matplotlib examples

matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f48adda05c0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8iEi60kryZjbGzN4ys51mdksTx1xhZr8ys21m9lCSQR46BN/6lvfFjxuX5JlF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\npiQvIpLHlORFRPLY/werOldKnzgB8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48ae064d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [0.5, 1.8, 0.7]\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8iEi60kryZjbGzN4ys51mdksTx1xhZr8ys21m9lCSQR46BN/6lvfFjxuX5JlF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\npiQvIpLHlORFRPLY/werOldKnzgB8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48add60208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure()\n",
    "axes = figure.gca()\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [0.5, 1.8, 0.7]\n",
    "\n",
    "axes.plot(x, y)\n",
    "\n",
    "figure.savefig('fig2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V27ZptdC4y8UQSkt9pfMaMCkVZATwQboCL8nQ1OSHTFZUqMDnu+uug9tu83+RSXJlMoRy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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48add8ceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure()\n",
    "axes = figure.gca()\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [0.5, 1.8, 0.7]\n",
    "\n",
    "axes.plot(x, y)\n",
    "\n",
    "axes.set_xbound(0, 4)\n",
    "axes.set_ybound(0, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JJQJOPjnNReRkYlZed9+dpi8aMwaWXDLvaKpDOdtQhpDWcn8B+DFwfhvPZ7MQ\nAaecAiNGOJmYVcKAAfCzn6XuxJMm5R1N9WtrCeWFiFgre94J+FtErDubw6pCrZVQIuDUU+G++1Iy\nWXTRvCMyax8iYI89oFMnuOUW9/wqZwnlmxmFa6WqqxYVksm99zqZmFWaBDfeCK++Cueck3c01a2t\nJZQm4MvCS2A+4Cs8l1fJRMBpp6V1sEeNgsUWyzsis/bpvfdgww3hiitg553zjiY/nstrBmoloZx2\nWuq66GRilr+//x369Eld9deu2mHb5VWJgY1WBoMGpV4mTiZm1WH99eGyy1IJ5eOP846m+jihVKnf\n/hbuusvJxKza7L477Lsv9O8PkyfnHU11cZVXFTr9dLj9dhg9GhZfPO9ozKyl5mYYOBAWWABuuql9\n9fxylVcNOeOMNCndqFFOJmbVqkMHGDIkraNywQV5R1M9OuUdgE135pkwbFgqmSyxRN7RmNmszD8/\n3H8/bLQR9OoFO+6Yd0T5q9oSiqRuku6S9IqklyRtKKm7pJGSXpX0Z0ndsn37S3pR0mOSumfbVpQ0\nPN9P0XpnnQW33eZkYlZLllkm9cI88EB48cW8o8lf1SYU4BLgwYhYHVgb+DdwPPBIRKwGjMpeAxwG\nrAdcy/RFvs4ETq5oxHNp8OA0AnfUKCcTs1qz0UZw4YXQty+MHZt3NPmqyoQiaSFgs4i4CdIo/Ij4\nHNiZNH8Y2b/9sudNpEGVXUlrs2wKfBARb1Q28jk3eDAMHZpKJp58zqw27b037LYb/PSnMGVK3tHk\npyp7eUlam1TaeJlUOvkHcCTwXkR0L9pvXET0kLQNcA7wHrAPcBewW0SMn8U1cu/ldfbZcPPN0Njo\nZGJW65qbU1fiRReFa6+t355fs+rlVa2N8p2AdYFDI+Ifki4iVW+1zAABEBGPkGY9RtI+wB+B1SQd\nDYwDjoiI78wVOmjQoG+eNzQ00NDQUPIPMjPnnJOSiUsmZvWhQ4dUdb3JJnDJJXDkkXlHVBqNjY00\nNja2at9qLaEsDjwVEStmrzclJZSVgIaI+EjSEsDorI2lcNx8wAhge1JS2QXYFegSEde3uEZuJZRz\nz4Ubbkglk6WWyiUEMyuTt95K7So33QQ77JB3NKVXc+NQIuIj4B1Jq2abtgZeAu4H9s+27Qfc1+LQ\nY4BLIqIJmDfb1kxqX6kKv/tdSiajRzuZmNWj5ZZLs1zsuy+8UtXr1pZeVZZQ4Jt2lOuBzsB/gQOA\njsCdwDIGcH3fAAASb0lEQVTAW8DAQjuJpCWBayNip+z1AGAQ8BnQLyI+bXH+ipdQzjsv1a02NkLP\nnhW9tJlV2M03p7FlY8bAIovkHU3peLbhGah0Qjn/fLjmmlQyWXrpil3WzHJ0zDFpbfqHHoLOnfOO\npjScUGagkgnlggvgqqtSycTJxKz9aGpKMxMvuyxceWXe0ZRGzbWh1JMLL0zJxCUTs/anY8c0ndJj\nj6WFuepdtXYbrgsXXZT+Khk9Ok3RYGbtz0ILwYgR0Ls3rLoqbLtt3hGVj0soZXLRRXD55U4mZgYr\nrgh33JFG1L/2Wt7RlI8TShlcfLGTiZl92xZbpF5fO+0En32WdzTl4Ub5Erv4Yrj00tQAv+yyJT+9\nmdW4I4+El1+GBx+ETjXY6OBG+Qq55JKUTEaPdjIxsxk7//w0TctRR+UdSek5oZTIpZem0sno0Wmk\nrJnZjHTqlJb4fvjhNDatntRggav6XHZZaoR3MjGz1lh44bTa46abpp5fW26Zd0Sl4RJKG11+eRpr\nMno0LL983tGYWa1YZRUYPhz22ANefz3vaErDCaUNrrgi1Yc6mZjZ3NhqKzjttLTa4+ef5x1N27mX\n11y68so0c/Do0bDCCiUMzMzanUMPhf/9Lw2A7Ngx72hmzb28Suyqq9KaJqNGOZmYWdtdfHFaOvjY\nY/OOpG2cUObQ1Ven1RZHj06jX83M2qpz57SGyogRab2kWuVeXnPgmmvSOvCjRjmZmFlpde+een5t\nvnnq+bXZZnlHNOdcQmmla66BwYNTMllppbyjMbN61KsX3HorDByY2lRqjRNKK1x7LZx1lpOJmZXf\ndtvBCSeknl8TJuQdzZxxL6/ZuPbaNKHbqFGw8soVCMzM2r0I+OUv4f334d57q6vnl3t5zaXrrnMy\nMbPKk9IMHBMmwIkn5h1N6zmhzMT118Ppp8OjjzqZmFnldekC99wDd98NQ4fmHU3ruMprBm64AQYN\nSiWTVVapbFxmZsVefhkaGlLVV+/eeUfjKq85cuONaSqERx91MjGz/K2xBtx8MwwYAG+/nXc0s+aE\nUuSmm+DUU1PJZNVV847GzCzp0weOPjr1/Jo4Me9oZs5VXpmbb4aTT04lk9VWyy8uM7MZiYCDDkrL\nB99zT1qkKw81WeUl6U1Jz0t6VtLfsm3dJY2U9KqkP0vqlm3vL+lFSY9J6p5tW1HS8NZca8gQOOkk\nJxMzq15Smkdw7NhUk1KNqjahAM1AQ0T8MCI2yLYdDzwSEasBo7LXAIcB6wHXAntm284ETp7dRYYM\nSd3ynEzMrNrNM08qndx2Gwwblnc031XNCUV8N76dgSHZ8yFAv+x5EzAf0BWYKmlT4IOIeGNWFxg6\ndHoy6dWrdIGbmZXLYoulOb+OPBL+9re8o/m2qm1DkfRfYBwQwDURcb2kzyKie9E+4yKih6RtgHOA\n94B9gLuA3SJi/CzOH0stFU4mZlaT7r8fDjkEnn4all66ctedVRtKNSeUJSPiA0mLAiOBw4H7IqJH\n0T6fRsQiLY7bB+gOjAGOJiWlIyJiUov94uWXg9VXL/cnMTMrj3PPhTvvhL/8Bbp2rcw1Z5VQqnb6\n+oj4IPv3E0n3AhsAH0laPCI+krQE8HHxMZLmA/YDtgf+COwC7ArsDVzf8hp33DHom+cNDQ00NDSU\n5bOYmZXDscfCSy/B/vvD7beXp+dXY2MjjY2Nrdq3KksokroCHSJioqT5SSWU3wJbA+Mi4lxJxwHd\nI+L4ouNOBZ6NiBGSGoEfAz/N9rusxTXatASwmVk1mDQJttwStt8+zfBRbrVYQlkc+IOkIMV4W0SM\nlPQP4E5JBwJvAQMLB0haElg/Ik7PNl0O/B34jOmN92ZmdWXeeeEPf4ANN0yj6gcOnP0x5VKVJZRK\ncAnFzOrJc8/BttvCQw/BeuuV7zo1ObDRzMxab5110sqy/fqldVTy4IRiZlYn+vdPC3P16wdff135\n67vKy8ysjkTAXnul57fdlqZsKSVXeZmZtRNSWtPp9ddh8ODKXrtae3mZmdlcmm++tCDXhhvC6qun\nqrBKcJWXmVmd+uc/YYcd4OGHU6N9KbjKy8ysHVpvPbjiCth5Z/jww/JfzwnFzKyODRwIBxwAu+yS\nRtWXk6u8zMzqXHMz7L57GlU/ZEjben65ysvMrB3r0CEtc/7SS3DeeeW7jnt5mZm1A127wn33pZ5f\nvXpB376lv4arvMzM2pExY2CnndJKtWutNefHu8rLzMyAVEK5+OJUQvnkk9Ke2yUUM7N26KST4PHH\n4ZFHYJ55Wn9cTS4BXG5OKGbWnjU3w4ABsPDCaaqW1vb8cpWXmZl9S4cOMHQoPPMMXHRRac7pXl5m\nZu3UAgvA/ffDRhulnl99+rTtfC6hmJm1Y8suC3ffDfvvDy+/3LZzOaGYmbVzvXvD+een7sRjx879\nedwob2ZmABx3XBqnMnIkdOky433cy2sGnFDMzL6tqSktH7zUUnD11TPu+eVeXmZmNlsdO8KwYfDk\nk3D55XN+vHt5mZnZNxZcMPX86t0bVlsNttuu9ce6hGJmZt+ywgpw552w997w6qutP84JxczMvmOz\nzeDss1PPr3HjWndMVScUSR0kPSPp/uz18pKelvSapOGSOmXbfy3pBUkPFG3bRNIFecZvNrcaGxvz\nDsGMgw6CHXdMqz5OnTr7/as6oQBHAMVDbc4FLoiIVYHxwEHZ9r0iYi3gKWD7bNspwBmVCtSslJxQ\nrFqcdx507gy/+c3s963ahCJpaaAPcH3R5q2Ae7LnQ4B+Rft3AboCUyXtDTwYEeMrFK6ZWV3q2BFu\nvx1GjYKrrpr1vtXcy+si4BigG4CkRYDPIqI5e/9doGf2/ArgaeAF4EngXqaXVMzMrA26dYMRI2CT\nTWa9X1UObJT0E+DHEfFrSQ3AUcABwNMRsUq2z9KkUsgPWhx7CvA8EMC+wNsR8X8zuEb1fXAzsxow\ns4GN1VpC2QToK6kPMB+wIHAJ0E1Sh6yUsjTwXvFBkpYC1o+IMyQ1AlsCp0jaOiIeLd53Zl+ImZnN\nnapsQ4mIEyNi2YhYEdgdGBURewOjgV2z3fYD7mtx6OmkxniAebN/m0ltK2ZmVkZVmVBm4XjgKEmv\nAT2AGwpvSFoHiIh4Pts0nNSm0ht4qNKBmpm1N1XZhmJmZrWn1kooZmZWIVJrV5pP6jahKFm3MHLe\nrJpI6i+pe95xmLWU/e48XtJyc7rGR90mFOA24EZg7bwDMSuQtLekp4FNgUl5x2NWLBum8QKwHtBx\nTo+vy7/es2LafMB/gPUkvRkRn8qralkOsvtRpJ6J1wO9I2JMvlGZzdAWwMkRcW/xxtb+7qyLEoqk\neYqeFz74GODvpDEtq0PqApZPhNZeSZonkmbgb8AdwORs4tP9JK2ec4jWjhX/7sxsDHwuqaukUyXt\nIWmh1v7urPmEIukE4PeSDpP0/YgIST2ADYFLgReBLSX9XNIKuQZr7UrRvXm4pNUj4iVgJPAAaTaH\njYEbJZ2d7V/zP49WO1r87iw0DTwBbECavqoTsAdwbmv/8KnZG1jSCpJGAWsC5wOrAftJ6hYR44BX\nI2ISaQqWY0lTt3yeW8DWbszk3jxQUlfgz8A1QL+I+CWwD7C/pKWK5qkzK5sZ3J+9gL0ldQQ+INXq\nPBcRpwIHAt8DlmvNuWu5DWUc8EBEXAjfzDa8L/C1pMWArSQ9Saq7vgWYACycHWdWTjO6N/cDpgEf\nA+dExFSAiHg9u0+XA97PKV5rX2Z0f+4fEU3Z2lMbAPNL6hERYyWNA5ZozYlrsoSStZN8DlxXtPkl\nYBmga0R8DPweuCMiNgYOJSWTeb9zMrMSmsW9uTQwT0Q0FZKJpPkkXUya9eHl757NrLRmcX/2zGp3\nPgFuJdXmXCjpImB9Unv0bNVEQpG0laRvMmShgSgiJhTttiHwTmENlIg4JyIuKdr/kIjwD62V1Bze\nmxOKjtsSKExY+pPsh9yspObw/vw8e+8vwCCgkVQFtnnW/jdbVZ1QJPWW9BKwP7BA0XYVGjCLBi4u\nR2roRNLGktYs7AsQEU0VDN3qXBvuzd5Z55DngF0j4siI+KqiwVvda+P9+f2ImBIRN0fE7yJiYquv\nW609abMGouuBhyNiWNH2wvT1SFoSmBAREyVdCnwBLAYsDxzpEomVQxvvzRWAI3xvWrnk+buzmkso\nC5Ea1B+U1EXSPpJWBroASLqQ1Kd/NUkLAgNIU9u/FBHb+QfWyqgt9+a2vjetzHL73Vk1vbwkHQ6s\nRVqV8QZSsluRNHXKUcBkYEdgvKSjSLHvHBGfZcefAdwZEZ/mEb/VL9+bVs2q6v6MiNwfpHq+p4Ed\ngMeAk0lTp5wDvA4MzPZbEPgUWLvo2C55x+9H/T58b/pRzY9quz+rpcpra+DciHgI+D9S995DgFNJ\nX8SC8E3PhGHA4vBNF7gpuURs7YXvTatmVXV/5ppQiqaaeJZUJCMi/kEa/r8msCRwDLC9