Cognitive_technologies/лр1-2/9_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Библиотеки Python для анализа данных и машинного обучения\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Основы работы с библиотекой `matplotlib`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С библиотекой `matplotlib` мы уже сталкивались, когда строили статистические графики на основе данных из датафреймов `pandas`. Но с помощью `matplotlib` можно строить не только гистограммы, графики плотности и диаграммы рассеяния, но и вообще любые графики. \n",
    "\n",
    "Для начала построим простенький график для визуализации данных в двух списках. Импортируем модуль pyplot из библиотеки и добавим питоновскую \"магическую\" строчку для того, чтобы графики отображались прямо в ipynb-файле."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим два небольших списка."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [-2, -0.5, 0, 2, 5, 8, 9, 10]\n",
    "Y = [4, 0.25, 0, 4, 25, 64, 81, 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим график."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117f3fd68>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P5NJBnbyOJPWgEb2I1OpgRSU/eXEen6/ZwZ9+eBI/PCXT60hSTyp6EfmGgxWV/Oyl+Xy6qpBxlw3gyuzOXkeSEKjoReRryiqquPnl+Xy0YjsP/mAAV53axetIEiIVvYhUK6+s4heT5/Nh7nZ+P6If1wxVyfuBil5EgEDJ3zplATOWb+N3l/Tj+tOyvI4kYaKiFxEqKqu47ZWFvLd0K/91cV9GnZ7ldSQJIxW9SCNXUVnF7VMX8c6SAu6/sA85Z3b1OpKEWchFb2ZxZrbAzN4O3u5qZrPNbLWZvWpmCaHHFJFIqKxy3PXaIt5alM/YC3oz+qxuXkeSCAjHiP6XQG6N238CHnXO9QR2AzlhOIaIhFlllePuaYv4x8J87v5eL3767e5eR5IICanozSwTuAiYELxtwLnAtOAuk4BLQzmGiIRfVZVj7N8X8/r8Ldxx3oncfE4PryNJBIU6on8M+BVQFbzdFtjjnKsI3s4D9J5pkShSVeW4740lvDYvj18O78mtw3t6HUkirN5Fb2YXA9udc/Nqbq5lV3eEx48xs7lmNrewsLC+MUTkOFRVOX49fSmvzNnMLef24LbvqOQbg1BG9GcAl5jZBuAVAlM2jwGtzOzQh6VlAvm1Pdg5N945l+2cy05L0wmFRSLNOccDby5j8uxN/Pzs7txx3okEZlvF7+pd9M65e51zmc65LOBq4CPn3LXAx8Dlwd1GAdNDTikiIXHO8bu3lvPirI385Kxu3P29Xir5RiQS6+jvAe4wszUE5uwnRuAYIlJHzjl+/3Yuz/97Az8+sytjL+itkm9kwvJ59M65T4BPgtfXAUPC8X1FJDTOOR58N5dnv1jPTWdkcf9FfVTyjZDeGSviU845/vT+Sp7513pGnXYCv7m4r0q+kVLRi/iQc46HZ6zk/z5dy3XDuvDbS/qp5BsxFb2IDz364Wqe/HgtI4d04b8v6a+Sb+RU9CI+8/iHq/nLzNVcld2ZP1zanyZNVPKNnYpexEee+Gg1j364istPyeSPlw1QyQugohfxjac+WcPDM1Zx2aBO/OmHJ6nkpZqKXsQHxn+2lv99fyUjTs7goSsGEqeSlxpU9CIxbsK/1vHguyv4/sAM/qySl1qo6EVi2HNfrOd/3snlogHpPHrlQOLj9E9avkk/FSIx6oUvN/C7t5Zzfr+OPHb1ySp5OSL9ZIjEoJdmbeQ305dxXt8O/GXkIJqq5OUo9NMhEmOmfLWJX/9jKd/p054nrxlMQrz+GcvR6SdEJIZMnbOZe19fwjm90njyWpW81I1+SkRixLR5edzz+mK+fWIaf7vuFJrFx3kdSWKEil4kBryxII+7py3izB7tePr6U0hsqpKXulPRi0S56Qu3cOfURZzWrS3P3JCtkpfjpqIXiWJvLcrn9lcXMqRrGyaOOlUlL/WioheJUu8uKeC2VxeSndWGZ288leYJKnmpHxW9SBR6f+lWbp2ygEGdW/HcjafSIiEsZ/2URkpFLxJlZizbyi8mz+ekzFSe/9EQkpqp5CU0KnqRKDIzdxs3T55P/06pTPrREJJV8hIGKnqRKPHxiu387KX59E1P4YWcIbRMbOp1JPEJFb1IFPh0VSE/eWkevTq25IWcoaSo5CWMVPQiHvvX6kLGvDCXHmnJvJgzhNTmKnkJL00Ainhky55S/jxjJW8s2ELvjim8/OOhtGqR4HUs8SEVvUgDK9pfzlOfruG5LzYAMOasbtx8Tg9N10jEqOhFGsjBikpe/HIjf/1oDcUHyrlsUCZ3fPdEOrVq7nU08TkVvUiEVVU53lyUz8MzVpK3u5SzTkxj7Pm96ZuR4nU0aSRU9CIR9MWaHTz4bi7L8ovpl5HCuMtO4sye7byOJY2Mil4kApbnFzPu/RV8tqqQTq2a89hVJ3PJwAyaNDGvo0kjpKIXCaOaK2lSEpvy64v6cP1pJ+gkIeKpehe9mXUGXgA6AlXAeOfc42bWBngVyAI2AFc653aHHlUkehWVlvPUJ19fSfPzb/cgtYVW0oj3QhnRVwB3Oufmm1lLYJ6ZfQDcCMx0zo0zs7HAWOCe0KOKRJ9DK2me+HgNRaXl/GBQJ+78bi+tpJGoUu+id84VAAXB63vNLBfoBIwAzg7uNgn4BBW9+IxW0kgsCcscvZllAYOA2UCH4C8BnHMFZtb+CI8ZA4wB6NKlSzhiiDQIraSRWBNy0ZtZMvB34DbnXLFZ3VYVOOfGA+MBsrOzXag5RCItt6CYce+t4FOtpJEYE1LRm1lTAiX/snPu9eDmbWaWHhzNpwPbQw0p4qX8PaX8ecYqXl+QV72S5rphJ+j8rRIzQll1Y8BEINc590iNu94ERgHjgpfTQ0oo4pFvrKT5Vjd+frZW0kjsCWVEfwZwPbDEzBYGt91HoOCnmlkOsAm4IrSIIg1LK2nEb0JZdfM5cKTJyeH1/b4iXqmqcry1OJ+H/qmVNOIvemesCIGVNH98L5elW4rpm57CizkD+FbPNK9jiYSFil4aNa2kkcZARS+NklbSSGOiopdGRStppDFS0UujoJU00pip6MXXtJJGREUvPqaVNCIBKnrxHa2kEfk6Fb34xuErae6/MHB2J62kkcZORS8xTytpRI5ORS8xSytpROpGRS8x5/CVNN/q2Y6xF/SmX0aq19FEopKKXmKKVtKIHD8VvcQEraQRqT8VvUS1/D2lPPLBKv4+XytpROpLRS9Rqai0nL99spbnvliPQytpREKhopeo8o2VNCd34o7vnkhm6xZeRxOJWSp6iQpaSSMSOSp68dy/1+zgQa2kEYkYFb145vCVNI9eNZARAztpJY1ImKnopcFpJY1Iw1LRS4PRShoRb6joJeK0kkbEWyp6iRitpBGJDip6iQitpBGJHjFd9O8uKeCVOZvpn5FC/06p9M9IpXOb5php1UZDcc6xY18Zawv3Bb62l7Bkyx7mbNitlTQiUSKmi76soorCvQcZ/9k6KqocACmJ8fTLSKV/p0D598tIpWu7JOJUNCEpr6xi4879rCvcx9rCkhrFvo/iAxXV+zVvGke3tCTuu7A3N5yWpZU0IlHAnHNeZyA7O9vNnTu33o8/UF7Jqm17WbqlmGX5RSzNLya3oJiyiioAWiTE0Sc9hf4ZKfQLjvx7dkimaVyTcD0F3yjaX87aHYECr1nom3bur/5lCtAhpRnd05LpnpZMt7SkwPX2yaSnJGr0LtJAzGyecy77mPv5oehrU15ZxdrCfSzdUszSLUUsyy9iWX4x+8sqAUiIa0Lv9JbVo/9+Gan07tiyUYxAK6sc+XtKWRMcka/bUVJd7Dv2Hazer2mckdX2UIknfa3YWyZqSaSI1xp90demqsqxfmcJy/KLWbaliKX5RSzdUkxRaTkAcU2Mnu2Tvzb10yc9heRmsTnDtb+sgnXVo/KS6qmW9TtKOBj8awegVYum9AiWeM1Cz2zdnHj91SMStTwtejM7H3gciAMmOOfGHW3/hir62jjnyNtdGpjy2VJcXf6HRrZm0LVdEv1rjPz7ZaTQqkWCJ3kP55xj+96DwRH51ws9v+hA9X5NDLq0afGNqZbuacm0SYqO5yIix8ezojezOGAVcB6QB8wBRjrnlh/pMV4W/ZFsLz5QXfqBqZ9ituwprb4/s3Xz/5R/cN4/rWWziOU5WFHJxp37D5tqCRT7voP/eTE0KSGuusC71yj0E9q2oFm8/6elRBqTuhZ9JOYkhgBrnHPrgkFeAUYARyz6aNQ+JZFzUxI5t3eH6m27Ssq+NvJftqWI95dtrb6/Q0qzwLRPRgo9O7QkOTGe5k3jAl8JgcvE4PXE+Ca1TovsLqmxVLHwP4W+add+arwWSkZqIt3bJ3P5KZnVhd4tLZkOKc20vFREviYSRd8J2Fzjdh4wNALHaXBtkhL4Vs+0r73xp/hAObn5xSytMe//ycrtXyvlI0mIa0Ji0ybVvwSKD1Swq6TsP/fHN6FbuyT6ZaRyycCM6pF613ZJJMXo6wYi0vAi0Ra1DSe/UXtmNgYYA9ClS5cIxGgYKYlNGdqtLUO7ta3eVlpWycZdJZSWVVJaXsmB8kpKy6ooLQ/eDm4vLa+ktCxw//6ySpKaxQdG5+2T6ZGWTEar5lr/LyIhi0TR5wGda9zOBPIP38k5Nx4YD4E5+gjk8EzzhDh6d0zxOoaICACRWDs3B+hpZl3NLAG4GngzAscREZE6CPuI3jlXYWa/AP5JYHnls865ZeE+joiI1E1EXtFzzr0LvBuJ7y0iIsdHb3sUEfE5Fb2IiM+p6EVEfE5FLyLicyp6ERGfi4qPKTazQmBjPR/eDtgRxjhe0nOJPn55HqDnEq1CeS4nOOeOeTLmqCj6UJjZ3Lp8elss0HOJPn55HqDnEq0a4rlo6kZExOdU9CIiPueHoh/vdYAw0nOJPn55HqDnEq0i/lxifo5eRESOzg8jehEROQpfFL2ZPWRmK8xssZm9YWatvM50vMzsfDNbaWZrzGys13nqw8w6m9nHZpZrZsvM7JdeZwqVmcWZ2QIze9vrLKEws1ZmNi347yTXzE7zOlN9mNntwZ+tpWY2xcwSvc5UV2b2rJltN7OlNba1MbMPzGx18LJ1JI7ti6IHPgD6O+dOInBi8ns9znNcgidUfxK4AOgLjDSzvt6mqpcK4E7nXB9gGHBzjD6Pmn4J5HodIgweB953zvUGBhKDz8nMOgG3AtnOuf4EPgb9am9THZfngfMP2zYWmOmc6wnMDN4OO18UvXNuhnOuInhzFoGzWsWS6hOqO+fKgEMnVI8pzrkC59z84PW9BMqkk7ep6s/MMoGLgAleZwmFmaUAZwETAZxzZc65Pd6mqrd4oLmZxQMtqOXsddHKOfcZsOuwzSOAScHrk4BLI3FsXxT9YX4EvOd1iONU2wnVY7YgAcwsCxgEzPY2SUgeA34FVHkdJETdgELgueA01AQzS/I61PFyzm0BHgY2AQVAkXNuhrepQtbBOVcAgYES0D4SB4mZojezD4Pzcod/jaixz/0Epg9e9i5pvdTphOqxwsySgb8Dtznnir3OUx9mdjGw3Tk3z+ssYRAPDAb+5pwbBJQQoSmCSArOX48AugIZQJKZXedtqtgQkTNMRYJz7jtHu9/MRgEXA8Nd7K0ZrdMJ1WOBmTUlUPIvO+de9zpPCM4ALjGzC4FEIMXMXnLOxWKx5AF5zrlDf11NIwaLHvgOsN45VwhgZq8DpwMveZoqNNvMLN05V2Bm6cD2SBwkZkb0R2Nm5wP3AJc45/Z7nacefHFCdTMzAvPAuc65R7zOEwrn3L3OuUznXBaB/x8fxWjJ45zbCmw2s17BTcOB5R5Gqq9NwDAzaxH8WRtODL6ofJg3gVHB66OA6ZE4SMyM6I/hCaAZ8EHg/z+znHM/9TZS3fnohOpnANcDS8xsYXDbfcFzCIu3bgFeDg4k1gE3eZznuDnnZpvZNGA+gSnaBcTQO2TNbApwNtDOzPKAB4BxwFQzyyHwi+yKiBw79mY5RETkePhi6kZERI5MRS8i4nMqehERn1PRi4j4nIpeRMTnVPQiIj6nohcR8TkVvYiIz/0/vlOcXgpzJ+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как можно заметить, в списке `Y` сохранены элементы списка `X`, возведенные в квадрат. Однако наш график не похож на ветвь параболы, он какой-то угловатый. Это нормально, потому что в списках у нас всего по 8 элементов, то есть, всего 8 точек на графике соединяются линиями. Если бы точек было больше, график был бы более гладким. Воспользуемся функцией `linspace` из библиотеки `numpy` (вот она нам и пригодилась!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.        , -1.87878788, -1.75757576, -1.63636364, -1.51515152,\n",
       "       -1.39393939, -1.27272727, -1.15151515, -1.03030303, -0.90909091,\n",
       "       -0.78787879, -0.66666667, -0.54545455, -0.42424242, -0.3030303 ,\n",
       "       -0.18181818, -0.06060606,  0.06060606,  0.18181818,  0.3030303 ,\n",
       "        0.42424242,  0.54545455,  0.66666667,  0.78787879,  0.90909091,\n",
       "        1.03030303,  1.15151515,  1.27272727,  1.39393939,  1.51515152,\n",
       "        1.63636364,  1.75757576,  1.87878788,  2.        ,  2.12121212,\n",
       "        2.24242424,  2.36363636,  2.48484848,  2.60606061,  2.72727273,\n",
       "        2.84848485,  2.96969697,  3.09090909,  3.21212121,  3.33333333,\n",
       "        3.45454545,  3.57575758,  3.6969697 ,  3.81818182,  3.93939394,\n",
       "        4.06060606,  4.18181818,  4.3030303 ,  4.42424242,  4.54545455,\n",
       "        4.66666667,  4.78787879,  4.90909091,  5.03030303,  5.15151515,\n",
       "        5.27272727,  5.39393939,  5.51515152,  5.63636364,  5.75757576,\n",
       "        5.87878788,  6.        ,  6.12121212,  6.24242424,  6.36363636,\n",
       "        6.48484848,  6.60606061,  6.72727273,  6.84848485,  6.96969697,\n",
       "        7.09090909,  7.21212121,  7.33333333,  7.45454545,  7.57575758,\n",
       "        7.6969697 ,  7.81818182,  7.93939394,  8.06060606,  8.18181818,\n",
       "        8.3030303 ,  8.42424242,  8.54545455,  8.66666667,  8.78787879,\n",
       "        8.90909091,  9.03030303,  9.15151515,  9.27272727,  9.39393939,\n",
       "        9.51515152,  9.63636364,  9.75757576,  9.87878788, 10.        ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(-2, 10, 100) # 100 точек\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117fd3eb8>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так график больше похож на параболу. Могли бы изобразить ее полностью, определенную на участке от -10 до 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1180f6f98>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10, 10, 200)\n",
