Categorical Variables

Question 1

What is the .value_counts()

Answer 1

the function returns an object containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element.

dataframe[‘column’]= dateframe[‘column’].value.counts()

Question 2

What is the .str.strip()?

Answer 2

Removes the white space characters

Question 3

What is qcut()?

Answer 3

“Quantile-based discretization function.” This basically means that .qcut() tries to divide up the underlying data into equal-sized bins. The function defines the bins using percentiles based on the distribution of the data

Question 4

How do you collapse data categories?

Answer 4

1. create the range: range=
2. Create names
3. dataframe[‘column’]= pd.cut(dataframe[‘other cloumn’}, bins= range, labels= names)
4. dataframe[[‘column’, ‘other colum]]

Question 5

What is the pd.cut()?

Answer 5

cut() function is used to separate the array elements into different bins . The cut function is mainly used to perform statistical analysis on scalar data

Question 6

How do you map fewer categories?

Answer 6

1. Create a mapping dictionary
2. dataframe[‘column’]= dataframe[‘column’].replace(mapping)
3. dataframe[‘column’].unique()

Question 7

what is unique()?

Answer 7

the function is used to find the unique elements of an array. Returns the sorted unique elements of an array.