Interpolation for missing values in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of linear interpolation on a DataFrame column

What is the output of the following code snippet that uses linear interpolation to fill missing values in a pandas DataFrame column?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, np.nan, 4, 5]})
df['A'] = df['A'].interpolate(method='linear')
print(df['A'].tolist())

A [1.0, nan, nan, 4.0, 5.0]

B [1.0, 2.0, 3.0, 4.0, 5.0]

C [1.0, 2.5, 3.5, 4.0, 5.0]

D [1.0, 1.0, 1.0, 4.0, 5.0]

💡 Hint

Linear interpolation fills missing values by connecting points with a straight line.

The missing values at positions 1 and 2 are filled by linear interpolation between 1 and 4, resulting in 2.0 and 3.0 respectively.

❓ data_output

intermediate

Result of time interpolation on a time-indexed DataFrame

Given a DataFrame indexed by dates with missing values, what is the result of using time-based interpolation?

Pandas

import pandas as pd
import numpy as np
idx = pd.to_datetime(['2024-01-01', '2024-01-02', '2024-01-04', '2024-01-05'])
df = pd.DataFrame({'Value': [10, np.nan, 40, 50]}, index=idx)
df_interpolated = df.interpolate(method='time')
print(df_interpolated['Value'].tolist())

A [10.0, 25.0, 40.0, 50.0]

B [10.0, 30.0, 40.0, 50.0]

C [10.0, nan, 40.0, 50.0]

D [10.0, 20.0, 40.0, 50.0]

💡 Hint

Time interpolation considers the time difference between index points.

The missing value on 2024-01-02 is interpolated based on time distance between 2024-01-01 (10) and 2024-01-04 (40), resulting in 20.0 (1 day out of 3 days: 10 + 30 * (1/3)).

🔧 Debug

advanced

Identify the error in interpolation code

What error will this code raise when trying to interpolate missing values in a DataFrame?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3]})
df['A'] = df['A'].interpolate(method='polynomial', order=2)
print(df)

A No error, outputs interpolated DataFrame

B TypeError: interpolate() got an unexpected keyword argument 'order'

C ValueError: method 'polynomial' requires specifying 'order' parameter

D AttributeError: 'Series' object has no attribute 'interpolate'

💡 Hint

Check if the 'order' parameter is correctly passed for polynomial interpolation.

The 'polynomial' method requires the 'order' parameter to be specified, otherwise it raises a ValueError.

🚀 Application

advanced

Choosing interpolation method for categorical data

You have a DataFrame column with missing categorical values like ['red', NaN, 'blue', NaN, 'green']. Which interpolation method is appropriate to fill missing values?

A Use method='polynomial' with order=2 for smooth category interpolation

B Use method='linear' to interpolate categories as numeric values

C Use method='nearest' to fill missing values with the closest non-missing category

D Use method='time' assuming the index is datetime

💡 Hint

Categorical data cannot be interpolated numerically.

Nearest method fills missing categorical values by copying the nearest valid category, which is suitable for categorical data.

🧠 Conceptual

expert

Effect of limit parameter in interpolation

What is the effect of setting the 'limit' parameter to 1 in pandas interpolate method when filling missing values?

A It limits the maximum number of consecutive NaNs to fill to 1, leaving longer gaps unfilled

B It limits the total number of NaNs filled in the entire DataFrame to 1

C It limits the interpolation to only the first missing value in the column

D It limits the interpolation to only numeric columns

💡 Hint

Think about how 'limit' controls consecutive missing values filled.

The 'limit' parameter restricts how many consecutive NaNs are filled. Setting limit=1 fills only one NaN in a row, leaving longer sequences partially unfilled.