pJ0knUwa\nxflytm919iiwmud706pZtd6fFU0okjaRtFLhdUyfauKvQAdJm2evXwTeAdaLiKHA1aTpvpcFdoyI\ndysYtrUDvjetmtXK/VmRRnlJ65Lq9LYgTYhX2F7oxvYf0mjN3ST9NSLezaZPWQUgIkZJagzPdWQl\n5nvTqlmt3Z9lLaFI6izpGuBa0sy/fwYasvc6Fn3ICcBfgHmA8yV1BroDnxTO5R9YKyXfm1bNavX+\nLHeV1zzA48BmEfEAaX6t1SV1imzkuqTfkhqLPgdOIX0Zf8leDylzfNZ++d60alaT92fJq7wkbQSM\ni4jXgC8j4raitzsCTRExLZsSZS1S0ez4iHgjO/5AYP749lwzZm3me9OqWT3cnyWbekXSwqR13DcH\nzgUuiogvsw+viGjORmv+FegVEZ9lXdciO76Dqw6sHHxvWjWrp/uzlFVe85Pq+Q7Lnm8OqXta9oV0\nAN7M9tmi8B5U1xdidcn3plWzurk/25RQJO0raQulNYffIzUg3QlMAjaUtFS2n7IPXVi/eFJhO7hR\n00rP96ZVs3q9P+c4oShZUtJo0ip0ewFXSfpeREyKNBX3I6QGoq0gZdOsZ8KX2TU3Kmwv1Qcx871p\n1aw93J9zlFCyDxak4fzvRcTWwK9IS0peW9gvIv5KKqL1ktRNUteYvh7JgRExqBTBmxX43rRq1l7u\nz1YlFEkdJQ0GBkvaAlgNaIJvFq46AuidvVdwHWlhl4eB/xWKcOH5jayEfG9aNWtv9+dsE0r2Qf9J\nKoa9DpwBTAW2lLQBfFOPNyh7FPyENEnZ88BaEfF+KQM3871p1aw93p+tGYfSDFwQEbcASPohadW5\nU4GrgPWyXgj3AltJWj4i3iQ1Hm0TEY+XJXIz35tW3drd/dmaKq9/AncqLSsJqS/0shFxM9BR0mFZ\nll2aNPDmTYCIuK8WvxCrKb43rZq1u/tztgklIr6KiMlFDUPbMn2emANI0wE8AAwHnoHpXdrMysn3\nplWz9nh/tnrqlSzLBmmBlvuzzROAE4HvA//L+lNXbZc2q0++N62ataf7c066DTcDnYGxwA+yzHoK\n0BwRTxS+ELMc+N60atZu7s85mstLafKyJ7PHTRFxQ7kCM5sTvjetmrWX+3NOE8rSwD7AhRExuWxR\nmc0h35tWzdrL/Vmy2YbNzKx9q+ia8mZmVr+cUMzMrCScUMzMrCScUMzMrCScUMzMrCScUMzKSFKT\npGckvSjpWUlHzW56DUnLSdqjUjGalYoTill5fRkR60bE90lzOf0YOG02x6wA7Fn2yMxKzAnFrEIi\nYizwC+DX8E1J5HFJ/8geG2W7ng1smpVsjpDUQdLvJI2R9Jykn2fHLyHpsWy/f0naJJ9PZpZ4YKNZ\nGUn6IiIWarFtHGnlvgmk+ZymSFoZGB4R62cLM/1fRPTN9v85sGhEDJbUhTQN+gDgp8A8EXF2Vo3W\nNVt73CwXrZ5t2MxKptCG0gW4XNI6pGVhV5nJ/tsBa0naNXu9ULbv34EbJXUG7ouI58sYs9lsOaGY\nVZCkFYFpEfGJpNOADyPiB9kU51/P7DDgsIh4eAbn24y0ZOzNki6IiFvLFrzZbLgNxay8vunRJWlR\n0tKvl2WbugEfZM/3BQor+00AFiw6x5+BQyR1ys6ziqSukpYFPs5mrr0eWLdsn8KsFVxCMSuveSU9\nQ6remgoMjYiLsveuBO6RtC/wEFBo//gX0CzpWeDmiLhE0vLAM1lbycdAP6ABOEbSVFIS2rcyH8ls\nxtwob2ZmJeEqLzMzKwknFDMzKwknFDMzKwknFDMzKwknFDMzKwknFDMzKwknFDMzKwknFDMzK4n/\nB4Y4uRwhUU5zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48adc137f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import date\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.ticker as mtick\n",
    "\n",
    "x = [date(2017, 6, 24), date(2017, 6, 25), date(2017, 6, 26)]   # даты\n",
    "y = [50, 100, 40]     # проценты\n",
    "\n",
    "figure = plt.figure()\n",
    "axes = figure.gca()\n",
    "\n",
    "axes.set_title('Tick format example')   # заголовок графика\n",
    "axes.set_xlabel('Dates')      # заголовок оси X          \n",
