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1.get_dummies ()

Pandas.get_dummies (data, prefix=None, prefix_sep='_', dummy_na=False, columns=None,sparse=False, drop_first=False): Convert categorical variable into dummy/indicator variables

> import pandas as pd > s = pd.Series (list ('abca')) > pd.get_dummies (s) ab c 0 1 01 01 02 0 0 13 1 0 02.pd.factorize ()

Pandas.factorize (values, sort=False, order=None, na_sentinel=-1,size_hint=None): Encode input values as an enumerated type or categorical variable

Series.factorize (sort=False, na_sentinel=-1): Encode the object as an enumerated type or categorical variable

Pandas has a method called factorize (), which creates numbers to represent category variables and maps an ID to each category, which ends up generating only one feature, unlike dummy.

Parameters:sort: boolean, default False

Sort by values

Na_sentinel: int, default-1

Value to mark "not found"

Returns:labels: the indexer to the original array

Uniques: the unique Index

Labels: the corresponding coded array

Uniques: the type that needs to be encoded

Supplement: the use and meaning of pandas.get_dummies

Get_dummies is the way to implement one hot encode using pandas.

The get_dummies parameters are as follows:

Pandas.get_dummies (data,prefix = None,prefix_sep ='_', dummy_na = False,columns = None,sparse = False,drop_first = False,dtype = None)

Data: array-like,Series or DataFrame

Prefix: string, string list or string dict. Default is None.

The string used to append the DataFrame column name. When calling get_dummies on DataFrame, pass a list with a length equal to the number of columns. Alternatively, a prefix can be a dictionary that maps column names to prefixes.

Prefix_sep: string, default is'_'

If a prefix is appended, the delimiter / delimiter should be used. Or pass the same list or dictionary as the prefix.

Dummy_na: bool, default is False

If you omit False NaN, add a column to indicate NaN.

Columns: similar list. Default is none.

The name of the column in the DataFrame to encode. If the column is None, then all D-type cells with column objects or categories will be converted.

Sparse: bool, default is False

Whether pseudo-coded columns should be supported by SparseArray (True) or regular NumPy array (False).

Drop_first: bool, default is False

Whether to get a kmurl dummy from the k classification level by deleting the first level.

New features in version 0.18.0.

Dtype: type D, default np.uint8

The data type of the new column. Only one dtype is allowed.

New features in version 0.23.0.

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