Dropping missing values (dropna) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

cleaned_df = df.[1]()

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

Adropna

Bisnull

Cfillna

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna instead of dropna, which fills missing values instead of dropping rows.

Using isnull, which only checks for missing values but does not remove them.

The dropna() method removes rows with missing values from the DataFrame.

2fill in blank

medium

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

Data Analysis Python

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

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

A0

B2

C1

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 which drops rows instead of columns.

Using invalid axis values like 2 or -1.

Setting axis=1 tells dropna() to drop columns with missing values.

3fill in blank

hard

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

Data Analysis Python

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

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

Aall

Bany

Cnone

Dsome

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'any' which drops rows if any column is missing.

Using invalid strings like 'none' or 'some'.

The how='all' option drops rows only if all values are missing in that row.

4fill in blank

hard

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

Data Analysis Python

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 keeps rows with fewer values.

Using axis=1 which drops columns instead of rows.

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

5fill in blank

hard

Fill all three blanks to drop columns with more than 2 missing values.

Data Analysis Python

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

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

Adf.shape[0] - 2

B1

CTrue

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using thresh as a fixed number instead of based on DataFrame shape.

Using axis=0 which drops rows instead of columns.

Setting inplace=False which does not modify original DataFrame.

thresh=df.shape[0] - 2 keeps columns with at most 2 missing values. axis=1 means columns. inplace=True modifies the original DataFrame.

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