matplotlib2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "matplotlib.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/chihsuanbjjh/binder/blob/master/matplotlib2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzV7Aoueuo5H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fd1f4e24-7bd6-472c-e9ca-12a8cc631b2b"
      },
      "source": [
        "print('hello')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjO0cHbVvWiO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e39671ed-f22f-4855-9039-53cb1f229508"
      },
      "source": [
        "print('hello2')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sX_0YawRL2pT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "d4c053cf-cc1c-4c28-a084-a1142a129e6d"
      },
      "source": [
        "import keyword\n",
        "print(keyword.kwlist)\n",
        "\n",
        "# list all built-in keywords\n",
        "#print(vars(__builtin__).keys())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['False', 'None', 'True', 'and', 'as', 'assert', 'break', 'class', 'continue', 'def', 'del', 'elif', 'else', 'except', 'finally', 'for', 'from', 'global', 'if', 'import', 'in', 'is', 'lambda', 'nonlocal', 'not', 'or', 'pass', 'raise', 'return', 'try', 'while', 'with', 'yield']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9T8pzng_OFlA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.plot([1, 2, 3, 4])\n",
        "plt.axis([0, 6, 0, 20])\n",
        "plt.scatter('a', 'b', c='c', s='d', data=data)\n",
        "\n",
        "plt.title('Histogram of IQ')\n",
        "plt.xlabel('entry a')\n",
        "plt.ylabel('entry b')\n",
        "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
        "\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(9, 3))\n",
        "\n",
        "plt.grid(False)\n",
        "\n",
        "plt.subplot(131)\n",
        "plt.bar(names, values)\n",
        "plt.subplot(132)\n",
        "plt.scatter(names, values)\n",
        "plt.subplot(133)\n",
        "plt.plot(names, values)\n",
        "plt.suptitle('Categorical Plotting')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnXHbgP0QIhU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "outputId": "ea820ae8-0db7-415d-cb5c-2fc44acde5b7"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(1)                # the first figure\n",
        "plt.subplot(211)             # the first subplot in the first figure\n",
        "plt.plot([1, 2, 3])\n",
        "plt.subplot(212)             # the second subplot in the first figure\n",
        "plt.plot([4, 5, 6])\n",
        "\n",
        "\n",
        "plt.figure(2)                # a second figure\n",
        "plt.plot([4, 5, 6])          # creates a subplot(111) by default\n",
        "\n",
        "plt.figure(1)                # figure 1 current; subplot(212) still current\n",
        "plt.subplot(211)             # make subplot(211) in figure1 current\n",
        "plt.title('Easy as 1, 2, 3') # subplot 211 title"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Easy as 1, 2, 3')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xS0o4rLx1aYCW1k6a24K8c9ktBr6+YBW/eNcGAn6He6uLyUpPjoHAWLBEbYxJ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\nSXL/G1eu8LmItzSiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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93T3TzBoBs81ssbvPKTiTmfUnFPR9i7ps8AYwCULXjC3yKxEpR+ZmbGP0lFRWbcvmyl4t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\niXMKehGROPf/ARLHvUXpwNsqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFuWF6mKQ7oD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "668afed1-c877-4b9d-f9f7-a305afbfdca6"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "mu, sigma = 100, 15\n",
        "x = mu + sigma * np.random.randn(10000)\n",
        "\n",
        "# the histogram of the data\n",
        "n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)\n",
        "\n",
        "\n",
        "plt.xlabel('Smarts')\n",
        "plt.ylabel('Probability')\n",
        "plt.title('Histogram of IQ')\n",
        "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
        "plt.axis([40, 160, 0, 0.03])\n",
        "plt.grid(False)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C9ARM6yzZWjBpTOVM4E7gr8ANEbFI0iRJx6VsU4ABkpqAbwDNjzKfCQwFzpU0L30GpWNn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\nMGZmloUDjJmZZeEAY2ZmWfwPzUzysgT7ig0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QlDRZEGxQJDS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pW8IN72fSUdw",
        "colab_type": "text"
      },
      "source": [
        "用物件的方式控制"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1uhqeDKSYhO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(2, 1, 1) # two rows, one column, first plot\n",
        "fig2 = plt.figure()\n",
        "ax2 = fig2.add_axes([0.15, 0.1, 0.7, 0.3])\n",
        "\n",
        "xtext = ax.set_xlabel('my xdata') # returns a Text instance\n",
