.ipynb_checkpoints/Matplotlib tutorials-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib\n",
    "### Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.\n",
    "\n",
    "Some of the major Pros of Matplotlib are:\n",
    "\n",
    "1. Generally easy to get started for simple plots\n",
    "2. Support for custom labels and texts\n",
    "3. Great control of every element in a figure\n",
    "4. High-quality output in many formats\n",
    "5. Very customizable in general"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\win10\\anaconda3\\lib\\site-packages (3.3.2)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from matplotlib) (1.19.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from matplotlib) (8.0.1)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from matplotlib) (2020.6.20)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from matplotlib) (1.3.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\win10\\anaconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six in c:\\users\\win10\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(0,10)\n",
    "y=np.arange(11,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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.scatter(x,y,c='g')\n",
    "plt.xlabel('X Axis')\n",
    "plt.ylabel('Y Axis')\n",
    "plt.title('Graph in 2D')\n",
    "plt.savefig('Test.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  4,  9, 16, 25, 36, 49, 64, 81])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2d diagram')"
      ]
     },
     "execution_count": 50,
     "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": [
    "## Line plot\n",
    "plt.plot(x,y,'ro',linestyle='dashed',linewidth=2,markersize=12)\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('y axis')\n",
    "plt.title('2d diagram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18ecc743580>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "##creating subplots\n",
    "## Assignment--plot x title y title\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(x,y,'r--')\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(y,x,'g--')\n",
    "plt.subplot(2,2,3)\n",
    "plt.plot(x,y,'b--')\n",
    "plt.subplot(2,2,4)\n",
    "plt.plot(x,y,'b*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YI0laRX2C/lvAliSXJjkb2AE8sGzMA8C7u2/fXA28UFVHeswpSVqhsU/dVNWxJLcCXwHOAO6qqieTvLfrvwPYC2wHDgEvAb/Wv+SJOCWniFbRWq5/LdcO1j9Na7l2mGL9Y38YK0laG7wyVpIaZ9BLUuPWVdAn2Zjkq0kOJnkyyfumXdNKJTkjyX/vrlFYU5Kcl+SzSb7d/T9447RrGlWSf9P9mTmQ5L4kPzLtmk4kyV1JjiY5sKTt1Un2JXm6e3/VNGs8kSH1/073Z+fxJPcnOW+KJQ41qPYlff82SSU5/1TWtK6CHjgGfKCqfhK4GrhlwG0bTnfvAw5Ou4gx/Sfgy1X1E8AVrJGfI8klwL8GZqvqdSx++WDHdKs6qbuBbcvadgMPVdUW4KFu/XR1Nz9c/z7gdVX108BfAR861UWN6G5+uHaSbASuA/7mVBe0roK+qo5U1f5u+fssBs0l061qdEk2AG8HPjXtWlYqyT8H3gTcCVBVL1fV3021qJU5E/jRJGcCr+A0vx6kqh4Gvres+Sbgnm75HuAdp7KmlRhUf1U9WFXHutVvsHhdzmlnyH97gN8D/h1DLhpdTesq6JdKshl4PfDNKZeyEv+RxT8o/zjlOsbxL4AF4L90p54+leScaRc1iqr6DvAfWDwSO8Li9SAPTreqsVx4/DqW7v2CKdfTx68DX5p2EaNKciPwnap6bBrzr8ugT/JK4HPA+6vqxWnXM4okNwBHq+qRadcypjOBq4BPVtXrgf/D6X3q4P/rzmXfBFwKXAyck+RXplvV+pXkwyyehr132rWMIskrgA8DH5lWDesu6JOcxWLI31tVn592PStwDXBjkmdZvFPoW5P81+mWtCLzwHxVHf8X1GdZDP614OeA/1lVC1X1f4HPAz8z5ZrG8fzxu8d270enXM+KJbkZuAH45Vo7FwH9OIsHCY91v78bgP1JXnOqClhXQZ8kLJ4jPlhVH592PStRVR+qqg1VtZnFDwL/rKrWzFFlVf0v4Lkkl3VN1wJ/OcWSVuJvgKuTvKL7M3Qta+SD5GUeAG7ulm8GvjDFWlYsyTbgg8CNVfXStOsZVVU9UVUXVNXm7vd3Hriq+504JdZV0LN4VPwuFo+GH+1e26dd1Dryr4B7kzwOXAn8++mWM5ruXyGfBfYDT7D4e3NaX46f5D7g68BlSeaTvAe4DbguydMsfvvjtmnWeCJD6r8dOBfY1/3u3jHVIocYUvt0a1o7//qRJI1jvR3RS9K6Y9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxv0/rT/tOKgmf1MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## bar plotss\n",
    "x1=[2,3,4,5]\n",
    "y1=[11,16,19,20]\n",
    "\n",
    "x2=[3,9,11,14]\n",
    "y2=[6,15,7,9]\n",
    "plt.bar(x1,y1,color='g')\n",
    "plt.bar(x2,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([4., 3., 3., 2., 3.]),\n",
       " array([ 5. , 21.4, 37.8, 54.2, 70.6, 87. ]),\n",
       " <BarContainer object of 5 artists>)"
      ]
     },
     "execution_count": 63,
     "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": [
    "## Histograms\n",
    "\n",
    "a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27]) \n",
    "plt.hist(a,bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x18ecca3b3a0>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca3b700>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca46b80>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca46ee0>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca5c3a0>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca5c700>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x18ecca3ba60>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca3bdc0>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca51280>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca515e0>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca5ca60>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca5cdc0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x18ecca3b040>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca46820>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca5c040>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x18ecca46160>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca51940>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca69160>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x18ecca464c0>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca51ca0>,\n",
       "  <matplotlib.lines.Line2D at 0x18ecca694c0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 65,
     "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": [
    "## Boxplot ussing Matplotlib\n",
    "data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "plt.boxplot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data to plot\n",
    "labels = 'Python', 'C++', 'Ruby', 'Java'\n",
    "sizes = [215, 130, 245, 210]\n",
    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']\n",
    "explode = (0.4, 0, 0, 0)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
    "autopct='%1.1f%%', shadow=False)\n",
    "\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}