pandas - 在分组数据帧后仅保留 True 值
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pandas - keep only True values after groupby a DataFrame
提问by Fabio Lamanna
I've been working on a DataFrame with User_IDs, DateTime objects and other information, like the following extract:
我一直在研究带有 User_ID、DateTime 对象和其他信息的 DataFrame，例如以下摘录：
User_ID;Latitude;Longitude;Datetime
222583401;41.4020375;2.1478710;2014-07-06 20:49:20
287280509;41.3671346;2.0793115;2013-01-30 09:25:47
329757763;41.5453577;2.1175164;2012-09-25 08:40:59
189757330;41.5844998;2.5621569;2013-10-01 11:55:20
624921653;41.5931846;2.3030671;2013-07-09 20:12:20
414673119;41.5550136;2.0965829;2014-02-24 20:15:30
414673119;41.5550136;2.0975829;2014-02-24 20:16:30
414673119;41.5550136;2.0985829;2014-02-24 20:17:30
I've grouped Users with:
我将用户分组为：
g = df.groupby(['User_ID','Datetime'])
and then check for no-single DataTime objects:
然后检查非单个 DataTime 对象：
df = df.groupby('User_ID')['Datetime'].apply(lambda g: len(g)>1)
I've obtained the following boolean DataFrame:
我已经获得了以下布尔数据帧：
User_ID
189757330 False
222583401 False
287280509 False
329757763 False
414673119 True
624921653 False
Name: Datetime, dtype: bool
which is fine for my purposes to keep only User_ID with a True masked value. Now I would like to keep only the User_ID values associated to the True values, and write them to a new DataFrame with pandas.to_csv, for instance. The expected DataFrame would contain only the User_ID with more than one DateTime object:
这对我的目的来说很好，只保留带有 True 掩码值的 User_ID。现在，我只想保留与 True 值关联的 User_ID 值，并将它们写入新的 DataFrame 中pandas.to_csv，例如。预期的 DataFrame 将仅包含具有多个 DateTime 对象的 User_ID：
User_ID;Latitude;Longitude;Datetime
414673119;41.5550136;2.0965829;2014-02-24 20:15:30
414673119;41.5550136;2.0975829;2014-02-24 20:16:30
414673119;41.5550136;2.0985829;2014-02-24 20:17:30
How may I have access to the boolean values for each User_ID? Thanks for your kind help.
我如何才能访问每个 User_ID 的布尔值？感谢您的帮助。
回答by EdChum
Assign the result of df.groupby('User_ID')['Datetime'].apply(lambda g: len(g)>1)to a variable so you can perform boolean indexing and then use the index from this to call isinand filter your orig df:
将 的结果分配df.groupby('User_ID')['Datetime'].apply(lambda g: len(g)>1)给变量，以便您可以执行布尔索引，然后使用此索引来调用isin和过滤您的原始文件：
In [366]:
users = df.groupby('User_ID')['Datetime'].apply(lambda g: len(g)>1)
users
Out[366]:
User_ID
189757330 False
222583401 False
287280509 False
329757763 False
414673119 True
624921653 False
Name: Datetime, dtype: bool
In [367]:
users[users]
Out[367]:
User_ID
414673119 True
Name: Datetime, dtype: bool
In [368]:
users[users].index
Out[368]:
Int64Index([414673119], dtype='int64')
In [361]:
df[df['User_ID'].isin(users[users].index)]
Out[361]:
User_ID Latitude Longitude Datetime
5 414673119 41.555014 2.096583 2014-02-24 20:15:30
6 414673119 41.555014 2.097583 2014-02-24 20:16:30
7 414673119 41.555014 2.098583 2014-02-24 20:17:30
You can then call to_csvon the above as normal
然后你可以to_csv正常调用上面的
回答by acushner
first, make sure you have no duplicate entries:
首先，确保您没有重复的条目：
df = df.drop_duplicates()
then, figure out the counts for each:
然后，找出每个的计数：
counts = df.groupby('User_ID').Datetime.count()
finally, figure out where the indexes overlap:
最后，找出索引重叠的位置：
df[df.User_ID.isin(counts[counts > 1].index)]
