pandas for Python - A Quick Guide - Data Transformation

This video tutorial covers the conversion of data to categorical data types using pandas. It explains how to convert string data to categories and discretize continuous variables. The tutorial highlights the advantages of using categorical data, such as reduced memory usage and enabling comparison operations. It also demonstrates the process of discretizing continuous variables into categorical variables using the pandas cut function.

What is one of the main advantages of converting string data to categorical data types in pandas?

• Enhances data visualization

• Improves data accuracy

• Reduces memory usage

• Increases the number of unique values

Which method is used to find the frequency of each label in a column?

• frequency()

• count_values()

• value_counts()

• label_count()

How can you convert a column to a categorical data type in pandas?

• Using pd.Categorical()

• Using pd.to_category()

• Using pd.to_categorical()

• Using pd.convert_to_category()

What function is used to create a series with random labels in pandas?

• random.choices()

• random.choice()

• random.select()

• random.pick()

How can you ensure the order of categories in a categorical column?

• By setting the 'order' parameter to True

• By using the 'sort' function

• By setting the 'ordered' parameter to True

• By using the 'arrange' function

What is discretization in the context of data preprocessing?

• Converting continuous variables into categorical variables

• Converting categorical data to numerical data

• Converting text data to numerical data

• Converting numerical data to text data

Which function is used to discretize a continuous variable into categories in pandas?

• pd.segment()

• pd.divide()

• pd.split()

• pd.cut()