做企业网站的,永川做网站的公司,网站的pdf目录怎么做的,专业的网站制作正规公司EDA(Exploratory Data Analysis)中文名称为探索性数据分析#xff0c;是为了在特征工程或模型开发之前对数据有个基本的了解。数据类型通常分为两类#xff1a;连续类型和离散类型#xff0c;特征类型不同#xff0c;我们探索的内容也不同。
1. 特征类型
1.1 连续型特征 …EDA(Exploratory Data Analysis)中文名称为探索性数据分析是为了在特征工程或模型开发之前对数据有个基本的了解。数据类型通常分为两类连续类型和离散类型特征类型不同我们探索的内容也不同。
1. 特征类型
1.1 连续型特征
定义取值为数值类型且数值之间的大小具有实际含义。例如收入。对于连续型变量需要进行EDA的内容包括
缺失值均值方差标准差最大值最小值中位数众数四分位数偏度最大取值类别对应的样本数
1.2 离散型特征
定义不具有数学意义的特征。如性别。对于离散型变量需要进行EDA的内容包括
缺失值众数取值个数最大取值类别对应的样本数每个取值对应的样本数
2. EDA目的
通过EDA需要达到以下几个目的
1可以有效发现变量类型、分布趋势、缺失值、异常值等。
2缺失值处理i删除缺失值较多的列通常缺失超过50%的列需要删除ii缺失值填充。对于离散特征通常将NAN单独作为一个类别对于连续特征通常使用均值、中值、0或机器学习算法进行填充。具体填充方法因业务的不同而不同。
3异常值处理主要针对连续特征。如Winsorizer方法处理。
4类别合并主要针对离散特征。如果某个取值对应的样本个数太少就需要将该取值与其他值合并。因为样本过少会使数据的稳定性变差且不具有统计意义可能导致结论错误。由于展示空间有限通常选择取值个数最少或最多的多个取值进行展示。
5删除取值单一的列。
6删除最大类别取值数量占比超过阈值的列。
3.实验
3.1 统计变量类型、分布趋势、缺失值、异常值等
#!/usr/bin/pythonimport pandas as pd
import numpy as npdef getTopValues(series, top 5, reverse False):Get top/bottom n valuesArgs:series (Series): data seriestop (number): number of top/bottom n valuesreverse (bool): it will return bottom n values if True is givenReturns:Series: Series of top/bottom n values and percentage. [value:percent, None]itype topcounts series.value_counts()counts list(zip(counts.index, counts, counts.divide(series.size)))if reverse:counts.reverse()itype bottomtemplate {0[0]}:{0[2]:.2%}indexs [itype str(i 1) for i in range(top)]values [template.format(counts[i]) if i len(counts) else None for i in range(top)]return pd.Series(values, index indexs)def getDescribe(series, percentiles [.25, .5, .75]):Get describe of seriesArgs:series (Series): data seriespercentiles: the percentiles to include in the outputReturns:Series: the describe of data include mean, std, min, max and percentilesd series.describe(percentiles)return d.drop(count)def countBlank(series, blanks []):Count number and percentage of blank values in seriesArgs:series (Series): data seriesblanks (list): list of blank valuesReturns:number: number of blanksstr: the percentage of blank valuesif len(blanks)0:isnull series.replace(blanks, None).isnull()else:isnull series.isnull()n isnull.sum()ratio isnull.mean()return (n, {0:.2%}.format(ratio))def isNumeric(series):Check if the seriess type is numericArgs:series (Series): data seriesReturns:boolreturn series.dtype.kind in ifcdef detect(dataframe): Detect dataArgs:dataframe (DataFrame): data that will be detectedReturns:DataFrame: report of detectingnumeric_rows []category_rows []for name, series in dataframe.items():# 缺失值比例nblank, pblank countBlank(series)# 最大类别取值占比biggest_category_percentage series.value_counts(normalizeTrue, dropnaFalse).values[0] * 100if isNumeric(series):desc getDescribe(series,percentiles[.01, .1, .5, .75, .9, .99])details desc.tolist()details_index [mean, std, min, 1%, 10%, 50%, 75%, 90%, 99%, max]row pd.Series(index[type, size, missing, unique, biggest_category_percentage, skew] details_index,data[series.dtype, series.size, pblank, series.nunique(), biggest_category_percentage, series.skew()] details)row.name namenumeric_rows.append(row)else:top5 getTopValues(series)bottom5 getTopValues(series, reverseTrue)details top5.tolist() bottom5[::-1].tolist()details_index [top1, top2, top3, top4, top5, bottom5, bottom4, bottom3, bottom2, bottom1]row pd.Series(index[type, size, missing, unique, biggest_category_percentage] details_index,data[series.dtype, series.size, pblank, series.nunique(), biggest_category_percentage] details)row.name namecategory_rows.append(row)return pd.DataFrame(numeric_rows), pd.DataFrame(category_rows)
demo(数据来自https://www.kaggle.com/competitions/home-credit-default-risk/data
import os
import eda
import pandas as pd
import numpy as npdata_dir ./df pd.read_csv(os.path.join(data_dir, bureau.csv))
numeric_df, category_df eda.detect(df)3.2 缺失值处理示例
#连续特征
df[col].fillna(-df[col].mean(), inplaceTrue)
#离散特征
df[col].fillna(nan, inplaceTrue)3.3 删除无用特征
def get_del_columns(df):del_columns {}for index, row in df.iterrows():if row[unique] 2:del_columns[row[Feature]] 取值单一continueif row[missing] 90:del_columns[row[Feature]] 缺失值数量大于90%continueif row[biggest_category_percentage] 99:del_columns[row[Feature]] 取值最多的类别占比超过99%continuedel_columns[row[Feature]] 正常return del_columns3.4 异常值处理
Winsorizer算法定义某个变量的上界和下界取值超过边界的话会用边界的值取代
class Winsorizer():Performs Winsorization 1-1*Warning: this class should not be used directly. def __init__(self,trim_quantile0.0):self.trim_quantiletrim_quantileself.winsor_limsNonedef train(self,X):# get winsor limitsself.winsor_limsnp.ones([2,X.shape[1]])*np.infself.winsor_lims[0,:]-np.infif self.trim_quantile0:for i_col in np.arange(X.shape[1]):lowernp.percentile(X[:,i_col],self.trim_quantile*100)uppernp.percentile(X[:,i_col],100-self.trim_quantile*100)self.winsor_lims[:,i_col][lower,upper]def trim(self,X):X_X.copy()X_np.where(Xself.winsor_lims[1,:],np.tile(self.winsor_lims[1,:],[X.shape[0],1]),np.where(Xself.winsor_lims[0,:],np.tile(self.winsor_lims[0,:],[X.shape[0],1]),X))return X_winsorizer Winsorizer (0.1)
anp.random.random((10,2))
print(转化前: , a)
winsorizer.train(a)
print(上界和下界: , winsorizer.winsor_lims)
b winsorizer.trim(a)
print(转化后: , b)4.总结
这篇文章只总结了EDA的常用做法实际应用过程中还需要根据具体业务来做调整。
