DALC_scalar7__pandas_심화_실습.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DALC_scalar7__pandas_심화_실습.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "LEpgg6MllqlP",
        "4ktdw0WEO16Y"
      ],
      "toc_visible": true,
      "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/juri123123/DALC_Scala/blob/main/DALC_scalar7__pandas_%EC%8B%AC%ED%99%94_%EC%8B%A4%EC%8A%B5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUQY0a9AmQB1"
      },
      "source": [
        "# 주관식\n",
        "셀을 더블클릭해서  : 다음에 답을 입력하세요."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0tu8WgkHe9n"
      },
      "source": [
        "* matplotlib 인터페이스에는 어떤 종류가 있나?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6nQsARbhNrvK"
      },
      "source": [
        "* matlab스타일의 장점은 무엇인가"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7Cz3tPHMFq8"
      },
      "source": [
        "* 객체지향스타일의 장점은 무엇인가\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KnVjMiLWfEO"
      },
      "source": [
        "* 객체지향스타일은 어떨때 사용하는가"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LEpgg6MllqlP"
      },
      "source": [
        "# 빅데이터 시각화\n",
        "* matplotlib사용\n",
        "* 선그림과 점그림 \n",
        "* 다중 서브플롯\n",
        "\n",
        "해당 질문 밑에 있는 코드셀에 코드를 입력하세요"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPLYsr50N48B"
      },
      "source": [
        "# matplotlib 인터페이스"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pSeljp9aWIsn"
      },
      "source": [
        "### matlab스타일 인터페이스"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0vQPFbZD9nu"
      },
      "source": [
        "* matplotlib라이브러리 사용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8u2sLMVNsbW"
      },
      "source": [
        "import matplotlib as mpl"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NHb_3yTGXk8"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_5uwi8CHNKW"
      },
      "source": [
        "* 그래프스타일 클래식으로 설정"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gRJmswLmrzq"
      },
      "source": [
        "plt.style.use('classic')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHp-kJ5vmr-u"
      },
      "source": [
        "import numpy as np\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mt_eg4AlI-2s"
      },
      "source": [
        "* 두 개의 패널 중 첫 번째 패널을 생성하고 현재 축을 설정"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wcQvP_EI3Yc"
      },
      "source": [
        "x = np.linspace(-10,10,100)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qM7c0PCJsVB"
      },
      "source": [
        "* 2행 1열 구조 그리드의 첫번째 패널에 사인 그래프 작성"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6IqwzzbpA7K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "23a64829-d867-467c-acbc-712caf10a5e2"
      },
      "source": [
        "plt.subplot(2,1,1)\n",
        "plt.plot(x, np.sin(x))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f80b2676d90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3-9VHZbSsu5"
      },
      "source": [
        "* 2행 1열 구조 그리드의 두번째 패널에 코사인 그래프 작성"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLB4WiwfSsvC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "be3a26bf-72aa-4780-c314-7d775d2ae9a8"
      },
      "source": [
        "plt.subplot(2,1,2)\n",
        "plt.plot(x, np.cos(x))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f80b27c3d10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXi9JehJLzzC"
      },
      "source": [
        "* 2행 1열의 축 객체의 배열을 형성하라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDiypRdGpA1r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "c64a4fcc-13aa-434b-e37e-5941674f2980"
      },
      "source": [
        "fig, ax = plt.subplots(2,1)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Jv9+vLVu2aGJigjUkAAA8IyYnJ+V0OtXV1aWlS5fO6VxsHQ8AAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIyzHEgikYhaW1v15ptvqrS0VPv379fIyMiv1t+7d09HjhzRli1btGXLFh05ckQ+ny8uTWN+9fX1mW4B/8UsFg9msbgwj8RjOZCcP39ePT09amxsVEdHh4qLi1VbW6upqalZ648eParx8XG1t7ervb1d4+Pjev/99+PWOOYP3+iLB7NYPJjF4sI8Eo/lQNLZ2any8nLl5uYqLS1Ne/bsUSgU0sDAQFTt6Oiorl69qqqqKjmdTjmdTlVVVeny5csaGxuL6wUAAIBnX7KVIp/Pp9HRURUVFc0cs9lsys/P19DQkP73f//3sXqv16uUlBTl5eXNHMvLy1NKSoq8Xq9eeOGFx+ojkYgkaXJy8qkvBPETDAaZxSLBLBYPZrG4MI/FYXoG05/jc2EpkAQCAUmSw+F47LjD4Zj52i/rf1n7pPrpxz45OTlW2sECOHHihOkW8F/MYvFgFosL81g8pqamZv3cj4WlQJKeni5JUYtSfT6fli9fPmv9bAtYfT7fzLketWzZMnk8Htntdi1ZssRS4wAAwKxIJKKpqSktW7ZszueyFEgcDodcLpcGBwe1atUqSVI4HJbX69XGjRuj6vPy8hQKhTQ8PKyXXnpJkjQ8PKxQKPTYY5xpSUlJysrKmst1AAAAA+Z6Z2Sa5UWtZWVl8ng8GhkZ0YMHD9Ta2qrk5GStXbs2qtblcmn16tVqaWnRxMSEJiYm1NLSoldffTVq/QgAAMCS/v5+SytRpvch6erqUiAQUGFhofbv36/c3FyNjY1p9+7damhoUElJiaSfF7ocO3ZMV69elSStWbNGNTU1cUtSAAAgcVgOJAAAAPPF0hqSuYpEImpra1N3d7f8fr8KCgpUU1OjlStXzlp/7949HTt2TFeuXJHE3ZV4imUW4+Pjamlp0fXr13X37l05nU5t2LBBu3btUmpqqoHuE0+s3xvT/H6/9u7dq7GxMX311Vey2WwL1HHieppZ9Pb2yuPx6M6dO3ruuee0fv16vfPOOwvYdWKKdRaDg4M6efKkvF6vkpKSVFJSourqarlcrgXuPPFcvHhRHR0dGh4eViAQ+M2fN3P5/F6Qv2XDLq+LRyyzmJqaUk5Ojj744AN1d3frww8/1JUrV3Tq1CkDnSemWL83ph0/fpxfk4+zWGfh8Xj08ccfq6amRl1dXfrXv/6lTZs2LXDXiSmWWTx8+FCHDx9Wbm6uPv/8c507d042m01Hjhwx0HnicTgcKisrU3V1taX6uXx+L0ggYZfXxSOWWWRnZ6uyslIrVqxQUlKSVqxYodLSUn3//fcGOk9Mscxj2uXLlzUyMqIdO3YsYKeJL5ZZ+P1+tba2at++fSopKZHNZpPdbldBQYGBzhNPrLO4e/euSktLlZqaqvT0dLndbg0NDRnoPPG88sor2rBhg7Kzs3+zdq6f3/MeSH5rl9df+q1dXvH0Yp3FbL799lvl5+fPV4u/K08zj4mJCTU3N+vQoUM8pomjWGdx48YN3b9/X7du3VJlZaW2bdumgwcP8jMqDmKdRUZGhrZu3aru7m7dv39fPp9Pvb29s/4GKObXXD+/5z2QzPcur7Au1ln80tmzZzU0NKS9e/fOS3+/N08zj6amJm3evPk315ggNrHOYmJiQpJ06dIlNTU16dNPP1VeXp4OHTrEXzWfo6f5vli3bp2+++47bd68WW+88Ybu3Lmjt99+e957xePm+vk974HkSbu8zrZra6y7vMK6WGfxqDNnzqirq0tNTU1sYhcnsc7j4sWLun37tv7yl78sSH+/J0/zc0qSdu7cqaysLKWlpemtt96S3+/XjRs35r/hBBbrLG7duqWDBw9q27Zt6unpUXd3t9asWaN9+/b95losxNdcP7/nPZA8usvrtOldXme79f/oLq/TnrTLK6yLdRbSz6vdP/roI124cEHNzc168cUXF6rdhBfrPL7++mvdvHlT27dvV1lZmd59911J0vbt29Xb27tgfSeiWGcxfYw/dRF/sc5ieHhYaWlp2rp1q1JTU2W321VRUaHbt29rZGRkIVv/3Zvr5/eCLGpll9fFI5ZZhMNhHT16VNeuXVNzczO/QjcPYplHdXW1zp49q9OnT+v06dP6+9//Lkk6efKk1q1bt9CtJ5xYZvH888/rtddeU3t7u3766ScFg0GdOXNGGRkZKi4uNtB9YollFoWFhQqFQvryyy8VDocVDAb12WefyW6385tocTD9fxoKhST9/FeWg8GgHj58GFU718/vBdkYjV1eF49YZnHt2jX97W9/U0pKStQCyp6eHkNXkFhi/d541PR82IckPmKdhd/v1/Hjx3Xp0iUlJSWpsLBQVVVVrO+Jg1hn8c0336itrU3/+c9/JEm5ubn661//qj/96U8mLyMh9Pb2qqGhIep4U1OT/vjHP8b185udWgEAgHEL8sgGAADgSQgkAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAuGSrhRcvXlRHR4eGh4cVCAT01VdfyWaz/Wr9vXv3dOzYMV25ckWStGbNGtXU1MjhcMy9awAAkFAs3yFxOBwqKytTdXW1pfqjR49qfHxc7e3tam9v1/j4uN5///2nbhQAACQuy4HklVde0YYNG5Sdnf2btaOjo7p69aqqqqrkdDrldDpVVVWly5cva2xsbE4NAwCAxDMva0i8Xq9SUlKUl5c3cywvL08pKSnyer3z8ZYAAOAZZnkNSSwCgcCsa0UcDocCgUDU8YcPH+rHH3+U3W7XkiVL5qMlAAAQZ5FIRFNTU1q2bJmSkuZ2j2NeAkl6erp8Pl/UcZ/Pp/T09KjjP/74o8rLy+ejFQAAMM88Ho+ysrLmdI55CSR5eXkKhUIaHh7WSy+9JEkaHh5WKBR67DHONLvdLkm6efOmMjMz56MlxKCurk719fWm24CYxWLCLBYX5rE4TE5OKicnZ+ZzfC4sB5JwOKxwOKxQKCRJCgaDstlsSk5OjrpN43K5tHr1arW0tOjdd9+VJLW0tOjVV1/VCy+8EHXu6cc0mZmZBJJFIDU1lTksEsxi8WAWiwvzWFzisdzC8gOff//733K73aqtrZUkvf7663K73bp+/brGxsZUWlqq69evz9TX1dUpMzNTO3fu1M6dO+V0OnX48OE5NwwAABKP5TskmzZt0qZNm3716z09PY/9OzMzU++9997TdwZj3G636RbwX8xi8WAWiwvzSDxsHY8ofKMvHsxi8WAWiwvzSDwEEgAAYByBBAAAGEcgAQAAxhFIAACAcQQSAABgHIEEAAAYRyABAADGEUgAAIBxBBIAAGAcgQQAABhHIAEAAMYRSAAAgHEEEgAAYByBBAAAGEcgAQAAxhFIAACAcQQSAABgHIEEAAAYRyABAADGEUgAAIBxBBIAAGAcgQQAABhHIAEAAMYRSAAAgHHJVgsjkYja2trU3d0tv9+vgoIC1dTUaOXKlbPWDw4O6uTJk/J6vUpKSlJJSYmqq6vlcrni1jwAAEgMlu+QnD9/Xj09PWpsbFRHR4eKi4tVW1urqampqNqHDx/q8OHDys3N1eeff65z587JZrPpyJEjcW0eAAAkBsuBpLOzU+Xl5crNzVVaWpr27NmjUCikgYGBqFq/36+7d++qtLRUqampSk9Pl9vt1tDQUFybBwAAicFSIPH5fBodHVVRUdHMMZvNpvz8/FlDRkZGhrZu3aru7m7dv39fPp9Pvb29Wrt2bfw6BwAACcNSIAkEApIkh8Px2HGHwzHztV9at26dvvvuO23evFlvvPGG7ty5o7fffnuO7QIAgERkaVFrenq6pJ/vlDzK5/Np+fLlUfW3bt3SwYMHVV1drddff13hcFjnzp3Tvn37dPr0adnt9lnfp66uTqmpqZIkt9stt9sd08UAAID51dfXp76+PklSMBiM23ktBRKHwyGXy6XBwUGtWrVKkhQOh+X1erVx48ao+uHhYaWlpWnr1q0zxyoqKvTJJ59oZGRE//M//zPr+9TX1yszM/NprgMAACyAR28YTE5O6sSJE3E5r+VFrWVlZfJ4PBoZGdGDBw/U2tqq5OTkWdeFFBYWKhQK6csvv1Q4HFYwGNRnn30mu92unJycuDQOAAASh+V9SCoqKhQIBHTgwAEFAgEVFhaqoaFBdrtdY2Nj2r17txoaGlRSUiKXy6UjR46ora1Np06dkiTl5uaqvr5eGRkZ83YxAADg2bSkv78/YroJv9+vLVu2aGJigkc2AAA8IyYnJ+V0OtXV1aWlS5fO6VxsHQ8AAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIxLtloYiUTU1tam7u5u+f1+FRQUqKamRitXrvzV1/T29srj8ejOnTt67rnntH79er3zzjtxaRwAACQOy4Hk/Pnz6unpUWNjo1asWKGzZ8+qtrZWZ8+eld1uj6r3eDz64osvdPjwYa1atUrBYFA3b96Ma/MAACAxWH5k09nZqfLycuXm5iotLU179uxRKBTSwMBAVK3f71dra6v27dunkpIS2Ww22e12FRQUxLV5AACQGCwFEp/Pp9HRURUVFc0cs9lsys/P19DQUFT9jRs3dP/+fd26dUuVlZXatm2bDh48KK/XG7/OAQBAwrAUSAKBgCTJ4XA8dtzhcMx87VETExOSpEuXLqmpqUmffvqp8vLydOjQIfl8vrn2DAAAEoylNSTp6emSFBUmfD6fli9f/qv1O3fuVFZWliTprbfe0hdffKEbN25o9erVs75PXV2dUlNTJUlut1tut9viZQAAgIXQ19envr4+SVIwGIzbeS0FEofDIZfLpcHBQa1atUqSFA6H5fV6tXHjxqj6/Px8SdKSJUtiaqa+vl6ZmZkxvQYAACycR28YTE5O6sSJE3E5r+VFrWVlZfJ4PBoZGdGDBw/U2tqq5ORkrV27Nqr2+eef12uvvab29nb99NNPCgaDOnPmjDIyMlRcXByXxgEAQOKwHEgqKirkdrt14MABlZWV6YcfflBDQ4PsdrvGxsZUWlqq69evz9T/4x//UHZ2tnbt2qU///nPGhoaUmNjo5YuXTovFwIAAJ5dS/r7+yOmm/D7/dqyZYsmJiZ4ZAMAwDNicnJSTqdTXV1dc77hwNbxAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAOMuBJBKJqLW1VW+++aZKS0u1f/9+jYyM/Obr/H6/duzYofXr1yscDs+pWQAAkJgsB5Lz58+rp6dHjY2N6ujoUHFxsWprazU1NfXE1x0/flw5OTlzbhQAACQuy4Gks7NT5eXlys3NVVpamvbs2aNQKKSBgYFffc3ly5c1MjKiHTt2xKVZAACQmCwFEp/Pp9HRURUVFc0cs9lsys/P19DQ0KyvmZiYUHNzsw4dOiSbzRafbgEAQEKyFEgCgYAkyeFwPHbc4XDMfO2XmpqatHnzZq1cuXKOLQIAgESXbKUoPT1d0s93Sh7l8/m0fPnyqPqLFy/q9u3beu+992Jqpq6uTqmpqZIkt9stt9sd0+sBAMD86uvrU19fnyQpGAzG7byWAonD4ZDL5ThKZiIAAAtwSURBVNLg4KBWrVolSQqHw/J6vdq4cWNU/ddff62bN29q+/btM7WStH37dlVVVWnTpk2zvk99fb0yMzOf6kIAAMD8e/SGweTkpE6cOBGX81oKJJJUVlYmj8ejl19+WdnZ2frkk0+UnJystWvXRtVWV1dr7969M/++ceOG/vnPf+rkyZNyOp1xaRwAACQOy4GkoqJCgUBABw4cUCAQUGFhoRoaGmS32zU2Nqbdu3eroaFBJSUlysjIUEZGxsxr//CHP0iSsrKyWOAKAACiLOnv74+YbsLv92vLli2amJjgkQ0AAM+IyclJOZ1OdXV1aenSpXM6F1vHAwAA4wgkAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA45KtFkYiEbW1tam7u1t+v18FBQWqqanRypUro2rHx8fV0tKi69ev6+7du3I6ndqwYYN27dql1NTUuF4AAAB49lm+Q3L+/Hn19PSosbFRHR0dKi4uVm1traampqJqp6amlJOTow8++EDd3d368MMPdeXKFZ06dSquzQMAgMRgOZB0dnaqvLxcubm5SktL0549exQKhTQwMBBVm52drcrKSq1YsUJJSUlasWKFSktL9f3338e1eQAAkBgsBRKfz6fR0VEVFRXNHLPZbMrPz9fQ0JClN/r222+Vn5//dF0CAICEZimQBAIBSZLD4XjsuMPhmPnak5w9e1ZDQ0Pau3fvU7QIAAASnaVFrenp6ZJ+vlPyKJ/Pp+XLlz/xtWfOnFFvb6+ampqUlZX1xNq6urqZRa9ut1tut9tKewAAYIH09fWpr69PkhQMBuN2XkuBxOFwyOVyaXBwUKtWrZIkhcNheb1ebdy4cdbXRCIRHTt2TN98842am5vlcrl+833q6+uVmZkZQ/sAAGAhPXrDYHJyUidOnIjLeS0vai0rK5PH49HIyIgePHig1tZWJScna+3atVG14XBYR48e1bVr1yyHEQAA8PtleR+SiooKBQIBHThwQIFAQIWFhWpoaJDdbtfY2Jh2796thoYGlZSU6IcfftCFCxeUkpKiysrKx87T09MT94sAAADPtiX9/f0R0034/X5t2bJFExMTPLIBAOAZMTk5KafTqa6uLi1dunRO52LreAAAYByBBAAAGEcgAQAAxhFIAACAcQQSAABgHIEEAAAYRyABAADGEUgAAIBxBBIAAGAcgQQAABhHIAEAAMYRSAAAgHEEEgAAYByBBAAAGEcgAQAAxhFIAACAcQQSAABgHIEEAAAYRyABAADGEUgAAIBxBBIAAGAcgQQAABhHIAEAAMYRSAAAgHEEEgAAYFyy1cJIJKK2tjZ1d3fL7/eroKBANTU1Wrly5az19+7d07Fjx3TlyhVJ0po1a1RTUyOHwxGfzgEAQMKwfIfk/Pnz6unpUWNjozo6OlRcXKza2lpNTU3NWn/06FGNj4+rvb1d7e3tGh8f1/vvvx+3xjF/+vr6TLeA/2IWiwezWFyYR+KxHEg6OztVXl6u3NxcpaWlac+ePQqFQhoYGIiqHR0d1dWrV1VVVSWn0ymn06mqqipdvnxZY2Njcb0AxB/f6IsHs1g8mMXiwjwSj6VA4vP5NDo6qqKiopljNptN+fn5Ghoaiqr3er1KSUlRXl7ezLG8vDylpKTI6/XGoW0AAJBILK0hCQQCkhS1/sPhcMx87Zf1s60V+bX6SCQiSZqcnLTSDuZZMBhkFosEs1g8mMXiwjwWh+kZTH+Oz4WlQJKeni7p5zslj/L5fFq+fPms9b+sna6fPtejpteh5OTkWGkHC+DEiROmW8B/MYvFg1ksLsxj8ZiamprzL61YCiQOh0Mul0uDg4NatWqVJCkcDsvr9Wrjxo1R9Xl5eQqFQhoeHtZLL70kSRoeHlYoFHrsMc60ZcuWyePxyG63a8mSJXO5HgAAsEAikYimpqa0bNmyOZ/L8q/9lpWVyePx6OWXX1Z2drY++eQTJScna+3atVG1LpdLq1evVktLi959911JUktLi1599VW98MILUfVJSUnKysqaw2UAAAAT4rWdx5L+/n5LD34ikYhaW1vV1dWlQCCgwsJC7d+/X7m5uRobG9Pu3bvV0NCgkpISST8/Vzp27JiuXr0qiX1IAADAr7McSAAAAOYLW8cDAADjLK8hmQu2nV88YpnF+Pi4WlpadP36dd29e1dOp1MbNmzQrl27lJqaaqD7xBPr98Y0v9+vvXv3amxsTF999ZVsNtsCdZy4nmYWvb298ng8unPnjp577jmtX79e77zzzgJ2nZhincXg4KBOnjwpr9erpKQklZSUqLq6Wi6Xa4E7TzwXL15UR0eHhoeHFQgEfvPnzVw+vxfkDgnbzi8escxiampKOTk5+uCDD9Td3a0PP/xQV65c0alTpwx0nphi/d6Ydvz4cX5NPs5inYXH49HHH3+smpoadXV16V//+pc2bdq0wF0nplhm8fDhQx0+fFi5ubn6/PPPde7cOdlsNh05csRA54nH4XCorKxM1dXVlurn8vm9IIGEbecXj1hmkZ2drcrKSq1YsUJJSUlasWKFSktL9f333xvoPDHFMo9ply