Assignment-02mar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "589de95a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans1:Matplotlib is a pakage in python and is used for visualiazation of data .\n",
    "# bar,hisogram,line,scatter,pi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5625904",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans2:Scatterplot is plot that visualise the data in dotted form .\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a0e8403",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "np.random.seed(3)\n",
    "x = 3 + np.random.normal(0, 2, 50)\n",
    "y = 3 + np.random.normal(0, 2, len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "43d900e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x23c23827220>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(x,y,c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8005e226",
   "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": [
    "plt.xlabel(\"x-axis\")\n",
    "plt.ylabel(\"y-axis\")\n",
    "plt.title(\"Graph\")\n",
    "plt.scatter(x,y,c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c6ca414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ans3:The subplot() function in matplotlib is used to create multiple plots within a single figure. \n",
    "x1 = np.array([0, 1, 2, 3, 4, 5])\n",
    "y1 = np.array([0, 100, 200, 300, 400, 500])\n",
    "x2 = np.array([0, 1, 2, 3, 4, 5])\n",
    "y2 = np.array([50, 20, 40, 20, 60, 70])\n",
    "x3 = np.array([0, 1, 2, 3, 4, 5])\n",
    "y3 = np.array([10, 20, 30, 40, 50, 60])\n",
    "x4 = np.array([0, 1, 2, 3, 4, 5])\n",
    "y4 = np.array([200, 350, 250, 550, 450, 150])\n",
    "fig, axs = plt.subplots(2, 2)\n",
    "axs[0, 0].plot(x1, y1)\n",
    "axs[0, 0].set_title('Line 1')\n",
    "axs[0, 1].plot(x2, y2)\n",
    "axs[0, 1].set_title('Line 2')\n",
    "axs[1, 0].plot(x3, y3)\n",
    "axs[1, 0].set_title('Line 3')\n",
    "axs[1, 1].plot(x4, y4)\n",
    "axs[1, 1].set_title('Line 4')\n",
    "fig.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f8af0f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans4:bar plot is used to show the data or raleton between the data in bar grph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aae695ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Profit')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "company = np.array([\"Apple\", \"Microsoft\", \"Google\", \"AMD\"])\n",
    "profit = np.array([3000, 8000, 1000, 10000])\n",
    "plt.bar(company,profit)\n",
    "plt.title(\"Profit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "40e1987f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans5:\n",
    "box1 = np.random.normal(100, 10, 200)\n",
    "box2 = np.random.normal(90, 20, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "35f3d6e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'value')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "data = [box1, box2]\n",
    "fig, ax = plt.subplots()\n",
    "ax.boxplot(data)\n",
    "plt.xlabel(\"Data\")\n",
    "plt.ylabel(\"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38b4a83a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#A box plot is a graphical representation of a dataset that displays the median, quartiles, and outliers of the data."
   ]
  }
 ],
 "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
}