2.0 -Data Visiulasiation Matplotlib Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#defining the dataset\n",
    "x = np.arange(0,10,0.1)\n",
    "y = 2*x + 5\n",
    "\n",
    "#plotting the datapoints\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5. ,  5.2,  5.4,  5.6,  5.8,  6. ,  6.2,  6.4,  6.6,  6.8,  7. ,\n",
       "        7.2,  7.4,  7.6,  7.8,  8. ,  8.2,  8.4,  8.6,  8.8,  9. ,  9.2,\n",
       "        9.4,  9.6,  9.8, 10. , 10.2, 10.4, 10.6, 10.8, 11. , 11.2, 11.4,\n",
       "       11.6, 11.8, 12. , 12.2, 12.4, 12.6, 12.8, 13. , 13.2, 13.4, 13.6,\n",
       "       13.8, 14. , 14.2, 14.4, 14.6, 14.8, 15. , 15.2, 15.4, 15.6, 15.8,\n",
       "       16. , 16.2, 16.4, 16.6, 16.8, 17. , 17.2, 17.4, 17.6, 17.8, 18. ,\n",
       "       18.2, 18.4, 18.6, 18.8, 19. , 19.2, 19.4, 19.6, 19.8, 20. , 20.2,\n",
       "       20.4, 20.6, 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. , 22.2, 22.4,\n",
       "       22.6, 22.8, 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2, 24.4, 24.6,\n",
       "       24.8])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cutomizing the Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#defining the dataset\n",
    "x = np.arange(0,10,1)\n",
    "y = 2*x + 5\n",
    "#changing the figure\n",
    "fig=plt.figure(figsize=(10,5))\n",
    "#plotting the datapoints\n",
    "plt.plot(x,y,linewidth = 2.0,linestyle = \":\", color = 'r',alpha = 0.5,marker ='o')\n",
    "plt.title(\"Line Plot Demo\")\n",
    "plt.xlabel(\"X-Axis\")\n",
    "plt.ylabel(\"Y-Axis\")\n",
    "plt.legend(['line1'],loc='best')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x = np.arange(0,10,1)\n",
    "y1 = 2*x+5\n",
    "y2 = 3*x+10\n",
    "\n",
    "#Create two or more plots in a fig\n",
    "plt.subplot(1,2,1)\n",
    "#PLotting the points\n",
    "plt.plot(x,y1)\n",
    "plt.title('Graph1')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(x,y2)\n",
    "plt.title('Graph2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#creating the dataset\n",
    "data = {'apples':20,'Mangoes':15,'Lemon':30,'Oranges':10}\n",
    "names = list(data.keys())\n",
    "values = list(data.values())\n",
    "\n",
    "fig = plt.figure(figsize = (10,5))\n",
    "#Creating the bar plot\n",
