pandas 通过 id 列表过滤熊猫数据框
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Filtering pandas data frame by a list of id's
提问by redrubia
I have a pandas dataframe, which has a list of user id's 'subscriber_id' and some other info.
我有一个 Pandas 数据框，其中包含用户 ID 的“subscriber_id”列表和其他一些信息。
I want to only select subscribers not in a given list A.
我只想选择不在给定列表 A 中的订阅者。
So if our data frame contains info for subscribers [1,2,3,4,5] and my exclude list is [2,4,5], I should now get a dataframe with information for [1,3]
因此，如果我们的数据框包含订阅者的信息 [1,2,3,4,5] 并且我的排除列表是 [2,4,5]，我现在应该得到一个包含 [1,3] 信息的数据框
I have tried using a mask as follows:
我曾尝试使用如下面罩：
temp = df.mask(lambda x: x['subscriber_id'] not in subscribers)
temp = df.mask(lambda x: x['subscriber_id'] not in subscribers)
but no luck!
但没有运气！
I am sure the not inis valid Python syntax, as I tested it on a list as follows:
我确定这not in是有效的 Python 语法，因为我在列表中对其进行了如下测试：
c = [1,2,3,4,5]
if 5 not in c:
print 'YAY'
>> YAY
Any suggestion or alternative way to filter the dataframe?
过滤数据框的任何建议或替代方法？
回答by unutbu
You could use the isinmethod:
您可以使用以下isin方法：
In [30]: df = pd.DataFrame({'subscriber_id':[1,2,3,4,5]})
In [31]: df
Out[31]:
subscriber_id
0 1
1 2
2 3
3 4
4 5
[5 rows x 1 columns]
In [32]: mask = df['subscriber_id'].isin([2,4,5])
In [33]: mask
Out[33]:
0 False
1 True
2 False
3 True
4 True
Name: subscriber_id, dtype: bool
In [34]: df.loc[~mask]
Out[34]:
subscriber_id
0 1
2 3
[2 rows x 1 columns]
If you use df.mask, then the input must be a boolean NDFrame or an array. lambda x: x['subscriber_id'] not in subscribersis a function, which is why it raised an exception.
如果使用df.mask，则输入必须是布尔型 NDFrame 或数组。lambda x: x['subscriber_id'] not in subscribers是一个函数，这就是它引发异常的原因。
Here is one way you could use df.mask, again with isinto form the boolean condition:
这是您可以df.mask再次使用 withisin来形成布尔条件的一种方法：
In [43]: df['subscriber_id'].mask(df['subscriber_id'].isin([2,4,5]).values)
Out[43]:
0 1
1 NaN
2 3
3 NaN
4 NaN
Name: subscriber_id, dtype: float64
