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Created November 10, 2024 09:35
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Jupyter notebook – Lab Session – 11 – Numpy Introduction
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Jupyter-notebook-Numpy-Introduction.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "aaff41b9",
"metadata": {},
"source": [
"# Numpy Introduction"
]
},
{
"cell_type": "markdown",
"id": "b87e66c8",
"metadata": {},
"source": [
"## Installing Numpy\n",
"## How To import NumPy\n",
"## Why use NumPy"
]
},
{
"cell_type": "markdown",
"id": "c33bf5a4",
"metadata": {},
"source": [
"# Terminology"
]
},
{
"cell_type": "markdown",
"id": "32691996",
"metadata": {},
"source": [
"## What is an array"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "56b12e52",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 3 4]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"a = np.array([[1, 2, 3, 4], [5, 6, 7 , 8], [9, 10, 11, 12]])\n",
"\n",
"print(a[0])"
]
},
{
"cell_type": "markdown",
"id": "88708177",
"metadata": {},
"source": [
"## What are the attributes of an array"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc10bf51",
"metadata": {},
"outputs": [],
"source": [
"[[0., 0., 0.],\n",
"[1., 1., 1.]]"
]
},
{
"cell_type": "markdown",
"id": "658f2df1",
"metadata": {},
"source": [
"## Whats the difference between a Python list and a NumPy array?"
]
},
{
"cell_type": "markdown",
"id": "8555b23e",
"metadata": {},
"source": [
"### Creating Array"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9418c5c9",
"metadata": {},
"outputs": [],
"source": [
"np.array()\n",
"np.zeros()\n",
"np.ones()\n",
"np.empty()\n",
"np.arange()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5d355575",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"a = np.array([1, 2, 3])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e97659f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0., 0.])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.zeros(6)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "23c45096",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 1., 1., 1., 1., 1.])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.ones(6)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "435fea4e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 1., 1., 1., 1., 1.])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Or even an empty array!\n",
"# The function empty creates an array whose initial content is random and depends on the state of the memory\n",
"\n",
"np.empty(6)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "72f4c92a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3]\n",
"[0 2 4 6 8]\n"
]
}
],
"source": [
"# you can create an array with a range of elements\n",
"\n",
"print(np.arange(4))\n",
"print(np.arange(0,10,2)) #(start, stop, step)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e77d2fcb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2, 7, 12, 17, 22, 27])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(2,29,5)"
]
},
{
"cell_type": "markdown",
"id": "727878f7",
"metadata": {},
"source": [
"## Add, Remove, and Sort"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9406c6ee",
"metadata": {},
"outputs": [],
"source": [
"np.append()\n",
"np.delete()\n",
"np.sort()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7d173ff1",
"metadata": {},
"outputs": [],
"source": [
"arr = np.array([1,2,3,4,5,6,7,8])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "76bf0ac1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6, 7, 8, 1, 2])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.append(arr, [1,2])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "85c5cc86",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 3, 4, 5, 6, 7, 8])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.delete(arr, 1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "08231037",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6, 7, 8])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sort(arr)"
]
},
{
"cell_type": "markdown",
"id": "9e8dba62",
"metadata": {},
"source": [
"## Shape and Size"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bcd78629",
"metadata": {},
"outputs": [],
"source": [
"ndarray.ndim() #jumlah axes, dimensi dr array\n",
"ndarray.size() #jumlah total elemen array\n",
"ndarray.shape() #menampilkan tuple integer"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "255df074",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0 1 2 3]\n",
" [4 5 6 7]]\n",
"\n",
" [[0 1 2 3]\n",
" [4 5 6 7]]\n",
"\n",
" [[0 1 2 3]\n",
" [4 5 6 7]]]\n"
]
}
],
"source": [
"array_example = np.array([[[0, 1, 2, 3],\n",
" [4, 5, 6, 7]],\n",
" \n",
" [[0, 1, 2, 3],\n",
" [4, 5, 6, 7]],\n",
" \n",
" [[0, 1, 2, 3],\n",
" [4, 5, 6, 7]]])\n",
"\n",
"print(array_example)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "387f348c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_example.ndim"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "29ff39e0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"24"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_example.size"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3943b6fe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 2, 4)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_example.shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "a73feeee",
