[英]check if one dataframe exists in another
我有 2 個數據df2
Overall
全面的
Time ID_1 ID_2
2020-02-25 09:24:14 140209 81625000
2020-02-25 09:24:14 140216 91625000
2020-02-25 09:24:18 140219 80250000
2020-02-25 09:24:18 140221 90250000
25/02/2020 09:42:02 143982 39075000
df2
ID_1 ID_2 Time Match?
140209 81625000 25/02/2020 09:24:14 no_match
143983 44075000 25/02/2020 09:42:02 no_match
143982 39075000 25/02/2020 09:42:02 match
143984 39075000 25/02/2020 09:42:02 no_match
我想檢查df2
是否存在於Overall
中,如果存在df2.Match?
同一行的說匹配。 如果是,則返回一個新列,表示是,如果它沒有說匹配,則返回否。
我努力了
Overall_1 = pds.merge(Overall, df2, on=….., how='left', indicator= 'Exist')
Overall_1.drop([...], inplace = True, axis =1 )
Overall_1['Exist']= np.where((Overall_1.Exist =='both') & (Overall_1.Match? == match), 'yes', 'no')
但錯誤盛行
TypeError: Cannot perform 'rand_' with a dtyped [bool] array and scalar of type [float]
因此生成Overall_1
dataframe 應該如下所示:
Time ID_1 ID_2 Exist
2020-02-25 09:24:14 140209 81625000 No
2020-02-25 09:24:14 140216 91625000 NaN
2020-02-25 09:24:18 140219 80250000 NaN
2020-02-25 09:24:18 140221 90250000 Nan
25/02/2020 09:42:02 143982 39075000 Yes
使用merge
和np.select.
import numpy as np
#df1 = Overall
df3 = pd.merge(df1,df2,on=['ID_1','ID_2','Time'],how='left',indicator='Exists')
col1 = df3['Match?']
col2 = df3['Exists']
conditions = [(col1.eq('match') & (col2.eq('both'))),
(col1.eq('no_match') & (col2.eq('both')))
]
choices = ['yes','no']
df3['Exists'] = np.select(conditions,choices,default=np.nan)
print(df3.drop('Match?',axis=1))
Time ID_1 ID_2 Exists
0 2020-02-25 09:24:14 140209 81625000 no
1 2020-02-25 09:24:14 140216 91625000 nan
2 2020-02-25 09:24:18 140219 80250000 nan
3 2020-02-25 09:24:18 140221 90250000 nan
4 2020-02-25 09:42:02 143982 39075000 yes
或者更簡單地使用replace
dict 和.merge
df3 = pd.merge(df1,df2,on=['ID_1','ID_2','Time'],how='left')\
.replace({'no_match' : 'no',
'match' : 'yes'})\
.rename(columns={'Match?' : 'Exists'})
print(df3)
Time ID_1 ID_2 Exists
0 2020-02-25 09:24:14 140209 81625000 no
1 2020-02-25 09:24:14 140216 91625000 NaN
2 2020-02-25 09:24:18 140219 80250000 NaN
3 2020-02-25 09:24:18 140221 90250000 NaN
4 2020-02-25 09:42:02 143982 39075000 yes
你可以試試: df_diff = pd.concat([Overall,df2]).drop_duplicates(keep=False)
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