9f1sjIiHbs2LGAnSa+WGbh9/vV2tqqffv2qaSkRDabTXa7XQUFBQY6TzyxzuLu3bsqLS1Vamqq0tPT5Xa7Z91FHLF75ZVXtGHDBmVnZ/9m7Vw/v+c9kLDt/OIR6yxm8+233yo/P3++WvxdeZp5TExMqLm5WYcOHeIxTRzFOosbN27o/v37unXrliorK7Vt2zYdPHiQn1FxEOssMjIytHXrVnV3d+v+/fvy+Xzq7e2ddUsKzK+5fn7PeyCZ723nYV2ss/ils2fPamhoSHv37p2X/n5vnmYeTU1N2rx582+uMUFsYp3FxMSEJOnSpUtqamrSp59+qry8PB06dGjWXaph3dN8X6xbt07fffedNm/erDfeeEN37tzR22+/Pe+94nFz/fye90DypG3nZ9tGPtZt52FdrLN41JkzZ9TV1aWmpiY2sYuTWOdx8eJF3b59W3/5y18WpL/fk6f5OSVJO3fuVFZWltLS0vTWW2/J7/frxo0b899wAot1Frdu3dLBgwe1bds29fT0qLu7W2vWrNG+fft+cy0W4muun9/zHkge3XZ+2vS287Pd+n902/lpT9p2HtbFOgvp59XuH330kS5cuKDm5ma9+OKLC9Vuwot1Hl9//bVu3ryp7du3q6ysbGYX5O3bt6u3t3fB+k5Esc5i+hh/6iL+Yp3F8PCw0tLStHXrVqWmpsput6uiokK3b9/WyMjIQrb+uzfXz+8FWdQ6ve38yMiIHjx4oNbWVkvbzk9MTGhiYuKJ284jNrHMIhwO6+jRo7p27Zqam5v5Fbp5EMs8qqurdfbsWZ0+fVqnT5/W3//+d0nSyZMntW7duoVuPeHEMovnn39er732mtrb2/XTTz8pGAzqzJkzysjIUHFxsYHuE0sssygsLFQoFNKXX36pcDisYDCozz77THa7nd9Ei4Pp/9NQKCTp57+yHAwG9fDhw6jauX5+L8hOrWw7v3jEMotr167pb3/7m1JSUqIWUPb09Bi6gsQS6/fGo6bnwz4k8RHrLPx+v44fP65Lly4pKSlJhYWFqqqqYn1PHMQ6i2+++UZtbW36z3/+I0nKzc3VX//6V/3pT38yeRkJobe3Vw0NDVHHm5qa9Mc//jGun99sHQ8AAIxj63gAAGAcgQQAABhHIAEAAMYRSAAAgHEEEgAAYByBBAAAGEcgAQAAxhFIAACAcQQSAABg3P8DMDZcZUE6DBEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "veju4mmRMgmx"
      },
      "source": [
        "* 위의 형성한 객체에 1행에는 넘파이패키지의 sin()함수를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6AJELNs3bHc",
        "outputId": "0d0c064a-49ab-4fc1-8fa2-75b90330ace9"
      },
      "source": [
        "ax[0].plot(x, np.sin(x))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f80ae4c45d0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoC3coUxpApz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KW7fLJ8eMjKZ"
      },
      "source": [
        "* 위의 형성한 객체에 1행에는 넘파이패키지의 sin()함수를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x06fkqr0VtJT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df5af217-4e98-427d-bc8b-502c58cfd471"
      },
      "source": [
        "ax[1].plot(x, np.cos(x))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f80ae4c4610>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5cXl2t8QXOM9"
      },
      "source": [
        "### 기본선그림"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U56XiDtTMpCq"
      },
      "source": [
        "### 선그림(lineplot)\n",
        "* 위와 같은 것이 %로 시작하는 것을 jupyter notebook의 magic command라고 한다.\n",
        "* %who는 변수명의 리스트를 보여주고 %magic 은 모든 매직명령어를 보여준다.\n",
        "* 전체적인 magic 명령어에 대해서는 다음 블로그를 참고\n",
        "* http://studymake.tistory.com/601\n",
        "* matplotlib 을 jupyter notebook에서 사용할 때, plot의 이미지를 보여주기 위해 magic command를 사용하는데,\n",
        "* 이때 우리가 사용하는 inline 이외에도 다음과 같은 요소를 사용할 수 있다.\n",
        "* Available matplotlib backends: ['osx', 'qt4', 'qt5', 'gtk3', 'notebook', 'wx', 'qt', 'nbagg','gtk', 'tk', 'inline']\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "julPwkbal-5w"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn-whitegrid')\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g5Hetg75aMch"
      },
      "source": [
        "*  Series를 통한 line plot 그리기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrVUy7LTZ9wZ",
        "outputId": "e0cf063c-87d1-48c2-abfb-b67fbf4fc6a2"
      },
      "source": [
        "s = pd.Series(np.random.randn(10).cumsum(), index=np.arange(0, 100, 10))\n",
        "s"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     2.280634\n",
              "10    0.837210\n",
              "20    0.915952\n",
              "30    1.731755\n",
              "40    1.902901\n",
              "50    3.130461\n",
              "60    4.635350\n",
              "70    3.991679\n",
              "80    3.389378\n",
              "90    4.413935\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alJg_1dZanBU"
      },
      "source": [
        "* 시리즈 s의 선그래프를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37a_XyCFamNX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "d1ebf15d-a459-4039-8164-ef4654c1b3bc"
      },
      "source": [
