{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "5d2d2387",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "0c5af0b5",
"metadata": {},
"outputs": [],
"source": [
"arr1=np.array((1,2,4,7,8,9,4,5,6,7,9))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2e0d0565",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"single dimentional: [1 2 4 7 8 9 4 5 6 7 9]\n"
]
}
],
"source": [
"print(\"single dimentional:\",arr1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "51a43691",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('int64')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr1.dtype"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f6520b45",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2-d arrays:\n",
" [[ 10 20 30 40]\n",
" [100 200 300 400]]\n"
]
}
],
"source": [
"arr2=np.array([[10,20,30,40],[100,200,300,400]])\n",
"print(\"2-d arrays:\\n\",arr2)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "319161ca",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"arr1: 1\n",
"arr2: 2\n"
]
}
],
"source": [
"#checking thair dimensions:\n",
"print(\"arr1:\",arr1.ndim)\n",
"print(\"arr2:\",arr2.ndim)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c71c8ff9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3-d array:\n",
" [[[ 1 2 3 4]\n",
" [ 10 20 30 40]\n",
" [100 200 300 400]]]\n",
"arr3: 3\n"
]
}
],
"source": [
"arr3=np.array([[[1,2,3,4],[10,20,30,40],[100,200,300,400]]])\n",
"print(\"3-d array:\\n\",arr3)\n",
"print(\"arr3:\",arr3.ndim)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "a426eb80",
"metadata": {},
"outputs": [],
"source": [
"#for casual cheking with data frames :\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "3c61781d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1\n",
"1 2\n",
"2 4\n",
"3 7\n",
"4 8\n",
"5 9\n",
"6 4\n",
"7 5\n",
"8 6\n",
"9 7\n",
"10 9\n",
"dtype: int64\n"
]
}
],
"source": [
"d_frame=pd.Series(arr1)\n",
"print(d_frame)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "aeed557b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3\n",
"0 10 20 30 40\n",
"1 100 200 300 400\n"
]
}
],
"source": [
"#array to data frame\n",
"#here the data frame support only the 2-d arry for printing other wise the print function will show error message:\n",
"d_frame=pd.DataFrame(arr2)\n",
"print(d_frame)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "88ae926a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 4)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d_frame.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "5d15a9ed",
"metadata": {},
"outputs": [],
"source": [
"#data frame to array\n",
"check1=np.array(d_frame)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "c4c87770",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 10 20 30 40]\n",
" [100 200 300 400]]\n"
]
}
],
"source": [
"print(check1)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "e84c9006",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1-D:\t [1 2 4 7 8 9 4 5 6 7 9] \n",
"\n",
"2-D:\t [[ 10 20 30 40]\n",
" [100 200 300 400]] \n",
"\n",
"3-D:\t [[[ 1 2 3 4]\n",
" [ 10 20 30 40]\n",
" [100 200 300 400]]]\n"
]
}
],
"source": [
"print(\"1-D:\\t\",arr1,\"\\n\")\n",
"print(\"2-D:\\t\",arr2,\"\\n\")\n",
"print(\"3-D:\\t\",arr3)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "df3e081a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 4 7 8 9 4 5 6 7 9]\n",
"[1 2 4 7 8 9 4]\n"
]
}
],
"source": [
"#nupy array indexing with step value:\n",
"print(arr1)\n",
"print(arr1[0:7])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "ab12aa05",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"first half of the elements: [1 2 4 7 8]\n"
]
}
],
"source": [
"#half of the elements are printing:\n",
"print(\"first half of the elements:\",arr1[:len(arr1)//2])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "55929e0a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"4\n",
"7\n",
"8\n",
"9\n",
"4\n",
"5\n",
"6\n",
"7\n",
"9\n"
]
}
],
"source": [
"for i in arr1:\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "0e15e750",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"10\n",
"20\n",
"30\n",
"40\n",
"100\n",
"200\n",
"300\n",
"400\n"
]
}
],
"source": [
"#iterating the 3-D array in one loop:\n",
"for i in np.nditer(arr3[:,::1]):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "141305fd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 4)"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr2.shape"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "a8cf04d8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 3, 4)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#doubt:ask to sir:\n",
