Python And sklearn-pandas：sklearn-pandas Introduction to library functions 、 install 、 A detailed introduction to how to use

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sklearn-pandas Introduction to library functions

sklearn-pandas Installation of library functions

sklearn-pandas How to use library functions

1、 Basic usage

2、 Case application

sklearn-pandas Introduction to library functions

          sklearn-pandas The module provides Scikit-Learn Machine learning methods and pandas The bridge between style data frameworks . especially , It provides a DataFrame The method of column mapping to transformation , These transformations will be recombined later into features .

sklearn-pandas Installation of library functions

pip install sklearn-pandas
pip install --user -i https://pypi.tuna.tsinghua.edu.cn/simple sklearn-pandas

sklearn-pandas How to use library functions

1、 Basic usage

>>> from sklearn_pandas import DataFrameMapper
>>> import pandas as pd
>>> import numpy as np
>>> import sklearn.preprocessing, sklearn.decomposition, \
... sklearn.linear_model, sklearn.pipeline, sklearn.metrics
>>> from sklearn.feature_extraction.text import CountVectorizer
>>> mapper = DataFrameMapper([
... ('pet', sklearn.preprocessing.LabelBinarizer()),
... (['children'], sklearn.preprocessing.StandardScaler())
... ])

2、 Case application

>>> from sklearn.base import TransformerMixin
>>> class DateEncoder(TransformerMixin):
... def fit(self, X, y=None):
... return self
...
... def transform(self, X):
... dt = X.dt
... return pd.concat([dt.year, dt.month, dt.day], axis=1)
>>> dates_df = pd.DataFrame(
... {'dates': pd.date_range('2015-10-30', '2015-11-02')})
>>> mapper_dates = DataFrameMapper([
... ('dates', DateEncoder())
... ], input_df=True)
>>> mapper_dates.fit_transform(dates_df)
array([[2015, 10, 30],
[2015, 10, 31],
[2015, 11, 1],
[2015, 11, 2]])