Experiment no.3 - Data Visualisation (1) divyani.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b150c6fe",
   "metadata": {},
   "source": [
    "# Data Visusalisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17c688c2",
   "metadata": {},
   "source": [
    "Experiment no.3 : Data Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a8693e4",
   "metadata": {},
   "source": [
    "Aim:To perform Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b81ff6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Name: Divyani Sadafale\n",
    "#Roll no.: 50\n",
    "#Sec: B\n",
    "#Subject: Data Science and Statistics (Lab 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7514d38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=20\n",
    "b=30\n",
    "c=a+b\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a24b736",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=(1,2,3,\"Ashish\",2.3,True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "717e84d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae9ed2bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fc0547d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 'Ashish', 2.3, True)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1::1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d56e1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "b=[1,2,3,\"Ashish\",2.3,True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "471d250d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9ef8ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a01c625",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f236233",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a078bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3a876c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(1,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "982a7642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3feed626",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=2*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ca19a49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b7ffae67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "66af340b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "\n",
    "plt.title(\"Line Chart\")\n",
    "plt.xlabel(\"X axis\")\n",
    "plt.ylabel(\"Y axis\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f8c02de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x,y)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2754c492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x,y)\n",
    "plt.title(\"Bar Chart\")\n",
    "plt.xlabel(\"X axis\")\n",
    "plt.ylabel(\"Y axis\")\n",
    "plt.show"
   ]
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}