    "plt.plot(x, x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поменяем цвет линии по умолчанию на какой-нибудь другой."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11815f208>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fSEHfAbGgn2PJOBczRa1CJeKhTKnKbK7E7anh7jNT0HfIHeNx/GacXsx5XYrI0Hp5KUfQZ0N1FexmFPQdEgn4uT0VZzZXIl2qel2OyNBZLla4tlrm+Hic0IDPN38zw/3Td9jx8ThBn/GyWvUiXffyYo6Q38dtQzDf/M0o6Dso5Pdxx/gI1/NllooVr8sRGRqLhTLzhQonxuMEfYo5HYEOW1umTK16ke5wzvHiQo5IwMexpFrzoKDvuIDPx4mJERYKFebzZa/LERl4V1fLLJeq3D2RwO8zr8vpCQr6LmhOierjpcUczjmvyxEZWA3neGkxSyLUXBBImhT0XeD3GXdNJlgpVbmyWvK6HJGBdTFTZLVS557JBD5Ta36Ngr5LDo9GGQ0FeGkhR0OtepFdV2s4Ti/mGI8EmRkJe11OT1HQd4mZce9Ugny1zvl0wetyRAbO2ZU8pXqDe6dGMbXm/wkFfRdNx8NMxUKcXlqlWm94XY7IwCjXGry2vMrMSJjJWMjrcnqOgr6Lmq36USr1Bq9pyUGRXfPq8iq1huOeyYTXpfQkBX2XpSJBDiYinF1ZpVjVkoMi7cpXapxL5zk8FmU0HPS6nJ6koPfA3ZMJHM0Jl0SkPS8u5jCMu9Wa35KC3gPxUOCNaYwzZU14JrJTi4UKV3Il7hiPD92C37dCQe+ROydGCPqMF+azuohKZAecc7ywkCUS8A39NMQ3o6D3SMjv466JBPOFCtc1NYLILbucK7FSqnLPZIKAJi67IR0dDx1LxRgJ+XlhPquLqERuQa3heGkhSzIc5NCopjq4GQW9h3xmvGlqlNVqnXMruohKZLvOrqxSrDW4b09CF0dtg4LeY3vjYfbEwpxeylGu6SIqkZsp1eq8upRn30iEyZimOtgOBb3HzIz79iSa83RouKXITa3NF3XvlIZTbpeCvgeMhoMcTcY4ny6Q1XBLkS0tFytczBa5PRVnJBTwupy+oaDvEXdNJgj4jOc13FJkU845npvPEvH7uHNyxOty+oqCvkeE/T7ummwOt7y2quGWIhtdzBZZKVW5dyqhdWBvkY5WDzmWjDEaCvD8fJZaQ616kTWVeoOXFnJMRIMc1HDKW6ag7yE+M94yPUqhVue15VWvyxHpGaeXcpTrDd68Z0zDKXeg7aA3M7+ZPWNmf9n6+qiZPWlmZ8zss2amyaFvwWQszMFEhNeWV1mt1LwuR8RzmXKVcysFjo7FSEY0O+VO7EaL/meA0+u+/nXgN51zx4EV4GO78BxD5d49o/jMeE4dszLknHM8P58l4DPu1nDKHWsr6M3sAPDPgU+2vjbgfcDnWrs8DnyknecYRtGAn7smRpjLl7mmeXBkiM3mSiwUKtw9mSDs15nmnWr3yP0W8J+BtUs6J4C0c27tnMMssL/N5xhKt6Xib3TM1tUxK0OoUm/w/HyWZCTIsWTM63L62o6D3sx+AJh3zj21fvMmu26aUmb2cTM7ZWanFhYWdlrGwPKZ8ebpUQrVOq+qY1aG0MuLzQ7Y+6fVAduudlr0DwM/aGYXgD+hecrmt4Ckma1dsnYAuLrZg51zjznnTjrnTk5NTbVRxuCaWtcxmyurY1aGx3Kxwrl0gduSMVLqgG3bjoPeOfcLzrkDzrkjwEeBv3PO/Wvgy8APtXZ7FPh821UOsTftGcVvxjNzGXXMylBoOMczcxkifp+WB9wlnejd+Hng58zsLM1z9p/qwHMMjUjAz71Toyy25vgQGXTn0gUy5Rr3TY8SVAfsrtiVWYGcc18BvtK6fQ54cDe+rzQdGYtyKVvkhfkse+NhIlobUwZUsVrn5cUc0/Ew+0ciXpczMPTnsg+YGfdPj1JrOF6Yz3pdjkjHPNdabe0te0bVAbuLFPR9YjQc5MTECJdzJeY0tl4G0JVckaurJe6aSBDXFMS7SkHfR06MjzAS9PPsXEaTnslAqdQbPDuXZSwc4Ph43OtyBo6Cvo/4fcb9e8fIV+ucXtRqVDI4XpjPUqk3eOveJD6dstl1Cvo+MxULc2QsxpmVPMvFitfliLRtLl/mYrbI8fG4Ji3rEAV9H3rTVIJowMdT19OaHkH6Wq3R4Jm5DCMhP3dNaMx8pyjo+1DQ7+OBvUlylboWFJe+9tJijkK1zlunk/h9OmXTKQr6PjUdD3N4LMqZ5TwrJZ3Ckf6zVKzw+kqBY8kYEzEtW9FJCvo+9qapUcIBH09dy+gUjvSVWqPBqWtpYkE/92ie+Y5T0PexkN/H/dNjZCs1zXApfeXFhRz5ap237h3TQt9doCPc52ZGIhwajfLq0qpG4UhfmMuXOZcucHsqzlQs7HU5Q0FBPwDevGeUSMDPqWtpao3GzR8g4pFKvcFT19MkQgHu0cyUXaOgHwBBv4+TM2OsVuu8sKBRONK7np/PUq41ODkzplE2XaSgHxBTsTDHU3HOpwtcXy15XY7Id7mSK3EpW+TExAipiEbZdJOCfoDcPZlgNBTgqesZyjWdwpHeUazVeWYuQzIc4M6JEa/LGToK+gHi9xlvm0lSbV1tqBWppBc453jqWpp6o8HJGc1l4wUF/YAZiwS5ezLB1dWSVqSSnnBmOc98ocJ9e8YYDWsuGy8o6AfQ8VScqViI5+ayZMtVr8uRIbZcrPDSYo59IxGOjEW9LmdoKegHkJlxciZJwGd8+6omPhNvVBsNvnMtTSTg44G9Y1oxykMK+gEVDfh560zzqtnntfygeOC5uSz5ap23zaQIaZFvT+noD7C98UhzyGWmwGxO5+uley5li1zKFrlzYoRJTVjmOQX9gLtnKkEqEuSZ6xnylZrX5cgQyJarPHM9w0Q0pKGUPUJBP+B8Zjw4kwTg29fSNDTkUjqo1mjw5NUVAj7jwX0aStkrFPRDIB4K8MDeMVZKVZ2vl45xzvH09Qy5Sp0H9yWJBvxelyQtCvohsT8R5Xgqzrl0gUuZgtflyAA6ly4wmytx92RCs1L2GAX9ELlnKsFkNMQzcxnSJY2vl92zXKzw/HyW6XiYE+Nxr8uRDRT0Q8RnzfOmQb+PJ6+uUKlrPhxpX7nW4NtX00QDft42k9R4+R6koB8ykYCft+9LUajWOXUtrflwpC0N53jy6gqlep23709qvHyP0qsyhCaiIe7bM8r1fJnTS1qCUHbu+fksi8UKD0yPaerhHqagH1LHkjEOjUZ5ZWlVF1PJjpxPFziXLnA8FefQWMzrcuQGFPRDysy4f3qM8UiQp66lWVHnrNyCxUKFZ+cy7ImFuXdKSwL2uh0HvZkdNLMvm9lpM3vJzH6mtX3czL5oZmdan1O7V67sJr/PeMf+FCG/n29eWaZYq3tdkvSBQrXOk1dXiAf9PLhPna/9oJ0WfQ34j865u4B3AD9lZncDnwC+5Jw7Dnyp9bX0qEjAz0P7U1Trjm9dWdFMl3JD1UaDb15Zpu5cq5GgkwL9YMevknPumnPu6dbtHHAa2A98GHi8tdvjwEfaLVI6aywS5G0zSVZKVZ66rpE4srmGc3z7appsucaD+5JaRKSP7MqfYzM7AtwPPAlMO+euQfOPAbBni8d83MxOmdmphYWF3ShD2rAvEeGeyQSzuRIvLea8Lkd6jHOOZ+eyzOXLvGV6jL3xiNclyS1oO+jNbAT4M+BnnXPbnkjFOfeYc+6kc+7k1NRUu2XILrhjPM6RsRivLed5fSXvdTnSQ15bznMhU+CO8ThHkxph02/aCnozC9IM+c845/68tXnOzGZa988A8+2VKN1iZrxlepS98TDPzWe5kit5XZL0gMvZIi8t5jjQ+q9P+k87o24M+BRw2jn3G+vuegJ4tHX7UeDzOy9Puq05TUKKVCTId66tsFioeF2SeGihUOap62kmoiHeulcjbPpVOy36h4F/A7zPzJ5tfXwI+DXg/WZ2Bnh/62vpIwGf8dD+cWKB5rBLLTA+nJaLFb45u0I8GOCd+1P4fQr5fhXY6QOdc18HtnrlH9np95XeEA74ePjAOF+5tMQ3Zpd5z6EJ4sEdv12kz2TLVf5hdplQwMe7Do5rGGWf06snW4qHAjx8YJxaw/G1y8sUq7qgahjkKzW+PruMz4x3HxjXAiIDQEEvN5SMBHn4wDiVWoOvzy5R