Dropping missing values (dropna) in Data Analysis Python - Mini Project: Build & Apply

Dropping Missing Values with dropna

📖 Scenario: You work in a small shop that keeps track of daily sales data. Sometimes, some sales records are missing because the cashier forgot to enter them. You want to clean the data by removing these missing entries before analyzing the sales.

🎯 Goal: You will create a small sales data table with some missing values, then use a configuration variable to decide how to drop these missing values using dropna. Finally, you will display the cleaned data.

📋 What You'll Learn

Create a pandas DataFrame called sales_data with specific sales records including missing values

Create a variable called drop_axis to decide whether to drop rows or columns with missing values

Use dropna on sales_data with the axis=drop_axis parameter to remove missing values

Print the cleaned DataFrame

💡 Why This Matters

🌍 Real World

Cleaning data by removing missing values is a common first step before analyzing or visualizing data in many fields like sales, healthcare, and finance.

💼 Career

Data analysts and data scientists often need to handle missing data to ensure their models and reports are accurate and reliable.

1

Create the sales data with missing values

Create a pandas DataFrame called sales_data with these exact entries: columns 'Day', 'Sales', and 'Customers'. The data should be:
Day: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
Sales: [200, 220, null, 250, 270]
Customers: [20, null, 25, 30, null]
Use None for missing values.

Data Analysis Python

# Create the sales_data DataFrame with missing values
# Your code here

Hint

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

2

Set the axis to drop missing values

Create a variable called drop_axis and set it to 0 to drop rows with missing values.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 220, None, 250, 270],
    'Customers': [20, None, 25, 30, None]
})

# Set drop_axis to 0 to drop rows with missing values
# Your code here

Hint

Set drop_axis to 0 to drop rows.

3

Drop missing values using dropna

Create a new DataFrame called cleaned_data by dropping missing values from sales_data using dropna with the parameter axis=drop_axis.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 220, None, 250, 270],
    'Customers': [20, None, 25, 30, None]
})

drop_axis = 0

# Use dropna on sales_data with axis=drop_axis and save to cleaned_data
# Your code here

Hint

Use dropna(axis=drop_axis) on sales_data.

4

Print the cleaned data

Print the cleaned_data DataFrame to see the result after dropping missing values.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 220, None, 250, 270],
    'Customers': [20, None, 25, 30, None]
})

drop_axis = 0

cleaned_data = sales_data.dropna(axis=drop_axis)

# Print cleaned_data
# Your code here

Hint

Use print(cleaned_data) to show the cleaned DataFrame.

Practice

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

easy

A. Duplicates rows with missing values

B. Fills missing values with zeros

C. Sorts the DataFrame by index

D. Removes rows or columns with missing values

Solution

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

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

2. Step 2: Identify what is removed

   It removes rows or columns that contain missing values, depending on parameters.

3. Final Answer:

   Removes rows or columns with missing values -> Option D

4. Quick Check:

   dropna() removes missing data [OK]

Hint: Remember: dropna means drop missing data [OK]

Common Mistakes:

• Confusing dropna with fillna
• Thinking it sorts data
• Assuming it duplicates data

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

easy

A. df.dropna(inplace=False, axis=2)

B. df.dropna(axis=1)

C. df.dropna(axis=0)

D. df.dropna(axis='columns')

Solution

1. Step 1: Understand axis parameter in dropna()

   axis=0 means drop rows, axis=1 means drop columns.

2. Step 2: Identify correct axis for dropping rows

   To drop rows with missing values, use axis=0.

3. Final Answer:

   df.dropna(axis=0) -> Option C

4. Quick Check:

   axis=0 drops rows [OK]

Hint: axis=0 drops rows, axis=1 drops columns [OK]

Common Mistakes:

• Using axis=1 to drop rows
• Using invalid axis=2
• Confusing inplace parameter

3. Given the DataFrame df below, what will df.dropna() return?

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

medium

A. Rows with index 0 only

B. Rows with index 0 and 1

C. Rows with index 1 and 2

D. Empty DataFrame

Solution

1. Step 1: Examine the DataFrame rows for missing values

   Row 0: A=1, B=4 (no missing)
   Row 1: A=2, B=NaN (missing in B)
   Row 2: A=NaN, B=6 (missing in A)

2. Step 2: Apply dropna() default behavior

   By default, dropna() drops rows with any missing values, so only row 0 remains.

3. Final Answer:

   Rows with index 0 only -> Option A

4. Quick Check:

   Only complete rows remain [OK]

Hint: dropna() keeps only rows without any NaN [OK]

Common Mistakes:

• Assuming it drops columns instead of rows
• Thinking it fills missing values
• Expecting multiple rows to remain

4. What is wrong with this code snippet if the goal is to drop columns with any missing values?

df.dropna(axis=2)

medium

A. axis=2 is invalid; axis should be 0 or 1

B. dropna cannot drop columns

C. Missing inplace=True to modify df

D. dropna only works on rows by default

Solution

1. Step 1: Check valid axis values for dropna

   Valid axis values are 0 (rows) and 1 (columns). axis=2 is invalid and causes an error.

2. Step 2: Understand error cause

   Using axis=2 will raise a ValueError because pandas does not support axis=2 for DataFrames.

3. Final Answer:

   axis=2 is invalid; axis should be 0 or 1 -> Option A

4. Quick Check:

   axis must be 0 or 1 [OK]

Hint: Only axis=0 or 1 allowed in dropna [OK]

Common Mistakes:

• Using axis=2 by mistake
• Forgetting axis parameter
• Confusing inplace with axis

5. You have a DataFrame df with some missing values. You want to drop only those rows where all columns are missing values. Which dropna() parameter should you use?

hard

A. dropna(axis=1)

B. dropna(how='all')

C. dropna(thresh=2)

D. dropna(how='any')

Solution

1. Step 1: Understand the how parameter in dropna()

   how='all' drops rows only if all values are missing.
   how='any' drops rows if any value is missing.

2. Step 2: Identify correct parameter for dropping rows with all missing

   Since the goal is to drop rows where all columns are missing, use how='all'.

3. Final Answer:

   dropna(how='all') -> Option B

4. Quick Check:

   how='all' drops rows with all NaN [OK]

Hint: Use how='all' to drop rows with all missing [OK]

Common Mistakes:

• Using how='any' which drops too many rows
• Confusing thresh with how
• Dropping columns instead of rows