        "s.plot()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80ae4d3d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXpe7lBWaY6U"
      },
      "source": [
        "*  DataFrame를 통한 line plot 그리기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "WRn4_EQNab0M",
        "outputId": "f0e08054-b13b-459a-b16d-5cafb4112064"
      },
      "source": [
        "df = pd.DataFrame(np.random.randn(10, 4).cumsum(axis=0),\n",
        "                  columns=[\"A\", \"B\", \"C\", \"D\"],\n",
        "                  index=np.arange(0, 100, 10))\n",
        "df"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-2.469360</td>\n",
              "      <td>-1.728100</td>\n",
              "      <td>1.677676</td>\n",
              "      <td>0.484898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>-3.893582</td>\n",
              "      <td>-1.883469</td>\n",
              "      <td>1.266973</td>\n",
              "      <td>0.800432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>-3.886980</td>\n",
              "      <td>-2.183428</td>\n",
              "      <td>0.969183</td>\n",
              "      <td>1.989567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>-4.012479</td>\n",
              "      <td>-2.306374</td>\n",
              "      <td>-0.256775</td>\n",
              "      <td>1.982858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>-4.028086</td>\n",
              "      <td>-1.972840</td>\n",
              "      <td>-1.028501</td>\n",
              "      <td>0.439835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>-3.814284</td>\n",
              "      <td>-1.882962</td>\n",
              "      <td>-1.841129</td>\n",
              "      <td>1.659104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>-4.570730</td>\n",
              "      <td>-2.386802</td>\n",
              "      <td>-1.524517</td>\n",
              "      <td>2.517901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>-4.660720</td>\n",
              "      <td>-1.670060</td>\n",
              "      <td>-1.856021</td>\n",
              "      <td>1.746146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>-6.315721</td>\n",
              "      <td>-2.204730</td>\n",
              "      <td>-1.320256</td>\n",
              "      <td>1.423838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>-6.928193</td>\n",
              "      <td>-0.610590</td>\n",
              "      <td>-1.276770</td>\n",
              "      <td>2.101883</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           A         B         C         D\n",
              "0  -2.469360 -1.728100  1.677676  0.484898\n",
              "10 -3.893582 -1.883469  1.266973  0.800432\n",
              "20 -3.886980 -2.183428  0.969183  1.989567\n",
              "30 -4.012479 -2.306374 -0.256775  1.982858\n",
              "40 -4.028086 -1.972840 -1.028501  0.439835\n",
              "50 -3.814284 -1.882962 -1.841129  1.659104\n",
              "60 -4.570730 -2.386802 -1.524517  2.517901\n",
              "70 -4.660720 -1.670060 -1.856021  1.746146\n",
              "80 -6.315721 -2.204730 -1.320256  1.423838\n",
              "90 -6.928193 -0.610590 -1.276770  2.101883"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DY8egKYQavl3"
      },
      "source": [
        "* 데이터프레임 df의 선그래프를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiXr_ycxamDZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "9cdf7950-f3b6-400e-8bf7-afae343ac5af"
      },
      "source": [
        "df.plot()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80ae1dfd10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hFLRAXVa2Xp"
      },
      "source": [
        "* 데이터프레임 df의 B열에 대해서만 선그래프를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYNO2wWaakKH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "f30832dd-bea7-4b24-fe53-bdc1722ba7e5"
      },
      "source": [
        "df.B.plot()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80ae4fdc50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aub_wxpJbEG2",
