import.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "import.ipynb",
      "provenance": [],
      "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/HPotty36/class2022Fall/blob/main/import.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXicrkYOzPRo"
      },
      "source": [
        "import numpy as np\n",
        "n = np.random.randn(100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKT4vn4MwyLA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "2b0aeb6e-b426-450b-964c-4374a09dfeb0"
      },
      "source": [
        "import matplotlib\n",
        "matplotlib.pyplot.hist(n)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 3.,  4.,  9., 11., 26., 19., 16., 10.,  1.,  1.]),\n",
              " array([-2.51307585, -1.99199295, -1.47091005, -0.94982715, -0.42874425,\n",
              "         0.09233865,  0.61342155,  1.13450445,  1.65558734,  2.17667024,\n",
              "         2.69775314]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "zQJ4LnbA-Hvz",
        "outputId": "bec7eeff-8cae-4c3d-f090-eaf78cdb5fa9"
      },
      "source": [
        "import matplotlib.pyplot\n",
        "matplotlib.pyplot.hist(n)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 4.,  4.,  4.,  8., 19., 21., 16., 13.,  6.,  5.]),\n",
              " array([-2.54804234, -2.0585933 , -1.56914426, -1.07969522, -0.59024618,\n",
              "        -0.10079714,  0.3886519 ,  0.87810094,  1.36754998,  1.85699902,\n",
              "         2.34644806]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWU6awi53c07",
        "outputId": "9b49fb47-6bac-4887-8815-e5ed130f6c56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.hist(n)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 4.,  4.,  4.,  8., 19., 21., 16., 13.,  6.,  5.]),\n",
              " array([-2.54804234, -2.0585933 , -1.56914426, -1.07969522, -0.59024618,\n",
              "        -0.10079714,  0.3886519 ,  0.87810094,  1.36754998,  1.85699902,\n",
              "         2.34644806]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## from import를 쓰면 import as처럼 축약해서 부를 수 있는 효과"
      ],
      "metadata": {
        "id": "v1zA2xRWzpdu"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "moIjn3Kv-Hqy",
        "outputId": "4b1e3880-1576-4b63-b4d0-192704221a8d"
      },
      "source": [
        "from matplotlib import pyplot\n",
