Missing data strategies decision in Pandas

Introduction

Sometimes data is incomplete or missing. We need to decide how to handle these gaps to keep our analysis accurate and useful.

When you find empty cells in a survey dataset.

When sensor data has gaps due to technical issues.

When customer information is partially missing in a sales database.

When cleaning data before building a machine learning model.

When summarizing data and missing values could affect results.

Syntax

Pandas

df.dropna()
df.fillna(value)
df.isna()
df.notna()

dropna() removes rows or columns with missing data.

fillna() replaces missing data with a value you choose.

Examples

Removes all rows that have any missing values.

Pandas

df.dropna()

Replaces all missing values with zero.

Pandas

df.fillna(0)

Fills missing ages with the average age.

Pandas

df['Age'].fillna(df['Age'].mean())

Removes columns that have any missing values.

Pandas

df.dropna(axis=1)

Sample Program

This code shows three ways to handle missing data: removing rows with missing values, filling missing values with zero, and filling missing ages with the average age.

Pandas

import pandas as pd

# Create a sample data frame with missing values
 data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
         'Age': [25, None, 30, None],
         'Score': [85, 90, None, 88]}

df = pd.DataFrame(data)

print('Original DataFrame:')
print(df)

# Strategy 1: Drop rows with any missing data
cleaned_drop = df.dropna()
print('\nDataFrame after dropna():')
print(cleaned_drop)

# Strategy 2: Fill missing values with a constant
filled_zero = df.fillna(0)
print('\nDataFrame after fillna(0):')
print(filled_zero)

# Strategy 3: Fill missing Age with mean age
mean_age = df['Age'].mean()
filled_mean = df.copy()
filled_mean['Age'] = filled_mean['Age'].fillna(mean_age)
print(f'\nMean Age: {mean_age}')
print('DataFrame after filling Age with mean:')
print(filled_mean)

Important Notes

Dropping rows can reduce data size and may lose important information.

Filling missing values keeps data size but may introduce bias if not chosen carefully.

Always consider the context and why data is missing before choosing a strategy.

Summary

Missing data can be handled by dropping or filling values.

Use dropna() to remove missing data rows or columns.

Use fillna() to replace missing data with meaningful values.

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