        "outputId": "0d125a67-1125-4409-c960-1e96c6591554"
      },
      "source": [
        "s2 = pd.Series(np.random.rand(16), index=list(\"abcdefghijklmnop\"))\n",
        "s2"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    0.867277\n",
              "b    0.506455\n",
              "c    0.804775\n",
              "d    0.375019\n",
              "e    0.713155\n",
              "f    0.754973\n",
              "g    0.820300\n",
              "h    0.483196\n",
              "i    0.952918\n",
              "j    0.267474\n",
              "k    0.987944\n",
              "l    0.002566\n",
              "m    0.572897\n",
              "n    0.160658\n",
              "o    0.765612\n",
              "p    0.813308\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xharyGBsOEqC"
      },
      "source": [
        "## 막대그래프그리기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_t3jDNz2a9l-"
      },
      "source": [
        "* s2열에 대해서 막대그래프를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qm1HkVEIa9l_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "0c385f4c-7e22-48f9-e5c5-c552d79d2374"
      },
      "source": [
        "s2.plot(kind='bar')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80ae3fe3d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hW7KURw4bRkl"
      },
      "source": [
        "s2열에 대해서 가로방향의 막대그래프를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LzKYe1TsbRkn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "ee7384a9-f867-4761-f4f2-c209e9e93c91"
      },
      "source": [
        "s2.plot.barh()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80adf48410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "ZojuOg-9blaB",
        "outputId": "135b300e-bf9a-42a5-886a-bb7770b501cf"
      },
      "source": [
        "df2 = pd.DataFrame(np.random.rand(6, 4), \n",
        "                   index=[\"one\", \"two\", \"three\", \"four\", \"five\", \"six\"],\n",
        "                   columns=pd.Index([\"A\", \"B\", \"C\", \"D\"], name=\"Genus\"))\n",
        "df2"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Genus</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>one</th>\n",
              "      <td>0.761878</td>\n",
              "      <td>0.331308</td>\n",
              "      <td>0.276131</td>\n",
              "      <td>0.299222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>0.525198</td>\n",
              "      <td>0.096368</td>\n",
              "      <td>0.551676</td>\n",
              "      <td>0.536652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>three</th>\n",
              "      <td>0.341793</td>\n",
              "      <td>0.433205</td>\n",
              "      <td>0.006734</td>\n",
              "      <td>0.501140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>four</th>\n",
              "      <td>0.837571</td>\n",
              "      <td>0.667773</td>\n",
              "      <td>0.270576</td>\n",
              "      <td>0.821929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>five</th>\n",
              "      <td>0.604812</td>\n",
              "      <td>0.191078</td>\n",
              "      <td>0.458852</td>\n",
              "      <td>0.958906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>six</th>\n",
              "      <td>0.721831</td>\n",
              "      <td>0.700848</td>\n",
              "      <td>0.971191</td>\n",
              "      <td>0.204360</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Genus         A         B         C         D\n",
              "one    0.761878  0.331308  0.276131  0.299222\n",
              "two    0.525198  0.096368  0.551676  0.536652\n",
              "three  0.341793  0.433205  0.006734  0.501140\n",
              "four   0.837571  0.667773  0.270576  0.821929\n",
              "five   0.604812  0.191078  0.458852  0.958906\n",
