japanize_matplotlib_test.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOdsvDy8vTR5NkyCR3Li7i5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hiro106/statistics-ml-cs-learning/blob/main/japanize_matplotlib_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install japanize-matplotlib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKnD9x3C1ZDt",
        "outputId": "622b313c-0fa1-4691-a391-5784e56c4223"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting japanize-matplotlib\n",
            "  Downloading japanize-matplotlib-1.1.3.tar.gz (4.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.1 MB 5.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from japanize-matplotlib) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (1.21.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (1.4.4)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->japanize-matplotlib) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->japanize-matplotlib) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->japanize-matplotlib) (1.15.0)\n",
            "Building wheels for collected packages: japanize-matplotlib\n",
            "  Building wheel for japanize-matplotlib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for japanize-matplotlib: filename=japanize_matplotlib-1.1.3-py3-none-any.whl size=4120275 sha256=6e583ed3f6026f3e16634a5321246e441b582975fc150080c36d967add0117b6\n",
            "  Stored in directory: /root/.cache/pip/wheels/83/97/6b/e9e0cde099cc40f972b8dd23367308f7705ae06cd6d4714658\n",
            "Successfully built japanize-matplotlib\n",
            "Installing collected packages: japanize-matplotlib\n",
            "Successfully installed japanize-matplotlib-1.1.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "F0UtFRZD1T1X",
        "outputId": "b95ca67e-71b7-4422-d438-d64dd9886eb5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import japanize_matplotlib\n",
        "japanize_matplotlib.japanize()\n",
        "\n",
        "x = range(6)\n",
        "y = np.linspace(0,5,6)\n",
        "\n",
        "plt.plot(x, y)\n",
        "plt.title('テスト')\n",
        "plt.xlabel('テスト')\n",
        "plt.ylabel('テスト')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "08wDlOwX1cYF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}