| Name | datar JSON |
| Version |
0.15.6
JSON |
| download |
| home_page | https://github.com/pwwang/datar |
| Summary | A Grammar of Data Manipulation in python |
| upload_time | 2024-03-14 04:12:20 |
| maintainer | |
| docs_url | None |
| author | pwwang |
| requires_python | >=3.8,<4.0 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
# datar
A Grammar of Data Manipulation in python
<!-- badges -->
[![Pypi][6]][7] [![Github][8]][9] ![Building][10] [![Docs and API][11]][5] [![Codacy][12]][13] [![Codacy coverage][14]][13] [![Downloads][20]][7]
[Documentation][5] | [Reference Maps][15] | [Notebook Examples][16] | [API][17]
`datar` is a re-imagining of APIs for data manipulation in python with multiple backends supported. Those APIs are aligned with tidyverse packages in R as much as possible.
## Installation
```shell
pip install -U datar
# install with a backend
pip install -U datar[pandas]
# More backends support coming soon
```
<!-- ## Maximum compatibility with R packages
|Package|Version|
|-|-|
|[dplyr][21]|1.0.8| -->
## Backends
|Repo|Badges|
|-|-|
|[datar-numpy][1]|![3] ![18]|
|[datar-pandas][2]|![4] ![19]|
|[datar-arrow][22]|![23] ![24]|
## Example usage
```python
# with pandas backend
from datar import f
from datar.dplyr import mutate, filter_, if_else
from datar.tibble import tibble
# or
# from datar.all import f, mutate, filter_, if_else, tibble
df = tibble(
x=range(4), # or c[:4] (from datar.base import c)
y=['zero', 'one', 'two', 'three']
)
df >> mutate(z=f.x)
"""# output
x y z
<int64> <object> <int64>
0 0 zero 0
1 1 one 1
2 2 two 2
3 3 three 3
"""
df >> mutate(z=if_else(f.x>1, 1, 0))
"""# output:
x y z
<int64> <object> <int64>
0 0 zero 0
1 1 one 0
2 2 two 1
3 3 three 1
"""
df >> filter_(f.x>1)
"""# output:
x y
<int64> <object>
0 2 two
1 3 three
"""
df >> mutate(z=if_else(f.x>1, 1, 0)) >> filter_(f.z==1)
"""# output:
x y z
<int64> <object> <int64>
0 2 two 1
1 3 three 1
"""
```
```python
# works with plotnine
# example grabbed from https://github.com/has2k1/plydata
import numpy
from datar import f
from datar.base import sin, pi
from datar.tibble import tibble
from datar.dplyr import mutate, if_else
from plotnine import ggplot, aes, geom_line, theme_classic
df = tibble(x=numpy.linspace(0, 2 * pi, 500))
(
df
>> mutate(y=sin(f.x), sign=if_else(f.y >= 0, "positive", "negative"))
>> ggplot(aes(x="x", y="y"))
+ theme_classic()
+ geom_line(aes(color="sign"), size=1.2)
)
```
```python
# very easy to integrate with other libraries
# for example: klib
import klib
from pipda import register_verb
from datar import f
from datar.data import iris
from datar.dplyr import pull
dist_plot = register_verb(func=klib.dist_plot)
iris >> pull(f.Sepal_Length) >> dist_plot()
```
## Testimonials
[@coforfe](https://github.com/coforfe):
> Thanks for your excellent package to port R (`dplyr`) flow of processing to Python. I have been using other alternatives, and yours is the one that offers the most extensive and equivalent to what is possible now with `dplyr`.
[1]: https://github.com/pwwang/datar-numpy
[2]: https://github.com/pwwang/datar-pandas
[3]: https://img.shields.io/codacy/coverage/0a7519dad44246b6bab30576895f6766?style=flat-square
[4]: https://img.shields.io/codacy/coverage/45f4ea84ae024f1a8cf84be54dd144f7?style=flat-square
[5]: https://pwwang.github.io/datar/
[6]: https://img.shields.io/pypi/v/datar?style=flat-square
[7]: https://pypi.org/project/datar/
[8]: https://img.shields.io/github/v/tag/pwwang/datar?style=flat-square
[9]: https://github.com/pwwang/datar
[10]: https://img.shields.io/github/actions/workflow/status/pwwang/datar/ci.yml?branch=master&style=flat-square
[11]: https://img.shields.io/github/actions/workflow/status/pwwang/datar/docs.yml?branch=master&style=flat-square
[12]: https://img.shields.io/codacy/grade/3d9bdff4d7a34bdfb9cd9e254184cb35?style=flat-square
[13]: https://app.codacy.com/gh/pwwang/datar
[14]: https://img.shields.io/codacy/coverage/3d9bdff4d7a34bdfb9cd9e254184cb35?style=flat-square
[15]: https://pwwang.github.io/datar/reference-maps/ALL/
[16]: https://pwwang.github.io/datar/notebooks/across/
[17]: https://pwwang.github.io/datar/api/datar/
[18]: https://img.shields.io/pypi/v/datar-numpy?style=flat-square
[19]: https://img.shields.io/pypi/v/datar-pandas?style=flat-square
[20]: https://img.shields.io/pypi/dm/datar?style=flat-square
[21]: https://github.com/tidyverse/dplyr
[22]: https://github.com/pwwang/datar-arrow
[23]: https://img.shields.io/codacy/coverage/5f4ef9dd2503437db18786ff9e841d8b?style=flat-square
[24]: https://img.shields.io/pypi/v/datar-arrow?style=flat-square
