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Demo pull request #1

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31b5e08
Modify the notebooks
smith-kyle Jan 17, 2024
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smith-kyle Jan 17, 2024
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smith-kyle Jan 17, 2024
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76 changes: 36 additions & 40 deletions pandas-example.ipynb
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Expand Up @@ -11,24 +11,24 @@
"cell_type": "markdown",
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Commented on notebook pandas-example.ipynb Cell 12 Line 1

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

"start a review" button didn't work for me - Android chrome

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

# Operating on Data in Pandas

Commenting on the header Operating on Data in Pandas

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

# Operating on Data in Pandas

Woo!

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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()

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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)).

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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!

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

A.subtract(B, fill_value=0)

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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.

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

A.subtract(B, fill_value=0)

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No changes needed!

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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.

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No changes needed!

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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

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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

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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.

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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!

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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

"metadata": {},
"source": [
"Copied from [https://github.com/jakevdp/PythonDataScienceHandbook](https://github.com/jakevdp/PythonDataScienceHandbook)"
"Copied from [https://github.com/jakevdp/PythonDataScienceHandbook](https://github.com/jakevdp/PythonDataScienceHandbook) with modifications to demonstrate notebook diffing."
]
},
{
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"metadata": {},
"source": [
"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.).\n",
"Pandas inherits much of this functionality from NumPy, and the ufuncs that we introduced in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb) are key to this.\n",
"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.\n",
"\n",
"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.\n",
"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.\n",
"We will additionally see that there are well-defined operations between one-dimensional ``Series`` structures and two-dimensional ``DataFrame`` structures."
"We will additionally see that there are well-defined operations between one-dimensional Series structures and two-dimensional DataFrame structures."
]
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Expand All @@ -48,7 +48,7 @@
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Expand All @@ -68,7 +68,7 @@
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Expand All @@ -77,26 +77,26 @@
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"A.add(B, fill_value=0)"
"A.subtract(B, fill_value=0)"
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"cell_type": "markdown",
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"Notice that indices are aligned correctly irrespective of their order in the two objects, and indices in the result are sorted.\n",
"Observe that the indices align accurately regardless of their sequence in the two objects, and the result's indices are organized in ascending order.\n",
"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.\n",
"Here we'll fill with the mean of all values in ``A`` (computed by first stacking the rows of ``A``):"
]
Expand Down Expand Up @@ -144,40 +144,36 @@
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>16.75</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8.00</td>\n",
" <td>3.00</td>\n",
" <td>12.75</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>16.75</td>\n",
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" <td>12.75</td>\n",
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" <td>56.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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" A B C\n",
"0 19.00 20.00 16.75\n",
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" A B C\n",
"0 10.0 26.0 55.0\n",
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Expand All @@ -200,7 +196,7 @@
"# Large cells? No problem. Cells are collapsed to showcase the diff\n",
"# Large cells? No problem. Cells are collapsed to showcase the diff\n",
"\n",
"fill = A.stack().mean()\n",
"fill = A.stack().sum()\n",
"A.add(B, fill_value=fill)\n",
"\n",
"# Large cells? No problem. Cells are collapsed to showcase the diff\n",
Expand All @@ -225,7 +221,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ufuncs: Operations Between DataFrame and Series\n",
"## Ufuncs: Operations Between DataFrame and Series with a changed header\n",
"\n",
"When performing operations between a ``DataFrame`` and a ``Series``, the index and column alignment is similarly maintained.\n",
"Operations between a ``DataFrame`` and a ``Series`` are similar to operations between a two-dimensional and one-dimensional NumPy array.\n",
Expand All @@ -234,20 +230,20 @@
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Expand All @@ -259,7 +255,7 @@
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Expand All @@ -268,11 +264,11 @@
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"metadata": {},
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Expand All @@ -285,7 +281,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### GitNotebooks v1 Features\n",
"### GitNotebooks v2 Features\n",
"\n",
"<table>\n",
" <thead><tr><th>Feature</th><th>Supported</th></tr></thead>\n",
Expand All @@ -296,15 +292,15 @@
" </tr>\n",
" <tr>\n",
" <td>Line comments</td>\n",
" <td>✗</td>\n",
" <td>✓</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Markdown comment</td>\n",
" <td>✗</td>\n",
" <td>✓</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Dataframe diffing</td>\n",
" <td>✗</td>\n",
" <td>✓</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
Expand Down
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