Python 根据列值删除 Pandas 中的 DataFrame 行
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Deleting DataFrame row in Pandas based on column value
提问by TravisVOX
I have the following DataFrame:
我有以下数据帧：
daysago line_race rating rw wrating
line_date
2007-03-31 62 11 56 1.000000 56.000000
2007-03-10 83 11 67 1.000000 67.000000
2007-02-10 111 9 66 1.000000 66.000000
2007-01-13 139 10 83 0.880678 73.096278
2006-12-23 160 10 88 0.793033 69.786942
2006-11-09 204 9 52 0.636655 33.106077
2006-10-22 222 8 66 0.581946 38.408408
2006-09-29 245 9 70 0.518825 36.317752
2006-09-16 258 11 68 0.486226 33.063381
2006-08-30 275 8 72 0.446667 32.160051
2006-02-11 475 5 65 0.164591 10.698423
2006-01-13 504 0 70 0.142409 9.968634
2006-01-02 515 0 64 0.134800 8.627219
2005-12-06 542 0 70 0.117803 8.246238
2005-11-29 549 0 70 0.113758 7.963072
2005-11-22 556 0 -1 0.109852 -0.109852
2005-11-01 577 0 -1 0.098919 -0.098919
2005-10-20 589 0 -1 0.093168 -0.093168
2005-09-27 612 0 -1 0.083063 -0.083063
2005-09-07 632 0 -1 0.075171 -0.075171
2005-06-12 719 0 69 0.048690 3.359623
2005-05-29 733 0 -1 0.045404 -0.045404
2005-05-02 760 0 -1 0.039679 -0.039679
2005-04-02 790 0 -1 0.034160 -0.034160
2005-03-13 810 0 -1 0.030915 -0.030915
2004-11-09 934 0 -1 0.016647 -0.016647
I need to remove the rows where line_raceis equal to 0. What's the most efficient way to do this?
我需要删除line_race等于的行0。执行此操作的最有效方法是什么？
采纳答案by tshauck
If I'm understanding correctly, it should be as simple as:
如果我理解正确，它应该很简单：
df = df[df.line_race != 0]
回答by Phillip Cloud
The best way to do this is with boolean masking:
最好的方法是使用布尔掩码：
In [56]: df
Out[56]:
line_date daysago line_race rating raw wrating
0 2007-03-31 62 11 56 1.000 56.000
1 2007-03-10 83 11 67 1.000 67.000
2 2007-02-10 111 9 66 1.000 66.000
3 2007-01-13 139 10 83 0.881 73.096
4 2006-12-23 160 10 88 0.793 69.787
5 2006-11-09 204 9 52 0.637 33.106
6 2006-10-22 222 8 66 0.582 38.408
7 2006-09-29 245 9 70 0.519 36.318
8 2006-09-16 258 11 68 0.486 33.063
9 2006-08-30 275 8 72 0.447 32.160
10 2006-02-11 475 5 65 0.165 10.698
11 2006-01-13 504 0 70 0.142 9.969
12 2006-01-02 515 0 64 0.135 8.627
13 2005-12-06 542 0 70 0.118 8.246
14 2005-11-29 549 0 70 0.114 7.963
15 2005-11-22 556 0 -1 0.110 -0.110
16 2005-11-01 577 0 -1 0.099 -0.099
17 2005-10-20 589 0 -1 0.093 -0.093
18 2005-09-27 612 0 -1 0.083 -0.083
19 2005-09-07 632 0 -1 0.075 -0.075
20 2005-06-12 719 0 69 0.049 3.360
21 2005-05-29 733 0 -1 0.045 -0.045
22 2005-05-02 760 0 -1 0.040 -0.040
23 2005-04-02 790 0 -1 0.034 -0.034
24 2005-03-13 810 0 -1 0.031 -0.031
25 2004-11-09 934 0 -1 0.017 -0.017
In [57]: df[df.line_race != 0]
Out[57]:
line_date daysago line_race rating raw wrating
0 2007-03-31 62 11 56 1.000 56.000
1 2007-03-10 83 11 67 1.000 67.000
2 2007-02-10 111 9 66 1.000 66.000
3 2007-01-13 139 10 83 0.881 73.096
4 2006-12-23 160 10 88 0.793 69.787
5 2006-11-09 204 9 52 0.637 33.106
6 2006-10-22 222 8 66 0.582 38.408
7 2006-09-29 245 9 70 0.519 36.318
8 2006-09-16 258 11 68 0.486 33.063
9 2006-08-30 275 8 72 0.447 32.160
10 2006-02-11 475 5 65 0.165 10.698
UPDATE:Now that pandas 0.13 is out, another way to do this is df.query('line_race != 0').
更新：现在 pandas 0.13 已经发布，另一种方法是df.query('line_race != 0').
回答by wonderkid2
But for any future bypassers you could mention that df = df[df.line_race != 0]doesn't do anything when trying to filter for None/missing values.
但是对于任何未来的绕过者，您可以提到df = df[df.line_race != 0]在尝试过滤None/missing 值时不会做任何事情。
Does work:
是否有效：
df = df[df.line_race != 0]
Doesn't do anything:
什么都不做：
df = df[df.line_race != None]
Does work:
是否有效：
df = df[df.line_race.notnull()]
回答by h3h325
The given answer is correct nontheless as someone above said you can use df.query('line_race != 0')which depending on your problem is much faster. Highly recommend.
尽管如此，给定的答案是正确的，因为上面有人说您可以使用df.query('line_race != 0')它，这取决于您的问题要快得多。强烈推荐。
回答by desmond
just to add another solution, particularly useful if you are using the new pandas assessors, other solutions will replace the original pandas and lose the assessors
只是添加另一个解决方案，如果您正在使用新的 pandas 评估器，则特别有用，其他解决方案将替换原始 Pandas 并失去评估器
df.drop(df.loc[df['line_race']==0].index, inplace=True)
回答by Amruth Lakkavaram
Another way of doing it. May not be the most efficient way as the code looks a bit more complex than the code mentioned in other answers, but still alternate way of doing the same thing.
另一种方法。可能不是最有效的方式，因为代码看起来比其他答案中提到的代码复杂一些，但仍然可以替代做同样的事情。
df = df.drop(df[df['line_race']==0].index)
回答by Loochie
Though the previou answer are almost similar to what I am going to do, but using the index method does not require using another indexing method .loc(). It can be done in a similar but precise manner as
虽然前面的答案几乎与我将要做的相似，但是使用 index 方法不需要使用另一个索引方法 .loc()。它可以以类似但精确的方式完成
df.drop(df.index[df['line_race'] == 0], inplace = True)
回答by Robvh
If you want to delete rows based on multiple values of the column, you could use:
如果要根据列的多个值删除行，可以使用：
df[(df.line_race != 0) & (df.line_race != 10)]
To drop all rows with values 0 and 10 for line_race.
删除所有值为 0 和 10 的行line_race。
回答by Prateek Kumar Singh
Just adding another way for DataFrame expanded over all columns:
只需为 DataFrame 添加另一种扩展到所有列的方法：
for column in df.columns:
df = df[df[column]!=0]
Example:
例子：
def z_score(data,count):
threshold=3
for column in data.columns:
mean = np.mean(data[column])
std = np.std(data[column])
for i in data[column]:
zscore = (i-mean)/std
if(np.abs(zscore)>threshold):
count=count+1
data = data[data[column]!=i]
return data,count
