Practical

practical.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Set 5 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.572 , 5.461 , 4.4704, 3.81  , 4.5974, 5.3086])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Height_list=[180,215,176,150,181,209]\n",
    "nparr=np.array(Height_list,dtype=None)\n",
    "nparr=nparr*0.0254\n",
    "nparr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T',\n",
       " '__abs__',\n",
       " '__add__',\n",
       " '__and__',\n",
       " '__array__',\n",
       " '__array_finalize__',\n",
       " '__array_function__',\n",
       " '__array_interface__',\n",
       " '__array_prepare__',\n",
       " '__array_priority__',\n",
       " '__array_struct__',\n",
       " '__array_ufunc__',\n",
       " '__array_wrap__',\n",
       " '__bool__',\n",
       " '__class__',\n",
       " '__complex__',\n",
       " '__contains__',\n",
       " '__copy__',\n",
       " '__deepcopy__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dir__',\n",
       " '__divmod__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__float__',\n",
       " '__floordiv__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__iand__',\n",
       " '__ifloordiv__',\n",
       " '__ilshift__',\n",
       " '__imatmul__',\n",
       " '__imod__',\n",
       " '__imul__',\n",
       " '__index__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__int__',\n",
       " '__invert__',\n",
       " '__ior__',\n",
       " '__ipow__',\n",
       " '__irshift__',\n",
       " '__isub__',\n",
       " '__iter__',\n",
       " '__itruediv__',\n",
       " '__ixor__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lshift__',\n",
       " '__lt__',\n",
       " '__matmul__',\n",
       " '__mod__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__neg__',\n",
       " '__new__',\n",
       " '__or__',\n",
       " '__pos__',\n",
       " '__pow__',\n",
       " '__radd__',\n",
       " '__rand__',\n",
       " '__rdivmod__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__rfloordiv__',\n",
       " '__rlshift__',\n",
       " '__rmatmul__',\n",
       " '__rmod__',\n",
       " '__rmul__',\n",
       " '__ror__',\n",
       " '__rpow__',\n",
       " '__rrshift__',\n",
       " '__rshift__',\n",
       " '__rsub__',\n",
       " '__rtruediv__',\n",
       " '__rxor__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__sub__',\n",
       " '__subclasshook__',\n",
       " '__truediv__',\n",
       " '__xor__',\n",
       " 'all',\n",
       " 'any',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'astype',\n",
       " 'base',\n",
       " 'byteswap',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'compress',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'copy',\n",
       " 'ctypes',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'data',\n",
       " 'diagonal',\n",
       " 'dot',\n",
       " 'dtype',\n",
       " 'dump',\n",
       " 'dumps',\n",
       " 'fill',\n",
       " 'flags',\n",
       " 'flat',\n",
       " 'flatten',\n",
       " 'getfield',\n",
       " 'imag',\n",
       " 'item',\n",
       " 'itemset',\n",
       " 'itemsize',\n",
       " 'max',\n",
       " 'mean',\n",
       " 'min',\n",
       " 'nbytes',\n",
       " 'ndim',\n",
       " 'newbyteorder',\n",
       " 'nonzero',\n",
       " 'partition',\n",
       " 'prod',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'ravel',\n",
       " 'real',\n",
       " 'repeat',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'round',\n",
       " 'searchsorted',\n",
       " 'setfield',\n",
       " 'setflags',\n",
       " 'shape',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'squeeze',\n",
       " 'std',\n",
       " 'strides',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'tobytes',\n",
       " 'tofile',\n",
       " 'tolist',\n",
       " 'tostring',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'var',\n",
       " 'view']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(nparr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Set 5 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False,  True])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BMI = [21,23,19,24,20]\n",
    "b=np.array(BMI)\n",
    "b<21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Set 5 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of girl is : 181.66666666666666\n",
      "Mean of Boy is  188.66666666666666\n"
     ]
    }
   ],
   "source": [
    "Height = [180,215,176,150,181,209]\n",
    "Gender = [\"GIRL\", \"GIRL\",\"BOY\", \"GIRL\", \"BOY\", \"BOY\"]\n",
    "girl=[]\n",
    "boy=[]\n",
    "for i in range(len(Gender)):\n",
    "    if(Gender[i]==\"GIRL\"):\n",
    "        girl.append(Height[i])\n",
    "    else:\n",
    "        boy.append(Height[i])\n",
    "#print(Gender[0])\n",
    "g=np.array(girl)\n",
    "b=np.array(boy)\n",
    "gmean=g.mean()\n",
    "bmean=b.mean()\n",
    "print(\"Mean of girl is :\",gmean)\n",
    "print(\"Mean of Boy is \",bmean)\n",
    "#dir(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median of girls is :  180.0\n",
      "Median of boys is :  181.0\n"
     ]
    }
   ],
   "source": [
    "gm=np.array(girl)\n",
    "g=np.median(gm)\n",
    "bm=np.array(boy)\n",
    "b=np.median(bm)\n",
    "print(\"Median of girls is : \", g)\n",
    "print(\"Median of boys is : \", b)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical 5 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[28 27 17]\n",
      " [58 66 32]\n",
      " [44 51 37]]\n"
     ]
    }
   ],
   "source": [
    "mat1=([1,3,2],[1,6,8],[5,2,1])\n",
    "mat2=([6,8,6],[6,3,3],[2,5,1])\n",
    "result=np.dot(mat1,mat2)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Set 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Set 6 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import turtle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "turtle.setup(800,600)\n",
    "board=turtle.Turtle()\n",
    "\n",
    "for i in range(1,20):\n",
    "    board.circle(i*6)\n",
    "turtle.done()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical 6 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *\n",
    "from tkinter.ttk import Combobox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "window=Tk()\n",
    "var = StringVar()\n",
    "var.set(\"one\")\n",
    "data=(\"one\", \"two\", \"three\", \"four\")\n",
    "cb=Combobox(window, values=data)\n",
    "cb.place(x=60, y=150)\n",
    "\n",
    "lb=Listbox(window, height=5, selectmode='multiple')\n",
    "for num in data:\n",
    "    lb.insert(END,num)\n",
    "lb.place(x=250, y=150)\n",
    "\n",
    "v0=IntVar()\n",
    "v0.set(1)\n",
    "r1=Radiobutton(window, text=\"male\", variable=v0,value=1)\n",
    "r2=Radiobutton(window, text=\"female\", variable=v0,value=2)\n",
    "r1.place(x=100,y=50)\n",
    "r2.place(x=180, y=50)\n",
    "                \n",
    "v1 = IntVar()\n",
    "v2 = IntVar()\n",
    "C1 = Checkbutton(window, text = \"Cricket\", variable = v1)\n",
    "C2 = Checkbutton(window, text = \"Tennis\", variable = v2)\n",
    "C1.place(x=100, y=100)\n",
    "C2.place(x=180, y=100)\n",
    "\n",
    "window.title('Hello Python')\n",
    "window.geometry(\"400x300+10+10\")\n",
    "window.mainloop()"
   ]
  }
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