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Why discretization?

The purpose of continuous attribute discretization is to simplify the data structure. Data discretization technology can be used to reduce the number of given continuous attribute values. Discretization method is often used as a tool for data mining.

Throwing away some information can make the model more robust and have stronger generalization ability.

What is the discretization of data

The discretization of continuous attributes is to divide the range of continuous attributes into several discrete intervals, and finally use different symbols or integer values to represent the attribute values that fall in each sub-interval.

Separate box

Case

1. First read the stock data and filter out the p_change data

Data = pd.read_csv (". / data/stock_day.csv") pairchange= data ['pairchange']

two。 Grouping stock rise and fall data

Tools used:

Pd.qcut (data, bins)-isobaric sub-box:

Grouping data groups will generally be used in conjunction with value_counts to count the number of each group

Series.value_counts (): count the number of grouping

# self-grouping qcut = pd.qcut (p_change, 10) # calculate the number of data allocated to each group qcut.value_counts ()

Pd.cut (data, bins)-equal width box:

# specify grouping interval bins = [- 100,7,-5,-3,0,3,5,7100] p_counts = pd.cut (p_change, bins)

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