numpy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "# Create an array from a list\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# Create an with regularly spaced values\n",
    "a_range = np.arange(0,10)\n",
    "print(a_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.   2.5  5.   7.5 10. ]\n"
     ]
    }
   ],
   "source": [
    "# Create an array with evenly spaced values\n",
    "a_linspace = np.linspace(0,10,5)\n",
    "print(a_linspace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02598765 0.58495376 0.24982591 0.41834936 0.79405447 0.35003785\n",
      " 0.18158589 0.51706652 0.71971943 0.09174729]\n"
     ]
    }
   ],
   "source": [
    "random_uniform = np.random.rand(10)\n",
    "print(random_uniform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.76264442  1.43310582  0.26237361  1.0454941   0.10129759 -0.55909846\n",
      " -1.67923553  0.60041701  1.71591351 -0.10632274]\n"
     ]
    }
   ],
   "source": [
    "random_normal = np.random.randn(10)\n",
    "print(random_normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "(2, 3)\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1,2,3],[4,5,6]])\n",
    "print(arr)\n",
    "shape = np.shape(arr)\n",
    "#dtype = np.dtype(arr) #int64\n",
    "size = np.size(arr)\n",
    "print(shape)\n",
    "print(size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "[5 8 9]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,5,8,9,5,9,6,8])\n",
    "first_element = a[0]\n",
    "print(first_element)\n",
    "sliced_array = a[1:4]\n",
    "print(sliced_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n"
     ]
    }
   ],
   "source": [
    "#Reshaping and manipulating arrays\n",
    "arr = np.array([[1,2,3],[4,5,6]])\n",
    "reshaped_arr = np.reshape(arr,(3,2))\n",
    "print(reshaped_arr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4]\n",
      " [2 5]\n",
      " [3 6]]\n"
     ]
    }
   ],
   "source": [
    "transposed_arr = np.transpose(arr)\n",
    "print(transposed_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 8 9 5 9 6 8]\n"
     ]
    }
   ],
   "source": [
    "# Filtering\n",
    "a = np.array([1,5,8,9,5,9,6,8])\n",
    "filtering_arr = a[a > 2]\n",
    "print(filtering_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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