Nuevo post en el blog de #juncotic! 💪
Seguimos con #python de la mano de @andrea_navarro
¿Han usado #Pandas para trabajar con datos?
Hoy Andrea nos explica cómo usarlo para ordenar columnas de un DataFrame, con ejemplos prácticos, y un CSV descargable para jugar con los datos 😃
Pueden leerlo acá: 👇
https://juncotic.com/ordenamiento-de-columnas-con-pandas/
Espero que les guste y sirva! 🙂
I've talked about creating data.frames and tibbles before, but it is an important topic so I have covered it again. This time specifically from the perspective of creating them from vectors. Post: www.spsanderson.com/steveondata/... #R #RStats #tibble #dplyr #tidyverse #dataframe #baseR #blog
I've talked about creating data.frames and tibbles before, but it is an important topic so I have covered it again. This time specifically from the perspective of creating them from vectors.
Post: https://www.spsanderson.com/steveondata/posts/2025-06-09/
#R #RStats #tibble #dplyr #tidyverse #dataframe #baseR #blog #Technology
I've talked about creating data.frames and tibbles before, but it is an important topic so I have covered it again. This time specifically from the perspective of creating them from vectors.
Post: https://www.spsanderson.com/steveondata/posts/2025-06-09/
#R #RStats #tibble #dplyr #tidyverse #dataframe #baseR #blog #Technology
Anybody using the narwhals #python package for #dataframe manipulations?
Very comfortable with #pandas and #polars syntax, but although #narwhals is supposed to be very close to polars, it is a subset and I find that there is a lot of basic stuff missing. Trying to figure out how to get the first part of a str.split.
in pandas I just add .str[0] and in polars .list.get(0)
with narwhals neither approaches are implemented. Any idea as what is supported in narwhals to do this?
#programming
Fahmatrix – A Lightweight, Pandas-Like DataFrame Library for Java
https://github.com/moustafa-nasr/fahmatrix
#HackerNews #Fahmatrix #Pandas #Java #DataFrame #Lightweight #Library
Computing travel time matrices in r⁵py from @geopandas #DataFrame is two lines of code:
(1) create an r5py.TransportNetwork from @openstreetmap and #GTFS data
(2) turn it into an r5py.TravelTimeMatrix()
Try it out in #binder: https://r5py.readthedocs.io/stable/user-guide/user-manual/quickstart.html
OPEN SOURCE 🚀
The Problem❔
There have been many instances where I needed to compare two dataframes and analyze their differences. To address this need, I created a fast Python library called "data_fingerprint" that does exactly that.
Check it out and let me know what you think! 🕵♂️
https://github.com/SimpleSimpler/data_fingerprint
#datascience #python #pandas #dataanalytics #dataengineering #dataframe #data
Parsing CSV with units in the header · Issue #166 · hgrecco/pint-pandas
https://github.com/hgrecco/pint-pandas/issues/166
Now we can read a #csv file with a header like `time / s,mass / g` into #pandas and call `.pint.quantify()` to get a #dataframe in which the columns have #units as in #Pints !
Handy for CSV restricted to single-row headers, as in Confluence Databases and Microsoft Lists.
Hi fedi 😊
I am struggling with #vegaaltair.
Do you have any resources of density faceted plots ?
I am trying to #plot densities of a selection of columns of a #dataframe with mean and median highlighted.
what I can do is #thresholding / binarize and obtain the boundary, but not save each boundary as its own file (hopefully as a #DataFrame)
I’ve written article about #pandera - #Python package used for #pandas #DataFrame validation: https://www.linkedin.com/pulse/do-you-use-dataframes-your-production-environment-schema-molinski-9swaf
(Sorry that this is on LinkedIn, but I’m trying to reach general audience there - my main goal is to promote the package and @pyOpenSci , and LinkedIn has larger community than my personal blog 😊)
[Перевод] 7 продвинутых приемов pandas для науки о данных
Pandas — это основная библиотека для работы с данными. Вот несколько приёмов, которые я использую, чтобы быстрее и проще выполнять повторяющиеся задачи по работе с данными.
https://habr.com/ru/articles/858894/
#pandas #datascience #numpy #matplotlib #анализ_данных #dataframe
Pandas НЕ для анализа данных
В среде питонистов библиотека Pandas пользуется большой популярностью и по большей мере известна в контексте DataSciense и анализа данных. DataFrame пандас позволяет не только всячески манипулировать данными, но и выводить их в нужном формате, предоставляя широкие возможности для кастомизации. Например, использовали ли вы объекты класса Styler , входящего в состав Pandas? Мне показалось интересным взглянуть на Pandas с этой стороны.
https://habr.com/ru/articles/822793/
#python #pandas #django #dataframe #html #server_side_rendering
Why Ibis?
Ibis defines a #Python #dataframe #API that executes on any query engine – the #frontend for any #backend data platform, with 20+ backends today. This allows Ibis to have excellent performance – as good as the backend it is connected to – with a consistent user experience.
Interesting quasi replacement for pandas (?). Worth looking into. #polars #dataframe #rust #pandas : "Polars — Why we have rewritten the string data type"(https://pola.rs/posts/polars-string-type/)
Interesting but so high level that I am not sure it is super useful. #polars #rust #dataframe #architecture #explainer #query #query-optimization : "Polars — A bird's eye view of Polars"(https://pola.rs/posts/polars_birds_eye_view/)
Ibis is the portable Python dataframe library. Ibis defines a Python dataframe API that executes on any query engine – the frontend for any backend data platform, with 20+ backends today.
Ibis provides a common API for data manipulation in Python, and compiling that API into the backend’s native language. This means you can learn a single API and use it across any supported backend.
