Dropping missing values with dropna() in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the dropna() function do in pandas?

The dropna() function removes rows or columns that contain missing values (NaN) from a DataFrame or Series.

beginner

How do you drop rows with any missing values in a DataFrame named df?

Use df.dropna(). This removes all rows that have at least one missing value.

beginner

What parameter do you use with dropna() to drop columns instead of rows?

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

intermediate

How can you drop rows only if all values are missing?

Use how='all' in dropna(), like df.dropna(how='all'). This keeps rows with at least one value.

intermediate

What does the thresh parameter do in dropna()?

thresh sets the minimum number of non-missing values required to keep a row or column. For example, df.dropna(thresh=2) keeps rows with at least 2 non-NaN values.

What is the default behavior of df.dropna()?

A Drops columns only if all values are missing

B Drops rows with any missing values

C Drops rows only if all values are missing

D Drops columns with any missing values

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

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

A Use <code>axis=1</code>

B Use <code>axis=0</code>

C Use <code>how='all'</code>

D Use <code>thresh=1</code>

Setting axis=1 tells pandas to drop columns instead of rows.

Which parameter keeps rows that have at least one non-missing value?

A <code>how='all'</code>

B <code>how='any'</code>

C <code>thresh=0</code>

D <code>axis=1</code>

how='all' drops rows only if all values are missing, so it keeps rows with at least one value.

What does df.dropna(thresh=3) do?

A Drops columns with fewer than 3 missing values

B Drops rows with more than 3 missing values

C Drops columns with exactly 3 missing values

D Drops rows with fewer than 3 non-missing values

thresh=3 means keep rows with at least 3 non-NaN values; others are dropped.

If you want to remove rows only when all columns are missing, which dropna() call is correct?

A <code>df.dropna(how='any')</code>

B <code>df.dropna(axis=1)</code>

C <code>df.dropna(how='all')</code>

D <code>df.dropna(thresh=1)</code>

how='all' drops rows only if every value is missing.

Explain how you would use dropna() to clean a dataset by removing rows with missing data. Include how to keep rows with at least some data.

Think about when to drop rows and when to keep them based on missing data.

Describe the role of the thresh parameter in dropna() and give an example of its use.

It controls how strict the dropping is based on data completeness.

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