NUMPY BASICS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NUMPY PROJECT \n",
    "\n",
    "basics of numpy or Numeric python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nd array, N-dimensional array examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "x=np.array([1,2,3])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "x=np.array([[1,2],[3,4]])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.+0.j 2.+0.j 3.+0.j 5.+0.j]\n"
     ]
    }
   ],
   "source": [
    "x=np.array([1,2,3,5],dtype=complex)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 2. 3.]\n",
      " [3. 4. 6.]]\n"
     ]
    }
   ],
   "source": [
    "x=np.array([[1,2,3],[3,4,6]],dtype=np.float64)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(b'sid',  60.) (b'athish', 100.)]\n"
     ]
    }
   ],
   "source": [
    "y=np.dtype([('name','S20'),('marks','f4')])\n",
    "x=np.array([('sid',60),('athish',100)],dtype=y)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting the shape and reshaping the array\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.array([[1,2,3],[56,23,55]])\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2],\n",
       "       [ 3, 56],\n",
       "       [23, 55]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.reshape(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.ndim=returns the number of array dimensions\n",
    "\n",
    "x.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#itemsize=returns length of each element in the array in bytes\n",
    "x.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : True\n",
       "  F_CONTIGUOUS : False\n",
       "  OWNDATA : True\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  WRITEBACKIFCOPY : False\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.empty([2,2],dtype=np.float64)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.ones([3,3])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "#.asarrary=converts list to array\n",
    "\n",
    "mylist=[1,2,3]\n",
    "mylist=np.asarray(mylist)\n",
    "print(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19, 21, 23])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.arange=returns ndarray containining evenly spaced values\n",
    "\n",
    "x=np.arange(1,24,2)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.69953378 1.74111836 1.78372044 1.82736491 1.87207729 1.9178837\n",
      " 1.96481091 2.01288635 2.06213811 2.11259497 2.16428642 2.21724267\n",
      " 2.27149467 2.32707411 2.38401349 2.44234607 2.50210594 2.56332804\n",
      " 2.62604813 2.69030287 2.75612981 2.82356742 2.8926551  2.96343324\n",
      " 3.0359432  3.11022734 3.18632909 3.26429291 3.34416437 3.42599014\n",
      " 3.50981804 3.59569706 3.68367739 3.77381044 3.86614889 3.96074669\n",
      " 4.05765914 4.15694286 4.25865588 4.36285763 4.46960902 4.57897243\n",
      " 4.69101176 4.8057925  4.92338171 5.04384813 5.16726215 5.29369589\n",
      " 5.42322324 5.55591989]\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "x=np.logspace(random.random(),random.random())\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INDEX SLICING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE ARRAY IS\n",
      "[[ 1  2  3]\n",
      " [56 34 87]\n",
      " [ 5  6  5]]\n"
     ]
    }
   ],
   "source": [
    "a=np.array([[1,2,3],[56,34,87],[5,6,5]])\n",
    "print(\"THE ARRAY IS\")\n",
    "print(a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THE BOX\n",
      "[[[ 1  2]\n",
      "  [56 34]]]\n"
     ]
    }
   ],
   "source": [
    "row=np.array([[0,0],[1,1]])\n",
    "col=np.array([[0,1],[0,1]])\n",
    "b=a[[row],[col]]\n",
    "print(\"THE BOX\")\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BROADCATING \n",
    "\n",
    "addition, subtraction and multiplication of array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FIRST ARRAY\n",
      "[[1. 2. 3.]\n",
      " [4. 5. 6.]\n",
      " [7. 8. 9.]]\n",
      "\n",
      "SECOND ARRAY \n",
      "\n",
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "a=np.array([[1,2,3],[4,5,6],[7,8,9]],dtype=np.float64)\n",
    "\n",
    "b=np.ones([3,3])\n",
    "\n",
    "print(\"FIRST ARRAY\")\n",
    "print(a)\n",
    "\n",
    "print(\"\\nSECOND ARRAY \\n\")\n",
    "print(b)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.  3.  4.]\n",
      " [ 5.  6.  7.]\n",
      " [ 8.  9. 10.]]\n"
     ]
    }
   ],
   "source": [
    "#addition\n",
    "\n",
    "Sum=a+b\n",
    "print(Sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0., -1., -2.],\n",
       "       [-3., -4., -5.],\n",
       "       [-6., -7., -8.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#difference\n",
    "differ=b-a\n",
    "differ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5., 10., 15.],\n",
       "       [20., 25., 30.],\n",
       "       [35., 40., 45.]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Multiplication\n",
    "\n",
    "Mul=5*b*a\n",
    "Mul"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARITHEMATIC OPERATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.],\n",
       "       [7., 8., 9.]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  4.,  9.],\n",
       "       [16., 25., 36.],\n",
       "       [49., 64., 81.]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.power(a,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.5       , 0.33333333],\n",
       "       [0.25      , 0.2       , 0.16666667],\n",
       "       [0.14285714, 0.125     , 0.11111111]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reciprocal(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STATISTICAL OPERATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.],\n",
       "       [7., 8., 9.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 2., 2.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ptp(a,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.5, 6.5, 7.5])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(a,75,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.581988897471611"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#standard variation\n",
    "np.std(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.666666666666667"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#variance\n",
    "np.var(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLOTTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.arange(20)\n",
    "y=3*x+6\n",
    "\n",
    "plt.title(\"DEMO\")\n",
    "plt.xlabel('distance')\n",
    "plt.ylabel('velocity')\n",
    "plt.plot(x,y,'og')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,1)\n",
    "z=4*x-3\n",
    "plt.plot(x,y,'*b')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(x,z,'-r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SAVING AND LOADING numpy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 12,  34,  12],\n",
       "       [ 45,   3,  45],\n",
       "       [242, 564, 666]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.array([[12,34,12],[45,3,45],[242,564,666]])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('firstsave',x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 12  34  12]\n",
      " [ 45   3  45]\n",
      " [242 564 666]]\n"
     ]
    }
   ],
   "source": [
    "z=np.load('firstsave.npy')\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook can be downloaded and edited for studying of numpy and matplotlib for ROBOTICS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AUTHOUR: Siddarth D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}