Filling missing values (fillna) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to fill missing values in the DataFrame with zero.

Data Analysis Python

import pandas as pd

data = {'A': [1, 2, None, 4], 'B': [None, 2, 3, 4]}
df = pd.DataFrame(data)
df_filled = df.[1](0)
print(df_filled)

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

Areplace

Bisnull

Cdropna

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'replace' instead of 'fillna' does not fill NaN values correctly.

Using 'dropna' removes rows instead of filling missing values.

The fillna method replaces missing values with the specified value, here zero.

2fill in blank

medium

Complete the code to fill missing values in column 'A' with the mean of that column.

Data Analysis Python

import pandas as pd

data = {'A': [1, 2, None, 4], 'B': [5, None, 7, 8]}
df = pd.DataFrame(data)
mean_value = df['A'].mean()
df['A'] = df['A'].[1](mean_value)
print(df)

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

Afillna

Breplace

Cdropna

Dinterpolate

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'replace' does not fill NaN values properly.

Using 'interpolate' fills missing values but differently than mean.

The fillna method fills missing values with the mean calculated.

3fill in blank

hard

Fix the error in the code to fill missing values in the DataFrame with the string 'missing'.

Data Analysis Python

import pandas as pd

data = {'A': [1, None, 3], 'B': [None, 'x', 'y']}
df = pd.DataFrame(data)
df_filled = df.[1]('missing')
print(df_filled)

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Afill

Bfill_missing

Cfillna

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fill' or 'fill_missing' which are not pandas methods.

Using 'replace' which does not fill NaN values correctly.

The correct method to fill missing values is fillna. Other options are invalid or do not exist.

4fill in blank

hard

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

Data Analysis Python

words = ['apple', 'bat', 'car', 'door']
lengths = {word: [1] for word in words if [2]
print(lengths)

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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.

Using the word in the condition instead of its length.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if the count is greater than zero.

Data Analysis Python

data = {'apple': 2, 'banana': 0, 'cherry': 5}
result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}
print(result)

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original key without uppercase conversion.

Using incorrect comparison operators like '<' or '==' in the condition.

The dictionary comprehension maps the uppercase key k.upper() to its value v only if the value is greater than zero (> 0).