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Python數(shù)據(jù)預(yù)處理時缺失值的不同處理方式總結(jié)

更新時間：2022年12月22日 08:15:23 作者：Python 集中營

在使用python做數(shù)據(jù)分析的時候，經(jīng)常需要先對數(shù)據(jù)做統(tǒng)一化的處理，缺失值的處理是經(jīng)常會使用到的。今天介紹的是使用差補法/均值/固定值等不同的方式完成數(shù)據(jù)填充從而保證數(shù)據(jù)的完整性，感興趣的可以了解一下

#?Importing?the?pandas?module?and?giving?it?the?alias?pd.
import?pandas?as?pd

#?Reading?the?excel?file?and?storing?it?in?a?dataframe.
data_frame?=?pd.read_excel('D:/test-data-work/data.xlsx')

#?Printing?the?dataframe.
print(data_frame)

#???????????姓名??????年齡??????班級?????成績???表現(xiàn)
#?0???Python?集中營??10.0??1210.0???99.0????A
#?1???Python?集中營??11.0??1211.0??100.0????A
#?2???Python?集中營??12.0??1212.0??101.0????A
#?3???Python?集中營??13.0??1213.0??102.0????A
#?4???Python?集中營??14.0??1214.0??103.0??NaN
#?5???Python?集中營??15.0??1215.0??104.0????A
#?6???Python?集中營??16.0??1216.0??105.0????A
#?7???Python?集中營??17.0?????NaN??106.0????A
#?8???Python?集中營??18.0??1218.0????NaN????A
#?9???Python?集中營??19.0??1219.0??108.0????A
#?10??Python?集中營???NaN??1220.0??109.0??NaN
#?11??Python?集中營???NaN?????NaN??110.0????A
#?12??Python?集中營???NaN??1222.0????NaN????A
#?13??Python?集中營??23.0??1223.0??112.0????A
#?14??Python?集中營??24.0??1224.0??113.0????A
#?15??Python?集中營??25.0?????NaN????NaN??NaN
#?16??Python?集中營???NaN??1226.0??115.0????A
#?17??Python?集中營??27.0??1227.0????NaN????A
#?18??Python?集中營??10.0??1210.0???99.0??NaN

源數(shù)據(jù)已經(jīng)讀取完成了，接下來使用四種常見的缺失值的處理方式來進行批量的數(shù)據(jù)填充。

1. 固定值填充

固定值填充也是一種比較簡單并且常用的填充方式，只需要給某個列填充自己想要填充的值即可。

這里我們把'表現(xiàn)'這一個列的空值全部填充成'B',fillna函數(shù)就是填充空值的意思。

#?Replacing?all?the?NaN?values?in?the?column?'表現(xiàn)'?with?the?value?'B'.
data_frame['表現(xiàn)']?=?data_frame['表現(xiàn)'].fillna('B')

#?Printing?the?dataframe.
print(data_frame)

#???????????姓名??????年齡??????班級?????成績?表現(xiàn)
#?0???Python?集中營??10.0??1210.0???99.0??A
#?1???Python?集中營??11.0??1211.0??100.0??A
#?2???Python?集中營??12.0??1212.0??101.0??A
#?3???Python?集中營??13.0??1213.0??102.0??A
#?4???Python?集中營??14.0??1214.0??103.0??B
#?5???Python?集中營??15.0??1215.0??104.0??A
#?6???Python?集中營??16.0??1216.0??105.0??A
#?7???Python?集中營??17.0?????NaN??106.0??A
#?8???Python?集中營??18.0??1218.0????NaN??A
#?9???Python?集中營??19.0??1219.0??108.0??A
#?10??Python?集中營???NaN??1220.0??109.0??B
#?11??Python?集中營???NaN?????NaN??110.0??A
#?12??Python?集中營???NaN??1222.0????NaN??A
#?13??Python?集中營??23.0??1223.0??112.0??A
#?14??Python?集中營??24.0??1224.0??113.0??A
#?15??Python?集中營??25.0?????NaN????NaN??B
#?16??Python?集中營???NaN??1226.0??115.0??A
#?17??Python?集中營??27.0??1227.0????NaN??A
#?18??Python?集中營??10.0??1210.0???99.0??B

2. 均值填充

均值填充就是將缺失值所在列的數(shù)據(jù)進行一次均值計算，計算出結(jié)果后再填充到缺失值所在的單元格上面。

使用均值填充的前提是這一列的數(shù)據(jù)可以進行均值計算，比如'成績'這一列都是數(shù)字可以使用mean函數(shù)做均值計算。

#?Replacing?all?the?NaN?values?in?the?column?'成績'?with?the?mean?of?the?column?'成績'.
data_frame['成績']?=?data_frame['成績'].fillna(data_frame['成績'].mean())

#?It's?printing?the?dataframe.
print(data_frame)

