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Demo pull request #7

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42ca6b4
Modify the notebooks
smith-kyle Jun 10, 2025
f1c8f2e
Change more markdown
smith-kyle Jan 17, 2024
b5d35ab
Modify the readme
smith-kyle Jul 17, 2024
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5 changes: 2 additions & 3 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@ This is a demo repository to showcase the ability to diff different types of fil

## Example Files

- `code-example.py`: Contains basic math functions like add, substract, multiply, divide, and modulus.
- `code-example.py`: Contains basic math functions like add, subtract, multiply, divide, and modulus.
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my test comment

- `pandas-example.ipynb`: Shows how to do operations on pandas DataFrames and Series, and includes some random data.

## How to Use
Expand All @@ -23,8 +23,7 @@ Just browse the files and make some changes. You can try to add new functions, f

## Known Issues

- Sometimes the notebook outputs are not shown in the diff.
- There may be some spelling errors in the code and markdowns.
- There may be some spelling errors in the code and markdown.
- The code is not tested for all edge cases.
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Please remove this line too.


Enjoy exploring the repo!
7 changes: 6 additions & 1 deletion code-example.py
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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):
return a % b
Comment on lines +20 to +21
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Is modulus used anywhere?



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__":
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68 changes: 32 additions & 36 deletions pandas-example.ipynb
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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,
"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 Down