[英]Compare two dataframes with same index using one column
I have the following two dataframes (samples).我有以下两个数据框(样本)。 I'd like to know which companies had their sales changed between the two dataframes.我想知道哪些公司的销售额在两个数据框之间发生了变化。 For example, AAPL is different in the second dataframe.例如,AAPL 在第二个数据帧中是不同的。
Sales 52W High 52W Low
Root
A 4.81B -0.1072 0.1082
AA 12.81B -0.3124 0.0709
AABA 266.05M -0.2038 0.0437
AAL 43.52B -0.3285 0.1131
AAN 3.61B -0.0208 0.4716
AAOI 321.80M -0.5196 0.5195
AAP 9.42B -0.0153 1.1190
AAPL 255.27B -0.0101 0.5210
AAXN 385.40M -0.1005 2.3432
ABB 35.52B -0.1870 0.0987
Sales 52W High 52W Low
Root
A 4.81B -0.1019 0.1149
AA 12.81B -0.3527 0.0082
AABA 266.05M -0.2212 0.0208
AAL 43.52B -0.3487 0.0797
AAN 3.61B -0.0196 0.4733
AAOI 321.80M -0.5478 0.4303
AAP 9.42B -0.0216 1.1218
AAPL 243.89B -0.0286 0.4957
AAXN 385.40M -0.0806 2.4171
ABB 35.52B -0.1838 0.1030
This you can using ne
(not equal)这你可以使用ne
(不等于)
df1.Sales.ne(df2.Sales)# the one mask as True is the different
Out[482]:
Root
A False
AA False
AABA False
AAL False
AAN False
AAOI False
AAP False
AAPL True
AAXN False
ABB False
Name: Sales, dtype: bool
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