NaN and None in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a pandas DataFrame with a None value.

Pandas

import pandas as pd

data = {'A': [1, 2, [1], 4]}
df = pd.DataFrame(data)
print(df)

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

A0

BNaN

CNone

D''

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'NaN' as a string instead of the actual NaN value.

Using 0 which is a valid number, not missing data.

None is used to represent missing data in Python and works in pandas DataFrames.

2fill in blank

medium

Complete the code to import the correct function to create a NaN value.

Pandas

import pandas as pd
from numpy import [1]

data = {'B': [1, 2, [1], 4]}
df = pd.DataFrame(data)
print(df)

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

Anan

BNaN

CNaT

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'NaN' with uppercase N which is not the correct import.

Using None which is different from NaN.

numpy.nan is the standard way to represent NaN (Not a Number) in pandas.

3fill in blank

hard

Fix the error in the code to check for NaN values in the DataFrame.

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({'C': [1, np.nan, 3]})
print(df.[1]())

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

Aisnulls

Bisna

Cisnan

Disnull

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'isnan' which is a numpy function, not a DataFrame method.

Using 'isnulls' which is not a valid method.

DataFrame has the method isna() to check for NaN values.

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

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

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 value instead of its length.

Using the word in the condition instead of its length.

The dictionary comprehension uses len(word) as value and filters words with length greater than 3.

5fill in blank

hard

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

Pandas

words = ['apple', 'bat', 'car', 'door']
result = { [1]: [2] for word in words if [3] }

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

Aword.upper()

Blen(word)

Clen(word) > 3

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Using the word itself instead of its length for values.

Incorrect condition for filtering.

The dictionary comprehension maps uppercase words to their lengths, filtering words longer than 3 characters.

Practice

1. What do NaN and None represent in a pandas DataFrame?

easy

A. Missing or null data values

B. Valid numeric data

C. Column headers

D. Index labels

Solution

1. Step 1: Understand the role of NaN and None in pandas

   Both NaN and None are used to mark missing or null data in pandas DataFrames.

2. Step 2: Differentiate from other DataFrame elements

   They do not represent valid data, headers, or indexes but indicate absence of data.

3. Final Answer:

   Missing or null data values -> Option A

4. Quick Check:

   NaN and None = Missing data [OK]

Hint: NaN and None always mean missing data in pandas [OK]

Common Mistakes:

• Thinking NaN is a valid number
• Confusing None with column names
• Assuming NaN is an index label

2. Which of the following is the correct way to check for missing values in a pandas DataFrame df?

easy

A. df.isna()

B. df.notna()

C. df.isnan()

D. df.ismissing()

Solution

1. Step 1: Recall pandas functions for missing data

   The correct function to check for missing values is isna(), which returns a boolean DataFrame indicating missing entries.

2. Step 2: Identify incorrect options

   notna() returns a boolean DataFrame indicating non-missing entries; isnan() and ismissing() do not exist in pandas.

3. Final Answer:

   df.isna() -> Option A

4. Quick Check:

   Use isna() to find missing values [OK]

Hint: Use df.isna() to find missing values in pandas [OK]

Common Mistakes:

• Using non-existent df.isnan()
• Using df.notna() which checks non-missing values
• Trying df.ismissing() which is invalid

3. Given the code:

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [None, 2, 3]})
result = df.isna().sum().to_dict()

What is the value of result?

medium

A. {'A': 0, 'B': 0}

B. {'A': 0, 'B': 1}

C. {'A': 1, 'B': 0}

D. {'A': 1, 'B': 1}

Solution

1. Step 1: Identify missing values in each column

   Column 'A' has one np.nan (missing), column 'B' has one None (missing).

2. Step 2: Use isna() and sum() to count missing per column

   df.isna() marks missing as True, sum() counts Trues per column, resulting in {'A': 1, 'B': 1}.

3. Final Answer:

   {'A': 1, 'B': 1} -> Option D

4. Quick Check:

   Count missing with isna().sum() = {'A':1,'B':1} [OK]

Hint: Count missing with df.isna().sum() per column [OK]

Common Mistakes:

• Ignoring None as missing in column B
• Confusing sum() to count non-missing
• Assuming np.nan and None are treated differently here

4. Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'X': [1, None, 3]})
df['X'] = df['X'].fillna(0)
print(df)

What is the output?

medium

A. X
0 1
1 3
2 0

B. X
0 1
1 None
2 3

C. X
0 1
1 0
2 3

D. X
0 1
1 NaN
2 3

Solution

1. Step 1: Identify missing value in column 'X'

   The second row has None, which pandas treats as missing (NaN).

2. Step 2: Use fillna(0) to replace missing with zero

   The fillna(0) replaces the missing value with 0, so the DataFrame becomes 1, 0, 3.

3. Final Answer:

   X 0 1 1 0 2 3 -> Option C

4. Quick Check:

   fillna(0) replaces None with 0 [OK]

Hint: fillna(0) replaces missing values with zero [OK]

Common Mistakes:

• Expecting None to remain after fillna
• Confusing fillna with dropna
• Assuming fillna changes non-missing values

5. You have a DataFrame df with columns 'A' and 'B' containing some NaN and None values. You want to replace all missing values in 'A' with the mean of 'A' and all missing values in 'B' with the string 'missing'. Which code correctly does this?

hard

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

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

C. df['A'].fillna(df['A'].mean())
df['B'].fillna('missing')

D. df.fillna(df.mean())
df['B'] = df['B'].fillna('missing')

Solution

1. Step 1: Replace missing in 'A' with its mean

   Use df['A'].fillna(df['A'].mean()) and assign back to 'A' to update the column.

2. Step 2: Replace missing in 'B' with 'missing'

   Use df['B'].fillna('missing') and assign back to 'B' to update that column.

3. Step 3: Check other options for correctness

   df.fillna({'A': df['A'].mean(), 'B': 'missing'}) does not assign back, so changes are not saved; df['A'].fillna(df['A'].mean()) df['B'].fillna('missing') also misses assignment; df.fillna(df.mean()) df['B'] = df['B'].fillna('missing') tries to fill numeric mean for all columns which fails for 'B'.

4. Final Answer:

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

5. Quick Check:

   Assign fillna results back to columns [OK]

Hint: Assign fillna results back to columns to save changes [OK]

Common Mistakes:

• Not assigning fillna result back to DataFrame
• Using fillna without specifying columns
• Trying to fill numeric mean into string column