed.

Solution

1. Step 1: Understand df.T usage

   T is a shortcut for transpose() in pandas, swapping rows and columns.

2. Step 2: Visualize the transposed DataFrame

   Original df has columns 'A' and 'B' with 2 rows. After transpose, columns become rows and rows become columns.

3. Final Answer:

   A DataFrame with rows and columns swapped, showing columns as rows. -> Option C

4. Quick Check:

   df.T = transpose DataFrame [OK]

Hint: Use df.T to quickly transpose DataFrame [OK]

Common Mistakes:

• Thinking df.T sorts data
• Believing df.T removes duplicates
• Assuming df.T causes error

4. What is wrong with this code snippet that tries to reshape a DataFrame df using pivot()?

df.pivot(index='id', columns='variable')

medium

A. Pivot cannot be used on DataFrames with numeric columns.

B. Missing the values parameter required for pivot.

C. The index parameter should be a list, not a string.

D. The columns parameter should be omitted.

Solution

1. Step 1: Check pivot() required parameters

   The pivot() function needs index, columns, and values to reshape data properly.

2. Step 2: Identify missing argument

   The code misses the values argument, which tells pivot what data to fill in the new table.

3. Final Answer:

   Missing the values parameter required for pivot. -> Option B

4. Quick Check:

   pivot() needs values parameter [OK]

Hint: Always include values= when using pivot() [OK]

Common Mistakes:

• Forgetting values parameter in pivot()
• Using string instead of list for index incorrectly
• Thinking pivot() can't handle numeric columns

5. You have a DataFrame df with columns ['id', 'year', 'sales_Q1', 'sales_Q2']. You want to reshape it to a long format with columns ['id', 'year', 'quarter', 'sales']. Which code correctly achieves this?

hard

A. df.stack(level=['quarter', 'sales'])

B. df.pivot(index=['id', 'year'], columns='quarter', values='sales')

C. df.transpose()

D. df.melt(id_vars=['id', 'year'], var_name='quarter', value_name='sales')

Solution

1. Step 1: Identify wide to long reshape method

   To convert columns like 'sales_Q1', 'sales_Q2' into rows, use melt().

2. Step 2: Use melt() with correct parameters

   Keep 'id' and 'year' fixed with id_vars, set var_name='quarter' and value_name='sales' to rename columns.

3. Step 3: Check other options

   pivot() is for long to wide, transpose() swaps rows and columns, stack() is for multi-index reshaping but not here.

4. Final Answer:

   df.melt(id_vars=['id', 'year'], var_name='quarter', value_name='sales') -> Option D

5. Quick Check:

   melt() with id_vars = long format reshape [OK]

Hint: Use melt() with id_vars to reshape wide to long [OK]

Common Mistakes:

• Using pivot() instead of melt() for wide to long
• Trying transpose() for reshaping columns
• Misusing stack() without multi-index