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pandas-dev
pandas
Python2013–2026
pandas-dev / pandas

pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Data & DatabasesScience & EngineeringLibraries & SDKs
Explore on GitHubRead Hacker News discussions
49Distinct people
56Mentions
0Explicit recommenders
50.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 5:05:26 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year56 dated mentions

Observed mentions: 2013-09-30 22:03:17 UTC to 2026-03-13 23:46:22 UTC.

02462013-01-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20132014-01-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 6 mentions; 1 submissions, 5 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 5 mentions; 2 submissions, 3 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 7 mentions; 4 submissions, 3 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 7 mentions; 1 submissions, 6 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 5 mentions; 2 submissions, 3 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20132013-01-01 00:00:00 UTC2014-01-01 00:00:00 UTC101
20142014-01-01 00:00:00 UTC2015-01-01 00:00:00 UTC404
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC404
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC303
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC615
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC523
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC202
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC743
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC303
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC716
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC514
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC404
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC523

What Hacker News says

4 sources
westurner·Jul 30, 2025
Is it possible to apply these optimizations to Arrow? Arrow's struct is called StructArray. Fields of StructArray have a StructType. StructType has .bit_width and .byte_width attrs in Python and probably the other implementations too: https://arrow.apache.org/docs/python/generated/pyarrow.Struc... Arrow supports bitfields with BooleanArray, and enums with categoricals but "BUG: Categorical columns using the PyArrow backend requires 4x more memory" (open) https://github.com/pandas-dev/pandas/issues/58062 : > On disk Parquet appears to store the category data as logical type String which is compressed with snappy and encoded Arrow Flight RPC handles nested structs with enums over the wire somehow too FWIU
In Writing memory efficient C structs
westurner·Mar 16, 2025
Can this be merged into pandas? Pandas does not currently install tqdm by default. pandas-dev/pandas//pyproject.toml [project.optional-dependencies] https://github.com/pandas-dev/pandas/blob/8943c97c597677ae98... Dask solves for various adjacent problems; IDK if pandas, dask, or dask-cudf would be faster with async? Dask docs > Scheduling > Dask Distributed (local) https://docs.dask.org/en/stable/scheduling.html#dask-distrib... : > Asynchronous Futures API Dask docs > Deploy Dask Clusters; local multiprocessing poll, k8s (docker desktop, podman-desktop,), public and private clouds, dask-jobqueue (SLURM,), dask-mpi: https://docs.dask.org/en/stable/deploying.html#deploy-dask-c... Dask docs > Dask DataFrame: https://docs.dask.org/en/stable/dataframe.html : > Dask DataFrames are a collection of many pandas DataFrames. > The API is the sam
Read the rest
e. The execution is the same. > [concurrent.futures and/or @dask.delayed] tqdm.dask: https://tqdm.github.io/docs/dask/#tqdmdask .. tests/tests_pandas.py: https://github.com/tqdm/tqdm/blob/master/tests/tests_pandas.... , tests/tests_dask.py: https://github.com/tqdm/tqdm/blob/master/tests/tests_dask.py tqdm with dask.distributed: https://github.com/tqdm/tqdm/issues/1230#issuecomment-222379... , not yet a PR: https://github.com/tqdm/tqdm/issues/278#issuecomment-5070062... dask.diagnostics.progress: https://docs.dask.org/en/stable/diagnostics-local.html#progr... dask.distributed.progress: https://docs.dask.org/en/stable/diagnostics-distributed.html... dask-labextension runs in JupyterLab and has a parallel plot visualization of the dask task graph and progress through it: https://github.com/dask/dask-labextension dask-jobqueue docs > Interactive Use > Viewing the Dask Dashboard: https://jobqueue.dask.org/en/latest/clusters-interactive.htm... https://examples.dask.org/ > "Embarrassingly parallel Workloads" tutorial re: "three different ways of doing this with Dask: dask.delayed, concurrent.Futures, dask.bag": https://examples.dask.org/applications/embarrassingly-parall...
In Show HN: Aiopandas – Async .apply() and .map() for Pandas, Faster API/LLMs Calls
swaraj·Feb 11, 2025
They are when the only reason they are flagged as security updates is because some a single group deems a very rare, obscure edge case as a HIGH severity vuln when in practice it rarely is => this leads to having to upgrade a minor version of a library that ends up causing breaking changes. This is the recent thread I'm down. Pandas 2.2 broke SQLalchemy backwards compatibility: https://stackoverflow.com/questions/38332787/pandas-to-sql-t... + https://github.com/pandas-dev/pandas/issues/57049#issuecomme...
In We are destroying software

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