Missing data strategies decision in Pandas - Mini Project: Build & Apply

Missing Data Strategies Decision

📖 Scenario: You are working as a data analyst for a small online store. You have collected customer data, but some values are missing. You need to decide how to handle these missing values before analysis.

🎯 Goal: Build a simple program that loads a dataset with missing values, sets a threshold for acceptable missing data, applies a strategy to handle missing values, and shows the cleaned data.

📋 What You'll Learn

Create a pandas DataFrame with missing values

Set a threshold variable for maximum allowed missing values per column

Use a method to drop columns exceeding the threshold

Fill remaining missing values with a fixed value

Print the cleaned DataFrame

💡 Why This Matters

🌍 Real World

Handling missing data is a common task in data science to prepare datasets for analysis or machine learning.

💼 Career

Data analysts and scientists must decide how to handle missing data to ensure accurate and reliable results.

1

Create a DataFrame with missing values

Create a pandas DataFrame called df with these exact columns and values:
'CustomerID': [101, 102, 103, 104, 105],
'Age': [25, None, 22, None, 28],
'City': ['New York', 'Los Angeles', None, 'Chicago', 'Houston'],
'PurchaseAmount': [250.0, 300.0, None, 150.0, None].

Pandas

import pandas as pd
# Create the DataFrame df with missing values
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists including None for missing data.

2

Set a threshold for missing data

Create a variable called max_missing and set it to 2. This will be the maximum number of missing values allowed per column.

Pandas

import pandas as pd

df = pd.DataFrame({
    'CustomerID': [101, 102, 103, 104, 105],
    'Age': [25, None, 22, None, 28],
    'City': ['New York', 'Los Angeles', None, 'Chicago', 'Houston'],
    'PurchaseAmount': [250.0, 300.0, None, 150.0, None]
})

# Set max_missing to 2
# Your code here

Hint

Just create a variable max_missing and assign the number 2.

3

Drop columns with too many missing values and fill remaining missing values

Create a new DataFrame called cleaned_df by dropping columns from df that have more than max_missing missing values. Then fill the remaining missing values in cleaned_df with 0.

Pandas

import pandas as pd

df = pd.DataFrame({
    'CustomerID': [101, 102, 103, 104, 105],
    'Age': [25, None, 22, None, 28],
    'City': ['New York', 'Los Angeles', None, 'Chicago', 'Houston'],
    'PurchaseAmount': [250.0, 300.0, None, 150.0, None]
})

max_missing = 2

# Drop columns with more than max_missing missing values and fill remaining missing with 0
# Your code here

Hint

Use dropna with axis=1 and thresh=len(df) - max_missing to drop columns. Then use fillna(0) to fill missing values.

4

Print the cleaned DataFrame

Print the cleaned_df DataFrame to see the result after handling missing data.

Pandas

import pandas as pd

df = pd.DataFrame({
    'CustomerID': [101, 102, 103, 104, 105],
    'Age': [25, None, 22, None, 28],
    'City': ['New York', 'Los Angeles', None, 'Chicago', 'Houston'],
    'PurchaseAmount': [250.0, 300.0, None, 150.0, None]
})

max_missing = 2

cleaned_df = df.dropna(axis=1, thresh=len(df) - max_missing)
cleaned_df = cleaned_df.fillna(0)

# Print cleaned_df
# Your code here

Hint

Use print(cleaned_df) to show the cleaned data.

Practice

1.

What is the main purpose of using dropna() in pandas?

easy

A. To remove rows or columns that contain missing data

B. To replace missing data with zeros

C. To fill missing data with the mean value

D. To convert data types of columns

Solution

1. Step 1: Understand dropna() function

   dropna() removes rows or columns that have missing values (NaN).

2. Step 2: Compare with other options

   Replacing missing data or converting data types are done by other functions like fillna() or astype().

3. Final Answer:

   To remove rows or columns that contain missing data -> Option A

4. Quick Check:

   dropna() removes missing data rows/columns [OK]

Hint: Remember: dropna drops missing data, fillna fills it [OK]

Common Mistakes:

• Confusing dropna with fillna
• Thinking dropna fills missing values
• Assuming dropna changes data types

2.

