Mailman 3 May 2020 - Pandas-dev

A case for a simplified (non-consolidating) BlockManager with 1D blocks
by Joris Van den Bossche June 11, 2020

June 11, 2020

Hi list, Rewriting the BlockManager based on a simpler collection of 1D-arrays is actually on our roadmap (see here <https://pandas.pydata.org/docs/dev/development/roadmap.html#block-manager-r…>), and I also touched on it in a mailing list discussion about pandas 2.0 earlier this year (see here <https://mail.python.org/pipermail/pandas-dev/2020-February/001180.html>). But since the topic came up again recently at the last online dev meeting (and also Uwe Korn who wrote a nice blog post <https://uwekorn.com/2020/05/24/the-one-pandas-internal.html> about this yesterday), I thought to do a write-up of my thoughts on why I think we should actually move towards a simpler, non-consolidating BlockManager with 1D blocks. *Simplication of the internals* It's regularly brought up as a reason to have 2D EextensionArrays (EAs) because right now we have a lot of special cases for 1D EAs in the internals. But to be clear: the additional complexity does not come from 1D EAs in itself, it comes from the fact that we have a mixture of 2D and 1D blocks. Solving this would require a consistent block dimension, and thus removing this added complexity can be done in two ways: have all 1D blocks, or have all 2D blocks. Just to say: IMO, this is not an argument in favor of 2D blocks / consolidation. Moreover, when going with all 1D blocks, we cannot only remove the added complexity from dealing with the mixture of 1D/2D blocks, we will *also* be able to reduce the complexity of dealing with 2D blocks. A BlockManager with 2D blocks is inherently more complex than with 1D blocks, as one needs to deal with proper alignment of the blocks, a more complex "placement" logic of the blocks, etc. I think we would be able to simplify the internals a lot by going with a BlockManager as a store of 1D arrays. *Performance* Performance is typically given as a reason to have consolidated, 2D blocks. And of course, certain operations (especially row-wise operations, or on dataframes with more columns as rows) will always be faster when done on a 2D numpy array under the hood. However, based on recent experimentation with this (eg triggered by the block-wise frame ops PR <https://github.com/pandas-dev/pandas/pull/32779>, and see also some benchmarks I justed posted in #10556 <https://github.com/pandas-dev/pandas/issues/10556#issuecomment-633703160> / this gist <https://gist.github.com/jorisvandenbossche/b8ae071ab7823f7547567b1ab9d4c20c>), I also think that for many operations and with decent-sized dataframes, this performance penalty is actually quite OK. Further, there are also operations that will *benefit* from 1D blocks. First, operations that now involve aligning/splitting blocks, re-consolidation, .. will benefit (e.g. a large part of the slowdown doing frame/frame operations column-wise is currently due to the consolidation in the end). And operations like adding a column, concatting (with axis=1) or merging dataframes will be much faster when no consolidation is needed. Personally, I am convinced that with some effort, we can get on-par or sometimes even better performance with 1D blocks compared to the performance we have now for those cases that 90+% of our users care about: - With limited effort optimizing the column-wise code paths in the internals, we can get a long way. - After that, if needed, we can still consider if parts of the internals could be cythonized to further improve certain bottlenecks (and actually cythonizing this will also be simpler for a simpler non-consolidating block manager). *Possibility to get better copy/view semantics* Pandas is badly known for how much it copies ("you need 10x the memory available as the size of your dataframe"), and having 1D blocks will allow us to address part of those concerns. *No consolidation = less copying.* Regularly consolidating introduces copies, and thus removing consolidation will mean less copies. For example, this would enable that you can actually add a single column to a dataframe without having to copy to the full dataframe. *Copy / view semantics* Recently there has been discussion again around whether selecting columns should be a copy or a view, and some other issues were opened with questions about views/copies when slicing columns. In the consolidated 2D block layout this will always be inherently messy, and unpredictable (meaning: depending on the actual block layout, which means in practice unpredictable for the user unaware of the block layout). Going with a non-consolidated BlockManager should at least allow us to get better / more understandable semantics around this. ------------------------------ *So what are the reasons to have 2D blocks?* I personally don't directly see reasons to have 2D blocks *for pandas itself* (apart from performance in certain row-wise use cases, and except for the fact that we have "always done it like this"). But quite likely I am missing reasons, so please bring them up. But I think there are certainly use cases where 2D blocks can be useful, but typically "external" (but nonetheless important) use cases: conversion to/from numpy, xarray, etc. A typical example that has recently come up is scikit-learn, where they want to have a cheap dataframe <-> numpy array roundtrip for use in their pipelines. However, I personally think there are possible ways that we can still accommodate for those use cases, with some effort, while still having 1D Blocks in pandas itself. So IMO this is not sufficient to warrant the complexity of 2D blocks in pandas. (but will stop here, as this mail is getting already long ..). Joris

