Skip to content

Minor text edits on multiple pages #207

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 9 commits into from
Mar 28, 2020
Merged

Minor text edits on multiple pages #207

Show file tree
Hide file tree
Changes from 8 commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
68ae044
Proofreading and minor reformatting of the page
Mar 28, 2020
846ce68
Minor text editing
Mar 28, 2020
4073ecf
Minor text editing
Mar 28, 2020
a2cbd0e
Minor text editing
Mar 28, 2020
85ca277
Minor text editing
Mar 28, 2020
499cc52
Minor text editing
Mar 28, 2020
987f18b
Minor text editing
Mar 28, 2020
faf1ec7
Minor text editing
Mar 28, 2020
6a39904
Fix video tutorial titles, put back year after NumPy talks
rgommers Mar 28, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion config.yaml
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,7 @@ params:
- title: Interoperable
text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU and sparse array libraries.
- title: Performant
text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
text: The core of NumPy is a well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
- title: Easy to use
text: NumPy's high level syntax makes it accessible to and productive for programmers from any background or experience level.
- title: Open source
Expand Down
2 changes: 1 addition & 1 deletion content/en/about.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@ The NumPy project is growing, we have separate teams for:
- Funding & grants
- Admin

See the [Team](/team) page for individual team members.
See the [Team](/team) page for the individual team members.


## Sponsors
Expand Down
2 changes: 1 addition & 1 deletion content/en/arraycomputing.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ pack newer algorithms and features geared towards machine learning and artificia
title="Array Computing Landscape">

**Array computing** is based on **arrays** data structures. *Arrays* are used
to organize vast amounts of data such that related set of values can be easily
to organize vast amounts of data such that a related set of values can be easily
sorted, searched, mathematically manipulated and transformed easily and quickly.

Array computing is *unique* as it involves operating on the data array *at
Expand Down
6 changes: 3 additions & 3 deletions content/en/contribute.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,9 +24,9 @@ Our community aspires to treat everyone equally, and to value all contributions.

## How to get started

For the process of contributing to the NumPy code base we have an extensive [developer guide](https://numpy.org/devdocs/dev/index.html). We don't work with assigning issues - if you see something of interest, please dive in!
For the process of contributing to the NumPy code base, we have an extensive [developer guide](https://numpy.org/devdocs/dev/index.html). We don't work with assigning issues - if you see something of interest, please dive in!

For other activities we will attempt to give some guidance on this page. If you're unsure of where to get started or how your skills matter to the project, _please reach out to us_! You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or on [GitHub](http://github.com/numpy/numpy) (open an issue or comment on a relevant issue). These are our preferred communication channels (open source is open by nature!), however if you prefer to discuss in private first, please reach out to our community coordinators at `[email protected]` or on [Slack](https://numpy-team.slack.com) (send an email to `[email protected]` for an invite the first time).
For other activities, we will attempt to give some guidance on this page. If you're unsure of where to get started or how your skills matter to the project, _please reach out to us_! You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an issue or comment on a relevant issue). These are our preferred communication channels (open source is open by nature!), however, if you prefer to discuss in private first, please reach out to our community coordinators at `[email protected]` or on [Slack](https://numpy-team.slack.com) (send an email to `[email protected]` for an invite the first time).

We also have a bi-weekly _community call_, the details of which are announced on the mailing list. You are very welcome to join this call!

Expand Down Expand Up @@ -68,7 +68,7 @@ We aim to translate this [numpy.org](https://numpy.org) website into multiple la

### Fundraising

NumPy has been an all-volunteer project for most of its history, however with the continuous growth of our user base we feel the need for funding to keep the project healthy. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) attempts to quantify the impact of recently received funding and the needs of the project. We have a number of ideas about how to obtain funding (and of course welcome more good ideas!), however these all take time to execute. Fundraising is also a skill that not many current team members have - we'd love your help!
NumPy has been an all-volunteer project for most of its history, however, with the continuous growth of our user base we feel the need for funding to keep the project healthy. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) attempts to quantify the impact of recently received funding and the needs of the project. We have a number of ideas about how to obtain funding (and of course welcome more good ideas!), these all take time to execute though. Fundraising is also a skill that not many current team members have - we'd love your help!


### Community coordination and outreach
Expand Down
52 changes: 25 additions & 27 deletions content/en/learn.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,11 +3,9 @@ title: Learn
sidebar: false
---

***Welcome to learning about NumPy!***
To learn how to use NumPy, you could begin with the resources listed under the **'Beginners'** section. Once you gain a broad view, you could try the more involved tutorials and resources listed under the **'Advanced'** section.

To learn how to use NumPy, you could begin with the resources listed under the 'beginners' section. Once you have a broad view into how to get started with using NumPy, you could try the more involved tutorials and resources listed under 'advanced' section.

