Interpolation for missing numerics in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of linear interpolation on missing values

What is the output DataFrame after applying linear interpolation to fill missing values in the 'Value' column?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Value': [1.0, None, None, 4.0, 5.0]})
df_interpolated = df.interpolate(method='linear')
print(df_interpolated)

A

   Value
0    1.0
1    2.0
2    3.0
3    4.0
4    5.0

B

   Value
0    1.0
1    1.0
2    1.0
3    4.0
4    5.0

C

   Value
0    1.0
1    2.5
2    3.5
3    4.0
4    5.0

D

   Value
0    1.0
1    NaN
2    NaN
3    4.0
4    5.0

💡 Hint

Linear interpolation fills missing values by connecting known points with straight lines.

Linear interpolation estimates missing values by assuming a straight line between known data points. Here, missing values at index 1 and 2 are filled as 2.0 and 3.0 respectively.

❓ data_output

intermediate

Count of missing values after interpolation

After applying forward fill interpolation on the following DataFrame, how many missing values remain?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Score': [None, 2, None, 4, None]})
df_filled = df.fillna(method='ffill')
missing_count = df_filled['Score'].isna().sum()
print(missing_count)

A 2

B 3

C 1

D 0

💡 Hint

Forward fill replaces missing values with the last known value, but the first missing value remains if no previous value exists.

The first value is missing and has no previous value to fill from, so it remains NaN. The other missing values get filled from previous known values, leaving 1 missing value.

🔧 Debug

advanced

Identify the error in interpolation code

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

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, None, 3], 'B': ['x', None, 'z']})
df_interpolated = df.interpolate()
print(df_interpolated)

A TypeError: unsupported operand type(s) for -: 'str' and 'float'

B ValueError: cannot interpolate non-numeric data

C No error, interpolation works fine

D KeyError: 'method'

💡 Hint

Interpolation requires numeric data; strings cause issues during calculation.

The column 'B' contains strings and None. Interpolation tries to subtract values to estimate missing data, which fails with strings causing a TypeError.

🚀 Application

advanced

Choosing interpolation method for time series data

You have a time series with missing hourly temperature readings. Which interpolation method is best to fill missing values preserving trends?

A Nearest neighbor interpolation

B Linear interpolation

C Polynomial interpolation of degree 5

D Backward fill

💡 Hint

Consider smooth gradual changes in temperature over time.

Linear interpolation estimates missing values by connecting points with straight lines, preserving gradual trends in time series data better than nearest or backward fill.

🧠 Conceptual

expert

Effect of interpolation on data variance

How does linear interpolation of missing numeric values generally affect the variance of the dataset?

A It increases variance by adding new extreme values

B It does not change variance at all

C It randomly changes variance depending on data

D It decreases variance by smoothing values between known points

💡 Hint

Think about how filling missing values with averages affects spread.

Linear interpolation fills gaps with values between known points, reducing variability and thus decreasing overall variance.