Dropping missing values (dropna) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the dropna() function do in data analysis?

The dropna() function removes rows or columns that contain missing values (NaN) from a dataset, helping to clean the data for analysis.

beginner

How can you use dropna() to remove columns with missing values instead of rows?

You can use dropna(axis=1) to remove columns that have any missing values instead of rows.

intermediate

What does the parameter how='all' do in dropna()?

The parameter how='all' tells dropna() to drop only those rows or columns where all values are missing.

intermediate

What is the effect of dropna(thresh=2) on a DataFrame?

It keeps only rows or columns with at least 2 non-missing values, dropping those with fewer than 2 valid entries.

beginner

Why is it important to drop missing values before analysis?

Missing values can cause errors or misleading results in calculations and models, so dropping them helps ensure accurate and reliable analysis.

What does df.dropna() do by default?

A Fills missing values with zero

B Drops columns with any missing values

C Drops rows with any missing values

D Keeps all rows and columns

dropna() removes rows that have at least one missing value by default.

How do you drop columns with missing values using dropna()?

A df.dropna(thresh=1)

B df.dropna(axis=0)

C df.dropna(how='all')

D df.dropna(axis=1)

Setting axis=1 tells dropna() to drop columns instead of rows.

What does dropna(how='all') do?

A Drops rows or columns where all values are missing

B Drops rows or columns with any missing value

C Drops no rows or columns

D Fills missing values with the mean

The how='all' option drops only rows or columns where every value is missing.

What does the thresh parameter control in dropna()?

A Minimum number of non-missing values required to keep the row/column

B Maximum number of missing values allowed

C Whether to drop rows or columns

D The value to replace missing data

thresh sets the minimum count of non-NaN values needed to keep a row or column.

Why might you choose to drop missing values instead of filling them?

A Because missing values are always errors

B To avoid introducing bias from incorrect guesses

C Because filling is always slower

D To increase dataset size

Dropping missing values avoids adding potentially wrong data that could bias results.

Explain how to use dropna() to clean a dataset by removing rows or columns with missing values.

Think about the axis and how parameters.

Describe why handling missing data is important before doing data analysis.

Consider the impact of missing values on results.

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