Interpolation for missing numerics in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to fill missing values in the 'score' column using linear interpolation.

Data Analysis Python

df['score'] = df['score'].[1]()

Drag options to blanks, or click blank then click option'

Adropna

Binterpolate

Cfillna

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() without specifying a value does not estimate missing values.

dropna() removes rows instead of filling missing values.

The interpolate() method fills missing numeric values by estimating intermediate values linearly.

2fill in blank

medium

Complete the code to interpolate missing values in the 'temperature' column using the 'time' method.

Data Analysis Python

df['temperature'] = df['temperature'].interpolate(method=[1])

Drag options to blanks, or click blank then click option'

A'time'

B'linear'

C'nearest'

D'index'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'linear' method when the index is a time series.

Using 'nearest' which picks closest values instead of interpolating.

The 'time' method uses the index as time values to interpolate missing data points.

3fill in blank

hard

Fix the error in the code to interpolate missing values forward in the 'humidity' column.

Data Analysis Python

df['humidity'] = df['humidity'].interpolate(method=[1], limit_direction='forward')

Drag options to blanks, or click blank then click option'

A'backfill'

B'pad'

C'ffill'

D'nearest'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'pad' which is an alias but not accepted by interpolate() method.

Using 'backfill' which fills backward, opposite of forward.

The 'ffill' method propagates last valid observation forward to fill gaps.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Data Analysis Python

lengths = {word: [1] for word in words if [2]

Drag options to blanks, or click blank then click option'

Alen(word)

Blen(word) > 3

Cword

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of 'len(word)' for values.

Using 'word > 3' which compares string to number causing error.

The dictionary comprehension assigns each word's length as value and filters words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase keys to values only if value is positive.

Data Analysis Python

result = [1]: [2] for [3] in data.items() if [2] > 0}}

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

Ck

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'item' instead of 'k' or 'v' causing errors.

Not converting keys to uppercase.

The comprehension uses uppercase keys, values from the dictionary, and iterates over key-value pairs.

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