osamutake/test-pyplot.ipynb

## test-pyplot.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# jupyterでPyplotを使う(python)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n%matplotlib inline\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\nmatplotlib.rcParams['axes.unicode_minus'] = False\nfig, ax = plt.subplots()\nax.plot(10*np.random.randn(100), 10*np.random.randn(100), 'o')\nax.set_title('Using hyphen instead of Unicode minus')\nplt.show()",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "pygments_lexer": "ipython3",
      "version": "3.5.2",
      "name": "python",
      "nbconvert_exporter": "python",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "jupyterでPyplotを使う(python)",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}
	{
	"cells": [
	{
	"metadata": {},
	"cell_type": "markdown",
	"source": "# jupyterでPyplotを使う(python)"
	},
	{
	"metadata": {
	"trusted": true
	},
	"cell_type": "code",
	"source": "import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n%matplotlib inline\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\nmatplotlib.rcParams['axes.unicode_minus'] = False\nfig, ax = plt.subplots()\nax.plot(10np.random.randn(100), 10np.random.randn(100), 'o')\nax.set_title('Using hyphen instead of Unicode minus')\nplt.show()",
	"execution_count": 2,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"image/png": 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\n",
	"text/plain": "<Figure size 432x288 with 1 Axes>"
	},
	"metadata": {}
	}
	]
	},
	{
	"metadata": {
	"trusted": true
	},
	"cell_type": "code",
	"source": "",
	"execution_count": null,
	"outputs": []
	}
	],
	"metadata": {
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3",
	"language": "python"
	},
	"language_info": {
	"mimetype": "text/x-python",
	"pygments_lexer": "ipython3",
	"version": "3.5.2",
	"name": "python",
	"nbconvert_exporter": "python",
	"codemirror_mode": {
	"version": 3,
	"name": "ipython"
	},
	"file_extension": ".py"
	},
	"gist": {
	"id": "",
	"data": {
	"description": "jupyterでPyplotを使う(python)",
	"public": true
	}
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}