EDA(Exploratory Data Analysis)探索性数据分析_数据探索与分析eda

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/python

import pandas as pd
import numpy as np

def getTopValues(series, top = 5, reverse = False):
    """Get top/bottom n values

    Args:
        series (Series): data series
        top (number): number of top/bottom n values
        reverse (bool): it will return bottom n values if True is given

    Returns:
        Series: Series of top/bottom n values and percentage. ['value:percent', None]
    """
    itype = 'top'
    counts = series.value_counts()
    counts = list(zip(counts.index, counts, counts.divide(series.size)))

    if reverse:
        counts.reverse()
        itype = 'bottom'

    template = "{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 series

    Args:
        series (Series): data series
        percentiles: the percentiles to include in the output

    Returns:
        Series: the describe of data include mean, std, min, max and percentiles
    """
    d = series.describe(percentiles)
    return d.drop('count')


def countBlank(series, blanks = []):
    """Count number and percentage of blank values in series

    Args:
        series (Series): data series
        blanks (list): list of blank values

    Returns:
        number: number of blanks
        str: the percentage of blank values
    """
    if 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 series's type is numeric

    Args:
        series (Series): data series

    Returns:
        bool
    """
    return series.dtype.kind in 'ifc'

def detect(dataframe):
    """ Detect data

    Args:
        dataframe (DataFrame): data that will be detected

    Returns:
        DataFrame: report of detecting
    """
    numeric_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(normalize=True, dropna=False).values[0] * 100
        if 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 = name
            numeric_rows.append(row)
        else:
            top5 = getTopValues(series)
            bottom5 = getTopValues(series, reverse=True)
            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 = name
            category_rows.append(row)
    return pd.DataFrame(numeric_rows), pd.DataFrame(category_rows)

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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 np

data_dir = "./"

df = pd.read_csv(os.path.join(data_dir, "bureau.csv"))
numeric_df, category_df = eda.detect(df)
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3.2 缺失值处理（示例）

#连续特征
df[col].fillna(-df[col].mean(), inplace=True)
#离散特征
df[col].fillna('nan', inplace=True)
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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"
            continue
        if row["missing"] > 90:
            del_columns[row["Feature"]] = "缺失值数量大于90%"
            continue
        if row["biggest_category_percentage"] > 99:
            del_columns[row["Feature"]] = "取值最多的类别占比超过99%"
            continue
        del_columns[row["Feature"]] = "正常"
    return del_columns
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3.4 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_quantile=0.0):
        self.trim_quantile=trim_quantile
        self.winsor_lims=None
        
    def train(self,X):
        # get winsor limits
        self.winsor_lims=np.ones([2,X.shape[1]])*np.inf
        self.winsor_lims[0,:]=-np.inf
        if self.trim_quantile>0:
            for i_col in np.arange(X.shape[1]):
                lower=np.percentile(X[:,i_col],self.trim_quantile*100)
                upper=np.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(X>self.winsor_lims[1,:],np.tile(self.winsor_lims[1,:],[X.shape[0],1]),np.where(X<self.winsor_lims[0,:],np.tile(self.winsor_lims[0,:],[X.shape[0],1]),X))
        return X_
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winsorizer = Winsorizer (0.1)
a=np.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)
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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。