"metadata": {},
"outputs": [],
"source": [
"arr_one = np.array([[1, 2, 3, 4, 5]])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "aca578cb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr_one.ndim"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "44959b48",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr_one.size"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "398962b6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 5)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr_one.shape"
]
},
{
"cell_type": "markdown",
"id": "1849e73e",
"metadata": {},
"source": [
"## Reshape"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "3a3839d0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3 4 5]\n"
]
}
],
"source": [
"a = np.arange(6)\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "4ce3f29f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 1]\n",
" [2 3]\n",
" [4 5]]\n"
]
}
],
"source": [
"b = a.reshape(3,2)\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "58fd04bb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0],\n",
" [1],\n",
" [2],\n",
" [3],\n",
" [4],\n",
" [5]])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.reshape(6,1)"
]
},
{
"cell_type": "markdown",
"id": "afb24ad4",
"metadata": {},
"source": [
"## Convert 1D to 2D"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14ea0887",
"metadata": {},
"outputs": [],
"source": [
"np.newaxis\n",
"np.expand_dims"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "000c153f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6,)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([1, 2, 3, 4, 5, 6])\n",
"a.shape"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "c1acf582",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 6)\n",
"[[1 2 3 4 5 6]]\n"
]
}
],
"source": [
"# you can use np.newaxis to add a new axis:\n",
"\n",
"a2 = a[np.newaxis]\n",
"print(a2.shape)\n",
"print(a2)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "17650b9b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 6)\n",
"[[1 2 3 4 5 6]]\n"
]
}
],
"source": [
"# you can convert a 1d array to a row vector by interesting an axis along the first dimension\n",
"\n",
"row_vector = a[np.newaxis, :]\n",
"print(row_vector.shape)\n",
"print(row_vector)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "f4e58a25",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(6, 1)\n",
"[[1]\n",
" [2]\n",
" [3]\n",
" [4]\n",
" [5]\n",
" [6]]\n"
]
}
],
"source": [
"# for a column vector, you can insert an axis along the second dimension\n",
"\n",
"col_vector = a[:, np.newaxis]\n",
"print(col_vector.shape)\n",
"print(col_vector)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "28a7e3c1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6,)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([1, 2, 3, 4, 5, 6])\n",
"a.shape"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "c8ea8a31",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6, 1)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# you can use np.expand_dims to add an axis at index position 1 with:\n",
"\n",
"b = np.expand_dims(a, axis=1)\n",
"b.shape"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "4a8366fd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 6)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# you can add an axis at index position 0 with:\n",
"\n",
"c = np.expand_dims(a, axis=0)\n",
"c.shape"
]
},
{
"cell_type": "markdown",
"id": "3280fc5d",
"metadata": {},
"source": [
"# Indexing and Slicing"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "ff43d6df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 3]\n",
"1\n",
"2\n",
"[1 2]\n",
"[2 3]\n",
"[2 3]\n"
]
}
],
"source": [
"data = np.array([1, 2, 3])\n",
"\n",
"print(data)\n",
"print(data[0])\n",
"print(data[1])\n",
"print(data[0:2])\n",
"print(data[1:])\n",
"print(data[-2:])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "01ff8488",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4],\n",
" [ 5, 6, 7, 8],\n",
" [ 9, 10, 11, 12]])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "c3a0c26e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 5 6 7 8 9 10 11 12]\n"
]
}
],
"source": [
"#you can easily print all of the values in the array that are more than 5\n",
"print(a[a>=5])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "c83c56f4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 5 6 7 8 9 10 11 12]\n",
"[ 5 6 7 8 9 10 11 12]\n"
]
}
],
"source": [
"five_up = (a >= 5)\n",
"\n",
"print(a[five_up])\n",
"print(a[a>=5])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "f1c60abe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 2 4 6 8 10 12]\n"
]
}
],
"source": [
"# you can select elements that are divisible by 2\n",
"\n",
"divisible_by_2 = a[a%2==0]\n",
"print(divisible_by_2)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "d9042779",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 3 4 5 6 7 8 9 10]\n"
]
}
],
"source": [
"# you can select elements that satisfy two conditions using the & and | operators\n",
"\n",
"c = a[(a > 2) & (a < 11)]\n",
"\n",
"print(c)"
]
},
{
"cell_type": "markdown",
"id": "7d3fd494",
"metadata": {},
"source": [
"# Creating Array from Existing Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "995335b3",
"metadata": {},
"outputs": [],
"source": [
"slicing indexing\n",
