Official account of the pandas project

pandas 3 has been released and marks the most significant evolution of #pandas in over ten years. No more `copy()` everywhere, and no more `lambda` gymnastics. Want examples? Read this hands-on article with the main changes: datapythonista.me/blog/whats…
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We're happy to announce the release of #pandas 2.3.0. You can install it with `pip install pandas` or `conda 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/whats…
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Hey we're the fastest at writing 2300 Parquet files, we can do it in 0 minutes! Oh, wait
#duckdb parquet writer is embarrassingly fast.
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For a second I thought this headline was about @pandas_dev and was like HELLSSS YEAH! sfstandard.com/2024/06/12/sa…
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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/whats…
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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/whats…
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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/whats…
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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/w…
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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/whats…
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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/whats…
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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!
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Can #pandas be lazy? There has been some discussion and a proof of concept about it recently.
breakfast
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This is the (much more efficient) workaround which you're encouraged to use instead - nice one @CaioLCastro ! Use `concat` a single time outside the loop, rather than multiple times inside it
Replying to @quant_arb
we use a hybrid approach. Append to a list, and then concat. res = [] for x in itersomething: res.append(calculations) pd.concat(res) df.append is gruesomely inefficient so maybe it is best to remove
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#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
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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!
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We are better than SQL. Except when SQL is better.
am i the only one who likes both pandas and SQL
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