matplotlib.2 basics.json

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c8d0b54388>]"
      ]
     },
     "metadata": {},
     "execution_count": 12
    },
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot([1, 2, 3],[1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [4, 2, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = [2, 10, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c8d278cc48>]"
      ]
     },
     "metadata": {},
     "execution_count": 17
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'info')"
      ]
     },
     "metadata": {},
     "execution_count": 18
    },
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y axis')"
      ]
     },
     "metadata": {},
     "execution_count": 19
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.ylabel('Y axis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'X axis')"
      ]
     },
     "metadata": {},
     "execution_count": 20
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.xlabel('X axis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c8d2f6d688>]"
      ]
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot([1, 2, 3],[4, 5, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<Figure size 7200x14400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 7200x14400 with 0 Axes>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize=(100,200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c8d2900088>"
      ]
     },
     "metadata": {},
     "execution_count": 28
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.subplot(1, 2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c8d24ebac8>,\n",
       " <matplotlib.lines.Line2D at 0x2c8d25b3288>]"
      ]
     },
     "metadata": {},
     "execution_count": 33
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(1, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 0 Axes>"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax1 = fig.add_subplot(1, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c8ecda3908>]"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "ax1.plot([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax2 = fig.add_subplot(2, 2, 1, facecolor='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c8d2b963c8>]"
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "ax2.plot([1, 2, 0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  }
 ]
}