Python Pandas 返回数据帧，其中值计数高于设定数字
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Python Pandas return DataFrame where value count is above a set number
提问by Emac
I have a Pandas DataFrame, and I want to return the DataFrame only if that Customer Number occurs more than a set number of times.
我有一个 Pandas DataFrame，并且我想仅在该客户编号出现的次数超过设定次数时才返回该 DataFrame。
Here is a sample of the DataFrame:
这是 DataFrame 的示例：
114 2017-04-26 1 7507 34 13
115 2017-04-26 3 77314 41 14
116 2017-04-27 7 4525 190 315
117 2017-04-27 7 5525 67 94
118 2017-04-27 1 6525 43 378
119 2017-04-27 3 7415 38 27
120 2017-04-27 2 7613 47 10
121 2017-04-27 2 77314 9 3
122 2017-04-28 1 227 17 4
123 2017-04-28 8 4525 205 341
124 2017-04-28 1 7415 31 20
125 2017-04-28 2 77314 8 2
And here is if that customer occurs more than 5 times, using this code:
如果该客户出现超过 5 次，请使用以下代码：
print(zip_data_df['Customers'].value_counts()>5)
7415 True
4525 True
5525 True
77314 True
6525 True
4111 True
227 True
206 False
7507 False
7613 False
4108 False
3046 False
2605 False
4139 False
4119 False
Now I expected if I did this:
现在我希望如果我这样做：
print(zip_data_df[zip_data_df['Customers'].value_counts()>5])
It would show me the whole DataFrame for customers that occur more than 5 times, but I got a Boolean error. I realize why it gives me an error now: one DataFrame is just telling me if that customer number occurs more than 5 times or not, and the other is showing me every time that customer number occurs. They don't match in length. But how do I get it so the dataframe will only return records where that customer occurs more than 5 times?
它会向我显示出现超过 5 次的客户的整个 DataFrame，但我得到了一个布尔错误。我意识到为什么它现在给我一个错误：一个 DataFrame 只是告诉我该客户编号是否出现超过 5 次，而另一个在每次出现该客户编号时都向我显示。它们的长度不匹配。但是我如何获得它以便数据框只会返回该客户出现超过 5 次的记录？
I'm sure there is some simple answer I'm overlooking, but I appreciate any help you can get me.
我确定我忽略了一些简单的答案，但我很感激你能给我的任何帮助。
回答by abe
So the issue here is indexing: value_counts() returns a Series indexed on 'Customers,' while zip_data_df seems to be indexed on something else. You can do something like:
所以这里的问题是索引：value_counts() 返回一个以“客户”为索引的系列，而 zip_data_df 似乎以其他方式索引。您可以执行以下操作：
cust_counts = zip_data_df['Customers'].value_counts().rename('cust_counts')
zip_data_df = zip_data_df.merge(cust_counts.to_frame(),
left_on='Customers',
right_index=True)
From there, you can select conditionally from zip_data_df like so:
从那里，您可以像这样从 zip_data_df 有条件地选择：
zip_data_df[zip_data_df.cust_counts > 5]
回答by DrTRD
I believe what you're looking for is:
我相信你正在寻找的是：
zip_data_df['Customers'].value_counts()[zip_data_df['Customers'].value_counts()>5]
回答by Zak Keirn
I had a similar problem and solved it this way.
我有一个类似的问题，并以这种方式解决了它。
cust_counts = zip_data_df['Customers'].value_counts()
cust_list = cust_counts[cust_counts > 5].index.tolist()
zip_data_df = zip_data_df[zip_data_df['Customers'].isin(cust_list)]
回答by Tom Wattley
you can get the job done with a handy little groupby transform here
你可以在这里通过一个方便的小分组转换来完成工作
subset_customers_df = zip_data_df[
zip_data_df.groupby('Customers')
['Customers'].transform('size')>5]
that works here for Pandas 0.25.3
这适用于 Pandas 0.25.3
回答by Koray Tugay
Haven 't tried but this should work:
没试过，但这应该有效：
cust_by_size = zip_data_df.groupBy("Customers").size()
cust_index_gt_5 = cust_by_size.index[cust_by_size > 5]
zip_data_cust_index_gt_5 = zip_data_df[zip_data_df["Customers"].isin(cust_index_gt_5)]
