plydata VS Pandas

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/

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)) ```  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() ```  For more detailed and advanced usage, see https://pwwang.github.io/datar/

Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.

In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.