pandas is very easy to implement, just convert it into Categories objects. The terms are called factors and levels, and levels are usually automatically converted to numerical storage.

c = ['A','A','A','B','B','C','C','C','C']
category = pd.Categorical(c)

Next, check the label of the category

print category.labels

There are ready-made ones in sklearn:

preprocessing.LabelEncoder().fit_transform(data)

See official documentation for details

You can directly convert between characters and numbers

I have never used it in practice. I don’t know if the map function can meet your needs. Please refer to the documentation for details
http://pandas.pydata.org/pand...

This is just 映射 logic. There is no need to use pandas and scikit-learn. It’s overkill and overkill

a = ['A','A','A','B','B','C','C','C','C']
result = [x for x in map(lambda c: ord(c) - ord('A'), a)]

If you have to use pandas, then isn’t this exactly Series

import pandas as pd
a = ['A','A','A','B','B','C','C','C','C']
result = pd.Series(a).map(lambda c: ord(c) - ord('A'))