Interpolation for missing numerics in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is interpolation in data analysis?

Interpolation is a method to estimate missing numeric values in data by using existing known values around them.

beginner

Name a common interpolation method used for filling missing numeric data.

Linear interpolation, which estimates missing values by connecting two known points with a straight line.

intermediate

How does pandas' interpolate() function help with missing data?

The pandas interpolate() function fills missing numeric values by estimating them using different methods like linear, time, or polynomial interpolation.

intermediate

Why is interpolation better than simply dropping missing values?

Interpolation keeps the data size intact and preserves trends by estimating missing values, while dropping data can lose important information.

advanced

What is a limitation of interpolation for missing numeric data?

Interpolation assumes that data changes smoothly between points, so it may not work well if data has sudden jumps or is very noisy.

Which method estimates missing values by drawing a straight line between known points?

A Linear interpolation

B Mean imputation

C Mode replacement

D Random sampling

Linear interpolation connects two known points with a straight line to estimate missing values.

What does pandas' interpolate() function do?

A Fills missing numeric values using estimation methods

B Converts numeric data to strings

C Drops rows with missing values

D Sorts data by index

pandas interpolate() fills missing numeric values by estimating them using methods like linear interpolation.

Why might interpolation not work well on some datasets?

A Because it requires categorical data

B Because it assumes smooth changes between points

C Because it deletes data

D Because it only works on text data

Interpolation assumes data changes smoothly, so it may fail if data has sudden jumps or noise.

Which of these is NOT a benefit of interpolation?

A Estimates missing values

B Preserves data trends

C Keeps data size intact

D Removes outliers automatically

Interpolation does not remove outliers; it estimates missing values while keeping data size and trends.

What type of data is interpolation mainly used for?

A Categorical data

B Text data

C Numeric data

D Image data

Interpolation is mainly used to estimate missing numeric data.

Explain what interpolation is and why it is useful for missing numeric data.

Think about how you can guess missing numbers using nearby known numbers.

Describe a situation where interpolation might not be a good choice for filling missing data.

Consider data that changes unpredictably or has sharp spikes.

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