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.