Interpolation for missing values in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to fill missing values in the DataFrame using interpolation.

Pandas

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

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

Ainterpolate

Bfillna

Cdropna

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() which replaces missing values but does not estimate intermediate values.

Using dropna() which removes rows with missing values instead of filling them.

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

2fill in blank

medium

Complete the code to interpolate missing values using linear method along the index.

Pandas

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

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

A'nearest'

B'polynomial'

C'linear'

D'spline'

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'nearest' which fills missing values with the nearest valid value.

Choosing 'polynomial' or 'spline' which require additional parameters.

The linear method interpolates missing values by connecting points with straight lines.

3fill in blank

hard

Fix the error in the code to interpolate missing values along columns instead of rows.

Pandas

df.interpolate(axis=[1], inplace=True)

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A0

B1

C'index'

D'columns'

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 which interpolates vertically along rows.

Using string values 'index' or 'columns' which are not valid for axis parameter.

Setting axis=1 interpolates along columns (horizontally).

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.

Pandas

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

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Comparing the word string directly to a number.

The dictionary comprehension maps each word to its length if the length is greater than 3.

5fill in blank

hard

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

Pandas

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

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

Ak.upper()

Bv

Ck, v

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'item' instead of unpacking key and value.

Not converting keys to uppercase.

The comprehension maps uppercase keys to their values only if the value is positive.