diff --git a/README.md b/README.md
index 7c3cabb..9a01422 100644
--- a/README.md
+++ b/README.md
@@ -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.
- `pandas-example.ipynb`: Shows how to do operations on pandas DataFrames and Series, and includes some random data.
## How to Use
@@ -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.
Enjoy exploring the repo!
diff --git a/code-example.py b/code-example.py
index 04c4594..e4b3601 100644
--- a/code-example.py
+++ b/code-example.py
@@ -14,7 +14,11 @@ 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
def main():
@@ -22,6 +26,7 @@ def main():
print("Subtraction:", subtract(10, 5))
print("Multiplication:", multiply(10, 5))
print("Division:", divide(10, 5))
+ print("Modulus:", modulus(10, 5))
if __name__ == "__main__":
diff --git a/pandas-example.ipynb b/pandas-example.ipynb
index 04ea17f..57878b8 100644
--- a/pandas-example.ipynb
+++ b/pandas-example.ipynb
@@ -11,7 +11,7 @@
"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."
]
},
{
@@ -19,16 +19,16 @@
"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
},
@@ -48,7 +48,7 @@
},
{
"cell_type": "code",
- "execution_count": 122,
+ "execution_count": 27,
"metadata": {
"collapsed": false
},
@@ -68,7 +68,7 @@
},
{
"cell_type": "code",
- "execution_count": 123,
+ "execution_count": 28,
"metadata": {
"collapsed": false
},
@@ -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``):"
]
@@ -144,40 +144,36 @@
" | \n",
" A | \n",
" B | \n",
- " C | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 19.00 | \n",
- " 20.00 | \n",
- " 16.75 | \n",
+ " 26.0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 8.00 | \n",
- " 3.00 | \n",
- " 12.75 | \n",
+ " 19.0 | \n",
"
\n",
" \n",
" | 2 | \n",
- " 16.75 | \n",
- " 10.75 | \n",
- " 12.75 | \n",
+ " 53.0 | \n",
+ " 56.0 | \n",
"
\n",
" \n",
"\n",
""
],
"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"
}
@@ -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",
@@ -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",
@@ -234,7 +230,7 @@
},
{
"cell_type": "code",
- "execution_count": 128,
+ "execution_count": 31,
"metadata": {
"collapsed": false
},
@@ -242,12 +238,12 @@
{
"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"
}
@@ -259,7 +255,7 @@
},
{
"cell_type": "code",
- "execution_count": 129,
+ "execution_count": 32,
"metadata": {
"collapsed": false
},
@@ -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"
}