matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(data_x, data_y):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(data_x, data_y)\n",
    "    ax.set_xlabel(\"x\")\n",
    "    ax.set_ylabel(\"y\")\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_data([1,2,3], [2,4,8])\n",
    "fig.set_size_inches([3,5])\n",
    "fig.savefig('my_figure.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fig.get_axes()"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}