Why handling missing data matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check for missing values in the DataFrame.

Pandas

missing = df.[1]()

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

Aisnull

Bdropna

Cfillna

Dhead

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() removes rows instead of showing missing values.

Using fillna() replaces missing values but does not identify them.

The isnull() function returns a DataFrame showing where values are missing (NaN).

2fill in blank

medium

Complete the code to remove rows with missing values from the DataFrame.

Pandas

clean_df = df.[1]()

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

Aisnull

Bdropna

Cinfo

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using isnull() only identifies missing values but does not remove them.

Using fillna() replaces missing values but does not remove rows.

The dropna() function removes rows that contain any missing values.

3fill in blank

hard

Fix the error in the code to fill missing values with zero.

Pandas

df_filled = df.[1](0)

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

Adropna

Bisnull

Cfillna

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using isnull() only checks for missing values but does not fill them.

The fillna(0) function replaces all missing values with zero.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B<=

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' includes words with length 3 or less, which is not desired.

Using 'word' instead of 'len(word)' does not give the length.

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

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase keys and values greater than zero.

Pandas

result = { [1]: [2] for k, v in data.items() if v [3] 0}

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' does not change key case.

Using '<' or '<=' includes values less than or equal to zero.

Using 'k' or 'v' incorrectly in the dictionary comprehension.

This dictionary comprehension creates keys as uppercase versions of original keys and includes only items where the value is greater than zero.