              "six    0.721831  0.700848  0.971191  0.204360"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EepLT6U2boT3"
      },
      "source": [
        "* 데이터프레임 df2의 누적막대그래프를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jR3NEUB8b5b"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlWHHtdSbn7-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "a76d599e-71d6-49fc-8367-28a19ee6510f"
      },
      "source": [
        "df2.plot(kind='bar',stacked=True)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80ad73f5d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3cbcWn5b6R8"
      },
      "source": [
        "* 데이터프레임 df2의 가로방향의 누적막대그래프를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bt37000Yb6SZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "9a3e287f-b7e5-4495-85a4-68b5e9364975"
      },
      "source": [
        "df2.plot(kind='barh', stacked=True)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f80ad63e710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzHCk1PuPF8K"
      },
      "source": [
        "* s3의 히스토그램을 그려라\n",
        "(히스토그램은 인덱스가 필요없다.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewQeK3lXtTUH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c705e88-32d7-4494-c595-63b421ba6348"
      },
      "source": [
        "s3 = pd.Series(np.random.normal(0, 1, size=200))\n",
        "s3"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     -0.142115\n",
              "1      0.247565\n",
              "2      1.682586\n",
              "3     -0.716165\n",
              "4      0.094111\n",
              "         ...   \n",
              "195    0.273143\n",
              "196    0.344833\n",
              "197    0.409582\n",
              "198    0.758074\n",
              "199   -0.769244\n",
              "Length: 200, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIUdDehpmrhn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLrkrDZSSHSE"
      },
      "source": [
        "* x축의 구간개수를50개로 설정하여 s3를 히스토그램으로그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGghPWtIt3SD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzaPAWphMqEo"
      },
      "source": [
        "## 산점도 그리기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4b-MSmvSMqFL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "f9395df0-a49e-47a8-bce5-9048c970da9c"
      },
      "source": [
        "x1 = np.random.normal(1, 1, size=(100, 1))\n",
        "x2 = np.random.normal(-2, 4, size=(100, 1))\n",
        "X = np.concatenate((x1, x2), axis=1)\n",
        "df3 = pd.DataFrame(X, columns=[\"x1\", \"x2\"])\n",
        "df3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.164951</td>\n",
              "      <td>-6.132267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.615138</td>\n",
              "      <td>3.221931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.213518</td>\n",
              "      <td>-0.539978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.171097</td>\n",
              "      <td>-3.248257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.938202</td>\n",
              "      <td>-2.150673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>2.202578</td>\n",
              "      <td>-5.027287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>0.698536</td>\n",
              "      <td>-8.378725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>0.496533</td>\n",
              "      <td>-5.162093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>-0.158065</td>\n",
              "      <td>-5.936603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>2.335152</td>\n",
              "      <td>-5.155524</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          x1        x2\n",
              "0   2.164951 -6.132267\n",
              "1   1.615138  3.221931\n",
              "2   1.213518 -0.539978\n",
              "3   3.171097 -3.248257\n",
              "4   1.938202 -2.150673\n",
              "..       ...       ...\n",
              "95  2.202578 -5.027287\n",
              "96  0.698536 -8.378725\n",
