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123 changes: 91 additions & 32 deletions Fundamentals/Numpy/essentials.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -16,9 +16,17 @@
"### Basic Array Creation:"
]
},
{
"cell_type": "markdown",
"id": "469b2a83",
"metadata": {},
"source": [
"**Note:** In NumPy, the term \"tensor\" is often used to refer to arrays of any dimension. A 1D array is a vector, a 2D array is a matrix, and higher-dimensional arrays are called tensors."
]
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 2,
"id": "dbd5d81e",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -104,7 +112,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 3,
"id": "83802b50",
"metadata": {},
"outputs": [
Expand All @@ -123,7 +131,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 4,
"id": "39dfb97c",
"metadata": {},
"outputs": [
Expand All @@ -140,6 +148,14 @@
"print(y.flags.f_contiguous) # Is it Fortran contiguous? "
]
},
{
"cell_type": "markdown",
"id": "0e6707f1",
"metadata": {},
"source": [
"**C order** means rows are stored one after another (row-major). **Fortran order** means columns are stored one after another (column-major). This affects how data is laid out in memory and can impact performance for some operations."
]
},
{
"cell_type": "markdown",
"id": "cc7bba51",
Expand All @@ -150,7 +166,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 5,
"id": "51a1dece",
"metadata": {},
"outputs": [
Expand All @@ -176,7 +192,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 6,
"id": "b1676c5e",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -213,7 +229,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"id": "8ba3caa5",
"metadata": {},
"outputs": [
Expand All @@ -230,36 +246,54 @@
"print(a + 5) # adds 5 to each element in the array"
]
},
{
"cell_type": "markdown",
"id": "26fdab69",
"metadata": {},
"source": [
"Vectorized operations in NumPy are fast because they are implemented in compiled C code under the hood, allowing operations on entire arrays without explicit Python loops. This leads to significant speedups for large data."
]
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 8,
"id": "03ec7a77",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ -0.02447243 -4.58678592 2.79514857 4.37957598 -5.01027197\n",
" 0.79991992 1.06516404 -6.75187828 -5.5635052 -2.75984379]\n",
" [ 5.07939064 -6.08044128 -2.0107042 -3.23687038 -1.9939912\n",
" -1.43513934 4.59965931 -1.24282259 -4.99097576 11.89869832]\n",
" [ -1.54279366 -5.47952922 3.42598619 4.50755354 3.87820602\n",
" -4.19837516 2.73886503 -5.62062562 4.98620218 4.14222144]\n",
" [ 10.81880802 -3.0316239 1.3985158 -8.4999524 8.4718774\n",
" 6.96113947 7.70438246 0.23762215 -1.93385122 3.39785957]\n",
" [ 2.22880788 2.62092499 1.47809868 -0.47977848 4.65140253\n",
" -0.25928333 5.94391666 4.55825208 -3.7509756 -8.45029718]\n",
" [ -1.25629205 -0.51275671 -8.27743996 1.48546517 0.08115635\n",
" 4.33144519 -12.68775831 5.41431455 -0.59448426 -7.32965964]\n",
" [ -1.39553701 -14.52411923 -0.93647355 -2.92908507 -0.73956153\n",
" 2.17402357 -1.53052591 -11.38280558 -4.81727417 6.00257577]\n",
" [ -2.49392955 -3.25959384 -0.51832825 -5.34498951 7.89476589\n",
" 1.51228891 -6.49448237 0.48378173 1.01601658 -0.53514001]\n",
" [ -6.83392948 2.16687329 -3.33322049 2.78308091 -0.59196055\n",
" 5.62299811 -2.12529449 4.46238733 2.07122165 -10.7969715 ]\n",
" [ -1.37557556 7.22198767 7.67152946 -1.69603545 -8.77947343\n",
" -7.58507589 7.01532742 3.83841619 2.02003209 -7.60197409]]\n"