    "plt.bar(names,values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customizing the Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoEAAAFNCAYAAAB/kbXqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAG/lJREFUeJzt3XmUZGWd5vHvI4WAQIEIbSMiBTZKI2oJhYor2rgig4g2Wm6oPQ4oo45tTzueGUUbW1zARu2DIo2yi0iJGyiIggsuZLHL2g4oi61oyVKIjBS/+SNutmGaVRkUGRmZ+X4/58SJG++9ce/vvicqeXjfeyNSVUiSJKktDxh1AZIkSZp5hkBJkqQGGQIlSZIaZAiUJElqkCFQkiSpQYZASZKkBhkCJWkWSXJ9kj1GXYek+c8QKGlO6sLSXUlWJvltkq8l2Xqaj7FxksO7Y92Z5OdJvpDkidN5nLWVZP8kq7o+WJnkuiSfSfKoUdcmafYzBEqay/aqqo2ALYFfAh9fm50kWTBJ23rAt4DHAi8CFgJ/DXwOeOGg+5kBP+j6YBNgD+AuYHmSnUZQi6Q5xBAoac6rqt8DXwB2HG9LsmeSi5LcnuSGJAf3rVuUpJK8IcnP6YW9iV4NPBx4cVVdXlWrqurOqvpCVfXvq5K8Ocm1wLVd2xHdMW9PsjzJ0/u2P7gbTTwlyR1JLkzy+AnHXpzk0iS3ddutP0AfrKqqn1bVm4DzgP4an5zk/CS3Jrkkye59685Ncki3fmWSryR5SJITu/ovSLKob/undG23dc9Pmao2SbOTIVDSnJfkQcB+wA/7mu8EXgNsCuwJHJjkxRPe+kx6o3vPm2S3ewDfqKo7ByjhxcCT+GMIvQBYDGwGnAScOiHI7Q2c2rf+9CTr9q3/W+D5wLbA44D9B6ih3zLg6QBJtgK+BhzSHe8dwGlJtujb/uX0Qu9WwCOBHwCf6ba/EnhPt6/Nun19DHgIcDjwtSQPuY/1SZoFDIGS5rLTk9wK3A48B/jw+IqqOreqLquqe6vqUuBkeqGv38Hd6N5dk+x7c+A/xl8kWdyNpN2e5OoJ236gqlaM76eqTqiq31TVPVV1GLAe8Oi+7Zd3I4p/oBek1gee3Lf+Y1V1c1WtAL5CL1DeFzfTC3AArwLOqKozur44GxjjT6e0P9ONIt4GnAn8tKq+WVX30AurT+i22xO4tqqO787tZOAqYK/7WJ+kWcAQKGkue3FVbUovZB0EnJfkLwGSPCnJt5PckuQ24AB6wa7fDWvY92/oXWsIQFVd3B3rJd3xVrufJH+f5MpuyvRWetfrbT7Z9lV1L3Aj8LC+9f/Rt/w7YKM11DmZrYAV3fI2wMu6AHtrV8/T+s+N3vWU4+6a5PX48R8G/GzCsX7WHU/SHGMIlDTnddfDLQNW0Qs40Jtm/TKwdVVtAnwSyMS3rmG35wDPTbLhICWML3TX//0jvSndB3fB8bYJx966b/sH0Lv28OYBjjOofYDvdss3AMdX1aZ9jw2r6tC12O/N9EJlv0cAN92PWiWNiCFQ0pyXnr2BB9O7hg1gY2BFVf2++0qXpfdxt8cBvwC+mGSnJOt01/UtmeJ9GwP3ALcAC5K8m96dxf12SfKS7m7itwF386fXM95nXX3bJvk4sDvw3m7VCcBeSZ43fg5Jdk/y8LU4zBnAo5IsTbIgyX70roP86v2pXdJoGAIlzWVfSbKS3jWB7wdeW1U/6da9CXhfkjuAdwOfvy877u44fhZwBb2bIW4HrgZ2pTfKtzrfoHdd3TX0pkp/z59PO3+J3o0sv6V3Q8ZLuusD18ZufX1wLr3AuWtVXdadxw30bkR5F71gegPwD6zF3/+q+g29r8v5e3rT5f8TeFFV/Xota5c0Qqla02yIJGk6dV9V81dV9apR1yKpbY4ESpIkNcgQKEmS1CCngyVJkhrkSKAkSVKDDIGSJEkNWjDqAuaCzTffvBYtWjTqMiRJkqa0fPnyX1fVFlNtZwgcwKJFixgbGxt1GZIkSVNKMvHnHSfldLAkSVKDDIGSJEkNMgRKkiQ1yBAoSZLUIEOgJElSgwyBkiRJDTIESpIkNcgQKEmS1CBDoCRJUoMMgZIkSQ3yZ+MGsWI5nJRRVyGtvaU16gokSbOMI4GSJEkNMgRKkiQ1yBAoSZLUIEOgJElSgwyBkiRJDTIESpIkNcgQKEmS1CBDoCRJUoMMgZIkSQ0yBEqSJDXIEChJktQgQ6AkSVKDDIGSJEkNMgRKkiQ1yBAoSZLUIEOgJElSgwyBkiRJDTIESpIkNcgQKEmS1CBDoCRJUoMMgZIkSQ0yBEqSJDVoRkJgkocn+VKSa5P8NMkRSR44E8eWJEnSnxt6CEwSYBlwelVtDzwK2Ah4/4TtFgy7FkmSJPXMRPB6NvD7qvoMQFWtSvI/gOuSXAc8C1gf2DDJfwG+BDwYWBf431X1pSSLgDOB7wFPAW4C9q6qu5LsCvwbcGe3/gVVtVOSdYBDgd2B9YB/rapPJdkSOAVY2J3/gVX13RnoB0mSpFljJqaDHwMs72+oqtuBn9MLYbsBr62qZwO/B/apqp3phcPDupFEgO3pBbnHALcC+3btnwEOqKrdgFV9h3kDcFtV7QrsCvzXJNsCS4FvVNVi4PHAxZMVneSNScaSjN1yx/3rAEmSpNlmJkYCA9Qa2s+uqhV9bf+c5BnAvcBWwEO7dddV1XhgWw4sSrIpsHFVnd+1nwS8qFt+LvC4JC/tXm9CL0heAByTZF16U9SThsCqOgo4CmDJdpmsfkmSpDlrJkLgT/jjqB0ASRYCW9Mbubuzb9UrgS2AXarqD0mupzdVDHB333argA3ohcbVCfDfq+obf7aiFzL3BI5P8uGqOu4+nZEkSdIcNxPTwecAD0ryGoDuWr3DgM8Cv5uw7SbAr7oA+CxgmzXtuKp+C9yR5Mld08v7Vn8DOLAb8SPJo5JsmGSb7hifpnct4c736+wkSZLmoKGHwKoqYB/gZUmuBa6hd+3fuybZ/ERgSZIxeqOCVw1wiDcARyX5Ab3Rv9u69qOBK4ALk1wOfIreyOfuwMVJLqI3QnnEWp6aJEnSnJVeRpu7kmxUVSu75XcCW1bVW6fzGEu2S40dMp17lGbY0rn971ySNLgky6tqyVTbzYfv5tszyf+idy4/A/YfbTmSJEmz35wPgVV1Cr3v/ZMkSdKA/O1gSZKkBhkCJUmSGmQIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWqQIVCSJKlBhkBJkqQGGQIlSZIaZAiUJElqkCFQkiSpQYZASZKkBhkCJUmSGmQIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhq0YNQFzAmb7QJLx0ZdhSRJ0rRxJFCSJKlBhkBJkqQGGQIlSZIaZAiUJElqkCFQkiSpQYZASZKkBhkCJUmSGmQIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWrQglEXMCesWA4nZdRVSJKkuWxpjbqCP+FIoCRJUoMMgZIkSQ0yBEqSJDXIEChJktQgQ6AkSVKDDIGSJEkNMgRKkiQ1yBAoSZLUIEOgJElSgwyBkiRJDTIESpIkNcgQKEmS1CBDoCRJUoMMgZIkSQ0yBEqSJDXIEChJktQgQ6AkSVKDDIGSJEkNMgRKkiQ1yBAoSZLUIEOgJElSgwyBkiRJDZoVITDJylHXIEmS1JJZEQIlSZI0s2ZtCEyyRZLTklzQPZ7atR+c5NgkZyW5PslLknwoyWVJvp5k3W67v0lyUdd+TJL1uvbrk7w3yYXduh1GeZ6SJEmjMGtDIHAE8NGq2hXYFzi6b90jgT2BvYETgG9X1WOBu4A9k6wPfBbYr2tfABzY9/5fV9XOwJHAOyY7eJI3JhlLMnbLHdN7YpIkSaM2m0PgHsAnklwMfBlYmGTjbt2ZVfUH4DJgHeDrXftlwCLg0cB1VXVN134s8Iy+fS/rnpd32/+ZqjqqqpZU1ZItNp5sC0mSpLlrwagLWIMHALtV1V39jUkA7gaoqnuT/KGqqlt9L71zyhT7vrt7XsXs7gNJkqShmM0jgWcBB42/SLL4Prz3KmBRkr/qXr8aOG8aa5MkSZrTZksIfFCSG/sebwfeAixJcmmSK4ADBt1ZVf0eeB1wapLL6I0QfnIolUuSJM1B+eNMqlZnyXapsUNGXYUkSZrTls5M5kqyvKqWTLXdbBkJlCRJ0gwyBEqSJDXIEChJktQgQ6AkSVKDDIGSJEkNMgRKkiQ1yBAoSZLUIEOgJElSgwYKgUlOS7JnEkOjJEnSPDBoqDsSWApcm+TQJDsMsSZJkiQN2UAhsKq+WVWvBHYGrgfOTnJ+ktclWXeYBUqSJGn6DTy9m+QhwP7A3wEXAUfQC4VnD6UySZIkDc2CQTZKsgzYATge2KuqftGtOiXJ2LCKkyRJ0nAMFAKBo6vqjP6GJOtV1d1VtWQIdUmSJGmIBp0OPmSSth9MZyGSJEmaOWscCUzyl8BWwAZJngCkW7UQeNCQa5MkSdKQTDUd/Dx6N4M8HDi8r/0O4F1DqkmSJElDtsYQWFXHAscm2beqTpuhmiRJkjRkU00Hv6qqTgAWJXn7xPVVdfgkb5MkSdIsN9V08Ibd80aTrKtprkWSJEkzZKrp4E91i9+squ/3r0vy1KFVNdtstgss9esQJUnS/DHoV8R8fMA2SZIkzQFTXRO4G/AUYIsJ1wQuBNYZZmGSJEkanqmuCXwgvesBFwAb97XfDrx0WEVJkiRpuKa6JvA84Lwkn62qn81QTZIkSRqyQX87eL0kRwGL+t9TVc8eRlGSJEkarkFD4KnAJ4GjgVXDK0eSJEkzYdAQeE9VHTnUSiRJkjRjBv2KmK8keVOSLZNsNv4YamWSJEkamkFHAl/bPf9DX1sB201vOZIkSZoJA4XAqtp22IVIkiRp5gw6EkiSnYAdgfXH26rquGEUJUmSpOEaKAQmeQ+wO70QeAbwAuB7gCFQkiRpDhp0JPClwOOBi6rqdUkeSu/rYtqwYjmclFFXIWk2W1qjrkCS7pNB7w6+q6ruBe5JshD4Fd4UIkmSNGcNOhI4lmRT4NPAcmAl8OOhVSVJkqShGvTu4Dd1i59M8nVgYVVdOryyJEmSNEyD3hjyjMnaquo701+SJEmShm3Q6eD+L4leH3givWnhZ097RZIkSRq6QaeD9+p/nWRr4ENDqUiSJElDN+jdwRPdCOw0nYVIkiRp5gx6TeDH6f1WMPSC4xOAS4ZVlCRJkoZr0GsCrwLW6ZZ/A5xcVd8fTkmSJEkatjWGwCTrAh8GXgNcDwT4C+DjwPeTPKGqLhp2kZIkSZpeU40EHgY8CNimqu4A6H4x5CNJjgSeD2w73BIlSZI03aYKgS8Etq+q//xRzKq6PcmBwK+BFwyzOEmSJA3HVHcH39sfAMdV1Srglqr64XDKkiRJ0jBNFQKvSPKaiY1JXgVcOZySJEmSNGxTTQe/GViW5PX0fiGkgF2BDYB9hlybJEmShmSNIbCqbgKelOTZwGPo3R18ZlWdMxPFSZIkaTgG/dm4bwHfGnItkiRJmiFr+7NxkiRJmsMMgZIkSQ0yBEqSJDXIEChJktQgQ6AkSVKDhhYCk1SS4/teL0hyS5KvDuuYkiRJGswwRwLvBHZKskH3+jnATUM8niRJkgY07OngM4E9u+VXACePr0jyxCTnJ7moe350175/kmVJvp7k2iQf6nvPG5Jck+TcJJ9O8omufZsk5yS5tHt+RNe+RZLTklzQPZ7atT8zycXd46IkGw+5HyRJkmaVYYfAzwEvT7I+8DjgR33rrgKeUVVPAN4N/HPfusXAfsBjgf2SbJ3kYcD/AZ5Mb1Rxh77tPwEcV1WPA04EPta1HwF8tKp2BfYFju7a3wG8uaoWA08H7ppYeJI3JhlLMnbLHWt9/pIkSbPSQL8Ysraq6tIki+iNAp4xYfUmwLFJtqf3m8Tr9q07p6puA0hyBbANsDlwXlWt6NpPBR7Vbb8b8JJu+XhgfPRwD2DHJOP7XdiN+n0fODzJicCyqrpxktqPAo4CWLJd6j6fvCRJ0iw2E3cHfxn4CH1TwZ1/Ar5dVTsBewHr9627u295Fb2wGgY3HtoeAOxWVYu7x1ZVdUdVHQr8HbAB8MMkO6x2T5IkSfPQTITAY4D3VdVlE9o34Y83iuw/wH5+DDwzyYOTLKA3vTvufODl3fIrge91y2cBB41vlGRx9/zIqrqsqj4IjPGnU8uSJEnz3tBDYFXdWFVHTLLqQ8AHknwfWGeA/dxE77rBHwHfBK4AbutWvwV4XZJLgVcDb+1rX9LdMHIFcEDX/rYklye5hN71gGeu3dlJkiTNTamaO5e7JdmoqlZ2I4FfBI6pqi8O+7hLtkuNHTLso0ia05bOnb+lkua3JMuraslU2821Xww5OMnFwOXAdcDpI65HkiRpThrq3cHTrareMeoaJEmS5oO5NhIoSZKkaWAIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWqQIVCSJKlBhkBJkqQGGQIlSZIaZAiUJElqkCFQkiSpQYZASZKkBhkCJUmSGmQIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWrQglEXMCdstgssHRt1FZIkSdPGkUBJkqQGGQIlSZIaZAiUJElqkCFQkiSpQYZASZKkBhkCJUmSGmQIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWqQIVCSJKlBC0ZdwJywYjmclFFXIUntWlqjrkCadxwJlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWqQIVCSJKlBhkBJkqQGGQIlSZIaZAiUJElqkCFQkiSpQYZASZKkBhkCJUmSGmQIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWqQIVCSJKlBhkBJkqQGGQIlSZIaZAiUJElqkCFQkiSpQfMuBCbZP8knRl2HJEnSbDbvQqAkSZKmNqtCYJLTkyxP8pMkb+zaViY5LMmFSc5JskXXfm6Sf0lyfpLLkzxxkv1tkeS0JBd0j6d27c9McnH3uCjJxjN7ppIkSaM1q0Ig8Pqq2gVYArwlyUOADYELq2pn4DzgPX3bb1hVTwHeBBwzyf6OAD5aVbsC+wJHd+3vAN5cVYuBpwN3TXxjkjcmGUsydssd03R2kiRJs8SCURcwwVuS7NMtbw1sD9wLnNK1nQAs69v+ZICq+k6ShUk2nbC/PYAdk4y/XtiN+n0fODzJicCyqrpxYiFVdRRwFMCS7VL3+8wkSZJmkVkTApPsTi+07VZVv0tyLrD+JJvWapYne/2Abn8TR/oOTfI14IXAD5PsUVVXrXXxkiRJc8xsmg7eBPhtFwB3AJ7ctT8AeGm3vBT4Xt979gNI8jTgtqq6bcI+zwIOGn+RZHH3/MiquqyqPgiMATtM98lIkiTNZrNmJBD4OnBAkkuBq4Efdu13Ao9Jshy4jS74dX6b5HxgIfD6Sfb5FuBfu30uAL4DHAC8LcmzgFXAFcCZQzgfSZKkWStVs/tytyQrq2qjSdrPBd5RVWPDrmHJdqmxQ4Z9FEnSai2d3f+tkmaTJMuraslU282m6WBJkiTNkNk0HTypyUYBu/bdZ7gUSZKkecORQEmSpAYZAiVJkhpkCJQkSWqQIVCSJKlBhkBJkqQGGQIlSZIaZAiUJElqkCFQkiSpQYZASZKkBhkCJUmSGmQIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWqQIVCSJKlBhkBJkqQGGQIlSZIatGDUBcwJm+0CS8dGXYUkSdK0cSRQkiSpQYZASZKkBhkCJUmSGmQIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWqQIVCSJKlBhkBJkqQGGQIlSZIaZAiUJElqUKpq1DXMeknuAK4edR3z3ObAr0ddxDxm/w6ffTxc9u/w2cfDN1N9vE1VbTHVRgtmoJD54OqqWjLqIuazJGP28fDYv8NnHw+X/Tt89vHwzbY+djpYkiSpQYZASZKkBhkCB3PUqAtogH08XPbv8NnHw2X/Dp99PHyzqo+9MUSSJKlBjgRKkiQ1yBA4hSTPT3J1kn9P8s5R1zPfJLk+yWVJLk4yNup65oMkxyT5VZLL+9o2S3J2kmu75wePssa5bjV9fHCSm7rP8sVJXjjKGueyJFsn+XaSK5P8JMlbu3Y/x9NgDf3rZ3iaJFk/yY+TXNL18Xu79m2T/Kj7DJ+S5IEjrdPp4NVLsg5wDfAc4EbgAuAVVXXFSAubR5JcDyypKr+bapokeQawEjiuqnbq2j4ErKiqQ7v/mXlwVf3jKOucy1bTxwcDK6vqI6OsbT5IsiWwZVVdmGRjYDnwYmB//Bzfb2vo37/Fz/C0SBJgw6pamWRd4HvAW4G3A8uq6nNJPglcUlVHjqpORwLX7InAv1fV/62q/wd8Dth7xDVJa1RV3wFWTGjeGzi2Wz6W3h98raXV9LGmSVX9oqou7JbvAK4EtsLP8bRYQ/9qmlTPyu7lut2jgGcDX+jaR/4ZNgSu2VbADX2vb8R/KNOtgLOSLE/yxlEXM489tKp+Ab3/AAB/MeJ65quDklzaTRc7VTkNkiwCngD8CD/H025C/4Kf4WmTZJ0kFwO/As4GfgrcWlX3dJuMPFMYAtcsk7Q5fz69nlpVOwMvAN7cTbNJc9GRwCOBxcAvgMNGW87cl2Qj4DTgbVV1+6jrmW8m6V8/w9OoqlZV1WLg4fRmFv96ss1mtqo/ZQhcsxuBrftePxy4eUS1zEtVdXP3/Cvgi/T+oWj6/bK7Dmj8eqBfjbieeaeqftn90b8X+DR+lu+X7jqq04ATq2pZ1+zneJpM1r9+hoejqm4FzgWeDGyaZPwne0eeKQyBa3YBsH13N88DgZcDXx5xTfNGkg27i5JJsiHwXODyNb9La+nLwGu75dcCXxphLfPSeDjp7IOf5bXWXVT/b8CVVXV43yo/x9Ngdf3rZ3j6JNkiyabd8gbAHvSuvfw28NJus5F/hr07eArdLfL/AqwDHFNV7x9xSfNGku3ojf4BLABOsn/vvyQnA7sDmwO/BN4DnA58HngE8HPgZVXljQ1raTV9vDu9abQCrgf+2/j1a7pvkjwN+C5wGXBv1/wuetet+Tm+n9bQv6/Az/C0SPI4ejd+rENvwO3zVfW+7r97nwM2Ay4CXlVVd4+sTkOgJElSe5wOliRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWqQIVCSpkGSVUku7nssuo/vPzrJjt3yu4ZRoyT18ytiJGkaJFlZVRutYf2Cvt8MvV/7kqTp4EigJA1Jkv2TnJrkK8BZSXZP8tW+9Z9Isn+3fG6SJUkOBTboRhNP7H5Z52tJLklyeZL9RnQ6kuaZBVNvIkkawAZJLu6Wr6uqfbrl3YDHVdWKJLtPtZOqemeSg7ofnifJvsDNVbVn93qTIdQuqUGGQEmaHneNB7cJzr6fP212GfCRJB8EvlpV370f+5Kk/+R0sCQN1519y/fwp39315/qzVV1DbALvTD4gSTvnt7yJLXKEChJM+dnwI5J1uumdf9mNdv9Icm6AEkeBvyuqk4APgLsPDOlSprvnA6WpBlSVTck+TxwKXAtcNFqNj0KuDTJhcBxwIeT3Av8AThwRoqVNO/5FTGSJEkNcjpYkiSpQYZASZKkBhkCJUmSGmQIlCRJapAhUJIkqUGGQEmSpAYZAiVJkhpkCJQkSWrQ/weRjS5DCg4guAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#creating the dataset\n",
