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Skipper Seabold jseabold HCA Healthcare Nashville, TN Engineerer of data and science

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538 Election Forecasting Model

jseabold/depy 121

DePy 2015 Talk

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These are the IPython notebook files for the CSC 432 Spring '13 course.

fonnesbeck/ngcm_pandas_2017 18

Python data analysis course for 2017 NGCM Summer Academy

jseabold/dask-pydata-chi-2016 17

Dask tutorial for PyData Chicago 2016

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Python data analysis course for 2015 NGCM Summer Academy

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scikit-learn course for 2017 NGCM Summer Academy

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Dask tutorial for PyData DC 2016

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Statsmodels: statistical modeling and econometrics in Python

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issue openedjseabold/pandas-selectable

Support non-boolean methods that return something to be compared?

I'm not sure if it's possible. But something like dta.select.A.str.strip() == ''. I think you could wrap the non-boolean methods so that they return a selectable column. Need to try it out.

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pull request commentpandas-dev/pandas

Deprecate groupby/pivot observed=False default

Been thinking about this a bit, given the comments and use cases. I have a proposal that may or may not be a good one.

What about making the default None and in the presence of a categorical, if the default isn't changed it raises an error with a message that makes users choose True or False.

This would be noisy but would avoid the temptation to guess and whatever "my way is the most typical" bias that could creep in. It wouldn't force a "stringsAsFactors" situaish just an "ugh SettingWithCopyWarning" situaish (which I can live with). It also could be temporary until there's another extension dtype ready that's more appropriate for situations where your strings aren't factors (or your factors aren't independent).

Thoughts?

jseabold

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issue commentjseabold/pandas-selectable

Inversion operator doesn't work for `.str`

Not sure there's a way to do this without some real magic. contains above returns a DataFrame and the inversion is on the DataFrame.

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issue openedjseabold/pandas-selectable

MultiIndex selection needs improvement

Will need to think about how to do this. I think maybe the pandas API could use some improvements here too. There's no attribute selection on the MultiIndex. Something like dta.select.index.level_name == 'foobar' would be aces.

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issue openedjseabold/pandas-selectable

Disable assignment

Currently not preventing assigning to a selectable column. E.g., something like dta.select.ColumnName = "I mistyped ==!" won't fail and leads to a bad time.

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issue openedjseabold/pandas-selectable

Add without examples to README

Show what the verbose code that this can replace

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issue openedjseabold/pandas-selectable

Inversion operator doesn't work for `.str`

Can't do ~dta.A.str.contains('foobar')

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A `select` accessor for easier subsetting of pandas DataFrames and Series

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Rename project

There's an existing project that I missed

https://pypi.org/project/pandas-select/

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PR opened jseabold/pandas-selectable

Rename project

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A `select` accessor to pandas DataFrames and Series for easier sub-setting

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pull request commentpandas-dev/pandas

Deprecate groupby/pivot observed=False default

For me this feels a bit like the "stringsAsFactors" saga on R

Ha, indeed. Now you've got me rethinking everything.

jseabold

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pull request commentpandas-dev/pandas

Deprecate groupby/pivot observed=False default

All the "problems" started o surface when someone discovered that categoricals save memory and time when dealing with strings.

Just want to gently push back on the (maybe perceived) notion that this is somehow an abuse of categoricals. High-cardinality, non-independent categoricals/factors are definitely a thing, and the default can not only explode memory but also gives non-sensical answers.

I think the departure from (the flavors of) SQL(s I use) was more unexpected for me.

Buut, as I mentioned in the issue thread, I've definitely wanted both behaviors of this just today. Is there some design philosophy here to fallback to for guidance (like prefer standard SQL semantics or refuse the temptation to guess or something)? I'm not sure just a type with sensible defaults is going to solve the issue for me. E.g., I want the observed=True behavior for two independent factors but not for two dependent factors and only when they'd both be included in a groupby. It's the presence of another one that leads me to want the behavior.

All this said, now that I know and am thinking about these things I think I'll always have to specify observed. Part of me just wants to raise an error if observed=None and force folks to think, but that'd be a pretty bad UX.

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pull request commentpandas-dev/pandas

Deprecate groupby/pivot observed=False default

Going to be a bit of (tedious) work to get the tests and doc builds passing on the warnings as errors runs, let me know whether this is likely to get merged, and I'll come back and fix the tests and look at the crosstab stuff.

jseabold

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issue commentpandas-dev/pandas

PERF: groupby with many empty groups memory blowup

Trying to think through the examples here and whether my current "usual" columns are more or less prevalent to not want sql group by semantics by default. E.g., for the case of grouping by cities and states, they're both factors and they're not independent. I guess you could say that they should be single city-state factors?

When you have a fixed set of categories that should persist across operations (e.g. survey results).

FWIW, I think this is what I use unstack for but am not sure. Could you post an example workflow to compare?

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pull request commentpandas-dev/pandas

Deprecate groupby/pivot observed=False default

Rebased to get rid of the merge conflict. Not sure why the coverage tests are saying I added untested lines.