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tslibs 2.0 and non-nanosecond datetime64/timedelta64
by Brock Mendel June 2, 2020

June 2, 2020

This is a discussion of what it would take to support non-nanosecond datetime64/timedelta64 dtypes and what decisions would need to be made along the way. The implementation would probably consist of: - add a NPY_DATETIMEUNIT attribute to Timestamp and Datetime64TZDtype - for timezone-related methods: - short-term: cast to nanosecond, use existing code, cast back to other unit - longer-term: update existing code to support non-nano units directly - comb through the code for all the places where we implicitly assume nano units and update - tests, so, so many tests We could then consider de-duplication. Tick is already redundant with Timedelta, and Timestamp[H] would render Period[H] redundant. With appropriate deprecation cycle, we could rip out a bunch of code. Another possibility is to try to upstream some code to numpy, which they have recently been receptive to (#16266 <https://github.com/numpy/numpy/pull/16266>, #16363 <https://github.com/numpy/numpy/pull/16363>, #16364 <https://github.com/numpy/numpy/pull/16364>, #16352 <https://github.com/numpy/numpy/issues/16352>, <https://github.com/numpy/numpy/issues/16195>#16195 <https://github.com/numpy/numpy/issues/16195>). @rgommers tells me that trying to implement a tz-aware datetime64 dtype in numpy would be "folly, that way madness lies", but that it might be more feasible once @seberg's dtype refactor lands. More realistically short-term, if we convinced numpy to update NPY_DATETIMEUNIT to include the anchored quarter/year/week units we use for Period, we could condense a lot of confusing enum-like code. Tangentially related: with zoneinfo (PEP 615) we should consider making those our canonical tzinfos and converting any dateutil/pytz tzinfos we encounter to those. They are implemented in C, so I'm _hopeful_ we can make some of our vectorized tzconversion code unnecessary. @pganssle has suggested we implement our own tzinfos, but I'm holding out hope we can keep that upstream.

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[ANN] pandaSDMX 1.0.0 released
by Dr. Leo May 15, 2020