Which of the following is the correct syntax to fill missing values in a DataFrame df with the value 0?

?

easy

A. df.fillna(0)

B. df.dropna(0)

C. df.fill(na=0)

D. df.replace(na=0)

Solution

1. Step 1: Recall fillna syntax

   The correct method to fill missing values is fillna() with the value as argument.

2. Step 2: Check each option

   df.fillna(0) uses df.fillna(0) which is correct. Others have wrong method names or parameters.

3. Final Answer:

   df.fillna(0) -> Option A

4. Quick Check:

   Use fillna(value) to fill missing values [OK]

Hint: fillna fills missing values; dropna removes rows/columns [OK]

Common Mistakes:

• Using dropna instead of fillna
• Wrong method name like fill() or replace()
• Passing parameters incorrectly

3.

Given the DataFrame df below, what will be the output of df.fillna(method='ffill')?

import pandas as pd
df = pd.DataFrame({'A': [1, None, 3, None], 'B': [None, 2, None, 4]})
print(df.fillna(method='ffill'))

medium

A. Removes rows with missing values

B. Fills missing values with zero

C. Fills missing values by propagating last valid observation forward

D. Raises an error due to wrong method

Solution

1. Step 1: Understand forward fill method

   method='ffill' fills missing values by copying the last known non-missing value forward.

2. Step 2: Apply to DataFrame

   For column A: 1 stays, None becomes 1, 3 stays, None becomes 3. For B: None stays None (no previous), 2 stays, None becomes 2, 4 stays.

3. Final Answer:

   Fills missing values by propagating last valid observation forward -> Option C

4. Quick Check:

   ffill copies last valid value forward [OK]

Hint: ffill copies previous value forward to fill NaNs [OK]

Common Mistakes:

• Thinking fillna(method='ffill') fills with zero
• Confusing ffill with dropping rows
• Expecting error from method parameter

4.

What is wrong with the following code if the goal is to remove all rows with missing data from DataFrame df?

df.fillna(drop=True)

medium

A. The code should use df.dropna(inplace=True) to modify in place

B. fillna() does not have a drop parameter; use dropna() instead

C. The parameter drop=True should be dropna=True

D. The code is correct and will remove rows with missing data

Solution

1. Step 1: Check method and parameters

   fillna() is for filling missing values and does not accept a drop parameter.

2. Step 2: Correct method for removing rows

   To remove rows with missing data, use dropna() instead.

3. Final Answer:

   fillna() does not have a drop parameter; use dropna() instead -> Option B

4. Quick Check:

   Use dropna() to remove missing data rows [OK]

Hint: Use dropna() to remove rows, fillna() to replace values [OK]

Common Mistakes:

• Using fillna() with invalid parameters
• Confusing fillna and dropna functions
• Expecting fillna to remove rows

5.

You have a DataFrame df with many missing values scattered randomly. You want to keep as much data as possible but avoid rows with more than 2 missing values. Which pandas method and parameter combination is best?

hard

A. Use df.fillna(method='bfill') to fill missing values backward

B. Use df.fillna(0) to replace all missing values with zero

C. Use df.dropna(axis=1) to drop columns with missing values

D. Use df.dropna(thresh=len(df.columns)-2) to drop rows with more than 2 missing values

Solution

1. Step 1: Understand the threshold parameter

   dropna(thresh=n) keeps rows with at least n non-missing values.

2. Step 2: Calculate threshold for max 2 missing values

   If total columns are len(df.columns), then rows must have at least len(df.columns) - 2 non-missing values to keep.

3. Step 3: Evaluate other options

   Filling with zero or backward fill changes data but does not drop rows. Dropping columns removes data unnecessarily.

4. Final Answer:

   Use df.dropna(thresh=len(df.columns)-2) to drop rows with more than 2 missing values -> Option D

5. Quick Check:

   dropna(thresh) keeps rows with enough non-NaNs [OK]

Hint: Use dropna(thresh) to keep rows with few missing values [OK]

Common Mistakes:

• Using fillna instead of dropping rows
• Dropping columns instead of rows
• Misunderstanding thresh parameter