plydata VS datar

Compare plydata vs datar and see what are their differences.

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
featured
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com
featured
plydata datar
2 4
274 256
- -
0.0 7.4
8 months ago about 2 months ago
Python Python
BSD 3-clause "New" or "Revised" License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

plydata

Posts with mentions or reviews of plydata. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-25.
  • datar: the dplyr in python
    3 projects | /r/Rlanguage | 25 Jun 2021
    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), y=['zero', 'one', 'two', 'three'] ) df >> mutate(z=f.x) """# output x y z 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 0 0 zero 0 1 1 one 0 2 2 two 1 3 3 three 1 """ df >> filter(f.x>1) """# output: x y 0 2 two 1 3 three """ df >> mutate(z=if_else(f.x>1, 1, 0)) >> filter(f.z==1) """# output: x y z 0 2 two 1 1 3 three 1 """ Works with plotnine # example grabbed from https://github.com/has2k1/plydata import numpy from datar.base import sin, pi 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)) https://preview.redd.it/w0hs4m8fyf771.png?width=697&format=png&auto=webp&s=eadd7473a9e3393c2d58531c0b2b12f849c27e5e Easy to integrate with other libraries import klib from pipda import register_verb from datar.datasets import iris from datar.dplyr import pull dist_plot = register_verb(func=klib.dist_plot) iris >> pull(f.Sepal_Length) >> dist_plot() https://preview.redd.it/w8b8ouagyf771.png?width=892&format=png&auto=webp&s=3cc8f04e63be710f593b2b6128073f65cf7ffaa4 For more detailed and advanced usage, see https://pwwang.github.io/datar/
    2 projects | /r/Python | 25 Jun 2021
    df = tibble( x=range(4), y=['zero', 'one', 'two', 'three'] ) df >> mutate(z=f.x) """# output x y z 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 0 0 zero 0 1 1 one 0 2 2 two 1 3 3 three 1 """ df >> filter(f.x>1) """# output: x y 0 2 two 1 3 three """ df >> mutate(z=if_else(f.x>1, 1, 0)) >> filter(f.z==1) """# output: x y z 0 2 two 1 1 3 three 1 """ ``` Works with plotnine ```python example grabbed from https://github.com/has2k1/plydata import numpy from datar.base import sin, pi 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)) ``` ![plotnine](https://github.com/pwwang/datar/raw/master/example.png) Easy to integrate with other libraries ```python import klib from pipda import register_verb from datar.datasets import iris from datar.dplyr import pull dist_plot = register_verb(func=klib.dist_plot) iris >> pull(f.Sepal_Length) >> dist_plot() ``` ![klib](https://github.com/pwwang/datar/raw/master/example2.png) For more detailed and advanced usage, see https://pwwang.github.io/datar/

datar

Posts with mentions or reviews of datar. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.
  • Difficulty transitioning between R and Python?
    2 projects | /r/datascience | 28 Oct 2022
  • What would it take to recreate dplyr in Python?
    3 projects | news.ycombinator.com | 17 Jan 2022
  • datar: the dplyr in python
    3 projects | /r/Rlanguage | 25 Jun 2021
    datar does not only mimic the piping syntax, but follows the API design from dplyr as much as possible, and is tested with its test cases.
    2 projects | /r/Python | 25 Jun 2021
    df = tibble( x=range(4), y=['zero', 'one', 'two', 'three'] ) df >> mutate(z=f.x) """# output x y z 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 0 0 zero 0 1 1 one 0 2 2 two 1 3 3 three 1 """ df >> filter(f.x>1) """# output: x y 0 2 two 1 3 three """ df >> mutate(z=if_else(f.x>1, 1, 0)) >> filter(f.z==1) """# output: x y z 0 2 two 1 1 3 three 1 """ ``` Works with plotnine ```python example grabbed from https://github.com/has2k1/plydata import numpy from datar.base import sin, pi 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)) ``` ![plotnine](https://github.com/pwwang/datar/raw/master/example.png) Easy to integrate with other libraries ```python import klib from pipda import register_verb from datar.datasets import iris from datar.dplyr import pull dist_plot = register_verb(func=klib.dist_plot) iris >> pull(f.Sepal_Length) >> dist_plot() ``` ![klib](https://github.com/pwwang/datar/raw/master/example2.png) For more detailed and advanced usage, see https://pwwang.github.io/datar/

What are some alternatives?

When comparing plydata and datar you can also consider the following projects:

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pipda - A framework for data piping in python

Dask - Parallel computing with task scheduling

brain-brew - Automated Anki flashcard creation and extraction to/from Csv