This is a curated collection of NumPy related educational resources. Some are very specific to NumPy, while others offer a broader view on numerical computing. It is a continuously evolving list. In case you would like to contribute, please refer to the section 'Contributing to NumPy Learning Resources' below.
This is a curated collection of NumPy related educational resources. Some are very specific to NumPy, while others offer a broader view on numerical computing. It is a continuously evolving list. In case you would like to contribute, please refer to the information at the bottom of this page.
***

## Beginners
Expand All @@ -17,62 +15,62 @@ There is tons of information about NumPy out there. If you are new to NumPy, we'
<i class="fad fa-chalkboard"></i> **Tutorials**

* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html)
* [SciPy Lectures](https://www.scipy-lectures.org/) *Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem.*
* [Towards Data Science, an introduction to NumPy by *Anne Bonner*](https://towardsdatascience.com/the-ultimate-beginners-guide-to-numpy-f5a2f99aef54)
* [SciPy Lectures](https://www.scipy-lectures.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem.
* [Towards Data Science, an introduction to NumPy *by Anne Bonner*](https://towardsdatascience.com/the-ultimate-beginners-guide-to-numpy-f5a2f99aef54)
* [Machine Learning Plus - Introduction to ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/)
* [Edureka - Learn NumPy Arrays with Examples ](https://www.edureka.co/blog/python-numpy-tutorial/)
* [Dataquest - NumPy Tutorial: Data Analysis with Python](https://www.dataquest.io/blog/numpy-tutorial-python/)
* [NumPy tutorial by *Nicolas Rougier*](https://github.com/rougier/numpy-tutorial)
* [Stanford CS231 by *Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/)
* [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial)
* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/)
* [NumPy User Guide](https://numpy.org/devdocs/user/index.html)

<i class="fas fa-books"></i> **Books**

* [Guide to NumPy by *Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) *This is a free version 1 from 2006. For a latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007).*
* [From Python to NumPy by *Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)
* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) by Juan Nunez-Iglesias, Stefan van der Walt and Harriet Dashnow
* [Guide to NumPy *by Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007).
* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)
* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt and Harriet Dashnow*

Besides the ones listed above, you may want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) of books on the subject of "Python+SciPy". Most of the books on this list are about the "SciPy ecosystem", which has NumPy at its core.
Besides the ones listed above, you may want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy". Most of the books on this list are about the "SciPy ecosystem", which has NumPy at its core.

<i class="far fa-file-video"></i> **Videos**

* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk), Alex Chabot-Leclerc, presented at SciPy 2019.`
* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc*

***

## Advanced

If you have the basic know-how of NumPy and how to use it, you can try these advanced tutorials, books and videos for a better understanding of specific NumPy concepts such as advanced indexing, splitting, stacking, linear algebra and more.
If you have the basic know-how of NumPy and how to use it, you can try these advanced tutorials, books and videos for a better understanding of the specific NumPy concepts such as advanced indexing, splitting, stacking, linear algebra and more.

<i class="fad fa-chalkboard"></i> **Tutorials**

* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html), Nicolas P. Rougier, 2016.
* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf), M. Scott Shell, 2014.
* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/), Stéfan van der Walt, 2008.
* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier*
* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell*
* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt*
* [NumPy in Python (Advanced)](https://www.geeksforgeeks.org/numpy-python-set-2-advanced/)
* [Advanced Indexing](https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm)
* [Machine Learning and Data Analytics with NumPy](https://www.machinelearningplus.com/python/numpy-tutorial-python-part2/)

<i class="fas fa-books"></i> **Books**

* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) by Jake Vanderplas
* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) by Wes McKinney
* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) by Robert Johansson
* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *by Jake Vanderplas*
* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *by Wes McKinney*
* [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson*

<i class="far fa-file-video"></i> **Videos**

* [Scientific Computing with Python - SciPy Japan, 2019](https://www.youtube.com/watch?v=cYugp9IN1-Q)
* [Advanced Indexing operations in NumPy arrays](https://www.youtube.com/watch?v=2WTDrSkQBng)
* [Scientific Computing with Python](https://www.youtube.com/watch?v=cYugp9IN1-Q)
* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng)

***

## NumPy Talks

* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) by Jaime Fernández (2016)
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The style changes are an improvement, but I'd like to keep the years because that gives relevant context. For talks about the state of NumPy or future plans, it matters when the talk was given.

Copy link
Member Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I see your point. I was going back and forth on whether to include the years for the talks.

* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) by Ralf Gommers (2019)
* [NumPy latest updates](https://www.youtube.com/watch?v=YFLVQFjRmPY) by Matti Picus, PyCon Israel (2019)
* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris (2019)
* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) by Travis Oliphant (2019)
* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández*
* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers*
* [NumPy. Latest Updates](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus*
* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris*
* [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant*

***

Expand Down
Loading