    "axes.set_ylabel('Percents')   # заголовок оси Y\n",
    "\n",
    "axes.xaxis.set_major_locator(\n",
    "    mdates.DayLocator())   # местоположение меток по оси X, на каждый день\n",
    "axes.xaxis.set_major_formatter(\n",
    "    mdates.DateFormatter('%Y-%m-%d'))     # формат меток по оси X, в формате strftime\n",
    "axes.yaxis.set_major_formatter(\n",
    "    mtick.FormatStrFormatter('%.0f%%'))   # формат меток по оси Y, в формате оператора %\n",
    "\n",
    "axes.plot(x, y)\n",
    "\n",
    "figure.autofmt_xdate()   # красиво расположить метки по оси X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48add023c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "values = [40, 60, 50]            # значения\n",
    "tick_labels = ['A', 'B', 'C']    # подписи к столбцам\n",
    "\n",
    "positions = np.arange(len(values))  # положения столбцов по оси X\n",
    "width = 0.35                        # ширина столбцов\n",
    "\n",
    "figure = plt.figure()\n",
    "axes = figure.gca()\n",
    "\n",
    "axes.bar(positions, values, width, tick_label=tick_labels, \n",
    "         align='center')    # по центру — красивее"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0220474171821168,\n",
       " 1.0000000328673935,\n",
       " -1.0000000246505483,\n",
       " 1.0091699670167189)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QkWMJcBxH6n1nBVEO+Da4/E6oFcDxwABgIHB/9v0XAIOzb32yvwLMBA7CmR/dNHWyETil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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48adbab470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = [40, 60, 50]       # значения\n",
    "labels = ['A', 'B', 'C']    # подписи\n",
    "\n",
    "figure = plt.figure()\n",
    "axes = figure.gca()\n",
    "\n",
    "axes.pie(values, labels=labels,\n",
    "        autopct='%.0f%%')   # расставить проценты\n",
    "axes.legend()\n",
    "\n",
    "axes.axis('equal')          # чтобы круг был круглым"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48adc72160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "values = [40, 60, 50]            # значения\n",
    "labels = ['one', 'two', 'three'] # надписи над столбцами\n",
    "tick_labels = ['A', 'B', 'C']    # подписи под столбцами\n",
    "\n",
    "positions = np.arange(len(values))\n",
    "width = 0.35                       \n",
    "\n",
    "figure = plt.figure()\n",
    "axes = figure.gca()\n",
    "\n",