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"description": "# datar\n\nA Grammar of Data Manipulation in python\n\n<!-- badges -->\n[![Pypi][6]][7] [![Github][8]][9] ![Building][10] [![Docs and API][11]][5] [![Codacy][12]][13] [![Codacy coverage][14]][13] [![Downloads][20]][7]\n\n[Documentation][5] | [Reference Maps][15] | [Notebook Examples][16] | [API][17]\n\n`datar` is a re-imagining of APIs for data manipulation in python with multiple backends supported. Those APIs are aligned with tidyverse packages in R as much as possible.\n\n## Installation\n\n```shell\npip install -U datar\n\n# install with a backend\npip install -U datar[pandas]\n\n# More backends support coming soon\n```\n\n<!-- ## Maximum compatibility with R packages\n\n|Package|Version|\n|-|-|\n|[dplyr][21]|1.0.8| -->\n\n## Backends\n\n|Repo|Badges|\n|-|-|\n|[datar-numpy][1]|![3] ![18]|\n|[datar-pandas][2]|![4] ![19]|\n|[datar-arrow][22]|![23] ![24]|\n\n## Example usage\n\n```python\n# with pandas backend\nfrom datar import f\nfrom datar.dplyr import mutate, filter_, if_else\nfrom datar.tibble import tibble\n# or\n# from datar.all import f, mutate, filter_, if_else, tibble\n\ndf = tibble(\n x=range(4), # or c[:4] (from datar.base import c)\n y=['zero', 'one', 'two', 'three']\n)\ndf >> mutate(z=f.x)\n\"\"\"# output\n x y z\n <int64> <object> <int64>\n0 0 zero 0\n1 1 one 1\n2 2 two 2\n3 3 three 3\n\"\"\"\n\ndf >> mutate(z=if_else(f.x>1, 1, 0))\n\"\"\"# output:\n x y z\n <int64> <object> <int64>\n0 0 zero 0\n1 1 one 0\n2 2 two 1\n3 3 three 1\n\"\"\"\n\ndf >> filter_(f.x>1)\n\"\"\"# output:\n x y\n <int64> <object>\n0 2 two\n1 3 three\n\"\"\"\n\ndf >> mutate(z=if_else(f.x>1, 1, 0)) >> filter_(f.z==1)\n\"\"\"# output:\n x y z\n <int64> <object> <int64>\n0 2 two 1\n1 3 three 1\n\"\"\"\n```\n\n```python\n# works with plotnine\n# example grabbed from https://github.com/has2k1/plydata\nimport numpy\nfrom datar import f\nfrom datar.base import sin, pi\nfrom datar.tibble import tibble\nfrom datar.dplyr import mutate, if_else\nfrom plotnine import ggplot, aes, geom_line, theme_classic\n\ndf = tibble(x=numpy.linspace(0, 2 * pi, 500))\n(\n df\n >> mutate(y=sin(f.x), sign=if_else(f.y >= 0, \"positive\", \"negative\"))\n >> ggplot(aes(x=\"x\", y=\"y\"))\n + theme_classic()\n + geom_line(aes(color=\"sign\"), size=1.2)\n)\n```\n\n\n\n```python\n# very easy to integrate with other libraries\n# for example: klib\nimport klib\nfrom pipda import register_verb\nfrom datar import f\nfrom datar.data import iris\nfrom datar.dplyr import pull\n\ndist_plot = register_verb(func=klib.dist_plot)\niris >> pull(f.Sepal_Length) >> dist_plot()\n```\n\n\n\n## Testimonials\n\n[@coforfe](https://github.com/coforfe):\n> Thanks for your excellent package to port R (`dplyr`) flow of processing to Python. I have been using other alternatives, and yours is the one that offers the most extensive and equivalent to what is possible now with `dplyr`.\n\n[1]: https://github.com/pwwang/datar-numpy\n[2]: https://github.com/pwwang/datar-pandas\n[3]: https://img.shields.io/codacy/coverage/0a7519dad44246b6bab30576895f6766?style=flat-square\n[4]: https://img.shields.io/codacy/coverage/45f4ea84ae024f1a8cf84be54dd144f7?style=flat-square\n[5]: https://pwwang.github.io/datar/\n[6]: https://img.shields.io/pypi/v/datar?style=flat-square\n[7]: https://pypi.org/project/datar/\n[8]: https://img.shields.io/github/v/tag/pwwang/datar?style=flat-square\n[9]: https://github.com/pwwang/datar\n[10]: https://img.shields.io/github/actions/workflow/status/pwwang/datar/ci.yml?branch=master&style=flat-square\n[11]: https://img.shields.io/github/actions/workflow/status/pwwang/datar/docs.yml?branch=master&style=flat-square\n[12]: https://img.shields.io/codacy/grade/3d9bdff4d7a34bdfb9cd9e254184cb35?style=flat-square\n[13]: https://app.codacy.com/gh/pwwang/datar\n[14]: https://img.shields.io/codacy/coverage/3d9bdff4d7a34bdfb9cd9e254184cb35?style=flat-square\n[15]: https://pwwang.github.io/datar/reference-maps/ALL/\n[16]: https://pwwang.github.io/datar/notebooks/across/\n[17]: https://pwwang.github.io/datar/api/datar/\n[18]: https://img.shields.io/pypi/v/datar-numpy?style=flat-square\n[19]: https://img.shields.io/pypi/v/datar-pandas?style=flat-square\n[20]: https://img.shields.io/pypi/dm/datar?style=flat-square\n[21]: https://github.com/tidyverse/dplyr\n[22]: https://github.com/pwwang/datar-arrow\n[23]: https://img.shields.io/codacy/coverage/5f4ef9dd2503437db18786ff9e841d8b?style=flat-square\n[24]: https://img.shields.io/pypi/v/datar-arrow?style=flat-square\n",
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