Numpy2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a2e30ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6fad7443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([1,2, 3, 4, 5, 6, 7, 8, 9])\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d496bcce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c92c1ec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n"
     ]
    }
   ],
   "source": [
    "zerodarr = np.array(23)\n",
    "print(zerodarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b508a17c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1 2 3]\n",
      "  [2 4 6]\n",
      "  [3 4 3]]]\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "oneDarr = np.array([[[1,2,3],[2,4,6],[3,4,3]]])\n",
    "print(oneDarr)\n",
    "print(oneDarr.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e8800488",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "[[[1 2 3]\n",
      "  [4 5 6]]\n",
      "\n",
      " [[1 2 3]\n",
      "  [4 5 6]]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array(42)\n",
    "b = np.array([1, 2, 3, 4, 5])\n",
    "c = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])\n",
    "\n",
    "print(a.ndim)\n",
    "print(b.ndim)\n",
    "print(c.ndim)\n",
    "print(d.ndim)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "debf7912",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(d[1][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aa278be7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd element on 1st row:  2\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\n",
    "\n",
    "print('2nd element on 1st row: ', arr[0, 1])\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51b22eef",
   "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
}