    "axes.bar(positions, values, width, tick_label=tick_labels, \n",
    "         align='center')\n",
    "\n",
    "y0, y1 = axes.get_ybound()   # размер графика по оси Y\n",
    "y_shift = 0.1 * (y1 - y0)    # дополнительное место под надписи\n",
    "\n",
    "for i, rect in enumerate(axes.patches):  # по всем нарисованным прямоугольникам\n",
    "    height = rect.get_height()  \n",
    "    label = labels[i]\n",
    "    x = rect.get_x() + rect.get_width() / 2  # посередине прямоугольника\n",
    "    y = y0 + height + y_shift / 2  # над прямоугольником в середине доп. места\n",
    "    axes.text(x, y, label, ha='center', va='center')  # выводим текст\n",
    "    \n",
    "axes.set_ybound(y0, y1 + y_shift)  # меняем размеры графика, чтобы надписи поместились"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0220474171821168,\n",
       " 1.0000000328673935,\n",
       " -1.0000000246505483,\n",
       " 1.0091699670167189)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kx+O88ortJN5x9Y8SHFv5BMd4ZHgzCbzlbHKjrzK0dLflRZbiwys3mCIbSRw3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\npauUUnmkpauUUnn0/ws4UDBnr5RIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48adb76c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Circle\n",
    "\n",
    "values = [40, 60, 50]       # значения\n",
    "labels = ['A', 'B', 'C']    # подписи\n",
    "\n",
    "figure = plt.figure()\n",
    "axes = figure.gca()\n",
    "\n",
    "axes.pie(values, labels=labels,\n",
    "        autopct='%.0f%%',   # расставить проценты\n",
    "        pctdistance=0.7)    # проценты чуть подальше от центра\n",
    "axes.add_patch(Circle((0, 0), # круг посередине\n",
    "                      radius=0.5, # радиусом 0.5\n",
    "                      facecolor='w', # с белой заливкой\n",
    "                      edgecolor='b')) # и чёрной окантовкой\n",
    "\n",
    "axes.axis('equal')          # чтобы круг был круглым"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vOV7MLIspOSuTdCbwZuDYscosXrx4+/zQ0BBDQ0M5m2AtGB4eZnh4uNfNcLyY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48adc7e630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "figure = plt.figure()\n",
    "grid = gridspec.GridSpec(3, 3)   # сетка 3х3\n",
    "\n",
    "figure.suptitle('Many graphs')   # общий заголовок\n",
    "\n",
    "axes0 = figure.add_subplot(grid[0, 0])  # ячейка в 0 строке и 0 столбце\n",
    "axes0.set_title('cell')\n",
    "\n",
