N.numpy8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1aa3f8c8-78d5-4b96-ba29-ebeb3262605c",
   "metadata": {},
   "source": [
    "# SLICING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4c6bf0e6-86b0-4c3a-b69b-3cd30d67c640",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2b038b05-0981-4ee5-8b0f-4553323c5ddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 21, 22, 23, 24, 25, 26, 27, 28, 29])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(20,30)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "eecee3f9-0fae-4154-b825-fb661a3273b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23, 24, 25, 26, 27, 28, 29])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "490ef4aa-ee36-43e8-b7ba-f061ac71f993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 22])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d6d2c840-d4c6-4fa8-93be-38ed780105c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 22, 24, 26, 28])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45687b02-0c6b-4061-a591-e052e2581314",
   "metadata": {},
   "source": [
    "* İki boyutlu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "1ec63123-8485-4bd0-a1e2-152ac882f593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 8, 9, 7, 8],\n",
       "       [2, 6, 1, 7, 1],\n",
       "       [3, 4, 8, 2, 8],\n",
       "       [6, 2, 7, 2, 7],\n",
       "       [1, 2, 5, 3, 7]], dtype=int32)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.random.randint(10, size = (5,5))\n",
    "m                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ae601727-e04a-4552-8bc3-39acd74ea8a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 3, 6, 1], dtype=int32)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "da66bab8-8876-4c3a-aafd-08c9bffe85c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 1], dtype=int32)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[3:,0]"
   ]
  }
 ],
 "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
}