0tWzA61Yq/P12WXqDcfDB8eJh/Rf3CBQ0MtNjUdDPHSgObXxN2aXNY/9gCrV6nzj8jKlWoOHD4wzpguiBoaCXrZlMhbmHfvHyVVqfGN2marCfqCUanW+dnmZfLXGQwdSjEc15fAgUdDLtk3Hwzy4L0W6VOXratkPjLWQL1TrPHRgXOu9DiAFvdySfSMR3r4/RaZc5WuXlyjXFPb9rFir87XLSxSqdR4+kFLIDygFvdyyfSMR3tk6jfO1y+qg7Vf/GPLNc/KTCvmBpaCXHZmOh3n4wDj5ap2vXmq2CKV/rFZq/P2lJUpvhLzOyQ8yBb3s2FQszLsOjFOqN/jq5SVylZrXJck2rJSq/P2lJWoNx7sPKeSHgYJe2jIRC/Hug+PUG46/v7TIUlFz4/Sy+XyZr11ewm/G9xya0ILeQ0JBL21LRUJ8z6EJgj4fX7u8xFXNetmTruSK/MOVZWIBP99zaIKELoYaGgp62RUjoQDvPTTBWDjIt66uaD77HuKc47XlVZ68miYZDvKeQxNEg5rWYJgo6GXXhAN+3n1w/I357F+Yz2pZQo/VG46nr2d4cSHH/kSEdx2c0ARlQ0j/u8muCvh8vGN/iufns5xZyZMpV3lwnxaR9kK5VudbV9MsFSvcOTHCXRMjmk9+SOm3T3adz4y3TI9x//QYC4UKX764qDntuyxTrvLlS0uslCq8bSbJ3ZMJhfwQU9BLxxxNxnjPoQlqDcdXLqqTtlsuZgp85eIS9YbjPQcnODga9bok8ZiCXjpqIhrinx2eJBEO8K2rKzw/n6Wh8/YdUW84nrqe5qnrGVKRII8cmdTkZALoHL10QSzo5z0HJ3hhIcvZlTyLhQoP7ksyouF9uyZXqfHklRWylRonWufjfTpVIy1q0UtX+H3N8/Zv35ciX63xdxcWuZQpeF1W33POcSFd4MsXFinV6zx0IMU9kwmFvPwTalJJV+1PREhFpvjOtRVOXc8wly9z3/QYYY3KuWXFWp2nW8dwMhri5EySmMbHyyYU9NJ1saCfdx+c4NWlVV5ZWmW+UOHN06PsH4loZMg2OOeYzZV4di5Dwznu2zPKbcmYjp1sSUEvnvCZcddkgpmRCE9fz/Dtq2n2jYR58/SYFqO+gUK1zvPzGa6ulhmPBHnrTFJTGchN6R0inkpGgrz38ARnlvOcXsqxcH6BuycTHE3GdJ55nXrDcXYlzytLq4DjnskEd4zH1YqXbVHQi+d8ZpyYGGFfIsKzcxmem89yPl3gTXtGmY5rMYy5fJnn5jKsVuvsGwlz355RYkH96sr26d0iPSMRCvCuA+NcXS3z4kKWb8wuszce5k17Rofy9ES6VOWlxRxz+TLxoJ+H9qfYOxLxuizpQ8P32yM9zczYn4iwNx7m7EqeV5dW+dvzCxwcjXLnxMhQjL3PlWu8vJTjSq5E0GfcO5ngtlQcv0+naWRnBv+3RvqS39c8nXN4LMpry3nOp/NcyhY5NBrlxMTIQLbwM+UqZ5abP2fAjDsnRjieihPU0FNp0+D9tshAiQT83LdnlDvG45xZznOuFfj7RsIcS8aZioX6ukPSOcdcvszZlTzzhQp+M25PxTkxHies0UeySxT00hciAT9v2jPK8fE4Z1fyXMgUuLpaJhHycywZ59BotK9avuV6g9lskXPpArlKjUjAxz2t0Uaa0ll2m4Je+kok4OfeqVHumkgwm2sG5XPzWV5cyDEzEubgaJTpeLgnh2bWG465QpnLmSLX8iUaDpLhICdnkhxIRHqyZhkMHQl6M/sA8NuAH/ikc+7XOvE8Mrz8PuPwWIzDYzFWShUupItcWS0y2+rA3BsPMzMSYU887GkLuVxrMF8oc221xPXVMjXnCPl9HE3GODwaIxkJelabDI9dD3oz8wO/C7wfmAW+Y2ZPOOde3u3nEoHm4uSpvSHe7EaZy5e5kitxPV/icq6E0bwoazIaYjIWIhUJEungue9irc5yscJyscpCoUK6teBKyO/jwGiU/YkIU7GQWu/SVZ1o0T8InHXOnQMwsz9M3TH8AAAHv0lEQVQBPgwo6KWjfGbMjESYGYngnGO5WOV6vsxisdnZeaa1YHkk4CMZDpIIBRgJBYgH/UQDfsIBH0Gf3bBz1zlHreEo1uoUaw0K1Tq5So1suUa2UqVUa7RqgVQkyN2TI+yJhUlFgn3daSz9rRNBvx+4vO7rWeDtHXgekS2ZGROxEBOxEJCg1nCslCqkS1XS5RqZUpX5QpnGhjVQfAYBn+G35geAA5yDWqNBZeMDWo9JhALsiYUZCwcYj4ZIhoMa9y49oxNBv9m7+7t+O8zs48DHAQ4dOtSBMkT+UcBnTMXCTMX+cUoF5xzFWoN8tUap1qBUq1OuN6g1mq32unMYzTe0mRHwGUGfj6DfiAaa/wXEgn6iAZ9a69LTOhH0s8DBdV8fAK5u3Mk59xjwGMDJkye1tpx0nZkRC/o1h7sMvE4MR/gOcNzMjppZCPgo8EQHnkdERLZh11v0zrmamf008Nc0h1d+2jn30m4/j4iIbE9HxtE7574AfKET31tERG6NrrUWERlwCnoRkQGnoBcRGXAKehGRAaegFxEZcOac99cqmdkCcHGHD58EFnexnN2ium6N6rp1vVqb6ro17dR12Dk3dbOdeiLo22Fmp5xzJ72uYyPVdWtU163r1dpU163pRl06dSMiMuAU9CIiA24Qgv4xrwvYguq6Narr1vVqbarr1nS8rr4/Ry8iIjc2CC16ERG5gb4IejP7YTN7ycwaZnZyw32/YGZnzexVM/v+LR5/1MyeNLMzZvbZ1vTJu13jZ83s2dbHBTN7dov9LpjZC639Tu12HZs836+Y2ZV1tX1oi/0+0DqGZ83sE12o67+Z2Stm9ryZ/YWZJbfYryvH62Y/v5mFW6/x2dZ76Uinaln3nAfN7Mtmdrr1/v+ZTfZ5r5ll1r2+v9TpulrPe8PXxZp+p3W8njezB7pQ04l1x+FZM8ua2c9u2Kdrx8vMPm1m82b24rpt42b2xVYWfdHMUls89tHWPmfM7NG2i3HO9fwHcBdwAvgKcHLd9ruB54AwcBR4HfBv8vg/BT7auv37wL/tcL3/A/ilLe67AEx28dj9CvCfbrKPv3XsjgGh1jG9u8N1fR8QaN3+deDXvTpe2/n5gZ8Efr91+6PAZ7vw2s0AD7RuJ4DXNqnrvcBfduv9tN3XBfgQ8Fc0F+h6B/Bkl+vzA9dpjjP35HgB7wEeAF5ct+2/Ap9o3f7EZu97YBw41/qcat1OtVNLX7TonXOnnXOvbnLXh4E/cc6VnXPngbM0Fyd/gzXXeHsf8LnWpseBj3Sq1tbz/Svgjzv1HB3wxoLuzrkKsLage8c45/7GOVdrffktmiuReWU7P/+Hab53oPleesQ6vH6gc+6ac+7p1u0ccJrmmsz94MPAH7mmbwFJM5vp4vM/ArzunNvphZhtc859FVjesHn9+2irLPp+4IvOuWXn3ArwReAD7dTSF0F/A5stRL7xF2ECSK8Llc322U3vBuacc2e2uN8Bf2NmT7XWze2Gn279+/zpLf5V3M5x7KQfp9n620w3jtd2fv439mm9lzI031td0TpVdD/w5CZ3v9PMnjOzvzKze7pU0s1eF6/fUx9l68aWF8drzbRz7ho0/5ADezbZZ9ePXUcWHtkJM/tbYO8md/2ic+7zWz1sk20bhxFta7Hy7dhmjT/CjVvzDzvnrprZHuCLZvZK6y//jt2oLuD3gF+l+TP/Ks3TSj++8Vts8ti2h2Nt53iZ2S8CNeAzW3ybXT9em5W6ybaOvY9ulZmNAH8G/KxzLrvh7qdpnp5YbfW//F/geBfKutnr4uXxCgE/CPzCJnd7dbxuxa4fu54Jeufc9+7gYdtZiHyR5r+NgVZLbNPFynejRjMLAP8SeOsNvsfV1ud5M/sLmqcN2gqu7R47M/sD4C83uWtbC7rvdl2tTqYfAB5xrZOTm3yPXT9em9jOz7+2z2zrdR7ju/8t33VmFqQZ8p9xzv35xvvXB79z7gtm9j/NbNI519E5XbbxunTkPbVNHwSeds7NbbzDq+O1zpyZzTjnrrVOZc1vss8szb6ENQdo9k/uWL+funkC+GhrRMRRmn+Zv71+h1aAfBn4odamR4Gt/kNo1/cCrzjnZje708ziZpZYu02zQ/LFzfbdLRvOi/6LLZ6v6wu6m9kHgJ8HftA5V9hin24dr+38/E/QfO9A8730d1v9cdotrT6ATwGnnXO/scU+e9f6CszsQZq/00sdrms7r8sTwI+2Rt+8A8isnbLogi3/q/bieG2w/n20VRb9NfB9ZpZqnWr9vta2netG73O7HzQDahYoA3PAX6+77xdpjph4Ffjguu1fAPa1bh+j+QfgLPB/gHCH6vxD4Cc2bNsHfGFdHc+1Pl6ieQqj08fufwMvAM+33mQzG+tqff0hmqM6Xu9SXWdpnod8tvXx+xvr6ubx2uznB/4LzT9EAJHWe+ds6710rAvH6F00/2V/ft1x+hDwE2vvM+CnW8fmOZqd2g91oa5NX5cNdRnwu63j+QLrRst1uLYYzeAeW7fNk+NF84/NNaDayq+P0ezX+RJwpvV5vLXvSeCT6x7746332lngx9qtRVfGiogMuH4/dSMiIjehoBcRGXAKehGRAaegFxEZcAp6EZEBp6AXERlwCnoRkQGnoBcRGXD/Hw3kxPqbLqM4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'lightblue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Или так:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11827b3c8>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список цветов в Python см. [здесь](https://matplotlib.org/users/colors.html). \n",
