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)

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

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)

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

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)

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

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).

Practice

1. What does the fillna() function do in a pandas DataFrame?

easy

A. It converts all values to strings.

B. It replaces missing values with a specified value or method.

C. It sorts the DataFrame by missing values.

D. It deletes rows with missing values.

Solution

1. Step 1: Understand the purpose of fillna()

   The fillna() function is designed to handle missing data by replacing NaN values.

2. Step 2: Identify what fillna() replaces missing values with

   It can replace missing values with a fixed value or use methods like forward fill or backward fill.

3. Final Answer:

   It replaces missing values with a specified value or method. -> Option B

4. Quick Check:

   fillna() replaces NaN values = D [OK]

Hint: fillna() fills missing spots, not deletes or sorts [OK]

Common Mistakes:

• Confusing fillna() with dropna() which deletes rows
• Thinking fillna() sorts data
• Assuming fillna() changes data types

2. Which of the following is the correct syntax to fill missing values in column 'Age' with 30 in a pandas DataFrame df?

easy

A. df['Age'].fill(30)

B. df.fillna['Age'](30)

C. df.fillna('Age', 30)

D. df['Age'].fillna(30)

Solution

1. Step 1: Identify the correct method call on a DataFrame column

   To fill missing values in a specific column, call fillna() on that column like df['Age'].fillna(30).

2. Step 2: Check syntax correctness

   df['Age'].fillna(30) uses correct syntax: column selection with brackets and method call with parentheses and argument.

3. Final Answer:

   df['Age'].fillna(30) -> Option D

4. Quick Check:

   Correct method call syntax = A [OK]

Hint: Use brackets for column, parentheses for fillna() [OK]

Common Mistakes:

• Using square brackets instead of parentheses for fillna()
• Calling fillna on DataFrame with wrong syntax
• Using fill() instead of fillna()

3. Given the DataFrame df below, what will be the output of df['Score'].fillna(0)?

import pandas as pd
df = pd.DataFrame({'Score': [10, None, 20, None, 30]})
print(df['Score'].fillna(0))

medium

A. [0, 0, 0, 0, 0]

B. [10, None, 20, None, 30]

C. [10, 0, 20, 0, 30]

D. [10, 10, 20, 20, 30]

Solution

1. Step 1: Understand fillna(0) effect on NaN values

   Calling fillna(0) replaces all missing (NaN) values with 0.

2. Step 2: Apply fillna(0) to the 'Score' column

   The original column has values [10, NaN, 20, NaN, 30]. After fillna(0), NaNs become 0, so result is [10, 0, 20, 0, 30].

3. Final Answer:

   [10, 0, 20, 0, 30] -> Option C

4. Quick Check:

   NaN replaced by 0 = C [OK]

Hint: fillna(value) replaces NaN with value directly [OK]

Common Mistakes:

• Expecting fillna() to remove rows
• Thinking fillna() copies previous values by default
• Confusing fillna(0) with fillna(method='ffill')

4. What is wrong with this code snippet to fill missing values in DataFrame df?

df.fillna(method='forward')

medium

A. The method name should be 'ffill', not 'forward'.

B. fillna() cannot use methods to fill missing values.

C. The method argument should be a number, not a string.

D. fillna() must be called on a column, not the whole DataFrame.

Solution

1. Step 1: Check valid method names for fillna()

   fillna() supports method='ffill' for forward fill and method='bfill' for backward fill.

2. Step 2: Identify the incorrect method name

   The code uses method='forward', which is invalid. The correct name is 'ffill'.

3. Final Answer:

   The method name should be 'ffill', not 'forward'. -> Option A

4. Quick Check:

   Correct method name = 'ffill' [OK]

Hint: Use 'ffill' or 'bfill' for method in fillna() [OK]

Common Mistakes:

• Using 'forward' instead of 'ffill'
• Thinking fillna() can't use methods
• Calling fillna() only on columns, not DataFrame

5. You have a DataFrame df with missing values in multiple columns. You want to fill missing values in column 'A' with the mean of 'A', and in column 'B' with the previous valid value. Which code correctly does this?

hard

A. df['A'] = df['A'].fillna(df['A'].mean())
df['B'] = df['B'].fillna(method='ffill')

B. df.fillna({'A': df['A'].mean(), 'B': 'ffill'})

C. df['A'].fillna(df['A'].mean())
df['B'].fillna(method='bfill')

D. df.fillna(method={'A': 'mean', 'B': 'ffill'})

Solution

1. Step 1: Fill column 'A' with its mean

   Calculate mean with df['A'].mean() and fill missing values using fillna() on column 'A'. Assign back to df['A'].

2. Step 2: Fill column 'B' with previous valid value

   Use fillna(method='ffill') on column 'B' to forward fill missing values. Assign back to df['B'].

3. Final Answer:

   df['A'] = df['A'].fillna(df['A'].mean()) df['B'] = df['B'].fillna(method='ffill') -> Option A

4. Quick Check:

   Mean fill + forward fill = B [OK]

Hint: Fill columns separately with correct methods, assign back [OK]

Common Mistakes:

• Using fillna() without assignment, so no change saved
• Passing 'ffill' as string value instead of method argument
• Trying to use method='mean' which is invalid