    "axes1 = figure.add_subplot(grid[0, 1:]) # ячейка в 0 строке с 1 столбца и до конца\n",
    "axes1.set_title('colspan')\n",
    "\n",
    "axes2 = figure.add_subplot(grid[1:, 0]) # ячейка c 1 строки и до конца в 0 столбце\n",
    "axes2.set_title('rowspan')\n",
    "\n",
    "axes3 = figure.add_subplot(grid[1:, 1:]) # большая ячейка\n",
    "axes3.set_title('big cell')\n",
    "\n",
    "figure.tight_layout()  # чтобы не налезали друг на друга"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el61091399501281672321158216663\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el61091399501281672321158216663\", {\"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"type\": \"zoom\", \"enabled\": false}, {\"button\": true, \"type\": \"boxzoom\", \"enabled\": false}], \"height\": 288.0, \"axes\": [{\"axesbgalpha\": null, \"paths\": [], \"xscale\": \"linear\", \"zoomable\": true, \"xdomain\": [1.0, 3.0], \"id\": \"el6109139950128258352\", \"ydomain\": [0.4, 1.8000000000000003], \"markers\": [], \"bbox\": [0.125, 0.125, 0.775, 0.775], \"ylim\": [0.4, 1.8000000000000003], \"collections\": [], \"xlim\": [1.0, 3.0], \"lines\": [{\"yindex\": 1, \"dasharray\": \"none\", \"xindex\": 0, \"alpha\": 1, \"coordinates\": \"data\", \"color\": \"#0000FF\", \"linewidth\": 1.0, \"data\": \"data01\", \"zorder\": 2, \"id\": \"el6109139950130487872\"}], \"texts\": [], \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"tickformat\": null, \"tickvalues\": null, \"nticks\": 5}, {\"position\": \"left\", \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"tickformat\": null, \"tickvalues\": null, \"nticks\": 9}], \"sharex\": [], \"axesbg\": \"#FFFFFF\", \"images\": [], \"sharey\": []}], \"width\": 432.0, \"data\": {\"data01\": [[1.0, 0.5], [2.0, 1.8], [3.0, 0.7]]}, \"id\": \"el6109139950128167232\"});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el61091399501281672321158216663\", {\"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"type\": \"zoom\", \"enabled\": false}, {\"button\": true, \"type\": \"boxzoom\", \"enabled\": false}], \"height\": 288.0, \"axes\": [{\"axesbgalpha\": null, \"paths\": [], \"xscale\": \"linear\", \"zoomable\": true, \"xdomain\": [1.0, 3.0], \"id\": \"el6109139950128258352\", \"ydomain\": [0.4, 1.8000000000000003], \"markers\": [], \"bbox\": [0.125, 0.125, 0.775, 0.775], \"ylim\": [0.4, 1.8000000000000003], \"collections\": [], \"xlim\": [1.0, 3.0], \"lines\": [{\"yindex\": 1, \"dasharray\": \"none\", \"xindex\": 