GITHUB . COM {}

Matching Content Categories {📚}

• Technology & Computing
• Health & Fitness
• Cryptocurrency

Content Management System {📝}

What CMS is github.com built with?

Github.com utilizes WORDPRESS.

Traffic Estimate {📈}

What is the average monthly size of github.com audience?

🚀🌠 Tremendous Traffic: 10M - 20M visitors per month


Based on our best estimate, this website will receive around 13,918,967 visitors per month in the current month.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Github.com Make Money? {💸}

Subscription Packages {💳}

How Much Does Github.com Make? {💰}

Subscription Packages {💳}

Prices on github.com are in US Dollars ($). They range from $4.00/month to $21.00/month.
We estimate that the site has approximately 6,945,396 paying customers.
The estimated monthly recurring revenue (MRR) is $29,170,665.
The estimated annual recurring revenues (ARR) are $350,047,976.

Wordpress Themes and Plugins {🎨}

What WordPress theme does this site use?

It is strange but we were not able to detect any theme on the page.

What WordPress plugins does this website use?

It is strange but we were not able to detect any plugins on the page.

Keywords {🔍}

index, issue, pandas, tickers, groupby, unsorted, sorted, performance, sign, perf, slows, crawl, works, fine, projects, closed, furechan, indexed, added, navigation, code, pull, requests, actions, security, version, confirmed, exists, import, ncols, sample, rawdata, runs, triage, reviewed, team, member, memory, execution, speed, lukemanley, jreback, milestone, github, type, footer, skip, content, menu, product,

Topics {✒️}

personal information perf comment metadata assignees np import pandas unsorted index slows latest version type projects projects milestone 1 issue exists triage issue import numpy rawdata = rawdata pandas works fine main branch tickers = tickers[ 5 closed 100% complete relationships pd nkeys indexed = rawdata github issue perf columns=columns sorted runs index groupby np code nkeys sample data columns= runs sign indexed sorted skip jump crawl checked reported confirmed reproducible nrows ncols = 50 %04d range ncols dataframe zeros

Payment Methods {📊}

• Braintree

Questions {❓}

• Already have an account?

Schema {🗺️}

DiscussionForumPosting:
      context:https://schema.org
      headline:PERF: groupby on an unsorted index slows to a crawl. works fine if index is sorted.
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this issue exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this issue exists on the main branch of pandas. ### Reproducible Example import numpy as np import pandas as pd nkeys, nrows, ncols = 50, 5000, 10 tickers = ["X%04d" % i for i in range(nkeys)] columns = ["C%d" % i for i in range(ncols)] sample = pd.DataFrame(np.zeros((nrows, ncols)), columns=columns) tickers = tickers[::-1] # to reverse the tickers data = {t: sample for t in tickers} rawdata = pd.concat(data, names=["ticker"]) rawdata = rawdata.reset_index().drop(columns="level_1") indexed = rawdata.set_index('ticker') indexed.groupby('ticker').apply(lambda x:x) ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 06d230151e6f18fdb8139d09abf539867a8cd481 python : 3.8.8.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.22000 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : English_United States.1252 pandas : 1.4.1 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 52.0.0.post20210125 Cython : 0.29.23 pytest : 6.2.3 hypothesis : None sphinx : 4.0.1 blosc : None feather : None xlsxwriter : 1.3.8 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : None jinja2 : 2.11.3 IPython : 7.22.0 pandas_datareader: 0.10.0 bs4 : 4.9.3 bottleneck : 1.3.2 fastparquet : None fsspec : 0.9.0 gcsfs : None matplotlib : 3.3.4 numba : 0.53.1 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 7.0.0 pyreadstat : None pyxlsb : None s3fs : 0.4.2 scipy : 1.6.2 sqlalchemy : 1.4.7 tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 zstandard : None </details> ### Prior Performance When indexed is sorted runs under 100 ms. When not indexed (ie ticker as column) also runs under 100 ms. Just remove the following code to check : tickers = tickers[::-1]
      author:
         url:https://github.com/furechan
         type:Person
         name:furechan
      datePublished:2022-03-27T13:57:55.000Z
      interactionStatistic:
         type:InteractionCounter
         interactionType:https://schema.org/CommentAction
         userInteractionCount:0
      url:https://github.com/46527/pandas/issues/46527
      context:https://schema.org
      headline:PERF: groupby on an unsorted index slows to a crawl. works fine if index is sorted.
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this issue exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this issue exists on the main branch of pandas. ### Reproducible Example import numpy as np import pandas as pd nkeys, nrows, ncols = 50, 5000, 10 tickers = ["X%04d" % i for i in range(nkeys)] columns = ["C%d" % i for i in range(ncols)] sample = pd.DataFrame(np.zeros((nrows, ncols)), columns=columns) tickers = tickers[::-1] # to reverse the tickers data = {t: sample for t in tickers} rawdata = pd.concat(data, names=["ticker"]) rawdata = rawdata.reset_index().drop(columns="level_1") indexed = rawdata.set_index('ticker') indexed.groupby('ticker').apply(lambda x:x) ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 06d230151e6f18fdb8139d09abf539867a8cd481 python : 3.8.8.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.22000 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : English_United States.1252 pandas : 1.4.1 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 52.0.0.post20210125 Cython : 0.29.23 pytest : 6.2.3 hypothesis : None sphinx : 4.0.1 blosc : None feather : None xlsxwriter : 1.3.8 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : None jinja2 : 2.11.3 IPython : 7.22.0 pandas_datareader: 0.10.0 bs4 : 4.9.3 bottleneck : 1.3.2 fastparquet : None fsspec : 0.9.0 gcsfs : None matplotlib : 3.3.4 numba : 0.53.1 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 7.0.0 pyreadstat : None pyxlsb : None s3fs : 0.4.2 scipy : 1.6.2 sqlalchemy : 1.4.7 tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 zstandard : None </details> ### Prior Performance When indexed is sorted runs under 100 ms. When not indexed (ie ticker as column) also runs under 100 ms. Just remove the following code to check : tickers = tickers[::-1]
      author:
         url:https://github.com/furechan
         type:Person
         name:furechan
      datePublished:2022-03-27T13:57:55.000Z
      interactionStatistic:
         type:InteractionCounter
         interactionType:https://schema.org/CommentAction
         userInteractionCount:0
      url:https://github.com/46527/pandas/issues/46527
Person:
      url:https://github.com/furechan
      name:furechan
      url:https://github.com/furechan
      name:furechan
InteractionCounter:
      interactionType:https://schema.org/CommentAction
      userInteractionCount:0
      interactionType:https://schema.org/CommentAction
      userInteractionCount:0

External Links {🔗}(3)

• How much does https://github.blog make?
• What's the income of https://pandas.pydata.org/docs/whatsnew/index.html?
• What's the financial outcome of https://www.githubstatus.com/?

Analytics and Tracking {📊}

• Site Verification - Google

Libraries {📚}

• Clipboard.js
• D3.js
• Lodash

Emails and Hosting {✉️}

Mail Servers:

• aspmx.l.google.com
• alt1.aspmx.l.google.com
• alt2.aspmx.l.google.com
• alt3.aspmx.l.google.com
• alt4.aspmx.l.google.com

Name Servers:

• dns1.p08.nsone.net
• dns2.p08.nsone.net
• dns3.p08.nsone.net
• dns4.p08.nsone.net
• ns-1283.awsdns-32.org
• ns-1707.awsdns-21.co.uk
• ns-421.awsdns-52.com
• ns-520.awsdns-01.net