"np.vstack()\n",
"np.hstack()\n",
"np.hsplit()\n",
".view()\n",
".copy()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "f0e0c97c",
"metadata": {},
"outputs": [],
"source": [
"arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "288b9209",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([4, 5, 6, 7, 8])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr1 = arr[3:8]\n",
"arr1"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "2f960253",
"metadata": {},
"outputs": [],
"source": [
"a_1 = np.array([[1, 1],\n",
" [2, 2]])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "fa52de6c",
"metadata": {},
"outputs": [],
"source": [
"a_2 = np.array([[3, 3],\n",
" [4, 4]])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "3eb0643f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 1],\n",
" [2, 2],\n",
" [3, 3],\n",
" [4, 4]])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.vstack((a_1, a_2))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "aa61f5a3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 1, 3, 3],\n",
" [2, 2, 4, 4]])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# or stack them horizontally with hstack\n",
"\n",
"np.hstack((a_1, a_2))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "cc7dbdb3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1 2 3 4 5 6 7 8 9 10 11 12]\n",
" [13 14 15 16 17 18 19 20 21 22 23 24]]\n"
]
}
],
"source": [
"arrsplit = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
" [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]])\n",
"\n",
"print(arrsplit)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "74520b62",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[ 1, 2, 3, 4],\n",
" [13, 14, 15, 16]]),\n",
" array([[ 5, 6, 7, 8],\n",
" [17, 18, 19, 20]]),\n",
" array([[ 9, 10, 11, 12],\n",
" [21, 22, 23, 24]])]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# if u wanted to split this array into three equally shaped arrays\n",
"\n",
"np.hsplit(arrsplit, 3)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "ec857d34",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4],\n",
" [ 5, 6, 7, 8],\n",
" [ 9, 10, 11, 12]])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "5f85845d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4],\n",
" [ 5, 6, 7, 8],\n",
" [ 9, 10, 11, 12]])"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# u can create a new array object that looks at the same data\n",
"\n",
"b = a.view()\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "7954549a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4],\n",
" [ 5, 6, 7, 8],\n",
" [ 9, 10, 11, 12]])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# using the copy method will make a complete copy of the array and its data (a deep copy)\n",
"\n",
"c = a.copy()\n",
"c"
]
},
{
"cell_type": "markdown",
"id": "09316b8a",
"metadata": {},
"source": [
"# Basic array operations"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "590b5881",
"metadata": {},
"outputs": [],
"source": [
"# Addition, Subtraction, Multiplication, Division, and More.."
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "337331d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([1, 2, 3, 4])\n",
"\n",
"# add all of the elements in the array\n",
"a.sum()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "91278025",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 1],\n",
" [2, 2]])"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = np.array([[1, 1], [2, 2]])\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "c3586ccf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 3])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# u can sum the rows\n",
"b.sum(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "80b3f077",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 4])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# u can sum the columns\n",
"b.sum(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "21857beb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = np.array([1, 2])\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "d0e59255",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 1.])"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ones = np.ones(2)\n",
"ones"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "fd78a236",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2., 3.])"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data + ones"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "5102b9d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 4])"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data * data"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "d5d15a26",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 1.])"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data / data"
]
},
{
"cell_type": "markdown",
"id": "72b7ff21",
"metadata": {},
"source": [
"# Broadcasting"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "a7719c79",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 4])"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data * 2"
]
},
{
"cell_type": "markdown",
"id": "5a1256f5",
"metadata": {},
"source": [
"# More Array Operations"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "b953600a",
"metadata": {},
"outputs": [],
"source": [
"# Maximum, minimum, sum, mean, product, standar deviation, and more.."
