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DOC: Emphasize NumPy in Ecosystem openers #242

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Merged
merged 2 commits into from
May 22, 2020

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bjnath
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@bjnath bjnath commented May 19, 2020

Brief description of what is fixed or changed

Puts NumPy in the opening sentence of each Ecosystem section,
closing issue #235.

Reduces text in the Visualization section, addressing @rgommers's concern in issue #238.

Closes gh-235.

Puts NumPy in the opening sentence of each Ecosystem section,
closing issue numpy#235.

Reduces text in the Visualization section, addressing a concern in numpy#238.

Closes #numpygh-235.
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bjnath commented May 20, 2020

The sentence "NumPy transforms the data in the burgeoning Python visualization landscape",
might read better as "NumPy crunches the numbers in the burgeoning Python visualization landscape."

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Looks good, less busy and more compact:

image

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The sentence "NumPy transforms the data in the burgeoning Python visualization landscape",
might read better as "NumPy crunches the numbers in the burgeoning Python visualization landscape."

Makes sense to tweak that phrase. I'm not 100% sure "crunches the numbers" captures it - there's not all that much number crunching going on in reality. Maybe "NumPy provides the foundation for the burgeoning ..." ?

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stefanv commented May 20, 2020 via email

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bjnath commented May 20, 2020

We haven't used 'foundation' in the others, so it's available.

Do you like "NumPy is a key ingredient"?

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bjnath commented May 20, 2020

or "the essential ingredient"

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bjnath commented May 20, 2020

"an essential component"

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+1 for "an essential component" as well. the other phrases are an improvement as well, but I like this one best.

<li>
<b>Presentation:</b>
<b>Display:</b>
<a href="./index.html/#tab-visual"> Data Visualization Tools</a>
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New issue found: this doesn't work as well as it should; it opens the correct tab, but the view jumps back to top of the page. Noting here, can deal with it later.

<a href="https://www.statsmodels.org/stable/index.html"> statsmodels</a>.
</li>

<li><b>Evaluate:</b>
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Will rethink the contents here - having NumPy right at the end again is a little odd perhaps.

retail, healthcare, marketing, finance, social media, automation, sales, travel,
etc.) as well as other core disciplines of data science, data engineering, and
data visualization. Tools such as <a href="https://mlflow.org">MLFlow</a> address
experiment hyperparameter and result tracking needs, while
<a href="https://dvc.org"> DVC</a> provides data version control for data science
and machine learning workflows.
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Still would like to make this tab a little more compact. Perhaps in a follow-up, all these changes look good. EDIT: followed up in gh-262

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+1 for "an essential component" as well. the other phrases are an improvement as well, but I like this one best.

Okay went with this, pushed a commit. If there's better ideas still, we can follow up in a new PR.

I will merge this, because there's lots of useful edits here and I want to make further changes to the content of some of these tabs.

Thanks a lot @bjnath!

@rgommers rgommers merged commit 0fdad49 into numpy:master May 22, 2020
bjnath added a commit to bjnath/numpy.org that referenced this pull request May 23, 2020
Proposed edit per @rgommers comment in numpy#242:
"Still would like to make this tab a little more compact."
rgommers added a commit to rgommers/numpy.org that referenced this pull request May 23, 2020
Follow up to numpygh-242, alternative to numpygh-262. The content rewrite and
inclusion of more relevant libraries attempts to make this sound
natural, sketch the breadth of the Python data science offerings,
and keeps some of the tools like DVC and MLFlow that beginning to
intermediate data scientists really need to learn about.

It does shrink the amount of content to a more reasonable size.
rgommers added a commit to rgommers/numpy.org that referenced this pull request May 24, 2020
Follow up to numpygh-242, alternative to numpygh-262. The content rewrite and
inclusion of more relevant libraries attempts to make this sound
natural, sketch the breadth of the Python data science offerings,
and keeps some of the tools like DVC and MLFlow that beginning to
intermediate data scientists really need to learn about.

It does shrink the amount of content to a more reasonable size.
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'Ecosystem' presentations and the slow reveal
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