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.

Practice

1. What is the main purpose of interpolation in data analysis when dealing with missing numeric values?

easy

A. To estimate missing values based on existing data points

B. To remove all rows with missing values

C. To convert numeric data into categories

D. To randomly fill missing values without any pattern

Solution

1. Step 1: Understand the role of interpolation

   Interpolation estimates missing numeric values by using known data points to fill gaps.

2. Step 2: Compare with other options

   Removing rows or random filling does not estimate values based on data trends.

3. Final Answer:

   To estimate missing values based on existing data points -> Option A

4. Quick Check:

   Interpolation = estimate missing values [OK]

Hint: Interpolation estimates missing numbers using nearby known values [OK]

Common Mistakes:

• Thinking interpolation deletes missing data
• Confusing interpolation with categorization
• Assuming interpolation fills values randomly

2. Which of the following is the correct syntax to perform linear interpolation on a pandas DataFrame column df['value'] to fill missing values?

easy

A. df['value'].interpolate(method='linear')

B. df.interpolate('linear', column='value')

C. interpolate(df['value'], method='linear')

D. df['value'].fillna(method='linear')

Solution

1. Step 1: Recall pandas interpolation syntax

   The correct method is to call interpolate() on the Series with method='linear'.

2. Step 2: Check other options for errors

   df['value'].fillna(method='linear') uses fillna incorrectly with method='linear', B uses wrong argument order, C is not a pandas function.

3. Final Answer:

   df['value'].interpolate(method='linear') -> Option A

4. Quick Check:

   pandas Series.interpolate(method='linear') = correct syntax [OK]

Hint: Use Series.interpolate(method='linear') to fill missing numerics [OK]

Common Mistakes:

• Using fillna with method='linear' which is invalid
• Calling interpolate as a standalone function
• Passing wrong argument names or order

3. Given the following code, what will be the output of print(df['score'].interpolate())?

import pandas as pd
df = pd.DataFrame({'score': [10, null, null, 40, 50]})

medium

A. [10.0, 10.0, 10.0, 40.0, 50.0]

B. [10, null, null, 40, 50]

C. [10.0, 20.0, 30.0, 40.0, 50.0]

D. [10.0, 25.0, 35.0, 40.0, 50.0]

Solution

1. Step 1: Understand linear interpolation on missing values

   Linear interpolation fills missing values by evenly spacing between known points. Between 10 and 40, two missing values become 20 and 30.

2. Step 2: Confirm the output array

   The filled column becomes [10.0, 20.0, 30.0, 40.0, 50.0].

3. Final Answer:

   [10.0, 20.0, 30.0, 40.0, 50.0] -> Option C

4. Quick Check:

   Linear interpolate fills gaps evenly [OK]

Hint: Linear interpolation fills gaps evenly between known points [OK]

Common Mistakes:

• Assuming missing values stay null
• Filling missing values with previous known value only
• Miscalculating intermediate values

4. Identify the error in this code snippet that attempts to interpolate missing values in df['temp']:

import pandas as pd
df = pd.DataFrame({'temp': [15, null, 25, null, 35]})
df['temp'] = df.interpolate(method='linear')

medium

A. There is no error; the code runs correctly

B. The method argument should be 'linear' without quotes

C. The assignment should be to df['temp'], not df['temp'] = ...

D. The interpolate method is called on the DataFrame, not the Series

Solution

1. Step 1: Check method call target

   The code calls interpolate() on df (DataFrame), but assigns to df['temp']. This mismatch causes error.

2. Step 2: Correct usage

   Interpolation should be called on the Series: df['temp'] = df['temp'].interpolate(method='linear').

3. Final Answer:

   The interpolate method is called on the DataFrame, not the Series -> Option D

4. Quick Check:

   Call interpolate on Series, not DataFrame for single column [OK]

Hint: Call interpolate on the Series, not the whole DataFrame [OK]

Common Mistakes:

• Calling interpolate on DataFrame but assigning to Series
• Using method argument without quotes
• Not assigning the result back to the column

5. You have a DataFrame df with a numeric column temperature containing missing values at the start and end of the data. Which interpolation method should you use to fill all missing values, including those at the edges, and how?

hard

A. Use df['temperature'].interpolate(method='linear') which fills all missing values by default

B. Use df['temperature'].interpolate(method='linear', limit_direction='both') to fill missing values including edges

C. Use df['temperature'].fillna(method='bfill') to fill missing values at edges only

D. Use df['temperature'].interpolate(method='nearest') which ignores edge missing values

Solution

1. Step 1: Understand edge missing values in interpolation

   Linear interpolation by default does not fill missing values at start or end (edges).

2. Step 2: Use limit_direction='both' to fill edges

   Setting limit_direction='both' allows interpolation to fill missing values at both start and end.

3. Step 3: Compare other options

   Use df['temperature'].interpolate(method='linear') which fills all missing values by default misses edge filling, C uses backfill only, D uses nearest which may not fill edges properly.

4. Final Answer:

   Use df['temperature'].interpolate(method='linear', limit_direction='both') -> Option B

5. Quick Check:

   limit_direction='both' fills edge missing values [OK]

Hint: Use limit_direction='both' to fill missing edges in interpolation [OK]

Common Mistakes:

• Assuming linear interpolation fills edges by default
• Using fillna instead of interpolate for edge missing values
• Ignoring limit_direction parameter