0, \"alpha\": 1, \"coordinates\": \"data\", \"color\": \"#0000FF\", \"linewidth\": 1.0, \"data\": \"data01\", \"zorder\": 2, \"id\": \"el6109139950130487872\"}], \"texts\": [], \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"tickformat\": null, \"tickvalues\": null, \"nticks\": 5}, {\"position\": \"left\", \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"tickformat\": null, \"tickvalues\": null, \"nticks\": 9}], \"sharex\": [], \"axesbg\": \"#FFFFFF\", \"images\": [], \"sharey\": []}], \"width\": 432.0, \"data\": {\"data01\": [[1.0, 0.5], [2.0, 1.8], [3.0, 0.7]]}, \"id\": \"el6109139950128167232\"});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.3git.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el61091399501281672321158216663\", {\"plugins\": [{\"type\": \"reset\"}, {\"button\": true, \"type\": \"zoom\", \"enabled\": false}, {\"button\": true, \"type\": \"boxzoom\", \"enabled\": false}], \"height\": 288.0, \"axes\": [{\"axesbgalpha\": null, \"paths\": [], \"xscale\": \"linear\", \"zoomable\": true, \"xdomain\": [1.0, 3.0], \"id\": \"el6109139950128258352\", \"ydomain\": [0.4, 1.8000000000000003], \"markers\": [], \"bbox\": [0.125, 0.125, 0.775, 0.775], \"ylim\": [0.4, 1.8000000000000003], \"collections\": [], \"xlim\": [1.0, 3.0], \"lines\": [{\"yindex\": 1, \"dasharray\": \"none\", \"xindex\": 0, \"alpha\": 1, \"coordinates\": \"data\", \"color\": \"#0000FF\", \"linewidth\": 1.0, \"data\": \"data01\", \"zorder\": 2, \"id\": \"el6109139950130487872\"}], \"texts\": [], \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"tickformat\": null, \"tickvalues\": null, \"nticks\": 5}, {\"position\": \"left\", \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"tickformat\": null, \"tickvalues\": null, \"nticks\": 9}], \"sharex\": [], \"axesbg\": \"#FFFFFF\", \"images\": [], \"sharey\": []}], \"width\": 432.0, \"data\": {\"data01\": [[1.0, 0.5], [2.0, 1.8], [3.0, 0.7]]}, \"id\": \"el6109139950128167232\"});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import mpld3\n",
    "\n",
    "figure = plt.figure()\n",
    "axes = figure.gca()\n",
    "\n",
    "x = [1, 2, 3]\n",
    "y = [0.5, 1.8, 0.7]\n",
    "\n",
    "axes.plot(x, y)\n",
    "\n",
    "mpld3.display()"
   ]
  }
 ],
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   "display_name": "Python 3",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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