Dropping missing values with dropna() in Pandas - Mini Project: Build & Apply

Dropping missing values with dropna()

📖 Scenario: You have a small dataset of sales records. Some records have missing values. You want to clean the data by removing any rows that have missing values.

🎯 Goal: Build a small pandas DataFrame with sales data, then use dropna() to remove rows with missing values, and finally display the cleaned data.

📋 What You'll Learn

Create a pandas DataFrame called sales_data with exact columns and values

Create a variable called cleaned_data that holds the DataFrame after dropping rows with missing values using dropna()

Print the cleaned_data DataFrame

💡 Why This Matters

🌍 Real World

Cleaning data by removing incomplete records is a common step before analysis or machine learning.

💼 Career

Data scientists and analysts often use <code>dropna()</code> to prepare datasets for accurate insights and models.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values:
'Product': ['Pen', 'Notebook', 'Pencil', 'Eraser', 'Marker'],
'Price': [1.5, 2.0, None, 0.5, 1.0],
'Quantity': [10, None, 15, 20, 10].
Use None for missing values.

Pandas

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

Hint

Use pd.DataFrame() with a dictionary. Use None for missing values.

2

Create a variable for cleaned data

Create a variable called cleaned_data that stores the result of calling dropna() on sales_data to remove rows with any missing values.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Pencil', 'Eraser', 'Marker'],
    'Price': [1.5, 2.0, None, 0.5, 1.0],
    'Quantity': [10, None, 15, 20, 10]
})

# Create cleaned_data by dropping rows with missing values from sales_data
# Your code here

Hint

Use dropna() on sales_data and assign it to cleaned_data.

3

Check the cleaned data

Use print() to display the cleaned_data DataFrame.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Pencil', 'Eraser', 'Marker'],
    'Price': [1.5, 2.0, None, 0.5, 1.0],
    'Quantity': [10, None, 15, 20, 10]
})

cleaned_data = sales_data.dropna()

# Print the cleaned_data DataFrame
# Your code here

Hint

Use print(cleaned_data) to see the cleaned DataFrame.

4

View the final cleaned data output

Run the program to see the printed cleaned_data DataFrame without any missing values.

Pandas

import pandas as pd

sales_data = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Pencil', 'Eraser', 'Marker'],
    'Price': [1.5, 2.0, None, 0.5, 1.0],
    'Quantity': [10, None, 15, 20, 10]
})

cleaned_data = sales_data.dropna()

print(cleaned_data)

# Run the code to see the cleaned data output

Hint

The output should show only rows without any missing values.

Practice

1. What does the dropna() function do in pandas?

easy

A. Sorts the DataFrame by index

B. Fills missing values with zeros

C. Removes rows or columns with missing values

D. Calculates the mean of columns

Solution

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

   The dropna() function is designed to remove data that contains missing values (NaN) from a DataFrame.

2. Step 2: Identify what dropna() removes

   It removes either rows or columns that have missing values, depending on the parameters used.

3. Final Answer:

   Removes rows or columns with missing values -> Option C

4. Quick Check:

   dropna() removes missing data [OK]

Hint: Remember: dropna() drops missing data, not fills it [OK]

Common Mistakes:

• Confusing dropna() with fillna()
• Thinking dropna() sorts data
• Assuming dropna() calculates statistics

2. Which of the following is the correct syntax to drop rows with any missing values in a DataFrame df?

easy

A. df.dropna(axis=0, how='any')

B. df.dropna(axis=1, how='any')

C. df.dropna(axis=0, how='all')

D. df.dropna(axis=1, how='all')

Solution

1. Step 1: Identify axis for rows

   In pandas, axis=0 means rows, so to drop rows, use axis=0.

2. Step 2: Use how='any' to drop rows with any missing values

   The how='any' parameter drops rows if any value is missing in that row.

3. Final Answer:

   df.dropna(axis=0, how='any') -> Option A

4. Quick Check:

   Drop rows with any NaN = axis=0, how='any' [OK]

Hint: axis=0 drops rows; how='any' drops if any NaN [OK]

Common Mistakes:

• Using axis=1 to drop rows
• Using how='all' to drop rows with any NaN
• Confusing axis and how parameters

3. Given the DataFrame:

import pandas as pd
data = {'A': [1, 2, None], 'B': [4, None, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

What is the result of df.dropna(axis=0, how='any')?

medium

A. Rows with index 0 and 1 only

B. Rows with index 0 and 2 only

C. All rows dropped

D. Only row with index 0

Solution

1. Step 1: Identify rows with any missing values

   Row 0: A=1, B=4, C=7 (no NaN)
   Row 1: A=2, B=NaN, C=8 (has NaN)
   Row 2: A=NaN, B=6, C=9 (has NaN)

2. Step 2: Drop rows with any NaN

   Only row 0 has no missing values, so only it remains after dropna(axis=0, how='any').

3. Final Answer:

   Only row with index 0 -> Option D

4. Quick Check:

   Drop rows with any NaN leaves only complete rows [OK]

Hint: Check which rows have any NaN to keep them [OK]

Common Mistakes:

• Keeping rows with NaN
• Confusing axis=0 and axis=1
• Misreading how='any' vs how='all'

4. You run the code:

df.dropna(axis=2)

and get an error. What is the problem?

medium

A. axis=2 is invalid; only 0 (rows) or 1 (columns) allowed

B. dropna() cannot be used on DataFrames

C. Missing values must be filled before dropping

D. You must specify how='any' or how='all'

Solution

1. Step 1: Understand valid axis values for dropna()

   In pandas, axis can only be 0 (rows) or 1 (columns) for dropna().

2. Step 2: Identify the error cause

   Using axis=2 is invalid and causes an error because it is not a supported axis.

3. Final Answer:

   axis=2 is invalid; only 0 (rows) or 1 (columns) allowed -> Option A

4. Quick Check:

   Valid axis values = 0 or 1 only [OK]

Hint: axis must be 0 or 1 for dropna() [OK]

Common Mistakes:

• Using axis=2 mistakenly
• Thinking dropna() needs fillna() first
• Assuming how parameter is mandatory

5. You have a DataFrame df with some rows having all values missing and some rows having only some missing values. Which dropna() call will remove only rows where all values are missing, keeping rows with partial data?

hard

A. df.dropna(axis=0, how='any')

B. df.dropna(axis=0, how='all')

C. df.dropna(axis=1, how='any')

D. df.dropna(axis=1, how='all')

Solution

1. Step 1: Understand how='all' parameter

   Using how='all' drops rows only if all values in that row are missing.

2. Step 2: Use axis=0 to drop rows

   Since we want to drop rows, axis=0 is correct.

3. Step 3: Confirm this keeps rows with some data

   Rows with some non-missing values are kept because not all values are missing.

4. Final Answer:

   df.dropna(axis=0, how='all') -> Option B

5. Quick Check:

   Drop rows with all NaN = axis=0, how='all' [OK]

Hint: Use how='all' to drop only fully empty rows [OK]

Common Mistakes:

• Using how='any' which drops partial rows
• Using axis=1 which drops columns
• Confusing how parameter effects