    "data = {'apples':20,'Mangoes':15,'Lemon':30,'Oranges':10}\n",
    "names = list(data.keys())\n",
    "values = list(data.values())\n",
    "\n",
    "fig = plt.figure(figsize = (10,5))\n",
    "#Creating the bar plot\n",
    "plt.barh(names,values,color=\"orange\")\n",
    "plt.title(\"Bar Graph Demo\")\n",
    "plt.xlabel(\"Fruits\")\n",
    "plt.ylabel(\"Quantity\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Creating the dataset\n",
    "a =[10,20,30,40,50,60,70,80]\n",
    "b =[5,3,2,5,6,1,4,2]\n",
    "x =[1,2,3,4,3,2,1,2]\n",
    "\n",
    "#Creating the scatter Plot\n",
    "plt.scatter(a,b)\n",
    "plt.scatter(a,x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customizing our Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Creating the dataset\n",
    "a =[10,20,30,40,50,60,70,80]\n",
    "b =[5,3,2,5,6,1,4,2]\n",
    "x =[1,2,3,4,3,2,1,2]\n",
    "\n",
    "#Creating the scatter Plot\n",
    "plt.scatter(a,b, c='g', s=200,edgecolors='y',marker='o',alpha =0.5)\n",
    "plt.scatter(a,x,c='r', s=400,edgecolors='b',marker='4',alpha =1)\n",
    "plt.legend(['b','x'],loc='best')\n",
    "plt.title(\"Scatter Plot Demo\")\n",
    "plt.xlabel(\"X-Axis\")\n",
    "plt.ylabel(\"Y-Axis\")\n",
    "plt.grid(True)\n",
    "plt.savefig('Scatter Plot.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a Histogram?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#prepraring the dataset\n",
    "\n",
    "numbers = [10,12,16,19,11,20,26,28,30,38,35,34,45,60,68,64,62,70,78,75,79,85,94,95]\n",
    "# creating the histogram\n",
    "plt.hist(numbers, bins = [0,20,40,60,80,100])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Customizing the Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "#prepraring the dataset\n",
    "\n",
    "numbers = [10,12,16,19,11,20,26,28,30,38,35,34,45,60,68,64,62,70,78,75,79,85,94,95]\n",
    "# creating the histogram\n",
    "plt.hist(numbers, bins = [0,20,40,60,80,100], edgecolor='#000000', color ='#FF2331')\n",
    "plt.title(\"Histogram Demo\")\n",
    "plt.xlabel(\"Range of values\")\n",
