N.numpy9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2abdaa81-a4f0-43cc-9d6c-f6741a163bd1",
   "metadata": {},
   "source": [
    "# FANCY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4a8b8fce-745b-4d8f-b265-f71116bd4937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.arange(0,30,3)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a13031e3-fe7d-4135-8717-2131ebcfdcf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(3)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ac25627d-fb15-46cd-bcf6-10cb08140f22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.int64(3), np.int64(9), np.int64(15))"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1], x[3], x[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "928ca5cb-7c8c-4c96-92f0-5489f05367e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fancy = [1,3,5]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f4abb89a-a2cf-4908-9604-509c60bc2f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  9, 15])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[fancy]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8feb564-bd5b-4793-9df6-b8cb50c4b152",
   "metadata": {},
   "source": [
    "* İki boyutlu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f425de6e-685c-4318-91ba-c032a0ccdd1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.arange(9).reshape(3,3)\n",
    "m"
   ]
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}