        "pyplot.hist(n)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 3.,  4.,  9., 11., 26., 19., 16., 10.,  1.,  1.]),\n",
              " array([-2.51307585, -1.99199295, -1.47091005, -0.94982715, -0.42874425,\n",
              "         0.09233865,  0.61342155,  1.13450445,  1.65558734,  2.17667024,\n",
              "         2.69775314]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALCklEQVR4nO3dT4gehR3G8eepsZcqNJIlDTZ2i4gQShvLklosxVZboilVCy3NQVIqrAcFBaGkeqjHlFa9tFgiCeaQWgQVhdhqGoQgtNKNBE3cWkVWGonJigdTeijRp4dM6nbdzfvu+2/y2/1+YNn3nXfend+L5stkdmbiJAIA1POptgcAAPSGgANAUQQcAIoi4ABQFAEHgKJWjXJja9asyfj4+Cg3CQDlHTp06L0kY/OXjzTg4+PjmpqaGuUmAaA8228vtJxDKABQFAEHgKIIOAAURcABoCgCDgBFEXAAKIqAA0BRBBwAiiLgAFDUSK/EBDoZ376vle3O7NjSynaBfrAHDgBFEXAAKIqAA0BRBBwAiuoYcNvrbb9g+zXbR23f1Sy/3/Y7tg83XzcOf1wAwFndnIVyWtI9SV62fbGkQ7b3N689lOTXwxsPALCYjgFPclzS8ebxKdvTki4d9mAAgHNb0jFw2+OSrpL0UrPoTtuv2N5te/Ui75m0PWV7anZ2tq9hAQAf6zrgti+S9ISku5N8IOlhSZdL2qgze+gPLPS+JDuTTCSZGBv7xD/pBgDoUVcBt32hzsR7b5InJSnJiSQfJvlI0iOSNg1vTADAfN2chWJJuyRNJ3lwzvJ1c1a7RdKRwY8HAFhMN2ehXCPpVkmv2j7cLLtX0lbbGyVF0oyk24cyIQBgQd2chfKiJC/w0rODHwcA0C2uxASAogg4ABRFwAGgKAIOAEURcAAoioADQFEEHACKIuAAUBQBB4CiCDgAFEXAAaAoAg4ARRFwACiKgANAUQQcAIoi4ABQFAEHgKIIOAAURcABoCgCDgBFEXAAKIqAA0BRBBwAiiLgAFAUAQeAogg4ABRFwAGgKAIOAEURcAAoioADQFEdA257ve0XbL9m+6jtu5rll9jeb/uN5vvq4Y8LADirmz3w05LuSbJB0tWS7rC9QdJ2SQeSXCHpQPMcADAiHQOe5HiSl5vHpyRNS7pU0k2S9jSr7ZF087CGBAB80pKOgdsel3SVpJckrU1yvHnpXUlrBzoZAOCcug647YskPSHp7iQfzH0tSSRlkfdN2p6yPTU7O9vXsACAj3UVcNsX6ky89yZ5sll8wva65vV1kk4u9N4kO5NMJJkYGxsbxMwAAHV3Fool7ZI0neTBOS89I2lb83ibpKcHPx4AYDGruljnGkm3SnrV9uFm2b2Sdkh63PZtkt6W9KPhjAgAWEjHgCd5UZIXefm6wY4DAOgWV2ICQFEEHACK6uYYOLDsjW/f19q2Z3ZsaW3bqI09cAAoioADQFEEHACKIuAAUBQBB4CiCDgAFEXAAaAoAg4ARRFwACiKgANAUQQcAIoi4ABQFAEHgKIIOAAURcABoCgCDgBFEXAAKIqAA0BRBBwAiiLgAFAUAQeAogg4ABRFwAGgqFVtDwCsdOPb97Wy3ZkdW1rZLgaHPXAAKIqAA0BRBBwAiiLgAFBUx4Db3m37pO0jc5bdb/sd24ebrxuHOyYAYL5u9sAflbR5geUPJdnYfD072LEAAJ10DHiSg5LeH8EsAIAl6OcY+J22X2kOsaxebCXbk7anbE/Nzs72sTkAwFy9BvxhSZdL2ijpuKQHFlsxyc4kE0kmxsbGetwcAGC+ngKe5ESSD5N8JOkRSZsGOxYAoJOeAm573Zynt0g6sti6AIDh6HgvFNuPSbpW0hrbxyT9QtK1tjdKiqQZSbcPcUYAwAI6BjzJ1gUW7xrCLACAJeBKTAAoioADQFEEHACKIuAAUBQBB4CiCDgAFEXAAaAoAg4ARRFwACiKgANAUQQcAIoi4ABQFAEHgKIIOAAURcABoCgCDgBFEXAAKIqAA0BRBBwAiiLgAFAUAQeAogg4ABRFwAGgKAIOAEURcAAoioADQFGr2h4A55/x7fvaHgFAF9gDB4CiCDgAFEXAAaAoAg4ARXUMuO3dtk/aPjJn2SW299t+o/m+erhjAgDm62YP/FFJm+ct2y7pQJIrJB1ongMARqhjwJMclPT+vMU3SdrTPN4j6eYBzwUA6KDX88DXJjnePH5X0trFVrQ9KWlSki677LIeNwdg0No8339mx5bWtr2c9P1LzCSRlHO8vjPJRJKJsbGxfjcHAGj0GvATttdJUvP95OBGAgB0o9eAPyNpW/N4m6SnBzMOAKBb3ZxG+Jikv0i60vYx27dJ2iHpO7bfkHR98xwAMEIdf4mZZOsiL1034FkAAEvAlZgAUBQBB4CiuB/4eYz7cgM4F/bAAaAoAg4ARRFwACiKgANAUQQcAIoi4ABQFAEHgKIIOAAURcABoCgCDgBFEXAAKIqAA0BRBBwAiiLgAFAUAQeAogg4ABRFwAGgKAIOAEURcAAoioADQFEEHACKIuAAUBQBB4CiCDgAFEXAAaAoAg4ARRFwACiKgANAUav6ebPtGUmnJH0o6XSSiUEMBQDorK+AN76V5L0B/BwAwBJwCAUAiuo34JH0vO1DticXWsH2pO0p21Ozs7N9bg4AcFa/Af9Gkq9KukHSHba/OX+FJDuTTCSZGBsb63NzAICz+gp4knea7yclPSVp0yCGAgB01nPAbX/G9sVnH0v6rqQjgxoMAHBu/ZyFslbSU7bP/pzfJ/nTQKYCAHTUc8CTvCXpKwOcBQCwBJxGCABFEXAAKGoQV2Iue+Pb97U9AgB8AnvgAFAUAQeAogg4ABRFwAGgKAIOAEURcAAoioADQFFlzgPnXGwA+H/sgQNAUQQcAIoi4ABQFAEHgKIIOAAURcABoCgCDgBFEXAAKIqAA0BRBBwAiiLgAFAUAQeAogg4ABRFwAGgKAIOAEWVuR84gOVjJd7ff2bHloH/TPbAAaAoAg4ARRFwACiKgANAUX0F3PZm26/bftP29kENBQDorOeA275A0m8l3SBpg6SttjcMajAAwLn1swe+SdKbSd5K8h9Jf5B002DGAgB00s954JdK+uec58ckfW3+SrYnJU02T/9l+/U+ttmGNZLea3uIEVkpn5XPufyc95/Vv+zr7V9YaOHQL+RJslPSzmFvZ1hsTyWZaHuOUVgpn5XPufyspM86Vz+HUN6RtH7O8883ywAAI9BPwP8m6QrbX7T9aUk/lvTMYMYCAHTS8yGUJKdt3ynpOUkXSNqd5OjAJjt/lD3804OV8ln5nMvPSvqs/+Mkbc8AAOgBV2ICQFEEHACKIuBdsP0r23+3/Yrtp2x/tu2ZhsH2D20ftf2R7WV3StZKufWD7d22T9o+0vYsw2R7ve0XbL/W/H97V9szjRoB785+SV9K8mVJ/5D085bnGZYjkn4g6WDbgwzaCrv1w6OSNrc9xAiclnRPkg2SrpZ0xzL+b7ogAt6FJM8nOd08/avOnPO+7CSZTlLtStlurZhbPyQ5KOn9tucYtiTHk7zcPD4laVpnrhBfMQj40v1U0h/bHgJLttCtH1bUH/blzPa4pKskvdTuJKPFv4nZsP1nSZ9b4KX7kjzdrHOfzvy1be8oZxukbj4nUIntiyQ9IenuJB+0Pc8oEfBGkuvP9brtn0j6nqTrUvjk+U6fcxnj1g/LkO0LdSbee5M82fY8o8YhlC7Y3izpZ5K+n+Tfbc+DnnDrh2XGtiXtkjSd5MG252kDAe/ObyRdLGm/7cO2f9f2QMNg+xbbxyR9XdI+28+1PdOgNL+EPnvrh2lJjy/TWz/I9mOS/iLpStvHbN/W9kxDco2kWyV9u/lzedj2jW0PNUpcSg8ARbEHDgBFEXAAKIqAA0BRBBwAiiLgAFAUAQeAogg4ABT1XxqfU77eOAeTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "ORo259i7_Uqi",
        "outputId": "8faa9ebd-66ff-4f86-e588-f114023e649d"
      },
      "source": [
        "from matplotlib.pyplot import hist\n",
        "hist(n)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 3.,  4.,  9., 11., 26., 19., 16., 10.,  1.,  1.]),\n",
              " array([-2.51307585, -1.99199295, -1.47091005, -0.94982715, -0.42874425,\n",
              "         0.09233865,  0.61342155,  1.13450445,  1.65558734,  2.17667024,\n",
              "         2.69775314]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}