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Issue35925 remove more trailing commas (#35959) * pandas/core/series.py * pandas/core/window/ewm.py * pandas/core/window/indexers.py * pandas/core/window/numba_.py * pandas/core/window/rolling.py * pandas/io/formats/css.py * pandas/io/formats/format.py * pandas/io/orc.py

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Issue35925 Remove trailing commas (#35996) * pandas/tests/test_multilevel.py * pandas/tests/test_nanops.py * pandas/.../test_moments_consistency_rolling.py * pandas/tests/window/moments/test_moments_ewm.py * pandas/.../test_moments_rolling.py * pandas/tests/window/test_base_indexer.py * pandas/tests/window/test_pairwise.py * pandas/tests/window/test_rolling.py * pandas/tseries/frequencies.py * pandas/util/_test_decorators.py

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pull request commentpandas-dev/pandas

Deprecate groupby/pivot observed=False default

Yeah, this is basically my position.

https://github.com/pandas-dev/pandas/issues/30552#issuecomment-602849215

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pull request commentpandas-dev/pandas

Deprecate groupby/pivot observed=False default

I started fixing up the doc warnings, but I think there's some more I need to think through with crosstab and dropna.

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pull request commentpandas-dev/pandas

Deprecate groupby/pivot observed=False default

Yeah, that makes sense as a better solution, and I was of two minds about whether to do this, but I struggled to find an example for when I'd ever want observed=False. Do you have one in mind? If I really wanted a cartesian product of things I don't actually have in my data, I don't think I'd reach for groupby to do that.

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pull request commentpandas-dev/pandas

Deprecate groupby/pivot observed=False default

Will see about avoiding pytest.warns though the CI stuff doesn't make it real clear why. I think the included warnings assertions didn't like asserting there wasn't a warning but I'll check again.

First question is, do you want this PR? Does it need discussion?

Second question is, what do y'all suggest I do about the warnings coming in the examples? Suppress them? Fix the examples to use use the new keyword?

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PR opened pandas-dev/pandas

Depr observed default
  • [x] Relates to #17594
  • [x] tests added / passed
  • [x] passes black pandas
  • [x] passes git diff upstream/master -u -- "*.py" | flake8 --diff
  • [x] whatsnew entry

Had a relatively small 70k data frame that I was trying to do a groupby sum on blow up on me today. This was the reason. I had something like zip codes and cities as categoricals, expected SQL-like groupby but instead got a cartesian product of 'cities' and 'zips'. Sounds like there was some previous desire to explore a new default.

Didn't try to do any wild stuff to keep up with the stacklevel depending on where this was called from.

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issue commentpbugnion/jupyterlab-sql

Extension incompatible with jupyterlab 2.0.1

I don't think all of the functionality is there on the fork, but I never used it before, so I don't know what's missing. I followed the recommended upgrade path though. Needs another set of eyes for sure. If anyone is interested the PR is #137. Would love to merge those changes back here. @pbugnion

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issue commentcode-ready/crc

[BUG] Error getting MAC address from UUID on macOS with hyperkit

Just ran into this issue on a fresh install of crc 1.11. This comment https://github.com/code-ready/crc/issues/882#issuecomment-637535245 worked for me, though I didn't need to install a new version of hyperkit. I just needed to copy the newer on over to ~/crc/.bin and to kill the running hyperkit (which crashed docker desktop) but allowed crc start to work.

arthurbarr

comment created time in 2 months

issue commentplotly/dash-table

Overflow for horizontal scrolling cuts the first column

No expert, but I think this may be part of what's going on. If you add a margin-left to the parent element of the table which is autoscrolling you'll shift the table elements to the right. Not sure if this is the actual root cause though or how the column size is being calculated. The margin here may not be big enough in all cases.

css=[{
    'selector': '.dash-spreadsheet-inner',
    'rule': 'margin-left: 20px
}]
gioxc88

comment created time in 3 months

issue commentplotly/jupyter-dash

Cannot stop server in JupyterLab using CTRL+C

Eh, doing some debugging I'm getting a 504 gateway timeout. This has to do with my work machine proxy, so it's a local issue.

Instead of raising an OSError and saying the port is in use, it may be more informative to call raise_for_status in the requests response? This would have pointed me in the right direction for this issue more quickly, at least.

msbuckas

comment created time in 3 months

issue commentplotly/jupyter-dash

Cannot stop server in JupyterLab using CTRL+C

FWIW, I'm seeing the same on OSX, but if I navigate to the 'http://localhost:8889' the app is running. Repro

app = JupyterDash(__name__)
app.layout = html.Div('Minimal')

app.run_server(mode="jupyterlab", host="localhost", port=8889)

JupyterLab version - 2.1.5 JupyterDash version - 0.2.1.post1 installed using pip. Conda doesn't find the package on conda-forge.

Happy to provide any other details.

msbuckas

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PR opened quantopian/qgrid

Update deprecated decorator usage

This becomes an error in ipywidgets 0.8.0.

See this commit.

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Update deprecated decorator usage This becomes an error in ipywidgets 0.8.0

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An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks

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