"arr3.shape"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "661a05b5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.ndim(arr3)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"id": "2e53a428",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(11,)"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr1.shape"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "f52621d0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"#accessign the nd arry elements:\n",
"#element - 1st row and first column\n",
"print(arr2[0,0])"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "78f2bb26",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"300\n"
]
}
],
"source": [
"#3-d arrray:\n",
"print(arr3[0,2,2])"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "f1dcee88",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 10 20 400 183 142]\n"
]
}
],
"source": [
"#for the absolute we willl create another array with -ve value\n",
"arr4=np.array([-10,-20,-400,-183,142])\n",
"print(abs(arr4))"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "27348c65",
"metadata": {},
"outputs": [],
"source": [
"#reshaping\n",
"#add or remove from the existing array"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "6248d02a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([1, 2, 4, 7, 8, 9]), array([4, 5, 6, 7, 9])]"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array_split(arr1,2)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "072c9d81",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[10, 20, 30, 40]]), array([[100, 200, 300, 400]])]"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array_split(arr2,2)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"id": "4883ec53",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[[ 1, 2, 3, 4],\n",
" [ 10, 20, 30, 40],\n",
" [100, 200, 300, 400]]]),\n",
" array([], shape=(0, 3, 4), dtype=int64),\n",
" array([], shape=(0, 3, 4), dtype=int64)]"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array_split(arr3,3)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "02a8971e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([1, 2, 4]), array([7, 8]), array([9, 4]), array([5, 6]), array([7, 9])]"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array_split(arr1,5)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"id": "e5a999ce",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"print(len(arr4))"
]
},
{
"cell_type": "code",
"execution_count": 106,
"id": "7b58a5c0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[ 1 2]\n",
" [ 3 4]\n",
" [ 5 6]]\n",
"\n",
" [[ 7 8]\n",
" [ 9 10]\n",
" [11 12]]]\n"
]
}
],
"source": [
"arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
"\n",
"newarr = arr.reshape(2, 3, 2)\n",
"print(newarr)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"id": "4bb019ce",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 4 7 8 9 4 5 6 7 9]\n"
]
}
],
"source": [
"#splitting the array:\n",
"print(arr1)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"id": "6fbdf5b6",
"metadata": {},
"outputs": [],
"source": [
"newarr2=np.array_split(arr1,3)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"id": "a254994b",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "only integer scalar arrays can be converted to a scalar index",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [111]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m newarr2:\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mnewarr2\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m)\n",
"\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index"
]
}
],
"source": [
"for i in newarr2:\n",
" print(newarr2[i])"
]
},
{
"cell_type": "code",
"execution_count": 117,
"id": "45ede19a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 4 7]\n",
"[8 9 4 5]\n",
"[6 7 9]\n"
]
}
],
"source": [
"print(newarr2[0])\n",
"print(newarr2[1])\n",
"print(newarr2[2])"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "b6dc5efb",
"metadata": {},
"outputs": [],
"source": [
"##searching arrays"
]
},
{
"cell_type": "code",
"execution_count": 126,
"id": "db8ca2e0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 4 7 8 9 4 5 6 7 9]\n",
"(array([], dtype=int64),)\n"
]
}
],
"source": [
"arr1"
]
},
{
"cell_type": "code",
"execution_count": 129,
"id": "b0d63d87",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([0, 2, 3]),)\n"
]
}
],
"source": [
"arr = np.array([1, 2, 1, 1, 5, 6, 7, 8])\n",
"\n",
"x = np.where(arr == 1)\n",
"\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"id": "32dd0731",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([4, 7]),)\n"
]
}
],
"source": [
"arr=np.array((1,2,3,4,5,2,3,5,6,7,8))\n",
"x=np.where(arr==5)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"id": "709be631",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n"
]
}
],
"source": [
"x = np.searchsorted(arr, 6)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 138,
"id": "eaa35f66",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 2 2 3 5 6 8 8]\n"
]
}
],
"source": [
"#sorting:\n",
"arr=np.array((2,5,2,8,0,3,6,8))\n",
