describe() method generates a high-level summary of the attributes of the given column. It is type-aware, meaning that its output changes based on the data type of the input. The output above only makes sense for numerical data
To see the mean of the points allotted (e.g. how well an averagely rated wine does), we can use the mean() function
To see a list of unique values we can use the unique() function
To see a list of unique values and how often they occur in the dataset, we can use the value_counts() method
In data science we often have a need for creating new representations from existing data, or for transforming data from the format it is in now to the format that we want it to be in later. There are two mapping methods that you will use often
map() should expect a single value from the Series (a point value, in the above example), and return a transformed version of that value. map() returns a new Series where all the values have been transformed by your function
Summary functions
describe()method generates a high-level summary of the attributes of the given column. It is type-aware, meaning that its output changes based on the data type of the input. The output above only makes sense for numerical datamean()functionunique()functionvalue_counts()methodMaps
map()should expect a single value from the Series (a point value, in the above example), and return a transformed version of that value. map() returns a new Series where all the values have been transformed by your function