Pandas DataFrame 删除 groupby 中的行
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Pandas DataFrame to drop rows in the groupby
提问by Zed Fang
I have a DataFrame with three columns Date, Advertiserand ID. I grouped the data firsts to see if volumns of some Advertisers are too small (For example when count()less than 500). And then I want to drop those rows in the group table.
我有三列的数据帧Date，Advertiser和ID。我首先对数据进行分组，以查看某些广告商的数量是否太小（例如count()小于 500 时）。然后我想删除组表中的那些行。
df.groupby(['Date','Advertiser']).ID.count()
The result likes this:
结果是这样的：
Date Advertiser
2016-01 A 50000
B 50
C 4000
D 24000
2016-02 A 6800
B 7800
C 123
2016-03 B 1111
E 8600
F 500
I want a result to be this:
我希望结果是这样的：
Date Advertiser
2016-01 A 50000
C 4000
D 24000
2016-02 A 6800
B 7800
2016-03 B 1111
E 8600
Followed up question:
后续问题：
How about if I want to filter out the rows in groupby in term of the total count()in date category. For example, I want to count()for a date larger than 15000. The table I want likes this:
如果我想根据count()日期类别中的总数过滤掉 groupby 中的行如何。例如，我想要count()一个大于 15000 的日期。我想要的表是这样的：
Date Advertiser
2016-01 A 50000
B 50
C 4000
D 24000
2016-02 A 6800
B 7800
C 123
采纳答案by Psidom
You have a Series object after the groupby, which can be filtered based on value with a chained lambdafilter:
在 之后有一个 Series 对象groupby，可以使用链式lambda过滤器根据值对其进行过滤：
df.groupby(['Date','Advertiser']).ID.count()[lambda x: x >= 500]
#Date Advertiser
#2016-01 A 50000
# C 4000
# D 24000
#2016-02 A 6800
# B 7800
#2016-03 B 1111
# E 8600
# F 500
