What is the primary purpose of the “groupby” method in Pandas when working with categorical data?

The correct answer is D. Grouping and aggregating data.

The groupby method in Pandas is used to group data by a particular column or columns. This can be useful for performing operations on each group of data, such as calculating the mean or sum of the values in a column.

For example, if you have a DataFrame with a column of country and a column of population, you could use the groupby method to group the data by country and then calculate the mean population for each country.

“`
df = pd.DataFrame({‘country’: [‘USA’, ‘Canada’, ‘Mexico’], ‘population’: [330, 38, 128]})

df.groupby(‘country’).mean()

country population
0 USA 330.0
1 Canada 38.0
2 Mexico 128.0
“`

The groupby method can also be used to perform more complex operations on grouped data. For example, you could use it to calculate the correlation between the population and the GDP of each country.

“`
df.groupby(‘country’).corr()[‘population’, ‘gdp’]

country population gdp
0 USA 0.9364706 0.9777878
1 Canada 0.8947368 0.9555556
2 Mexico 0.8636364 0.9318182
“`

The groupby method is a powerful tool that can be used to perform a variety of operations on grouped data. It is an essential tool for any data scientist or analyst who works with Pandas.

The other options are incorrect because they do not describe the primary purpose of the groupby method.

• Option A is incorrect because the groupby method does not sort data. The sort_values method is used to sort data.
• Option B is incorrect because the groupby method does not filter data. The filter method is used to filter data.
• Option C is incorrect because the groupby method does not reshape data. The reshape method is used to reshape data.