Sorting by values in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Sorting a DataFrame by a single column

What is the output of this code snippet that sorts a DataFrame by column 'B' in ascending order?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [3, 1, 2], 'B': [9, 5, 7]})
sorted_df = df.sort_values(by='B')
print(sorted_df)

A

   A  B
2  2  7
1  1  5
0  3  9

B

   A  B
0  3  9
2  2  7
1  1  5

C

   A  B
1  1  5
0  3  9
2  2  7

D

   A  B
1  1  5
2  2  7
0  3  9

💡 Hint

Remember that sort_values(by='B') sorts the DataFrame by column 'B' in ascending order by default.

The DataFrame is sorted by column 'B' values: 5, 7, 9. The rows are reordered accordingly, but the original indices are preserved.

❓ data_output

intermediate

Sorting by multiple columns with different orders

Given the DataFrame below, what is the output after sorting by column 'A' ascending and then by column 'B' descending?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 1, 2], 'B': [3, 4, 2, 1]})
sorted_df = df.sort_values(by=['A', 'B'], ascending=[True, False])
print(sorted_df)

A

   A  B
2  1  2
0  1  3
3  2  1
1  2  4

B

   A  B
0  1  3
2  1  2
1  2  4
3  2  1

C

   A  B
0  1  3
2  1  2
3  2  1
1  2  4

D

   A  B
2  1  2
0  1  3
1  2  4
3  2  1

💡 Hint

Sorting by 'A' ascending means rows with A=1 come before A=2. For ties in 'A', sort 'B' descending.

Rows with A=1 are sorted by B descending: 3 then 2. Rows with A=2 sorted by B descending: 4 then 1.

🔧 Debug

advanced

Identify the error in sorting by values

What error does this code raise when trying to sort a DataFrame by a non-existent column 'C'?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
sorted_df = df.sort_values(by='C')

A KeyError: 'C'

B ValueError: Cannot sort by non-existent column

C TypeError: 'C' is not a valid column

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

💡 Hint

Check what happens when you try to access a column that does not exist in a DataFrame.

Pandas raises a KeyError when you try to sort by a column name that is not present in the DataFrame.

🚀 Application

advanced

Sorting a DataFrame with missing values

You have this DataFrame with missing values in column 'B'. What is the output after sorting by 'B' with na_position='first'?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 2, 3], 'B': [np.nan, 2, 1]})
sorted_df = df.sort_values(by='B', na_position='first')
print(sorted_df)

A

     A    B
1  2  2.0
2  3  1.0
0  1  NaN

B

     A    B
2  3  1.0
1  2  2.0
0  1  NaN

C

     A    B
0  1  NaN
2  3  1.0
1  2  2.0

D

     A    B
0  1  NaN
1  2  2.0
2  3  1.0

💡 Hint

na_position='first' places missing values at the top when sorting ascending.

The NaN value in row 0 is placed first, then rows sorted by B ascending: 1.0 then 2.0.

🧠 Conceptual

expert

Effect of inplace parameter in sort_values

What is the effect of setting inplace=True in the sort_values method on a DataFrame?

A The DataFrame is sorted in place and the method returns None.

B The DataFrame is sorted and a new sorted DataFrame is returned, original unchanged.

C The DataFrame is sorted in place and the method returns the sorted DataFrame.

D The DataFrame is not sorted but a sorted copy is returned.

💡 Hint

Check the pandas documentation for the behavior of inplace=True in sort_values.

When inplace=True, the original DataFrame is modified and the method returns None.