Python机器学习与深度学习之二：数据预处理

最新推荐文章于 2025-03-29 15:50:55 发布

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最新推荐文章于 2025-03-29 15:50:55 发布

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分类专栏： Python机器学习与深度学习

本文链接：https://blog.youkuaiyun.com/fjqlldg/article/details/110260932

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本文介绍了使用Python进行数据预处理的方法，包括最值化调整、标准化、正态化及二值化等常见操作，并展示了如何利用sklearn库实现这些转换。

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一、最值化调整数据

import pandas as pd
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler

iris  = datasets.load_iris()

names = ['separ-length','separ-width','petal-length','petal-width','class']
data  = pd.read_csv(r'iris.csv',names = names)

array = data.values
X     = array[:,0:4]
Y     = array[:,4]
transformer = MinMaxScaler(feature_range=(0,1))

newX  = transformer.fit_transform(X)
np.set_printoptions(precision = 3)

print(newX)

运行结果：

二、标准化数据

import pandas as pd
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler

iris  = datasets.load_iris()

names = ['separ-length','separ-width','petal-length','petal-width','class']
data  = pd.read_csv(r'iris.csv',names = names)

array = data.values
X     = array[:,0:4]
Y     = array[:,4]
transformer = StandardScaler().fit(X)

newX  = transformer.transform(X)
np.set_printoptions(precision = 3)

print(newX)

三、正态化（归一化）数据

import pandas as pd
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.preprocessing import Normalizer

iris  = datasets.load_iris()

names = ['separ-length','separ-width','petal-length','petal-width','class']
data  = pd.read_csv(r'iris.csv',names = names)

array = data.values
X     = array[:,0:4]
Y     = array[:,4]
transformer = Normalizer().fit(X)

newX  = transformer.transform(X)
np.set_printoptions(precision = 3)

print(newX)

四、二值化数据

import pandas as pd
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.preprocessing import Binarizer

iris  = datasets.load_iris()

names = ['separ-length','separ-width','petal-length','petal-width','class']
data  = pd.read_csv(r'iris.csv',names = names)

array = data.values
X     = array[:,0:4]
Y     = array[:,4]
transformer = Binarizer(threshold = 0.0).fit(X)

newX  = transformer.transform(X)
np.set_printoptions(precision = 3)

print(newX)

运行结果：