isin pandas 的问题 [英] Problems with isin pandas
问题描述
抱歉，我刚刚问了这个问题:在数据帧中调节时具有多个 Or 的 Pythonic 方式 但将其标记为过早回答，因为它通过了我过于简单的测试用例，但不能更普遍地工作.(如果可以合并并重新打开这个问题就太好了...)
Sorry, I just asked this question: Pythonic Way to have multiple Or's when conditioning in a dataframe but marked it as answered prematurely because it passed my overly simplistic test case, but isn't working more generally. (If it is possible to merge and reopen the question that would be great...)
这里是完整的问题:
sum(data['Name'].isin(eligible_players))
> 0
sum(data['Name'] == "Antonio Brown")
> 68
"Antonio Brown" in eligible_players
> True
基本上，如果我理解正确的话，我会显示安东尼奥·布朗在符合条件的球员中，并且他在数据框中.但是，由于某种原因，.isin() 无法正常工作.
Basically if I understand correctly, I am showing that Antonio Brown is in eligible players and he is in the dataframe. However, for some reason the .isin() isn't working properly.
正如我在之前的问题中所说，我正在寻找一种方法来检查许多 ors 以选择正确的行
As I said in my prior question, I am looking for a way to check many ors to select the proper rows
____ 编辑 ____
____ EDIT ____
In[14]:
eligible_players
Out[14]:
Name
Antonio Brown 378
Demaryius Thomas 334
Jordy Nelson 319
Dez Bryant 309
Emmanuel Sanders 293
Odell Beckham 289
Julio Jones 288
Randall Cobb 284
Jeremy Maclin 267
T.Y. Hilton 255
Alshon Jeffery 252
Golden Tate 250
Mike Evans 236
DeAndre Hopkins 223
Calvin Johnson 220
Kelvin Benjamin 218
Julian Edelman 213
Anquan Boldin 213
Steve Smith 213
Roddy White 208
Brandon LaFell 205
Mike Wallace 205
A.J. Green 203
DeSean Jackson 200
Jordan Matthews 194
Eric Decker 194
Sammy Watkins 190
Torrey Smith 186
Andre Johnson 186
Jarvis Landry 178
Eddie Royal 176
Brandon Marshall 175
Vincent Jackson 175
Rueben Randle 174
Marques Colston 173
Mohamed Sanu 171
Keenan Allen 170
James Jones 168
Malcom Floyd 168
Kenny Stills 167
Greg Jennings 162
Kendall Wright 162
Doug Baldwin 160
Michael Floyd 159
Robert Woods 158
Name: Pts, dtype: int64
和
In [31]:
data.tail(110)
Out[31]:
Name Pts year week pos Team
28029 Dez Bryant 25 2014 17 WR DAL
28030 Antonio Brown 25 2014 17 WR PIT
28031 Jordan Matthews 24 2014 17 WR PHI
28032 Randall Cobb 23 2014 17 WR GB
28033 Rueben Randle 21 2014 17 WR NYG
28034 Demaryius Thomas 19 2014 17 WR DEN
28035 Calvin Johnson 19 2014 17 WR DET
28036 Torrey Smith 18 2014 17 WR BAL
28037 Roddy White 17 2014 17 WR ATL
28038 Steve Smith 17 2014 17 WR BAL
28039 DeSean Jackson 16 2014 17 WR WAS
28040 Mike Evans 16 2014 17 WR TB
28041 Anquan Boldin 16 2014 17 WR SF
28042 Adam Thielen 15 2014 17 WR MIN
28043 Cecil Shorts 15 2014 17 WR JAC
28044 A.J. Green 15 2014 17 WR CIN
28045 Jordy Nelson 14 2014 17 WR GB
28046 Brian Hartline 14 2014 17 WR MIA
28047 Robert Woods 13 2014 17 WR BUF
28048 Kenny Stills 13 2014 17 WR NO
28049 Emmanuel Sanders 13 2014 17 WR DEN
28050 Eddie Royal 13 2014 17 WR SD
28051 Marques Colston 13 2014 17 WR NO
28052 Chris Owusu 12 2014 17 WR NYJ
28053 Brandon LaFell 12 2014 17 WR NE
28054 Dontrelle Inman 12 2014 17 WR SD
28055 Reggie Wayne 11 2014 17 WR IND
28056 Paul Richardson 11 2014 17 WR SEA
28057 Cole Beasley 11 2014 17 WR DAL
28058 Jarvis Landry 10 2014 17 WR MIA
推荐答案
(另外:一旦您发布了您实际使用的内容，只需几秒钟即可发现问题.)
(Aside: once you posted what you were actually using, it only took seconds to see the problem.)
Series.isin(something) 迭代 something 以确定您要测试成员资格的事物集.但是您的 eligible_players 不是列表，而是系列.对 Series 的迭代是对 values 的迭代，即使成员资格 (in) 是相对于索引的:
Series.isin(something) iterates over something to determine the set of things you want to test membership in. But your eligible_players isn't a list, it's a Series. And iteration over a Series is iteration over the values, even though membership (in) is with respect to the index:
In [72]: eligible_players = pd.Series([10,20,30], index=["A","B","C"])
In [73]: list(eligible_players)
Out[73]: [10, 20, 30]
In [74]: "A" in eligible_players
Out[74]: True
所以在你的情况下，你可以使用 eligible_players.index 来代替传递正确的名字:
So in your case, you could use eligible_players.index instead to pass the right names:
In [75]: df = pd.DataFrame({"Name": ["A","B","C","D"]})
In [76]: df
Out[76]:
Name
0 A
1 B
2 C
3 D
In [77]: df["Name"].isin(eligible_players) # remember, this will be [10, 20, 30]
Out[77]:
0 False
1 False
2 False
3 False
Name: Name, dtype: bool
In [78]: df["Name"].isin(eligible_players.index)
Out[78]:
0 True
1 True
2 True
3 False
Name: Name, dtype: bool
In [79]: df["Name"].isin(eligible_players.index).sum()
Out[79]: 3
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