We're happy to announce the release of #pandas 2.2.2. You can install it with `pip install pandas` or `mamba install -c conda-forge pandas`. Thanks to all contributors and sponsors who made this release possible! The release notes can be found at: pandas.pydata.org/docs/whatsnew/…

We're happy to announce the release of #pandas 2.2.1. You can install it with `pip install pandas` or `mamba install -c conda-forge pandas`. Thanks to all contributors and sponsors who made this release possible! The release notes can be found at: pandas.pydata.org/docs/whatsnew/…

How fast can a CSV file be processed? I explain in detail comparing many options such as pandas, DuckDB, polars data, #Python , #R , #rustlang and more in this new blog post:

datapythonista.me/blog/how-fast-…

We're happy to announce the release of #pandas 2.2.0. You can install it with `pip install pandas` or `mamba install -c conda-forge pandas`. Thanks to all contributors and sponsors who made this release possible! The release notes can be found at: pandas.pydata.org/docs/whatsnew/…

We are excited to announce a release candidate for #pandas 2.2.0 has just been released.

If all goes well, we'll release #pandas 2.2.0 in about 2 weeks.

Full list of changes and contributors: pandas.pydata.org/docs/dev/whats…

We're happy to announce the release of #pandas 2.1.4.

You can install it with `pip install pandas` or `mamba install -c conda-forge pandas`. Thanks to all contributors and sponsors who made this release possible!

The release notes can be found at: pandas.pydata.org/docs/whatsnew/…

A new edition of the data manipulation, analysis and visualization in #python course at Ghent University by Joris Van den Bossche and myself is scheduled on 29, 31 January and 2 February 2024. For more information, see shorturl.at/qDVW9 #pandas #seaborn #matplotlib

We're happy to announce the release of #pandas 2.1.2. You can install it with `pip install pandas` or `mamba install -c conda-forge pandas`. Thanks to all contributors and sponsors who made this release possible!

The release notes can be found at:
pandas.pydata.org/docs/whatsnew/…

We're happy to announce the release of #pandas 2.1.1. You can install it with `pip install pandas` or `mamba install -c conda-forge pandas`. You can find what's new in this version in the release notes. Thanks to all contributors and sponsors who made this release possible!

Can #pandas be lazy?

There has been some discussion and a proof of concept about it recently.

Felipe I can't answer that: read the source code of every project you find interesting!

For me I was a pandas user, wanted to improve IO support with Stata files, and dug into the code to figure out how it worked. My first pr 🥹
github.com/pandas-dev/pan…

This is the (much more efficient) workaround which you're encouraged to use instead - nice one Caio L. de Castro !

Use `concat` a single time outside the loop, rather than multiple times inside it

#pandas has two internal ways to store strings: NumPy and PyArrow (faster).

pandas 3.0 will change the default and strings will use PyArrow when for example calling read_csv.

You can get this change now in pandas 2.1 with:

pandas.options.future.infer_string = True

We're happy to announce the release of #pandas 2.1.0. You can install it with `pip install pandas` or `mamba install -c conda-forge pandas`. You can find what's new in this version in the release notes. Thanks to all contributors and sponsors who made this release possible!

Do you know how to extend #pandas with a fast language like #rustlang ?

Core developer Marc Garcia shows you how in this step by step tutorial at EuroSciPy.

m.youtube.com/watch?v=iUEzNm…

Is it possible to write dataframe-agnostic code that works with #pandas dataframes but also with dataframes from other libraries? Are we there yet?

Core developer Marco Gorelli gives you the answer in this EuroSciPy talk.

m.youtube.com/watch?v=gAGG21…

Do you want to learn more about #pandas 2.0 and beyond?

Core developers Joris Van den Bossche and Richard Shadrach gave a talk about it at EuroSciPy.

youtu.be/NK7RuG4rQpI

We are better than SQL.

Except when SQL is better.

Artificial intelligence may not be so intelligent if it uses pandas .apply() when not strictly necessary.

Our operations are usually vectorized (very fast), .apply() is usually not, so it may be very slow.

Avoid loops and apply if a pandas operation exists for what you need.

We are excited to announce a release candidate for #pandas 2.1.0 has just been released.

If all goes well, we'll release #pandas 2.1.0 in about 1 week.

Full list of changes and contributors: pandas.pydata.org/docs/dev/whats…