"[[-8.42345984e+00 -1.01916172e+01 -7.45918410e-01 3.96268543e+00\n",
" -5.72833093e+00 2.91438727e+00 4.06279948e+00 4.12523109e+00\n",
" 3.00733531e-01 9.78858080e-02]\n",
" [ 1.59576119e+00 3.26355065e+00 -1.06319812e+01 4.70533281e+00\n",
" -1.48214898e+00 -2.52620053e+00 2.65741680e-01 4.16388773e-01\n",
" 9.22887221e+00 2.71577359e+00]\n",
" [-9.66200392e-01 -2.17186285e+00 6.75360270e+00 5.99673669e+00\n",
" -9.85259070e+00 -4.46350024e+00 -3.23534501e+00 -3.02228031e+00\n",
" 6.64894340e+00 -2.07698746e-01]\n",
" [ 4.43036087e+00 -3.90701380e+00 -4.51090876e+00 4.16867809e+00\n",
" -5.70280260e-01 1.95887322e+00 -1.60934887e-01 -1.66273426e+00\n",
" 2.61067655e+00 -2.44358783e+00]\n",
" [ 3.51765638e+00 4.65760575e+00 -8.96849909e-01 -1.06300686e+00\n",
" -5.01294087e+00 3.82284722e+00 3.77360233e+00 2.68585738e+00\n",
" 8.32719266e+00 -9.34176480e+00]\n",
" [-3.47242746e+00 -3.58623833e+00 -9.61253561e-01 1.39347319e+00\n",
" 1.32623086e+00 -1.41734816e+00 -3.96135494e+00 -3.41007842e+00\n",
" -4.21338940e+00 -7.24046272e+00]\n",
" [-1.66593856e+01 -1.88375474e-01 5.39244429e+00 9.63068424e-01\n",
" 4.29069316e+00 -6.24799863e+00 -6.41083656e+00 4.27256832e+00\n",
" -7.06174952e+00 6.95411295e+00]\n",
" [ 2.57333024e-02 7.24614163e+00 5.25180381e+00 6.67631814e+00\n",
" 2.84832739e-01 -5.26630346e+00 -1.59437223e-03 -9.95875547e+00\n",
" 2.31109628e+00 6.40286987e-02]\n",
" [ 1.19716494e+01 1.71500697e-01 2.77296476e-01 -1.01661562e+01\n",
" -1.77701099e+00 -5.46968459e+00 8.52619956e+00 2.01530821e+00\n",
" 6.04138132e+00 9.10008895e+00]\n",
" [ 4.87703646e+00 -1.68493879e+00 -7.19759065e+00 1.09044935e-01\n",
" 5.07082145e-01 -1.14342681e+01 4.98130023e+00 9.19224842e-01\n",
" -5.40917816e+00 -2.29185723e+00]]\n"
]
}
],
Expand Down Expand Up @@ -296,7 +330,7 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 9,
"id": "9456ddb6",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -327,7 +361,7 @@
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 10,
"id": "2032b66c",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -379,6 +413,31 @@
"id": "0afac608",
"metadata": {},
"source": [
"#### Advanced Indexing\n",
"\n",
"- Advanced indexing occurs when you use lists or boolean arrays to index into an array.\n",
"- **Warning:** Advanced indexing creates copies of the data, and modifying the copy will not affect the original array.\n",
"\n",
"For example:\n",
"\n",
"```python\n",
"my_array = np.arange(10)\n",
"print('my_array:', my_array) # [0 1 2 3 4 5 6 7 8 9]\n",
"\n",
"# Using a list for advanced indexing\n",
"advanced_indexed_array = my_array[[1, 2, 8]]\n",
"print('advanced_indexed_array:', advanced_indexed_array) # [1 2 8]\n",
"\n",
"# Modify the advanced indexed array\n",
"advanced_indexed_array[0] = 99\n",
"print('After modifying advanced_indexed_array:')\n",
"print('my_array:', my_array) # [0 1 2 3 4 5 6 7 8 9]\n",
"print('advanced_indexed_array:', advanced_indexed_array) # [99 2 8]\n",
"```\n",
"\n",
"Even though `advanced_indexed_array` was created from `my_array`, it is a separate copy, and changes to it do not affect the original `my_array`.\n",
"\n",
"\n",
"### NumPy Internals"
]
},
Expand All @@ -392,7 +451,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 11,
"id": "fb47a3b2",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -503,7 +562,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "kernel_from_scratch",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
Expand All @@ -517,7 +576,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
"version": "3.12.1"
}
},
"nbformat": 4,
Expand Down