这篇文章给大家介绍怎么在Python中实现数据规范化,内容非常详细,感兴趣的小伙伴们可以参考借鉴,希望对大家能有所帮助。
Python主要应用领域有哪些
1,云计算,典型应用OpenStack.2,网前端开发,众多大型网站均为Python开发。3。人工智能应用,基于大数据分析和深度学习而发展出来的人工智能本质上已经无法离开python.4,系统运维工程项目,自动化运维的标配就是python + Django/flask.5,金融理财分析,量化交易,金融分析。6,大数据分析。
<强>数据规范化强>
为了消除指标之间的量纲和取值范围差异的影响,需要进行标准化(归一化)处理,将数据按照比例进行缩放,使之落入一个特定的区域,便于进行综合分析。
数据规范化方法主要有:
——最小——最大规范化
-零均值规范化
<强>数据示例强>
<强>代码实现强>
# - *安康;编码:utf-8 - * - #数据规范化 import pandas  as pd import numpy  as np 时间=datafile & # 39; normalization_data.xls& # 39;, #参数初始化 data =, pd.read_excel(丢失,header =, None), #读取数据 (data 作用;data.min ())/(data.max(),安康;data.min()), #最小——最大规范化 (data 作用;data.mean ())/data.std(), #零-均值规范化
从命令行可以看到下面的输出:
祝辞祝辞祝辞(data-data.min ())/(data.max () -data.min (
,,,,,,,,,0,,,,,,,,1,,,,,,,,2,,,,,,,,3
0,0.074380,0.937291,0.923520,1.000000
1,0.619835,0.000000,0.000000,0.850941
2,0.214876,0.119565,0.813322,0.000000
3,0.000000,1.000000,1.000000,0.563676
4,1.000000,0.942308,0.996711,0.804149
5,0.264463,0.838629,0.814967,0.909310
6,0.636364,0.846990,0.786184,0.929571祝辞祝辞祝辞(data-data.mean ())/data.std ()
引用>
,,,,,,,,,0,,,,,,,,1,,,,,,,,2,,,,,,,,3
0 -0.905383,0.635863,0.464531,0.798149
1,0.604678 -1.587675 -2.193167,0.369390
2 -0.516428 - -1.304030,-1.111301 0.147406 -2.078279
3,0.784628,0.684625 - -0.456906
4,1.657146,0.647765,0.675159,0.234796
5 -0.379150,0.401807,0.152139,0.537286
6,0.650438,0.421642,0.069308,0.595564上述代码改为使用<代码> 代码>打印语句打印,如下:
# - *安康;编码:utf-8 - * - #数据规范化 import pandas  as pd import numpy  as np 时间=datafile & # 39; normalization_data.xls& # 39;, #参数初始化 data =, pd.read_excel(丢失,header =, None), #读取数据 打印(data 安康;data.min ())/(data.max(),安康;data.min())), #最小——最大规范化 打印(data 安康;data.mean ())/data.std()), #零-均值规范化可输出如下打印结果:
,,,,,,,,,0,,,,,,,,1,,,,,,,,2,,,,,,,,3
引用>
0,0.074380,0.937291,0.923520,1.000000
1,0.619835,0.000000,0.000000,0.850941
2,0.214876,0.119565,0.813322,0.000000
3,0.000000,1.000000,1.000000,0.563676
4,1.000000,0.942308,0.996711,0.804149
5,0.264463,0.838629,0.814967,0.909310
6,0.636364,0.846990,0.786184,0.929571
,,,,,,,,,0,,,,,,,,1,,,,,,,,2,,,,,,,,3
0 -0.905383,0.635863,0.464531,0.798149
1,0.604678 -1.587675 -2.193167,0.369390
2 -0.516428 - -1.304030,-1.111301 0.147406 -2.078279
3,0.784628,0.684625 - -0.456906
4,1.657146,0.647765,0.675159,0.234796
5 -0.379150,0.401807,0.152139,0.537286
6,0.650438,0.421642,0.069308,0.595564关于怎么在Python中实现数据规范化就分享到这里了,希望以上内容可以对大家有一定的帮助,可以学到更多知识。如果觉得文章不错,可以把它分享出去让更多的人看的到。
怎么在Python中实现数据规范化