    "\n",
    "Теперь изменим тип линии. По умолчанию используется сплошная линия, но ее можно заменить на пунктирную или что-то подобное:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1182db4a8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nEUm+0aO9S+W6dbGT5A0d0edCSYlPN/jcczBhQmLGwhbJO1u2+Fg2jRpBaWnsNHlDR/S5sOOO8Ktfed/6kSNjpxFJrgce8GtXbr0VGus4tpKGQMiVrVvhoINg40aYOdPnqxSRzFm/3ocL79wZ3nyzKD45awiEfFNSAr//PcyfD0OGxE4jkjz33APLlvn/WREU+frQZ5tc6tkTfvYzOOKI2ElEkqd3b78a/cgjYyfJO2q6EREpUGq6yWfLlvl0ZkuWxE4iUvgWLIBevfy7VEtNNzFs3AgPPwwheLdLEWm466+HV15Rd8rt0BF9DJ07w6WX+kTFM2bETiNSuN5807ssX3990U34XR8q9LHccIOPgXPNNX5kLyL1U1HhczO3b1/0wxDXRoU+lpYtfaz6l1+G55+PnUak8IwY8eUEP82axU6T19RGH9Mll/iJ2UMOiZ1EpPCcfDLcfTecf37sJHlP3StFRAqUulcWklmz4LTT4OOPYycRyX/Ll8Phh4MODutMhT4fhAAvvgg33xw7iUj++/nPYfJkn9hH6kSFPh9885vQrx8MHuwDnolI9SZM8G7JV14J++wTO03BUKHPF7/6lU95dtVV6m4pUp2KCrjsMmjbVvMw15MKfb7YbTcv9mPHqrulSHVGjvTulL//veaBrSd1r8wnF1/sR/Pdu8dOIpJ/zjrL+86fc07sJAVHhT6fNG4MV1zht0PQmNoilTZv9sl6zj03dpKClHbTjZmVmNm7ZvZc6n5nM5tgZvPMbISZaSql+iov9xO08+fHTiIS34wZsNdePq6NNEgm2uivAGZXuX87cHcIoQuwBvhRBrZRXNq3h8WL/eheJ2almIUAl18OmzZB166x0xSstAq9mXUATgHuT9034Digcgbsh4Az0tlGUWrb1vvUv/ACPPts7DQi8YwYAePHwy23eIcFaZB0j+j/CPwMqEjd3w1YG0LYkrq/GGif5jaK02WXefPNFVfAhg2x04jk3tq13t24rAx++tPYaQpagwu9mZ0KrAwhTKq6uJpVq217MLO+ZlZuZuWrVq1qaIzk2mEHuO8++PBDeOSR2GlEcu+JJ2DlSr+QsKQkdpqC1uBBzczsVuAHwBagFNgFeAroAewRQthiZt8Gbg4h9Nje79KgZtvx6qtwzDHQSJc8SBGaNQu6dYudIm9lfVCzEMKAEEKHEEInoDfwagjh+8B44Lup1foAoxu6DQGOO86L/MqVfmWgSNJt2QLvv++3VeQzIhuHidcDV5vZfLzNflgWtlFc5syBb3wDHnwwdhKR7Bs40Av87Nm1ryt1ovHoC0FFhTffzJjhRb9Nm9iJRLJjyRLYbz846ijvdaaLBrdL49EnSaNG8Oc/w7p1PkemSFJdcYU33QwcqCKfQSr0haJrVxgwAIYP94HPRJLmySdh1Cj45S9h771jp0kUFfpCMmCAt9W/+mrsJCKZN3eu95m/9trYSRJHbfSF5tNPYZddYqcQyY4vvvBrSKRO1EafVJVFfupUn05NpNC98YYPcwAq8lmiYYoL0datcPbZ/k/x7rtQWho7kUjDrFsHffr4a3j6dB+qWzJOR/SFqKTEeyXMmeOzUokUql/8AhYuhKFDVeSzSIW+UPXoARddBHfc4ePXixSat96CP/0JLrnE+81L1uhkbCFbu9ZHuGzZ0ov9jjvGTiRSN+vXw4EHejPktGmaA7aB6noyVp+VClnz5v6R9x//0MUlUlh22skvjjrwQBX5HNARvYjkVkWFRmPNEHWvLDavvw7du/tHYpF8tXYtHHSQZk7LMRX6pKio8L7I110XO4lIzS6/3MeYb9s2dpKiokKfFMce6wOeDRoEL74YO43IVz31lM+WdsMNPtSB5Iza6JNk40b41rfg44/94pNWrWInEnHLlsEBB0DHjvD227oCNkPURl+MSkvhL3+B1athyJDYaUS+9Nhjfv7oL39RkY9AR/RJ9O67fsJLXS4ln3zwAXTuHDtFouiIvpgdfLAX+YUL4b33YqeRYvbuu96MCCryEemCqaTauhVOPBGaNIGJE/0CFZFc+uwzOOccH5tp1iz/LlHoiD6pSkrgnnt8nllNPygxXHqpN9fcf7+KfGQq9EnWs6f3qx88GJ54InYaKSbDh8PDD8ONN8LRR8dOU/R0MjbpNm/2f7Q5c2DKFLWTSvbNm+fniQ45xKe91PDDWaOTseKaNPGubeedB61bx04jxaBTJ7jqKn/dqcjnBR3RF5vNm734i2RaCH4CVnMa54yO6OWrli3zj9OPPBI7iSTRoEGw//6waFHsJLINFfpi0rq1D4vw05/65OIimTJxIlx5pQ9z0L597DSyDRX6YtK4MYwYAS1awFlnwZo1sRNJEnz8MZx7LrRr5z1tNNZ83tEzUmx23927Wi5aBBdc4MMbizTUF1/4RVHLl8PIkT6tpeQdFfpidMQRcPfd3mavo3pJx+ef+yfFoUM19HAeU6+bYhWCH42pB440VAg+ppKmBoxGvW5k+8y8yH/6qbevTpoUO5EUkjff9KkrV65UkS8ADX6GzGxPMxtvZrPNbKaZXZFa3tLMxprZvNT3FpmLKxm3cSNMmACnnw5Ll8ZOI4Vg0SI4+2xYvFhjyxeIdN6KtwDXhBC6AocDl5hZN6A/MC6E0AUYl7ov+apNG5+o+ZNP4IwzYMOG2Ikkn332mR8UbNgAo0d7Dy7Jew0u9CGEZSGEyanbnwGzgfZAL+Ch1GoPAWekG1Ky7IADfBCq8nK46CJvexXZ1pYt3sw3fTo8/jh07Ro7kdRRRhrXzKwTcDAwAdg9hLAM/M0AaFPDz/Q1s3IzK1+1alUmYkg6evWC3/0Oxo+HJUtip5F8tGKFD1g2eLCPjCoFI+1eN2b2NeB14LchhCfNbG0IoXmVx9eEELb7+U69bvJECLBqlTfniFTn88+hadPYKSQlJ71uzGwHYBQwPITwZGrxCjNrm3q8LbAynW1IDpl5ka+ogP794amnYieSfPDoo96kt3mzinyBSqfXjQHDgNkhhLuqPPQM0Cd1uw8wuuHxJIrNm+H1131o4zffjJ1GYnrtNfjhD+H993XupoClc0R/JPAD4Dgzm5L6Ohm4DTjBzOYBJ6TuSyEpLfWeOB07eg+L2bNjJ5IY3nkHTjsN9tnHP93tuGPsRNJADZ4VIITwJmA1PNy9ob9X8kSrVvDSSz5cwoknwt/+5hNKSHGYOdNPuLZuDWPHagybAqdL2qRme+8NY8Z4U86MGbHTSC6tWgW77eZFvl272GkkTZrnS7bvwANhwQJo1szvb90KJSVxM0n2VM5AdswxMGuWpgJMCB3RS+0qi/zjj8Phh8Pq1XHzSHasXOkjUA4a5PdV5BNDhV7qrkULmDYNTjrJB0OT5FixAo49FubPh/32i51GMkyFXuruhBN80pLJk/22xrJPhuXLvch/+CG88ILflkRRoZf6Of10GDUKpkyB446D9etjJ5J0bNjgz+NHH8GLL3rbvCSOGuGk/k4/HZ55xi+m0pWShW2nnaBfPzj4YDj66NhpJEs0w5Skb+ZM2HVX6NAhdhKpq3nz/OTrkUfGTiJpqOtYNzqil/Rs2QJnnunTEr70Euy7b+xEUpvJk/2E+i67+FXP6l2TeGqjl/Q0bgx//au31R95JPzjH7ETyfa8+qq3w++0Ezz/vIp8kVChl/SVlXmBb9HCT+w9/XTsRFKdUaP8SL5jR/j73+Eb34idSHJEhV4y4+tfh7fe8tmqhgzRSIf5aPRof1N+4w1o3z52GskhfW6TzGnd2psGKip8bPvVq/0krYZMiGfTJvj4Yy/sQ4b4c6OeUkVHR/SSWc2awc47+8nZnj3h5JN1YVUsq1b5hW0nnOBj2JSWqsgXKRV6yY4ddoC+fX0O2m99y7tgSu7MmAH/+Z8+pvwvf+kDlUnRUqGX7Pnxj32GonXrfDA0naTNjYcf9iK/YYPPFNa7d+xEEpkKvWTXEUdAeTl07Qo33OD97iV7tmyBgQPhsMPg3Xf9uxQ9nYyV7OvQwXt6LF/u/bbXr/fbX/967GTJ8d57PitYy5beP755c/WRl3/REb3kRmnpl1MR9u8PBx0Ew4dHjZQIIcCwYXDooXDVVb6sVSsVefk3KvSSe9dd54X+/PP965//jJ2oMC1dCqee6udCysrglltiJ5I8pUIvudexo/fGuflmGDECunXzk4ZSd6+/Dvvv7/vxnntg3DjYc8/YqSRPqdBLHI0bw003waRJfqJ2r71iJyoMlVccd+0KRx3l8wJcfjk00r+y1EyvDonrgAO8C2anTl7Ezj/fe42od86/27QJbr0Vunf3q1vbtPE5ATRejdSBCr3kj08/9XbnSy/1NvyxY2Mnyg8vv+xviD//ufem+eyz2ImkwKjQS/7YdVdva37ySb/Y58QTfTarZctiJ4tjxQofQqJHDz+Kf/FF3ze77ho7mRQYFXrJL2Y+kcnMmXDbbTBnDnzta/7Ypk1xs+XK55/79xYtvNjffjtMn+5jB4k0gAq95KfSUrj+epg1ywdJ27LF5zW94AK/OCiJpk+HPn38ROuGDT4+TXk5/Oxnvj9EGkiFXvJb5YU/mzb5pBkjR3ohPOssb+Yp9HHvK5tkTjzR2+FHjoTvftdH/wT/hCOSJhV6KQzNmsGdd8KHH/oR7htvwPHH+zy1hezvf/d2+JkzvVfNokX+d+6yS+xkkiAW8uCIqKysLJSXl8eOIYVk40Z46ik45xw/6v/Nb7yZ5/vf96PjfByWd+NG70HzyCN+3cAf/uCfSJ5+Gk45JT8zS14zs0khhLLa1svKEb2Z9TSzuWY238z6Z2MbUuRKS+G8875s2qmo8CJ62mmwxx7eH/+ZZ+JmrPTCC561dWvo1cuvG6g8wVx58llFXrIo44XezEqAgcBJQDfgPDPrluntiPybm27ybpjPPefFfswYePTRLx//zW98VMeVK7ObY9ky7wJ5443+5gN+xP7KK/C97/mb0dKlPvyDSI5kvOnGzL4N3BxC6JG6PwAghHBrTT+jphvJuK1bYe1a2G0376K4555fnuBs39578FxyiXdZ3LjR2/53390vSNreCdAQvrywq2NHP3fwyitwxx3ezr5kia/XpInP8tSli+fYeWfNnSsZF7Pppj2wqMr9xallIrlTUuJFHryAr13rTSZ33QXHHuuFvXIu2+nTvSdPy5beJNSqlRfx55/3x19/3Ydo2G03bypq3twHYpswwR/ftMnnZz3uOD+R+tZb/mbQpYs/3ry5irxElY1Bq6s7HPrKxwYz6wv0BejYsWMWYohU0bQpfOc7/rWtzp19bPzly/3of906v2hp99398ZYt/ed23tmLdvPm0K6dF3vwE6mnnJK7v0WkntR0IyJSoGI23bwDdDGzzmbWBOgN5En3BxGR4pPxppsQwhYzuxQYA5QAD4QQZmZ6OyIiUjdZmVgyhPAC8EI2freIiNSPhkAQEUk4FXoRkYRToRcRSTgVehGRhFOhFxFJuLwYptjMVgELG/jjrYCPMxgnU5SrfpSr/vI1m3LVTzq59gohtK5tpbwo9Okws/K6XBmWa8pVP8pVf/maTbnqJxe51HQjIpJwKvQiIgmXhEI/JHaAGihX/ShX/eVrNuWqn6znKvg2ehER2b4kHNGLiMh2FEShN7NzzGymmVWYWdk2jw1ITUI+18x61PDznc1sgpnNM7MRqeGTM51xhJlNSX19aGZTaljvQzObnlov64Pwm9nNZrakSraTa1gvpxO6m9kdZjbHzKaZ2VNm1ryG9XKyv2r7+81sx9RzPD/1WuqUrSxVtrmnmY03s9mp1/8V1axzjJl9UuX5/WW2c6W2u93nxdyfUvtrmpkdkoNM+1bZD1PM7FMzu3KbdXK2v8zsATNbaWYzqixraWZjU7VorJm1qOFn+6TWmWdmfdIOE0LI+y+gK7Av8BpQVmV5N2AqsCPQGXgfKKnm5x8HeqduDwb+J8t57wR+WcNjHwKtcrjvbgaurWWdktS+2xtoktqn3bKc60Sgcer27cDtsfZXXf5+4GJgcOp2b2BEDp67tsAhqds7A+9Vk+sY4LlcvZ7q+rwAJwMv4jPOHQ5MyHG+EmA53s88yv4C/gs4BJhRZdnvgf6p2/2re90DLYEFqe8aGGPUAAAEGElEQVQtUrdbpJOlII7oQwizQwhzq3moF/BYCGFTCOEDYD5wWNUVzMyA44CRqUUPAWdkK2tqe+cCj2ZrG1lwGDA/hLAghLAZeAzft1kTQng5hLAldfdtoEM2t1eLuvz9vfDXDvhrqXvquc6aEMKyEMLk1O3PgNkUzvzLvYCHg3sbaG5mbXO4/e7A+yGEhl6ImbYQwhvA6m0WV30d1VSLegBjQwirQwhrgLFAz3SyFESh3466TES+G7C2SlHJ9mTlRwMrQgjzang8AC+b2aTUvLm5cGnq4/MDNXxUjD2h+0X40V91crG/6vL3/2ud1GvpE/y1lROppqKDgQnVPPxtM5tqZi+a2TdzFKm25yX2a6o3NR9sxdhflXYPISwDfyMH2lSzTsb3XVYmHmkIM3sF2KOah24IIYyu6ceqWbZtN6I6TVZeF3XMeB7bP5o/MoSw1MzaAGPNbE7qnb/BtpcLGAT8Bv+bf4M3K1207a+o5mfT7o5Vl/1lZjcAW4DhNfyajO+v6qJWsyxrr6P6MrOvAaOAK0MIn27z8GS8eWJd6vzL00CXHMSq7XmJub+aAKcDA6p5ONb+qo+M77u8KfQhhOMb8GOLgT2r3O8ALN1mnY/xj42NU0di1a2TkYxm1hg4Czh0O79jaer7SjN7Cm82SKtw1XXfmdlQ4LlqHqrLfsx4rtRJplOB7iHVOFnN78j4/qpGXf7+ynUWp57nXfnqx/KMM7Md8CI/PITw5LaPVy38IYQXzOx/zaxVCCGrY7rU4XnJymuqjk4CJocQVmz7QKz9VcUKM2sbQliWaspaWc06i/FzCZU64OcnG6zQm26eAXqnekR0xt+ZJ1ZdIVVAxgPfTS3qA9T0CSFdxwNzQgiLq3vQzJqZ2c6Vt/ETkjOqWzdTtmkXPbOG7eV8Qncz6wlcD5weQvi8hnVytb/q8vc/g792wF9Lr9b05pQpqXMAw4DZIYS7alhnj8pzBWZ2GP4//c8s56rL8/IMcEGq983hwCeVTRY5UOOn6hj7axtVX0c11aIxwIlm1iLV1HpialnD5eLsc7pfeIFaDGwCVgBjqjx2A95jYi5wUpXlLwDtUrf3xt8A5gNPADtmKeeDQL9tlrUDXqiSY2rqaybehJHtffcIMB2YlnqRtd02V+r+yXivjvdzlGs+3g45JfU1eNtcudxf1f39wK/xNyKA0tRrZ37qtbR3DvbRUfhH9mlV9tPJQL/K1xlwaWrfTMVPah+Rg1zVPi/b5DJgYGp/TqdKb7ksZ2uKF+5dqyyLsr/wN5tlwBep+vUj/LzOOGBe6nvL1LplwP1Vfvai1GttPnBhull0ZayISMIVetONiIjUQoVeRCThVOhFRBJOhV5EJOFU6EVEEk6FXkQk4VToRUQSToVeRCTh/h/kauCEx7ZKQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red', linestyle = '--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1183f75f8>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red', linestyle = '-.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список всех типов линий см. [здесь](https://matplotlib.org/gallery/lines_bars_and_markers/line_styles_reference.html). