DOC: expanding comparison with R section #12472
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This is the beginning of a quick reference section. It's incomplete, just did a rough translation of http://nbviewer.jupyter.org/urls/gist.githubusercontent.com/TomAugspurger/6e052140eaa5fdb6e8c0/raw/811585624e843f3f80b9b6fe89e18119d7d2d73c/dplyr_pandas.ipynb into tables. Should try to get some R experts to comment, and it would be nice to have the pandas versions link to docs for the functions being used, but I'm terrible at reStructuredText and gave up for the moment.
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Thanks for this. There's a slightly updated version here, but I can't really remember what changed. reST can be a bit of a pain. For linking to methods, you can use e.g. |
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Yeah, I tried :meth: once and didn't like it. But seriously, I couldn't get it to format well, or deal with the example
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| ``filter(df, col1 == 1, col2 == 1)`` ``df.query('col1 == 1 & col2 == 1')`` | ||
| ``df[df$col1 == 1 & df$col2 == 1,]`` ``df[(df.col1 == 1) & (df.col2 == 1)]`` | ||
| ``select(df, col1, col2)`` ``df[['col1', 'col2']]`` | ||
| ``select(df, col1:col3)`` No one-line equivalent, but see [#select_range]_ |
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Isn't this equivalent to df.loc[:, 'col1':'col3'] ?
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Probably. I'm not a pandas expert. I was going off http://nbviewer.jupyter.org/urls/gist.githubusercontent.com/TomAugspurger/6e052140eaa5fdb6e8c0/raw/811585624e843f3f80b9b6fe89e18119d7d2d73c/dplyr_pandas.ipynb which said there wasn't an equivalent. @TomAugspurger is that correct?
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@jorisvandenbossche based on my understanding of python, 'col1':'col3' would have to parse correctly as a range, and I don't think it does. But I'd be happy to be wrong.
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It does work in this case, I've updated that notebook here. I can never remember the rules on slicing unsorted indexes, so I prefer to be explicit. For the comparison though I think it's fine to use 'col1':'col3'
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If the labels are actual column names, this works perfectly as expected (just from the one label to the other, regardless of the order). It's only when you use labels that are not included, that the index needs to be sorted
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Can you update this as well?
| =========================================== =========================================== | ||
| R pandas | ||
| =========================================== =========================================== | ||
| ``arrange(df, col1, col2)`` ``df.sort(['col1', 'col2'])`` |
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sort is deprecated, pls change it to sort_values.
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can you update |
- sort -> sort_values - unique -> drop_duplicates
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@jreback thanks for the poke |
| @@ -55,7 +55,7 @@ R pandas | |||
| ``select(df, col1, col2)`` ``df[['col1', 'col2']]`` | |||
| ``select(df, col1:col3)`` No one-line equivalent, but see [#select_range]_ | |||
| ``select(df, -(col1:col3))`` ``df.drop(cols_to_drop, axis=1)`` but see [#select_range]_ | |||
| ``distinct(select(df, col1))`` ``df.col1.unique()`` | |||
| ``distinct(select(df, col1))`` ``df[['col1']].drop_duplicates()`` | |||
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in R does this return a different shape (e.g. Series/DataFrame distinction) if you provide 1 vs multiple columns?)
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I don't know, let me see if I can reproduce.
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> mtcars
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
> distinct(select(mtcars, gear))
gear
1 4
2 3
3 5
> distinct(select(mtcars, gear, carb))
gear carb
1 4 4
2 4 1
3 3 1
4 3 2
5 3 4
6 4 2
7 3 3
8 5 2
9 5 4
10 5 6
11 5 8
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@jreback I think it's the same type either way
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hmm that's interesting. ok best then to show the frame result then (which i think is what you did) (even for 1 column)
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This is the simplest way I could find to select a single series in R:
> distinct(select(mtcars, gear))$gear
[1] 4 3 5
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@leifwalsh thanks. I merged this in. Pls have a look at the built docs (prob take a few hours). http://pandas-docs.github.io/pandas-docs-travis/comparison_with_r.html not really sure if there is a way to make the 3 tables (sorting, transforming, grouping) be the same width ..... |
git diff upstream/master | flake8 --diffThis is the beginning of a quick reference section. It's incomplete,
just did a rough translation of
http://nbviewer.jupyter.org/urls/gist.githubusercontent.com/TomAugspurger/6e052140eaa5fdb6e8c0/raw/811585624e843f3f80b9b6fe89e18119d7d2d73c/dplyr_pandas.ipynb
into tables. Should try to get some R experts to comment, and it would
be nice to have the pandas versions link to docs for the functions being
used, but I'm terrible at reStructuredText and gave up for the moment.