"print(np.sort(arr))"
]
},
{
"cell_type": "code",
"execution_count": 139,
"id": "66b658a1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['azar' 'parmesh' 'ravi' 'sanath']\n"
]
}
],
"source": [
"arr=np.array((\"sanath\",\"ravi\",\"parmesh\",\"azar\"))\n",
"print(np.sort(arr))"
]
},
{
"cell_type": "code",
"execution_count": 140,
"id": "2aef5294",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[10 20 29]\n",
" [ 0 20 83]]\n"
]
}
],
"source": [
"#we can also sort the 2-d array:\n",
"arr=np.array([[20,10,29],[0,20,83]])\n",
"print(np.sort(arr))"
]
},
{
"cell_type": "code",
"execution_count": 143,
"id": "60eeeed6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 3 4]\n"
]
}
],
"source": [
"arr=np.array([1,2,3,4])\n",
"x=[True,False,True,True]\n",
"print(arr[x])"
]
},
{
"cell_type": "code",
"execution_count": 144,
"id": "35d8dba5",
"metadata": {},
"outputs": [],
"source": [
"#nupy rondom class working:\n",
"#we can predict theese numbers:\n",
"#machine will generate this:\n",
"from numpy import random"
]
},
{
"cell_type": "code",
"execution_count": 153,
"id": "859d5dbb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"77"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x=random.randint(100)\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 148,
"id": "7cfa7040",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"88\n"
]
}
],
"source": [
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 154,
"id": "a1748723",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.88562907, 0.22932804, 0.90857795, 0.2838597 , 0.76885629,\n",
" 0.87201837, 0.5121362 , 0.89441225, 0.67149354, 0.03825343])"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x=random.rand(10)\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 170,
"id": "62ef85d8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 16, 29, 50, 6, 0, 99, 11, 88, 33])"
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#generating 10 randoom numbers in given range:\n",
"#and this method is also called as the creating the 2-d array:\n",
"x=random.randint(100,size=10)\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 158,
"id": "34d5e7ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[40, 55, 86, 42, 85, 35, 18, 13, 11, 35],\n",
" [66, 31, 61, 65, 23, 49, 65, 30, 90, 3],\n",
" [90, 53, 95, 32, 45, 85, 31, 10, 52, 69]])"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#creating the 3-d array from the rndom :\n",
"x=random.randint(100,size=(3,10))\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 159,
"id": "8ae2661e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 10)"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.shape"
]
},
{
"cell_type": "code",
"execution_count": 171,
"id": "37d6319d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"29"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#upto now we have created random array:\n",
"# but now we can get the random element from the generatd arry:\n",
"#only for the 1-d array\n",
"y=random.choice(x)\n",
"y"
]
},
{
"cell_type": "code",
"execution_count": 174,
"id": "7decc1e0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[5, 7, 9, 7, 3],\n",
" [9, 9, 5, 7, 7],\n",
" [5, 5, 5, 9, 5]])"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#now we can get the choice of number from the 2-d array by using the size:\n",
"arr2 = random.choice([3, 5, 7, 9], size=(3, 5))\n",
"arr2"
]
},
{
"cell_type": "code",
"execution_count": 179,
"id": "415e52b4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 3 2 4]\n"
]
}
],
"source": [
"#rndom permutatiions\n",
"arr=np.array([1,2,3,4])\n",
"random.shuffle(arr)\n",
"print(arr)"
]
},
{
"cell_type": "code",
"execution_count": 181,
"id": "40dbec86",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4 1 2 3]\n"
]
}
],
"source": [
"print(random.permutation(arr))"
]
},
{
"cell_type": "code",
"execution_count": 189,
"id": "bb115669",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5, 5, 5, 96, 86]\n"
]
}
],
"source": [
"#zip method:\n",
"x1=[1,2,3,93,82]\n",
"x2=[4,3,2,3,4,]\n",
"z=[]\n",
"for i,j in zip(x1,x2):\n",
" z.append(i+j)\n",
"print(z)"
]
},
{
"cell_type": "code",
"execution_count": 199,
"id": "2dfc2e91",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 5 5 5 96 86]\n",
"[-3 -1 1 90 78]\n",
"[ 4 6 6 279 328]\n",
"[ 0.25 0.66666667 1.5 31. 20.5 ]\n",
"[ 1 8 9 804357 45212176]\n",
"[1 2 1 0 2]\n",
"[1 2 1 0 2]\n",
"(array([ 0, 0, 1, 31, 20]), array([1, 2, 1, 0, 2]))\n"
]
}
],
"source": [
"#arithmatic operations:\n",
"print(np.add(x1,x2))\n",
"print(np.subtract(x1,x2))\n",
"print(np.multiply(x1,x2))\n",
"print(np.divide(x1,x2))\n",
"print(np.power(x1,x2))\n",
"print(np.mod(x1,x2))\n",
"print(np.remainder(x1,x2))\n",
"print(np.divmod(x1,x2))\n",
"print(np.(x1,x2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b93fdd9",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}