#???????????姓名??????年齡??????班級??????????成績?表現(xiàn)
#?0???Python?集中營??10.0??1210.0???99.000000??A
#?1???Python?集中營??11.0??1211.0??100.000000??A
#?2???Python?集中營??12.0??1212.0??101.000000??A
#?3???Python?集中營??13.0??1213.0??102.000000??A
#?4???Python?集中營??14.0??1214.0??103.000000??B
#?5???Python?集中營??15.0??1215.0??104.000000??A
#?6???Python?集中營??16.0??1216.0??105.000000??A
#?7???Python?集中營??17.0?????NaN??106.000000??A
#?8???Python?集中營??18.0??1218.0??105.733333??A
#?9???Python?集中營??19.0??1219.0??108.000000??A
#?10??Python?集中營???NaN??1220.0??109.000000??B
#?11??Python?集中營???NaN?????NaN??110.000000??A
#?12??Python?集中營???NaN??1222.0??105.733333??A
#?13??Python?集中營??23.0??1223.0??112.000000??A
#?14??Python?集中營??24.0??1224.0??113.000000??A
#?15??Python?集中營??25.0?????NaN??105.733333??B
#?16??Python?集中營???NaN??1226.0??115.000000??A
#?17??Python?集中營??27.0??1227.0??105.733333??A
#?18??Python?集中營??10.0??1210.0???99.000000??B

可以發(fā)現(xiàn)計算出的均值是105.733333，已經(jīng)都填充到'成績'這一列的缺失值上面了。

3. 中位數(shù)填充

中位數(shù)填充和均值填充差不多是一樣的，不同的是使用median函數(shù)來計算缺失值所在列的中位數(shù)。

#?Replacing?all?the?NaN?values?in?the?column?'年齡'?with?the?median?of?the?column?'年齡'.
data_frame['年齡']?=?data_frame['年齡'].fillna(data_frame['年齡'].median())

#?It's?printing?the?dataframe.
print(data_frame)

#???????????姓名??????年齡??????班級??????????成績?表現(xiàn)
#?0???Python?集中營??10.0??1210.0???99.000000??A
#?1???Python?集中營??11.0??1211.0??100.000000??A
#?2???Python?集中營??12.0??1212.0??101.000000??A
#?3???Python?集中營??13.0??1213.0??102.000000??A
#?4???Python?集中營??14.0??1214.0??103.000000??B
#?5???Python?集中營??15.0??1215.0??104.000000??A
#?6???Python?集中營??16.0??1216.0??105.000000??A
#?7???Python?集中營??17.0?????NaN??106.000000??A
#?8???Python?集中營??18.0??1218.0??105.733333??A
#?9???Python?集中營??19.0??1219.0??108.000000??A
#?10??Python?集中營??16.0??1220.0??109.000000??B
#?11??Python?集中營??16.0?????NaN??110.000000??A
#?12??Python?集中營??16.0??1222.0??105.733333??A
#?13??Python?集中營??23.0??1223.0??112.000000??A
#?14??Python?集中營??24.0??1224.0??113.000000??A
#?15??Python?集中營??25.0?????NaN??105.733333??B
#?16??Python?集中營??16.0??1226.0??115.000000??A
#?17??Python?集中營??27.0??1227.0??105.733333??A
#?18??Python?集中營??10.0??1210.0???99.000000??B

4. 插補法填充

差補法填充可以根據(jù)該列的上一個數(shù)據(jù)和下一個數(shù)據(jù)得到該單元格需要插入的數(shù)據(jù)是多少。

比如：上一個班級是1220，下一個班級是1222，那么該單元格需要插入的數(shù)據(jù)應(yīng)該是1221。

#?Replacing?all?the?NaN?values?in?the?column?'班級'?with?the?interpolated?values?of?the?column?'班級'.
data_frame['班級']?=?data_frame['班級'].interpolate()

#?It's?printing?the?dataframe.
print(data_frame)

#???????????姓名??????年齡??????班級??????????成績?表現(xiàn)
#?0???Python?集中營??10.0??1210.0???99.000000??A
#?1???Python?集中營??11.0??1211.0??100.000000??A
#?2???Python?集中營??12.0??1212.0??101.000000??A
#?3???Python?集中營??13.0??1213.0??102.000000??A
#?4???Python?集中營??14.0??1214.0??103.000000??B
#?5???Python?集中營??15.0??1215.0??104.000000??A
#?6???Python?集中營??16.0??1216.0??105.000000??A
#?7???Python?集中營??17.0??1217.0??106.000000??A
#?8???Python?集中營??18.0??1218.0??105.733333??A
#?9???Python?集中營??19.0??1219.0??108.000000??A
#?10??Python?集中營??16.0??1220.0??109.000000??B
#?11??Python?集中營??16.0??1221.0??110.000000??A
#?12??Python?集中營??16.0??1222.0??105.733333??A
#?13??Python?集中營??23.0??1223.0??112.000000??A
#?14??Python?集中營??24.0??1224.0??113.000000??A
#?15??Python?集中營??25.0??1225.0??105.733333??B
#?16??Python?集中營??16.0??1226.0??115.000000??A
#?17??Python?集中營??27.0??1227.0??105.733333??A
#?18??Python?集中營??10.0??1210.0???99.000000??B

到此這篇關(guān)于Python數(shù)據(jù)預(yù)處理時缺失值的不同處理方式總結(jié)的文章就介紹到這了,更多相關(guān)Python數(shù)據(jù)預(yù)處理內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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