pandas for Python - A Quick Guide - Handling Missing Values and Duplicates

The video tutorial covers data preprocessing, focusing on handling missing data and duplicates in datasets. It explains how to identify missing values and duplicates using pandas, and demonstrates methods to address these issues, such as removing or replacing missing values and eliminating duplicate rows. The tutorial uses the Titanic dataset for practical examples, highlighting the importance of data cleaning in data analysis.

What is the first step in a data analysis project?

• Data visualization

• Data collection

• Data modeling

• Data preprocessing

Which method can be used to check for missing values in a pandas DataFrame?

• isnull()

• dropna()

• fillna()

• duplicated()

What is a potential downside of removing rows with missing values?

• It can lead to data duplication

• It can result in loss of valuable data

• It can increase data processing time

• It can introduce new missing values

How can missing values in a numerical column be replaced?

• With the median value

• With the average value

• With the maximum value

• With the minimum value

What method is used to replace missing values with a specific value in pandas?

• interpolate()

• dropna()

• fillna()

• replace()

Which method helps in identifying duplicate rows in a DataFrame?

• fillna()

• duplicated()

• isnull()

• dropna()

What is the purpose of the drop_duplicates() method in pandas?

• To replace missing values

• To remove missing values

• To remove duplicate rows

• To sort the DataFrame