I've been working on a DataFrame with User_IDs, DateTime objects and other information, like the following extract:
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:
g = df.groupby(['User_ID','Datetime'])
and then check for no-single DataTime objects:
df = df.groupby('User_ID')['Datetime'].apply(lambda g: len(g)>1)
I've obtained the following boolean DataFrame:
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:
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.
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 isin and filter your orig df:
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_csv on the above as normal
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