May 15, 2020

[ANN] pandaSDMX 1.0.0 released Hi, Two years after the 0.9 release I am pleased to announce the availability of pandaSDMX 1.0.0. This is a major feature release including rewrites in virtually all areas. Certain backwards-incompatible API changes appeared inevitable, but are largely outweighed by a host of new and enhanced features. Highlights include: • more complete and accurate implementation of the SDMX information model including hierarchical code-lists and facets. This feature was overdue. It will considerably facilitate the interpretation and representation of data and metadata. • Better handling of idiosyncrasies of data sources should ease data acquisition in corner-cases • Streamlined API and more informative string representations for interactive data acquisition and exploration • The information model has been decoupled from the XML and JSON readers so that arbitrary data sources outside the SDMX ecosystem can be embedded programmatically. This shift in architecture could eventually seed the transformation of pandaSDMX from a pure client library to an end-to-end SDMX platform for the generation of SDMX files served over HTTP. • Easier and more flexible configuration of HTTP connections through user-provided requests Sessions • vastly extended test suite and streamlined documentation • Modern code base leveraging typing and pydantic Quick start and links ------------------------- • Installation (requires Python 3.7): $ pip install pandasdmx • Documentation: https:pandasdmx.readthedocs.io/ • Github: https://github.com/dr-leo/pandaSDMX Roadmap --------- • add an intake driver/plugin exposing SDMX datasets and metadata • provide conda package InternationalStrings: re-implement support for locale selection • support SDMXJSON structure-messages which have recently been added to the SDMX standard • Fix a few known issues Help wanted! Credits ----------- Many great people have generously contributed to this release. Even the lion's share of the development was temporarily shouldered by a single collaborator. A big thanks to all of them! What is pandaSDMX? ---------------------- pandaSDMX is an Apache 2.0-licensed Python library that implements SDMX 2.1 (ISO 17369:2013), a format for exchange of statistical data and metadata used by national statistical agencies, central banks, and international organisations. pandaSDMX can be used to: • explore the data and metadata available from many data providers such as the World Bank, International Monetary Fund, Eurostat, the ECB, OECD, and United Nations; • parse data and metadata in SDMX-ML (XML) or SDMX-JSON formats—either: o from local files, or o retrieved from SDMX web services, with query validation and caching; • convert data and metadata into pandas objects, for use with the analysis, plotting, and other tools in the Python data ecosystem.

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[ANN] pandaSDMX 1.0.0 released
by Dr. Leo May 15, 2020

May 15, 2020

Hi, Two years after the 0.9 release I am pleased to announce the availability of pandaSDMX 1.0.0. This is a major feature release including rewrites in virtually all areas. Certain backwards-incompatible API changes appeared inevitable, but are largely outweighed by a host of new and enhanced features. Highlights include: • more complete and accurate implementation of the SDMX information model including hierarchical code-lists and facets. This feature was overdue. It will considerably facilitate the interpretation and representation of data and metadata. • Better handling of idiosyncrasies of data sources should ease data acquisition in corner-cases • Streamlined API and more informative string representations for interactive data acquisition and exploration • The information model has been decoupled from the XML and JSON readers so that arbitrary data sources outside the SDMX ecosystem can be embedded programmatically. This shift in architecture could eventually seed the transformation of pandaSDMX from a pure client library to an end-to-end SDMX platform for the generation of SDMX files served over HTTP. • Easier and more flexible configuration of HTTP connections through user-provided requests Sessions • vastly extended test suite and streamlined documentation • Modern code base leveraging typing and pydantic Quick start and links ------------------------- • Installation (requires Python 3.7): $ pip install pandasdmx • Documentation: https:pandasdmx.readthedocs.io/ • Github: https://github.com/dr-leo/pandaSDMX Roadmap --------- • add an intake driver/plugin exposing SDMX datasets and metadata • provide conda package InternationalStrings: re-implement support for locale selection • support SDMXJSON structure-messages which have recently been added to the SDMX standard • Fix a few known issues Help wanted! Credits ----------- Many great people have generously contributed to this release. Even the lion's share of the development was temporarily shouldered by a single collaborator. A big thanks to all of them! What is pandaSDMX? ---------------------- pandaSDMX is an Apache 2.0-licensed Python library that implements SDMX 2.1 (ISO 17369:2013), a format for exchange of statistical data and metadata used by national statistical agencies, central banks, and international organisations. pandaSDMX can be used to: • explore the data and metadata available from many data providers such as the World Bank, International Monetary Fund, Eurostat, the ECB, OECD, and United Nations; • parse data and metadata in SDMX-ML (XML) or SDMX-JSON formats—either: o from local files, or o retrieved from SDMX web services, with query validation and caching; • convert data and metadata into pandas objects, for use with the analysis, plotting, and other tools in the Python data ecosystem.

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