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Demo Pull Request #4

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0c279d6
Modify the notebooks
smith-kyle Jan 17, 2024
1f87d11
Change more markdown
smith-kyle Jan 17, 2024
64f949b
Show newly added features
smith-kyle Jan 17, 2024
2b229cf
Change python example
smith-kyle Jul 17, 2024
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7 changes: 6 additions & 1 deletion code-example.py
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        raise ValueError("Division by zero is not allowed")


def modulus(a, b):

how

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    return a * b


def divide(a, b):
    if b != 0:
        return a / b
    else:
        raise ValueError("Division by zero is not allowed")

another comment

Original file line number Diff line number Diff line change
Expand Up @@ -14,14 +14,19 @@ def divide(a, b):
if b != 0:
return a / b
else:
return None
raise ValueError("Division by zero is not allowed")


def modulus(a, b):
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hey don't do this

return a % b


def main():
print("Addition:", add(10, 5))
print("Subtraction:", subtract(10, 5))
print("Multiplication:", multiply(10, 5))
print("Division:", divide(10, 5))
print("Modulus:", modulus(10, 5))


if __name__ == "__main__":
Expand Down
76 changes: 36 additions & 40 deletions pandas-example.ipynb
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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.).

test comment

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my reply in github

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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.).

new review comment

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my gh reply

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# Operating on Data in Pandas

Hey @owen-connor I'm going to ping you for a demo

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Copied from [https://github.com/jakevdp/PythonDataScienceHandbook](https://github.com/jakevdp/PythonDataScienceHandbook) with modifications to demonstrate notebook diffing.

here's my review comment

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fill = A.stack().sum()
A.add(B, fill_value=fill)

Looks good

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A = pd.DataFrame(rng.randint(0, 20, (2, 2)),
                 columns=list('AB'))
B = pd.DataFrame(rng.randint(0, 10, (3, 3)),

test

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Suggested change
"A = rng.randint(10, size=(4, 4))\n",

Original file line number Diff line number Diff line change
Expand Up @@ -11,24 +11,24 @@
"cell_type": "markdown",
"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."
]
},
{
"cell_type": "markdown",
"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."
]
},
{
"cell_type": "code",
"execution_count": 121,
"execution_count": 26,
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ok

"metadata": {
"collapsed": true
},
Expand All @@ -48,7 +48,7 @@
},
{
"cell_type": "code",
"execution_count": 122,
"execution_count": 27,
"metadata": {
"collapsed": false
},
Expand All @@ -68,7 +68,7 @@
},
{
"cell_type": "code",
"execution_count": 123,
"execution_count": 28,
"metadata": {
"collapsed": false
},
Expand All @@ -77,26 +77,26 @@
"data": {
"text/plain": [
"0 2.0\n",
"1 5.0\n",
"2 9.0\n",
"3 5.0\n",
"1 3.0\n",
"2 3.0\n",
"3 -5.0\n",
"dtype: float64"
]
},
"execution_count": 123,
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A.add(B, fill_value=0)"
"A.subtract(B, fill_value=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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",
" <th>0</th>\n",
" <td>19.00</td>\n",
" <td>20.00</td>\n",
" <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",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>16.75</td>\n",
" <td>10.75</td>\n",
" <td>12.75</td>\n",
" <td>53.0</td>\n",
" <td>56.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 19.00 20.00 16.75\n",
"1 8.00 3.00 12.75\n",
"2 16.75 10.75 12.75"
" A B C\n",
"0 10.0 26.0 55.0\n",
"1 16.0 19.0 55.0\n",
"2 53.0 56.0 52.0"
]
},
"execution_count": 127,
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
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 @@
},
{
"cell_type": "code",
"execution_count": 128,
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 5, 5, 9],\n",
" [3, 5, 1, 9],\n",
" [1, 9, 3, 7]])"
"array([[7, 7, 2, 5],\n",
" [4, 1, 7, 5],\n",
" [1, 4, 0, 9]])"
]
},
"execution_count": 128,
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -259,7 +255,7 @@
},
{
"cell_type": "code",
"execution_count": 129,
"execution_count": 32,
"metadata": {
"collapsed": false
},
Expand All @@ -268,11 +264,11 @@
"data": {
"text/plain": [
"array([[ 0, 0, 0, 0],\n",
" [ 2, 0, -4, 0],\n",
" [ 0, 4, -2, -2]])"
" [-3, -6, 5, 0],\n",
" [-6, -3, -2, 4]])"
]
},
"execution_count": 129,
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
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
106 changes: 5 additions & 101 deletions sklearn-example.ipynb
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