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "4c4dcd4f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.max()\n",
"data.min()\n",
"data.sum()"
]
},
{
"cell_type": "markdown",
"id": "8c010b4c",
"metadata": {},
"source": [
"# Matrices"
]
},
{
"cell_type": "markdown",
"id": "f9e85cc8",
"metadata": {},
"source": [
"## Creating Matrices"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "9f142675",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.49292191, 0.1593913 ],\n",
" [0.43512884, 0.26043418],\n",
" [0.69031586, 0.26080554]])"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.ones((3,2))\n",
"np.zeros((3,2))\n",
"np.random.random((3,2))"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "d1bfa2bc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1. 1.]\n",
" [1. 1.]\n",
" [1. 1.]]\n",
"[[0. 0.]\n",
" [0. 0.]\n",
" [0. 0.]]\n",
"[[0.05451766 0.09732209]\n",
" [0.73913936 0.98199571]\n",
" [0.95590904 0.83658468]]\n"
]
}
],
"source": [
"print(np.ones((3,2)))\n",
"print(np.zeros((3, 2)))\n",
"print(np.random.random((3,2)))"
]
},
{
"cell_type": "markdown",
"id": "4d706525",
"metadata": {},
"source": [
"## Matrix Arithmetic"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "4234e095",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2]\n",
" [3 4]]\n"
]
}
],
"source": [
"data = np.array([[1,2], [3,4]])\n",
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "2d7d2a47",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1. 1.]\n",
" [1. 1.]]\n"
]
}
],
"source": [
"ones = np.ones([2, 2])\n",
"print(ones)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "0ea72500",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[2. 3.]\n",
" [4. 5.]]\n"
]
}
],
"source": [
"print(data + ones)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "be158ec2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1. 1.]]\n"
]
}
],
"source": [
"ones_row = np.ones([1,2])\n",
"print(ones_row)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "f788b7b2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[2. 3.]\n",
" [4. 5.]]\n"
]
}
],
"source": [
"print(data + ones_row)"
]
},
{
"cell_type": "markdown",
"id": "3c1caf5f",
"metadata": {},
"source": [
"# Dot Product"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "c572160a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2 3]\n",
" [4 5 6]]\n",
"(2, 3)\n",
"[[ 7 8]\n",
" [ 9 10]\n",
" [11 12]]\n",
"(3, 2)\n"
]
}
],
"source": [
"a_1 = np.array([[1,2,3], [4,5,6]])\n",
"print(a_1)\n",
"print(a_1.shape)\n",
"\n",
"a_2 = np.array([[7,8], [9,10], [11,12]])\n",
"print(a_2)\n",
"print(a_2.shape)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "9fdd7ff1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 58, 64],\n",
" [139, 154]])"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.dot(a_1, a_2)"
]
},
{
"cell_type": "markdown",
"id": "0c773ae1",
"metadata": {},
"source": [
"# Matrix Indexing"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "8a1a1c4c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2]\n",
" [3 4]\n",
" [5 6]]\n",
"[1 2]\n",
"[3 4]\n",
"2\n",
"[[3 4]\n",
" [5 6]]\n",
"[2 4]\n"
]
}
],
"source": [
"data = np.array([[1,2], [3,4], [5,6]])\n",
"\n",
"print(data)\n",
"print(data[0])\n",
"print(data[1])\n",
"print(data[0,1])\n",
"print(data[1:3])\n",
"print(data[0:2,1])"
]
},
{
"cell_type": "markdown",
"id": "237cf1ab",
"metadata": {},
"source": [
"# Matrix Aggregation"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "6deb8b16",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2]\n",
" [3 4]\n",
" [5 6]]\n"
]
}
],
"source": [
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "c686fb64",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n",
"1\n",
"21\n"
]
}
],
"source": [
"print(data.max())\n",
"print(data.min())\n",
"print(data.sum())"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "c3fd9c93",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5 6]\n",
"[2 4 6]\n"
]
}
],
"source": [
"print(data.max(axis=0))\n",
"print(data.max(axis=1))"
]
},
{
"cell_type": "markdown",
"id": "64b8aa99",
"metadata": {},
"source": [