              "97  0.496533 -5.162093\n",
              "98 -0.158065 -5.936603\n",
              "99  2.335152 -5.155524\n",
              "\n",
              "[100 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhu60hvjSLeb"
      },
      "source": [
        "* df3의 x1열을 x축으로 x2열을 y축으로 산점도를 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4ykGVibMWSi"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fV6uu19zMvX0"
      },
      "source": [
        "##  플롯 조정\n",
        "* color\n",
        "\n",
        "값 색상\n",
        "\n",
        "\"b\" blue\n",
        "\n",
        "\"g\" green\n",
        "\n",
        "\"r\" red\n",
        "\n",
        "\"c\" cyan\n",
        "\n",
        "\"m\" magenta\n",
        "\n",
        "\"y\" yellow\n",
        "\n",
        "\"k\" black\n",
        "\n",
        "\"w\" white\n",
        "\n",
        "* marker\n",
        "\n",
        "값 마킹\n",
        "\n",
        "\".\" point\n",
        "\n",
        "\",\" pixel\n",
        "\n",
        "\"o\" circle\n",
        "\n",
        "\"v\" triangle_down\n",
        "\n",
        "\"^\" triangle_up\n",
        "\n",
        "\"<\" triangle_left\n",
        "\n",
        "\">\" triangle_right\n",
        "\n",
        "\"8\" octagon\n",
        "\n",
        "\"s\" square\n",
        "\n",
        "\"p\" pentagon\n",
        "\n",
        "\"*\" star\n",
        "\n",
        "\"h\" hexagon\n",
        "\n",
        "\"+\" plus\n",
        "\n",
        "\"x\" x\n",
        "\n",
        "\"D\" diamond\n",
        "\n",
        "* line style\n",
        "\n",
        "값 라인 스타일\n",
        "\n",
        "\"-\" solid line\n",
        "\n",
        "\"--\" dashed line\n",
        "\n",
        "\"-.\" dash-dotted line\n",
        "\n",
        "\":\" dotted line\n",
        "\n",
        "\"None\" draw nothing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyBDJFiGMX3h"
      },
      "source": [
        "* plt.plot(np.random.randn(50)에서 색상은 초록, 마킹모양은 원으로 선스타일은 dashed line으로 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWRSFv_DMvX2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1H0Z-LfjMzNK"
      },
      "source": [
        "* y=x그래프를 solid green 옵션으로 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFi6-Ut8MzNN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-2CkyUXM8he"
      },
      "source": [
        "* y=x+1그래프를 dashed cyan 옵션으로 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVc87VZMM8hg"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZj4OZD_NEQ5"
      },
      "source": [
        "* y=x+2 그래프를 dashdot black 옵션으로 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RW646EeRNEQ6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxETcM-fNl-w"
      },
      "source": [
        "* y=x+3그래프를 dotted red 옵션으로 그려라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQMrrvhbNJ5Q"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ezZWnu4O_yG"
      },
      "source": [
        "* 코사인 그래프를 그리고 x축의 범위를 (-1,11)로 y축의 범위를 (1.5,1.5)으로 설정해라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNNVxVlRPACn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wv7UR8SlPAOi"
      },
      "source": [
        "* 자연상수 그래프(np.exp(x)그래프를 그리고 y축의 스케일을 로그로 설정해라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oo5xRwzXPAOj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_du-yBjh8Gz"
      },
      "source": [
        "* 사인그래프를 그리고 축의 레이블를 a sine curve로 지정하고 x축의 레이블을 x, y축의 레이블을 sin(x)라 설정해라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60clgNFPj_XN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XtjJ_JSBPAg8"
      },
      "source": [
        "* 사인그래프를 그리고 옵션을 dash green , 레이블을 'sin(x)'로 \n",
        "* 사인그래프를 그리고 옵션을 dott blue , 레이블을 'cos(x)'로\n",
        "* 두그래프의 x축의 범위를 동기화\n",
        "* 그래프의 제목을 'curves of sine and cosine'\n",
        "* x레이블을 'x'로\n",
        "* 범례표시해라"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJST_ZDnPAg_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}