    "plt.ylabel(\"Frequence of values\")\n",
    "plt.grid(True)\n",
    "plt.savefig(\"Histogram Plot.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a Box Plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Data Preparation\n",
    "total = [20,4,1,30,20,10,20,70,30,10]\n",
    "order = [10,3,1,15,17,2,30,44,2,1]\n",
    "discount = [30,10,20,5,10,20,50,60,20,45]\n",
    "data = list([total, order,discount])\n",
    "#Plotting the data\n",
    "plt.boxplot(data, showmeans = True)\n",
    "plt.title(\"Box Plot Demo\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a violin plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#Data Preparation\n",
    "total = [20,4,1,30,20,10,20,70,30,10]\n",
    "order = [10,3,1,15,17,2,30,44,2,1]\n",
    "discount = [30,10,20,5,10,20,50,60,20,45]\n",
    "data = list([total, order,discount])\n",
    "#Plotting the data\n",
    "plt.violinplot(data, showmeans = True, showmedians = True)\n",
    "plt.title(\"Violin Plot Demo\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#importing the required libraries\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#preparing the dataset\n",
    "label = ['Dog','Cat','Wolf','Lion']\n",
    "sizes = [50,45,60,80]\n",
    "\n",
    "#creating the pie chart \n",
    "plt.pie(sizes, labels = label,autopct='%1.1f%%',shadow=True, startangle = 90,explode = (0,0.1,0,0))\n",
    "plt.title(\"Pie Chart Demo\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a Doughnut Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#define the dataset\n",
    "group_names = [\"GroupA\",\"GroupB\",\"GroupC\"]\n",
    "group_size = [20,30,50]\n",
    "size_centre = [5]\n",
    "a,b,c = [plt.cm.Blues,plt.cm.Reds,plt.cm.Greens]\n",
    "#Creating the doughnut chart\n",
    "pie1 = plt.pie(group_size,labels = group_names, radius = 1.5,colors = [a(0.5),b(1.5),c(1.0)])\n",
    "pie2 = plt.pie(size_centre,radius = 1.0,colors = 'w')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create a Area Chart?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#define the dataset\n",
    "x=range(1,15)\n",
    "y=[1,4,6,8,4,5,3,2,4,1,5,6,8,7]\n",
    "\n",
    "#creating the area plot\n",
    "plt.stackplot(x,y,colors = 'Black', alpha = 0.5)\n",
    "#Merge area chart with the line chart\n",
    "plt.plot(x,y,color = 'g')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}