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]()

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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])

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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]')

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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])

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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

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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.