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.