Demo pull request #1

smith-kyle · 2023-10-14T04:49:01Z

No description provided.

gitnotebooks · 2023-10-14T04:49:05Z

Found 2 changed notebooks. Review the changes at https://gitnotebooks.com/GitNotebooks/demo/pull/1

smith-kyle · 2023-10-14T04:53:48Z

We can see dataframe diffs??

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smith-kyle · 2023-10-14T04:54:27Z

Inline comments on the left and right!

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smith-kyle · 2023-10-14T04:55:02Z

Plots are rendered too!

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smith-kyle · 2023-10-14T04:55:39Z

Markdown rendering ftw!

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smith-kyle · 2023-10-14T04:59:50Z

Comments are marked Outdated when the line changes after the comment was posted

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gitnotebooks-demo-user · 2024-01-17T21:07:37Z

Nice.

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pkaminski · 2024-01-24T18:54:38Z

Test comment

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pkaminski · 2024-01-24T18:55:14Z

Code comment.

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pkaminski · 2024-01-24T18:55:56Z

Really?

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smith-kyle · 2024-02-09T22:26:31Z

Commented on notebook pandas-example.ipynb Cell 12 Line 5

## Ufuncs: Operations Between DataFrame and Series

When performing operations between a ``DataFrame`` and a ``Series``, the index and column alignment is similarly maintained.
Operations between a ``DataFrame`` and a ``Series`` are similar to operations between a two-dimensional and one-dimensional NumPy array.
Consider one common operation, where we find the difference of a two-dimensional array and one of its rows:

Comment on markdown cells!

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Jujuben3885

Hello and hi

smith-kyle · 2024-05-02T21:44:58Z

sklearn-example.ipynb

@@ -16,7 +16,7 @@
    },


Commented on notebook sklearn-example.ipynb Cell 5 From line 2 to 3

iris['PCA2'] = X_2D[:, 1] sns.lmplot(x="PCA1", y="PCA2", hue='species', data=iris, fit_reg=False);

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Can I add comments

Seems like better separation among the classes now. Have you tried something like a kernelPCA?

asah · 2024-08-08T06:36:42Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 12 Line 1

## Ufuncs: Operations Between DataFrame and Series with a changed header

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asah

Test

asah

I submitted a review but the review charges button is still available... So I'm submitting another... ?

smith-kyle · 2024-08-29T17:28:03Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 1 Line 1

# Operating on Data in Pandas

Commenting on the header Operating on Data in Pandas

smith-kyle · 2024-09-04T00:03:57Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 1 Line 1

# Operating on Data in Pandas

Woo!

user-demo-user · 2024-09-23T21:56:57Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 11 Line 18

# Large cells? No problem. Cells are collapsed to showcase the diff # Large cells? No problem. Cells are collapsed to showcase the diff # Large cells? No problem. Cells are collapsed to showcase the diff # Large cells? No problem. Cells are collapsed to showcase the diff # Large cells? No problem. Cells are collapsed to showcase the diff # Large cells? No problem. Cells are collapsed to showcase the diff fill = A.stack().mean()

Comment Test

user-demo-user

LGTM

user-demo-user · 2024-09-23T21:59:46Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 5 Line 1

Any item for which one or the other does not have an entry is marked with ``NaN``, or "Not a Number," which is how Pandas marks missing data (see further discussion of missing data in [Handling Missing Data](03.04-Missing-Values.ipynb)).

Leaving a diff comment

user-demo-user

LGTM!!!!!

user-demo-user · 2024-09-23T22:09:32Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 3 Line 5

One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.). Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in [Computation on NumPy Arrays: Universal Functions](https://gitnotebooks.com/blog) are key to this. Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will *preserve index and column labels* in the output, and for binary operations such as addition and multiplication, Pandas will automatically *align indices* when passing the objects to the ufunc. This means that keeping the context of data and combining data from different sources–both potentially error-prone tasks with raw NumPy arrays–become essentially foolproof ones with Pandas.

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Resolving this comment!

user-demo-user

LGTM

user-demo-user · 2024-09-23T22:13:17Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 8 Line 1

A.subtract(B, fill_value=0)

Leaving a comment!

user-demo-user · 2024-09-23T22:13:17Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 9 Line 1

Observe that the indices align accurately regardless of their sequence in the two objects, and the result's indices are organized in ascending order.

Adding another comment

user-demo-user

Needs 2 changes

user-demo-user · 2024-09-23T22:18:38Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 8 Line 1

A.subtract(B, fill_value=0)

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No changes needed!

user-demo-user · 2024-09-23T22:18:39Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 9 Line 1

Notice that indices are aligned correctly irrespective of their order in the two objects, and indices in the result are sorted.