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кроме того, можно изменить толщину линии, добавив аргумент `linewidth`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118459710>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x**2, 'red', linewidth = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь построим график, состоящий только из точек (можно считать диаграммой рассеяния)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1185738d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь будем менять цвет точек и тип точек (тип маркера) одновременно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1185d1c88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1186eefd0>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = '*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11874c390>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wTuA+gMw8m5lzzU7VsTXAq6oLHF3FBa5et1pl5g+B35y3eDuwt7q/F7itF9suIvTn+Wvg35seYoWKu6B6RGwCbgIONztJLfcAnwb6/YrtrwfawNerw1D3RsTapodaqcz8JfBF4ARwCvhdZn6/2alquz4zT8HCjhIw2IuN9E3oI+I/q+Ny5/+zfdE6f8fC4YP7m5u0I8u6oHq/iIhXA98BPp6Zv296nk5ExDZgNjOPND1LF6wBhoGvZuZNwBl6dIigl6rj19uBzcAQsDYi/qrZqfpDT64w1QuZ+e5LfT0ixoFtwNbsv3NGl3VB9X4QEa9gIfL3Z+ZDTc9Twy3A+6rLYr4SeE1EfDMz+zEsJ4GTmfnH/7vaRx+GHng38PPMbANExEPAO4BvNjpVPc9HxPrMPBUR64HZXmykb/boLyUibgU+A7wvM//Q9Dwd+DGwJSI2R8SVLPyC6ZGGZ1qxiAgWjgMfzcwvNT1PHZn52czcmJmbWHg/ftCnkSczfwU8FxFvqBZtBaYbHKlTJ4CbI+Kq6t+1rfThL5XP8wgwXt0fBx7uxUb6Zo9+Cf8E/Cnw6ML7z48y82+aHWn5MvOFiPhb4D/4/wuqP9PwWJ24BfgQ8NOIeKpa9rnqGsJq1keB+6sdiePAhxueZ8Uy83BE7AOeYOEQ7ZP00SdkI+IB4F3AdRFxErgb+DzwYETcwcIPstt7su3+O8ohSVqJIg7dSJIuztBLUuEMvSQVztBLUuEMvSQVztBLUuEMvSQVztBLUuH+DwIl4cTCB7zPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='green', marker = '2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Список маркеров смотри [здесь](https://matplotlib.org/api/markers_api.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если бы все строки кода со `scatter()` были в одной ячейке, то графики бы просто накладывались друг на друга."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11886a438>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZZdfRS5JGF0MvSYUz9JJUOEMvSYUz9JJUOEMvSYUz9JJUuFFxHX1EDAC/HYGHOht4aQQep2o+j9GjhOcAPo/RZqSex99n5rC/Q2ZUhH6kRET/ibx5YLTzeYweJTwH8HmMNm/28/DUjSQVztBLUuFKC/36qgcYIT6P0aOE5wA+j9HmTX0eRZ2jlyT9rdKO6CVJRyku9BHxLxHxbETsiIh/jYizqp7pREXE1RHx3xHxfESsqHqeZkTE+RHxeETsjIhnIuKGqmdqRUScHhFPRcSPqp6lWRFxVkQ82PjvYmdEvL/qmZoREZ9v/Jt6OiLui4i3Vj3TiYiIeyPiYEQ8fcS6iRHxWETsanye0M4Zigs98Bjwzsx8F/Ac8KWK5zkhEXE6sBaYC8wCFkTErGqnasprwLLMvBiYDXy6Q5/H624AdlY9RIu+Dfw4M2cC76YDn09ETAGuB3oy850M/VGja6ud6oR9F7j6qHUrgM2ZOR3Y3Fhum+JCn5k/zczXGotPMvQ3azvBZcDzmbk7M18F7gfmVTzTScvM/Zm5vXH7LwxFZUq1UzUnIqYCHwXurnqWZkXE24APAvcAZOarmflytVM1bQxwRkSMAcZxjL9FPRpl5s+BPxy1eh6wsXF7I9DbzhmKC/1R/hH4j6qHOEFTgL1HLO+jQwP5uoiYBlwKbK12kqZ9C/gi8NeqB2nBO4AB4DuNU1B3R8T4qoc6WZn5O+DrwB5gP/CnzPxptVO1ZFJm7oehgyPgnHburCNDHxH/2ThPd/THvCO2+QpDpxHq1U16UuIY6zr2kqiI6AJ+CHwuM/9c9TwnKyI+BhzMzG1Vz9KiMcB7gHWZeSlwiDafJmiHxjnsecCFwGRgfERcV+1UnWPE/zj4myEzP3y8r0fEIuBjwJzsnOtH9wHnH7E8lQ75X9OjRcRbGIp8PTMfqnqeJl0OfDwiPgK8FXhbRHw/MzstLvuAfZn5+v9VPUgHhh74MPCbzBwAiIiHgA8A3690quYdiIjzMnN/RJwHHGznzjryiP54IuJqYDnw8cwcrHqek/ALYHpEXBgRYxn6QdMjFc900iIiGDofvDMzv1H1PM3KzC9l5tTMnMbQa7GlAyNPZv4e2BsRMxqr5gC/rnCkZu0BZkfEuMa/sTl04A+Vj/AIsKhxexHwcDt31pFH9MO4E/g74LGhfw88mZn/VO1Iw8vM1yLiM8BPGLqi4N7MfKbisZpxOfBJ4FcR8cvGui9n5r9XONOp7rNAvXEAsRv4VMXznLTM3BoRDwLbGTol+xQd8i7ZiLgPuBI4OyL2AauArwKbImIxQ9/ErmnrDJ1zZkOS1IziTt1Ikv4/Qy9JhTP0klQ4Qy9JhTP0klQ4Qy9JhTP0klQ4Qy9JhftfkHk+YNeE/i4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = 'o')\n",
    "plt.scatter(X, Y, color ='blue', marker = '*')\n",
    "plt.scatter(X, Y, color ='green', marker = '2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если присмотреться, то на красных точках можно увидеть синие звездочки и зеленые треугольники. Чтобы такого не происходило (например, если вы создаете и сохраняете графики в цикле в пределах одной ячейки), нужно добавить строку с функцией `clf()`, которая очищает координатную плоскость для следующего графика (*clf* ‒ от *clear figure*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1189585c0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, Y, color ='red', marker = 'o')\n",
    "plt.clf()\n",
    "plt.scatter(X, Y, color ='blue', marker = '*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На плоскости представлен только последний график с синими звездочками, красные точки от первого графика был стерты с помощью `clf()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При построении графиков стоит иметь в виду, что функция `plot()` всегда соединяет точки, причем последовательно, в том порядке, в котором они следуют в списках или массивах. Из-за этой особенности, допустив ошибку, связанную с заданием неверной области определения функции, можно получить некорректные графики. Построим для примера гиперболу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118644630>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-100, 100, 100)\n",
