NaN and None in Pandas - Mini Project: Build & Apply

Handling NaN and None in Pandas

📋 What You'll Learn

💡 Why This Matters

🌍 Real World

Handling missing data is common in real-world datasets like sales, weather, or survey data where some entries might be missing or not recorded.

💼 Career

Data scientists and analysts must know how to detect and handle missing values to prepare clean data for analysis and modeling.

1

Create a DataFrame with missing values

Import pandas as pd and numpy as np. Create a DataFrame called sales_data with a column 'Day' containing 'Monday', 'Tuesday', 'Wednesday', 'Thursday', and 'Friday'. Add a column 'Sales' with values 200, None, 150, np.nan, and 300 respectively.

Pandas

# Import pandas and numpy
# Create the sales_data DataFrame with missing values
# Your code here

Hint

Use pd.DataFrame to create the table. Use None and np.nan to represent missing values.

2

Count missing values in the DataFrame

Create a variable called missing_count that stores the total number of missing values in the sales_data DataFrame using the isna() and sum() methods.

Pandas

import pandas as pd
import numpy as np

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Sales': [200, None, 150, np.nan, 300]
})

# Count missing values in sales_data
# Your code here

Hint

Use sales_data.isna() to find missing values, then sum twice to get total count.

3

Remove rows with missing values

Create a new DataFrame called clean_sales by removing all rows with missing values from sales_data using the dropna() method.

Pandas

import pandas as pd
import numpy as np

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Sales': [200, None, 150, np.nan, 300]
})

missing_count = sales_data.isna().sum().sum()

# Remove rows with missing values and save to clean_sales
# Your code here

Hint

Use dropna() on the DataFrame to remove rows with missing values.

4

Print the cleaned DataFrame

Print the clean_sales DataFrame to show the sales data without missing values.

Pandas

import pandas as pd
import numpy as np

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Sales': [200, None, 150, np.nan, 300]
})

missing_count = sales_data.isna().sum().sum()

clean_sales = sales_data.dropna()

# Print the clean_sales DataFrame
# Your code here

Hint

Use print(clean_sales) to display the DataFrame without missing values.

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