kuroipearls/test.ipynb

## test.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH5UEl43zzvHZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0416d988d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Prepare the data\n",
    "x = np.linspace(0, 10, 100)\n",
    "\n",
    "# Plot the data\n",
    "plt.plot(x, x, label='linear')\n",
    "\n",
    "# Add a legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MY FIRST CHART IN JUPYTER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH5UEl43zzvHZAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7f0416d988d0>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"import matplotlib.pyplot as plt\n",
	"import numpy as np\n",
	"\n",
	"# Prepare the data\n",
	"x = np.linspace(0, 10, 100)\n",
	"\n",
	"# Plot the data\n",
	"plt.plot(x, x, label='linear')\n",
	"\n",
	"# Add a legend\n",
	"plt.legend()\n",
	"\n",
	"# Show the plot\n",
	"plt.show()"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# MY FIRST CHART IN JUPYTER"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"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": "3.6.0"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}