pandas

“Still Pandas has a place in my arsenal. Mostly because I know it better and I like it.”

“always found pandas more convinient than polars for quick and dirty data crunching”

“Pandas is the swiss knife tool of data science that lets you do anything with the”

“Most folks just need to learn how to use pandas well and that will open enough doors”

“Pandas is great because it allows us to validate on a sampling or smaller time window dataset”

“Pandas is the swiss knife tool of data science that lets you do anything with the data”

“we finally nixed Pandas from Data Ingestion, now reading excel files takes 2 seconds instead of 2 minutes”

“the ergonomics of good pandas documentation and community knowledge make it a better choice”

“being able to throw pandas at something or matplotlib or whatever is super valuable”

“certain idioms did not work efficiently because they escaped the pandas kernel, causing memory copy and other bad behavior”

“Pandas is the most established, although has a lot of technical limitations, as well as complexities.”

“Pandas is so verbose and hard to debug, most of the times struggle to be performant on small datasets.”

“load data in pandas and display it in a Jupyter notebook with style.background_gradient()”

“a non-professional programmer can use scikit-learn and pandas and matplotlib to put their skills on wheels”

“There are a lot of parts on pandas that made me stop using it long ago but first class duplicates handling is not among them.”

“Pandas is clearly quite ergonomic for various exploratory interactive analyses, but the API is, imo, awful.”

“Pandas is fantastic for doing iterative and interactive research on semi-structured data.”

“has been open source for almost two decades, and is supported by many companies”

“The first time I read pandas code, I saw something along the lines of df[df["age"] < 3] and thought I was having a stroke.”

“I personally agree with the criticisms of the syntax, but Pandas is real and it was not easy to build it.”

“its creator (Wes McKinney) has changed the data ecosystem for the better with the learnings coming from pandas”

“The pandas example is a good approximation of how I think and want to transform/process data even if it is ugly under the hood.”

“Pandas deserves a ton of respect in my opinion. I built my career on knowing it well and using it daily for a decade”

“For better or worse, like Excel and like the simpler programming languages of old, Pandas lets you overwrite data in place.”

“Pandas is good, I've used it extensively, but agree it's not fit for production use.”

“It was the bridge I crossed that moved me from living in Excel to living in code.”

“Let your analysts use DuckDB or pandas/polars then instead of quirky command line tools.”

“So much of my life time has been lost trying to get dataframe and non-dataframe libraries to work together.”

“it started as an sql alchemy and pandas service. Then when it was too slow, I got involved”

“think pandas stores basically just ints floats and strings as it is numpy underneath”

“it is a pretty language but obfuscates the relational database with a non-relational lanuguage”

“Pandas and Numpy made it quite easy to get up and running with a lot of analytics.”

“Pandas and Matplotlib and SciPy all used via an ipython notebook were almost a replacement”

“Code using pandas is testable and reusable in much the same way as any other code”

“in any context where performance or scientific computing ecosystem interoperability matters”

“the vectorisation of operations in it and other table based formats mean they’re not easy to replace performantly”

“The thousand-plus data integrity tests I've written in pandas tell a different story”

“Pandas is really poor dataframe experience outside of it's original use case which was financial time series.”

“it's a solved problem and Python is eating good in terms of quality dataframe libraries”

“the author can use Pandas or Polars or another data-science-related library/framework in Python to get the job done”