    "y = 1/x\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Полученный график не совсем похож на гиперболу! Как известно, в точке $x=0$ график уходит на бесконечность, линия при $x=0$ отсутствует. А здесь она есть! Избавимся от нее, построив график \"по кусочкам\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1188bccf8>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.linspace(-100, 0, 100) # x < 0\n",
    "y1 = 1/x1\n",
    "\n",
    "x2 = np.linspace(0, 100, 100) # x > 0\n",
    "y2 = 1/x2\n",
    "\n",
    "plt.plot(x1, y1, 'blue')\n",
    "plt.plot(x2, y2, 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В завершение попробуем построить целый рисунок (*figure*), состоящий сразу из нескольких графиков (подграфиков). Построим разные типы функций: $y=x^2$, $y=x^3$, $y=e^x$ и $y=|x|$. Сначала создадим соответствующие массивы значений:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-100, 100, 100)\n",
    "y = x ** 2\n",
    "z = x ** 3\n",
    "r = np.exp(x)\n",
    "m = abs(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим рисунок (*figure*):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А теперь будем добавлять в него графики, указывая их расположение. В функции `subplot()` указывается число. Первые две цифры ‒ это число графиков в строке и столбце (здесь 2 на 2, поэтому `22`). Последняя цифра ‒ это положение графика: левый верхний угол (`1`), правый верхний угол (`2`), левый нижний угол (`3`), правый нижний угол (`4`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "plt.plot(x, y) # x^2\n",
    "plt.title('parabola')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(x, z) # x^3\n",
    "plt.title('hyperbola')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.plot(x, r) # e^x\n",
    "plt.title('exponent')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.plot(x, m) # |x|\n",
    "plt.title('abs')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Строка `plt.grid(True)` нужна для того, чтобы на графиках были добавлены линии разметки, привычные нам \"клеточки\", которые позволяют удобным образом определять координаты точек на графике."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И напоследок: как сохранить график в файл. Очень просто. Например, так."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v3y84IlYB7wXuaXuWJiLiVcDbgXsBMvNPmfliu1N1bAB4ZUQMAJcwz7vXLVeZ+SPgN2ds3gLsrm/vBm7qxbGLCP0Z/g74j7aHWKLi3lA9ItYC1wEH252kkbuATwF/bnuQhl4LzABfq09D3RMRQ20PtVSZ+UvgC8BR4AQwm5nfb3eqxkYy8wRUL5SAK3txkL4JfUT8V31e7syPLXP2+Ueq0wf3tTdpRxb1hur9IiJWAN8GPp6Zv2t7nk5ExPuAk5l5qO1ZumAAeDPwlcy8DvgDPTpF0Ev1+estwDXAa4ChiNja7lT9oSfvMNULmfmuc30+IsaB9wGbs/+uGV3UG6r3g4gYpIr8fZn5QNvzNHAD8P76bTFfAbwqIr6Rmf0YluPA8cx8+f+u9tKHoQfeBfw8M2cAIuIB4G3AN1qdqpkXIuKqzDwREVcBJ3txkL55RX8uEXEj8Gng/Zn5x7bn6UARb6geEUF1HvhIZn6x7XmayMzPZOaqzFxL9Xz8oE8jT2b+CjgWEa+rN20GDrc4UqeOAtdHxCX1f2ub6cMfKp/hIWC8vj0OPNiLg/TNK/oF/Cvw18DD1fPPo5n59+2OtHiZ+VJE/APwn5x+Q/WnWh6rEzcAHwJ+GhFP1Ns+W7+HsNr1UeC++oXEc8CHW55nyTLzYETsBR6jOkX7OH30G7IRcT/wDuCKiDgO3Al8DpiKiFupvpHd3JNj999ZDknSUhRx6kaSdHaGXpIKZ+glqXCGXpIKZ+glqXCGXpIKZ+glqXCGXpIK9//iqiCxUV+OWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.scatter(X, Y, color ='red', marker = '*')\n",
    "plt.savefig('MyScatter.png') # ищем файл в рабочей папке (рядом с текущим ipynb-файлом)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы достаточно кратко обсудили возможности библиотеки `matplotlib`, но на этом ее возможности не заканчиваются. Кому интересно, стоит посмотреть документацию по `matplotlib`, а также заглянуть в галерею с примерами графиков, которые можно адаптировать под свои задачи и данные.\n",
    "\n",
    "Если кто-то привык работать в R и полюбил вид графиков `ggplot2`, можно установить [библиотеку](http://ggplot.yhathq.com/install.html) `ggplot` для Python. Кроме того, можно поработать с библиотекой [seaborn](https://seaborn.pydata.org/), она предоставляет много возможностей для построения статистических графиков, и выглядят ти графики тоже очень симпатично.\n",
    "\n",
    "Если хочется более продвинутой интерактивной графики, связанной со статистическими моделями, стоит обратить внимание на библиотеку GraphLab. Она интересна не только графикой, но и другими вещами, но есть один минус: библиотека платная. Однако есть возможность получить доступ по учебной лицензии и даже продлевать ее, особенно, если учесть, что GraphLab используется в некоторых курсах на Coursera, что тоже облегачает получение доступа ([пример](https://www.coursera.org/learn/ml-foundations/home/welcome) такого курса)."
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}