Daniel Mahler

http://linkedin.com/in/dmahler

Principal Machine Learning Scientist @ Volley

Aug
16
revised What is the best stemming method in Python?
deleted 32 characters in body
Aug
12
accepted DataFrame from dicts with automatic date parsing
Jul
23
awarded Popular Question
Jul
13
awarded Yearling
Jul
13
awarded Yearling
Jul
8
comment displaying embedded newlines in a text column of a Pandas DataFrame
That looks great. I need to rry it out
Jul
6
awarded Enlightened
Jul
6
awarded Nice Answer
Jun
26
awarded Popular Question
Jun
26
comment Anaconda 4.7.5 - Warning about conda-build <3.18.3 and issues with python packages
My problem wasa little more complcated. With 4.6 I use pip_interop_enabled: true channel_priority: strict to keep my environment sane. With these settings 4.7 starts to want to delete essential packages and without them it gets hopelessly confused. I ended up reinstalling from scratch and pinning 4.6.
Jun
26
comment Anaconda 4.7.5 - Warning about conda-build <3.18.3 and issues with python packages
FWIW I fried my mian working environment earlier today this way & spent the rest of the day trying to repair it -- so far unsuccessfully
Jun
23
awarded Notable Question
Jun
20
comment gathering a large dataframe back into master in dask distributed
ok, but why would df.head(df.shape[0].compute(), -1) not have the same problem as compute()? Don't they do the same thing?
Jun
18
comment reading a Dask DataFrame from CSVs in a deep S3 path hierarchy
@mdurant It is suboptimal for processing with dask etc, but that is the data I need to process.
Jun
18
comment gathering a large dataframe back into master in dask distributed
I do want the whole dataframe collected. Although large it easily fits on. on my machine. The second snippet df.head(df.shape[0].compute(), -1) does what I need quite quicikly. I am really asking why doescompute stall?
Jun
13
asked gathering a large dataframe back into master in dask distributed
Jun
12
accepted reading a Dask DataFrame from CSVs in a deep S3 path hierarchy
Jun
12
revised reading a Dask DataFrame from CSVs in a deep S3 path hierarchy
added 248 characters in body
Jun
12
asked reading a Dask DataFrame from CSVs in a deep S3 path hierarchy
Jun
3
accepted Download actual notebook from JupyterLab
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