Dropping missing values with dropna() in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to drop rows with any missing values from the DataFrame.

Pandas

cleaned_df = df.[1]()

Drag options to blanks, or click blank then click option'

Afillna

Bisnull

Cdropna

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() instead of dropna()

Using isnull() which only checks for missing values

Using replace() which replaces values but does not drop rows

The dropna() method removes rows with missing values.

2fill in blank

medium

Complete the code to drop columns with any missing values from the DataFrame.

Pandas

cleaned_df = df.dropna(axis=[1])

Drag options to blanks, or click blank then click option'

A-1

B2

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 which drops rows instead of columns

Using axis=2 or -1 which are invalid for dropna()

Setting axis=1 drops columns with missing values.

3fill in blank

hard

Fix the error in the code to drop rows only if all values are missing.

Pandas

cleaned_df = df.dropna(how='[1]')

Drag options to blanks, or click blank then click option'

Aany

Ball

Cnone

Dsome

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'any' which drops rows with any missing value

Using invalid strings like 'none' or 'some'

Using how='all' drops rows only if all values are missing.

4fill in blank

hard

Fill both blanks to drop rows with less than 3 non-missing values.

Pandas

cleaned_df = df.dropna(thresh=[1], axis=[2])

Drag options to blanks, or click blank then click option'

A3

B2

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using thresh less than 3 which drops too few rows

Using axis=1 which drops columns instead of rows

The thresh=3 keeps rows with at least 3 non-missing values, and axis=0 means rows.

5fill in blank

hard

Fill all three blanks to drop columns with less than 2 non-missing values and reset the index.

Pandas

cleaned_df = df.dropna(thresh=[1], axis=[2]).[3](drop=True)

Drag options to blanks, or click blank then click option'

A1

B2

Dreset_index

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 which drops rows instead of columns

Not resetting the index after dropping rows or columns

Using incorrect thresh value

Use thresh=2 to keep columns with at least 2 non-missing values, axis=1 to drop columns, and reset_index(drop=True) to reset the index without adding the old index as a column.

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