p4_2개의_데이터그래프_만들기.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "p4_2개의 데이터그래프 만들기.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOG2gFpYW3QiCp4MAo5f2Wm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/witheunjin/what-did-i-study-today/blob/master/p4_2%EA%B0%9C%EC%9D%98_%EB%8D%B0%EC%9D%B4%ED%84%B0%EA%B7%B8%EB%9E%98%ED%94%84_%EB%A7%8C%EB%93%A4%EA%B8%B0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "MWW51vXzYXzg",
        "outputId": "dee6c810-71ea-4171-ea2a-c3a036ba46db"
      },
      "source": [
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "D1 = np.array([[1.0,1.2,3,4,5,6],\r\n",
        "               [1.5,3,2.3,5.3,3.8,5.5]])\r\n",
        "D2 = np.array([[-0.6,1.0,1.2,3,4,5,6],\r\n",
        "               [2.9,1.5,3,2.3,5.3,3.8,5.5]])\r\n",
        "\r\n",
        "fig,(ax1,ax2) = plt.subplots(1,2,sharex=True, sharey=True)\r\n",
        "fig.set_size_inches((15,6))\r\n",
        "\r\n",
        "ax1.plot(D1[0], D1[1], 'ko', markersize=10)\r\n",
        "ax1.set_xlim([-1,7])\r\n",
        "ax1.set_ylim([1,6])\r\n",
        "ax1.set_title('D1', fontsize=18)\r\n",
        "\r\n",
        "ax2.plot(D2[0], D2[1], 'ko', markersize=10)\r\n",
        "ax2.set_xlim([-1,7])\r\n",
        "ax2.set_ylim([1,6])\r\n",
        "ax2.set_title('D2', fontsize=18)\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}