Python for Data Analysis: Step-By-Step with Projects - Tackling Missing Data (Dropping)

The video tutorial covers methods to handle missing data in dataframes using the pandas library. It explains how to drop rows or columns with missing values using the dropna method. The tutorial also discusses the use of the 'thresh' parameter to control the number of non-missing values required to retain rows or columns. The video emphasizes the trade-offs of dropping data and suggests imputation as an alternative strategy for handling missing data.

What is the default behavior of the dropna method in pandas?

• It drops columns with any missing values.

• It drops rows with any missing values.

• It does nothing to missing values.

• It fills missing values with zeros.

How does the 'thresh' parameter affect the dropna method?

• It changes the method to fill missing values instead of dropping them.

• It specifies the minimum number of non-missing values required in a row.

• It specifies the maximum number of missing values allowed in a row.

• It only applies to columns, not rows.

What happens to rows with more missing values than the 'thresh' parameter allows?

• They are moved to a separate dataframe.

• They are dropped from the dataframe.

• They are filled with zeros.

• They are kept in the dataframe.

When using dropna with axis=1, what is being removed?

• Rows with missing values.

• Columns with missing values.

• All missing values are filled.

• The entire dataframe is reset.

What does setting the 'thresh' parameter to 300 do when dropping columns?

• Drops columns with more than 300 missing values.

• Keeps columns with at least 300 non-missing values.

• Drops columns with less than 300 non-missing values.

• Keeps columns with at least 300 missing values.

What is a potential downside of dropping rows or columns with missing data?

• It automatically fills missing values with averages.

• It can lead to data duplication.

• It increases the size of the dataset.

• It may result in loss of valuable information.

What alternative method is suggested for handling missing data instead of dropping?

• Data visualization

• Data imputation

• Data duplication

• Data encryption