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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, 7:02:26 AM 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
gjreda·Jul 6, 2014
SEEKING WORK - Remote (based in Chicago) (part-time) Who I can help: People/companies/startups looking to gain some value out of the data they're collecting, specifically around user behavior. Experience: First data hire at GrubHub and currently leading the data analytics team. I take pride in being a data generalist (can do the data science or data engineering thing, while keeping business value in mind - just because some 'discovery' is interesting, doesn't mean it's valuable for the business). Prior to GrubHub, I spent three years as an analytics consultant at one of the Big Four. Technologies: My day-to-day stack is some combination of: Python + pandas + MySQL + Amazon Elastic MapReduce + Hive + Amazon Redshift. Others: HTML, CSS, d3js, R, Flask (python), nearly all RDBMS platforms (SQL Server, Oracle, Postgres, SQLite), Google Analyt
Read the rest
ics Projects: - Open Source contributions to pandas (https://github.com/pydata/pandas) and tweepy (https://github.com/tweepy/tweepy) - My blog (http://www.gregreda.com), which has been at the top of HN several times (tutorials on web scraping, pandas, unix commands) Links: Website - http://www.gregreda.com LinkedIn - https://www.linkedin.com/in/gjreda GitHub - https://github.com/gjreda Stack Overflow - http://stackoverflow.com/users/1419514/gjreda Email - gjreda@gmail.com
In Ask HN: Freelancer? Seeking freelancer? (July 2014)
birken·Jun 10, 2014
Pandas (data analysis library for python) has a lot of cython and C optimizations for datetime string parsing: They have their own C function which parses ISO-8601 datetime strings: https://github.com/pydata/pandas/blob/2f1a6c412c3d1cbdf56610... They have a version of strptime written in cython: https://github.com/pydata/pandas/blob/master/pandas/tslib.py... I'm not saying these are better/worse than your solution, I haven't done any benchmarks and the pandas functions sometimes cut a few corners, but perhaps there is something useful there for reference anyways. They also don't deal directly in datetime.datetime objects, they use pandas specific intermediate objects, but should be simple enough to grok. Having done some work with dateutil, I will tell you that dateutil.parser.parse is slow, but its main use case shouldn't be converting
Read the rest
strings to datetimes if you already know the format. If you know the format already you should use datetime.strptime or some faster variant (like the one above). There is a nice feature of pandas where given a list of datetime-y strings of an arbitrary format, it will attempt to guess the format using dateutil's lexer (https://github.com/pydata/pandas/blob/master/pandas/tseries/...) combined with trial/error, and then try to use a faster parser instead of dateutil.parser.parse to convert the array if possible. In the general case this resulted in about a 10x speedup over dateutil.parser.parse if the format was guessable.
In Show HN: A fast ISO8601 date-time parser for Python

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