"# Transposing and Reshaping"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "8a3afd0a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2]\n",
" [3 4]\n",
" [5 6]]\n"
]
}
],
"source": [
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "fee15967",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 3 5]\n",
" [2 4 6]]\n"
]
}
],
"source": [
"print(data.T)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"id": "27ccf4a5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1]\n",
" [2]\n",
" [3]\n",
" [4]\n",
" [5]\n",
" [6]]\n"
]
}
],
"source": [
"data_col = np.array([[1,2,3,4,5,6]]).T\n",
"print(data_col)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "30d62c2f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6]])"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_col.reshape(2,3)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "16c0d3bc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2],\n",
" [3, 4],\n",
" [5, 6]])"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_col.reshape(3,2)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "15ed9ae3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 1 2]\n",
" [3 4 5]]\n"
]
}
],
"source": [
"arr = np.arange(6).reshape((2,3))\n",
"print(arr)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "6d285b71",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[1 2]\n",
" [3 4]]\n",
"\n",
" [[5 6]\n",
" [7 8]]]\n"
]
}
],
"source": [
"ndarr = np.array([[[1, 2], [3,4]],\n",
" [[5, 6], [7, 8]]])\n",
"print(ndarr)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"id": "47c8cce4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[1. 1.]\n",
" [1. 1.]\n",
" [1. 1.]]\n",
"\n",
" [[1. 1.]\n",
" [1. 1.]\n",
" [1. 1.]]\n",
"\n",
" [[1. 1.]\n",
" [1. 1.]\n",
" [1. 1.]]\n",
"\n",
" [[1. 1.]\n",
" [1. 1.]\n",
" [1. 1.]]]\n"
]
}
],
"source": [
"print(np.ones((4,3,2)))"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "1273244e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0. 0.]\n",
" [0. 0.]\n",
" [0. 0.]]\n",
"\n",
" [[0. 0.]\n",
" [0. 0.]\n",
" [0. 0.]]\n",
"\n",
" [[0. 0.]\n",
" [0. 0.]\n",
" [0. 0.]]\n",
"\n",
" [[0. 0.]\n",
" [0. 0.]\n",
" [0. 0.]]]\n"
]
}
],
"source": [
"print(np.zeros((4,3,2)))"
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "cf8167e9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.79568529 0.70048239]\n",
" [0.01458117 0.75937567]\n",
" [0.85644883 0.67510868]]\n",
"\n",
" [[0.82429833 0.84009191]\n",
" [0.35086451 0.54936671]\n",
" [0.99348265 0.7521005 ]]\n",
"\n",
" [[0.07473488 0.77458928]\n",
" [0.22606195 0.18627498]\n",
" [0.05133246 0.80805047]]\n",
"\n",
" [[0.3770231 0.14832845]\n",
" [0.63940574 0.88564686]\n",
" [0.77236064 0.18111657]]]\n"
]
}
],
"source": [
"print(np.random.random((4,3,2)))"
]
},
{
"cell_type": "markdown",
"id": "b9cf6c1c",
"metadata": {},
"source": [
"# Flatten N-Dimensional Array"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "ec926afb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1 2 3 4]\n",
" [ 5 6 7 8]\n",
" [ 9 10 11 12]]\n"
]
}
],
"source": [
"arrflat = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
"print(arrflat)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"id": "100f5989",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# u can use flatten to flatten ur array into 1d array\n",
"\n",
"arrflat.flatten()"
]
},
{
"cell_type": "markdown",
"id": "0000188d",
"metadata": {},
"source": [
"# Working with Math Formulas"
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "e8d3ba68",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'n' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_15384/3104529244.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0merror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobserved\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mprediction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'n' is not defined"
]
}
],
"source": [
"error = (1/n) * np.sum(np.square(observed - prediction))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7827f49e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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