Practical Data Science using Python - Pandas DataFrame 6

The video tutorial covers handling missing values in data frames using the fillna function, validating data operations, and performing data aggregation with group by and aggregation functions. It also explores advanced data filtering using Lambda functions and demonstrates creating pivot tables for data analysis. The tutorial emphasizes the importance of choosing appropriate strategies based on business needs and data characteristics.

What function is used to replace missing values in a data frame with specified values?

• replace

• fillna

• interpolate

• dropna

When is it advisable to delete rows with missing values?

• When the data set is very large

• When there are only a few rows with missing values

• When the missing values are in important columns

• When there are many missing values

What should you do to ensure the correctness of operations on a data frame?

• Check for null values before and after the operation

• Use a fixed value for replacement

• Always use the fillna function

• Delete all rows with missing values

Which dataset is used to demonstrate grouping and aggregation in the video?

• Gapminder Data

• Census Data

• World Bank Data

• Kaggle Data

What is the purpose of using the group by function in data frames?

• To merge multiple data frames

• To filter the data

• To sort the data

• To perform aggregate functions on grouped data

What does the size function return when used with group by?

• The average size of each group

• The number of records in each group

• The total number of groups

• The total number of columns

What is a pivot table used for in data frames?

• To visualize data

• To create a summary of data with aggregate functions

• To merge data frames

• To clean data

Which function is used to apply multiple aggregation functions on a grouped data frame?

• apply

• transform

• agg

• map

What is the role of a Lambda function in filtering data?

• To visualize data

• To create unnamed functions for simple operations

• To perform complex calculations

• To name a function

Which of the following is NOT a statistical operation mentioned for aggregation?

• Correlation

• Variance

• Median

• Mean