Commenting on other side

No changes needed!

user-demo-user

LGTM!

owen-connor · 2024-10-08T21:47:12Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 3 Line 4

One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.). Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in [Computation on NumPy Arrays: Universal Functions](https://gitnotebooks.com/blog) are key to this. Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will *preserve index and column labels* in the output, and for binary operations such as addition and multiplication, Pandas will automatically *align indices* when passing the objects to the ufunc.

comment

apurvtwr · 2024-10-19T00:40:00Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 5 Line 1

Any item for which one or the other does not have an entry is marked with ``NaN``, or "Not a Number," which is how Pandas marks missing data (see further discussion of missing data in [Handling Missing Data](03.04-Missing-Values.ipynb)).

Can I suggest the change here ? +- type changes in git

owen-connor

Finished review

owen-connor · 2024-10-31T17:06:03Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 9 Line 2

Observe that the indices align accurately regardless of their sequence in the two objects, and the result's indices are organized in ascending order. As was the case with ``Series``, we can use the associated object's arithmetic method and pass any desired ``fill_value`` to be used in place of missing entries.

Comment

smith-kyle · 2024-11-02T23:16:12Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 3 Line 4

One of the essential pieces of NumPy is the ability to perform quick element-wise operations, both with basic arithmetic (addition, subtraction, multiplication, etc.) and with more sophisticated operations (trigonometric functions, exponential and logarithmic functions, etc.). Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in [Computation on NumPy Arrays: Universal Functions](https://gitnotebooks.com/blog) are key to this. Pandas includes a couple useful twists, however: for unary operations like negation and trigonometric functions, these ufuncs will *preserve index and column labels* in the output, and for binary operations such as addition and multiplication, Pandas will automatically *align indices* when passing the objects to the ufunc.

my comment!

owen-connor

Make some changes

owen-connor · 2024-11-21T16:06:08Z

sklearn-example.ipynb

@@ -16,7 +16,7 @@
    },


Commented on notebook sklearn-example.ipynb Cell 5 Line 3

iris['PCA1'] = X_2D[:, 0] iris['PCA2'] = X_2D[:, 1] sns.lmplot(x="PCA1", y="PCA2", data=iris, hue='species', fit_reg=False, markers=["o", "s", "D"], palette="Set1");

Comment

owen-connor · 2024-11-21T16:06:08Z

sklearn-example.ipynb

@@ -16,7 +16,7 @@
    },


Commented on notebook sklearn-example.ipynb Cell 6 Line 1

We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!

comment 2

owen-connor · 2024-11-21T16:06:09Z

pandas-example.ipynb

      ]
    },
    {
      "cell_type": "markdown",


Commented on notebook pandas-example.ipynb Cell 5 Line 1

Any item for which one or the other does not have an entry is marked with ``NaN``, or "Not a Number," which is how Pandas marks missing data (see further discussion of missing data in [Handling Missing Data](03.04-Missing-Values.ipynb)).

comment 3

smith-kyle

Here are some comments

smith-kyle · 2024-11-21T18:09:47Z

sklearn-example.ipynb

@@ -16,7 +16,7 @@
    },


Commented on notebook sklearn-example.ipynb Cell 5 From line 1 to 2

iris['PCA1'] = X_2D[:, 0] iris['PCA2'] = X_2D[:, 1]

Please idk change something here

reply in GitHub

smith-kyle · 2024-11-21T18:09:47Z

sklearn-example.ipynb

@@ -16,7 +16,7 @@
    },


Commented on notebook sklearn-example.ipynb Cell 7 Line 1

### Unsupervised learning: Dimensionality reduction

Thanks for deleting this

smith-kyle force-pushed the my-branch branch from 27a7142 to 4ce2010 Compare October 14, 2023 05:44

smith-kyle force-pushed the my-branch branch 4 times, most recently from ad9610f to 211f47a Compare January 17, 2024 20:33

smith-kyle added 3 commits January 17, 2024 13:06

Modify the notebooks

31b5e08

Change more markdown

11f2988

Show newly added features

76f98aa

smith-kyle force-pushed the my-branch branch from 535262a to 76f98aa Compare January 17, 2024 21:06

Jujuben3885 approved these changes Mar 22, 2024

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smith-kyle commented Nov 2, 2024

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smith-kyle commented Nov 21, 2024

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smith-kyle assigned owen-connor Jul 31, 2025

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Demo pull request #1

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smith-kyle commented Oct 14, 2023 •

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