        "ytext = ax.set_ylabel('my ydata')\n",
        "\n",
        "\n",
        "plt.figure(1)                # the first figure\n",
        "plt.subplot(211) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yywR1cL89pUI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "10740c09-553a-404b-89bc-e454b3b49195"
      },
      "source": [
        "x = np.linspace(0, 2, 100)\n",
        "\n",
        "plt.plot(x, x, label='linear')\n",
        "plt.plot(x, x**2, label='quadratic')\n",
        "plt.plot(x, x**3, label='cubic')\n",
        "\n",
        "plt.xlabel('x label')\n",
        "plt.ylabel('y label')\n",
        "\n",
        "plt.title(\"Simple Plot\")\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9HVLj1NbAir/DvEehstgqwDFiGkT6Zy1Y2xK8iIRhFQkJdbfz4fGGe5RSzYMx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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BptqitJ9v4X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "outputId": "14602dab-5c28-4640-ddef-10d2c34bcf8b"
      },
      "source": [
        "# First create some toy data:\n",
        "x = np.linspace(0, 2*np.pi, 400)\n",
        "y = np.sin(x**2)\n",
        "y2 = x*x\n",
        "# Creates just a figure and only one subplot\n",
        "fig, ax = plt.subplots(figsize=(8,10),\n",
        "                       edgecolor = '#000000'\n",
        "                       \n",
        "                       ) #Returns:fig,axes\n",
        "\n",
        "ax.plot(x, y,\n",
        "        label = 'sin(x^2)',\n",
        "        color = 'red',\n",
        "        # marker = 'o',\n",
        "        linewidth = 1,\n",
        "        )\n",
        "ax.plot(x, y2,\n",
        "        label = 'x^2',\n",
        "        color = 'blue',\n",
        "        marker = '+',\n",
        "        )\n",
        "\n",
        "#開啟格線\n",
        "ax.grid(color='r',\n",
        "        linestyle='--',\n",
        "        linewidth = 1,\n",
        "        alpha = 0.3,\n",
        "        )\n",
        "\n",
        "#標題的設定\n",
        "ax.set_title('Simple plot')\n",
        "print(\"title is :\",ax.get_title())\n",
        "\n",
        "#x軸的設定\n",
        "ax.set_xlabel( 'x label = Time',\n",
        "              fontsize=18,\n",
        "              fontstyle = 'italic',\n",
        "            #   position=(0,0),\n",
        "              color = '#00ff00',\n",
        "            #   horizontalalignment='left', #label與fig的相位置\n",
        "              visible=True, # False 就隱藏\n",
        "              )\n",
        "print(\"x label is :\",ax.get_xlabel())\n",
        "\n",
        "#y軸的設定\n",
        "ax.set_ylabel( 'y label = Amplitude',\n",
        "              fontsize=18,\n",
        "              fontstyle = 'normal',\n",
        "            #   position=(0,0),\n",
        "              color = '#FFff00',\n",
        "            #   horizontalalignment='left', #label與fig的相位置\n",
        "              visible=True, # False 就隱藏\n",
        "              )\n",
        "print(\"y label is :\",ax.get_ylabel())\n",
        "\n",
        "#圖例的設定\n",
        "ax.set_label('Label via method')\n",
        "ax.legend()\n",
        "\n",
        "#x軸的座標\n",
        "ax.set_xticks(np.linspace(0, 6, 10),) #設定數值\n",
        "# ax.set_xticklabels('xtick') # 設定文字\n",
        "\n",
        "# 開啟/關閉外框\n",
        "ax.set_frame_on(1) #True/False\n",
        "\n",
        "#設定軸的數值範圍\n",
        "ax.set_ylim(-2,2)\n",
        "\n",
        "#存檔\n",
        "fig.savefig(time.strftime('%m_%d_%I_%M')+\".png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "title is : Simple plot\n",
            "x label is : x label = Time\n",
            "y label is : y label = Amplitude\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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phFiR43khhHhcCLFWCLFMCHGqaY0AgOpqK6cNBYFWkkRBoM33KxmslQbWSgNr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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XB0unTyCTOL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "354b9543-546e-4e57-c2f5-f3151fd3f5fa"
      },
      "source": [
        "import time\n",
        "time.strftime('%m_%d_%I_%M')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'02_13_06_42'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0bgsq4cBhCu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "bb1142a7-1c2c-4056-a762-5190d52dbc5d"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "print(fig)\n",
        "print(ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Figure(432x288)\n",
            "AxesSubplot(0.125,0.125;0.775x0.755)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8CWpCYMvSU0YfElq4n8BzPZcum6w2goAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i44mrcP-BLpi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# Creates two subplots and unpacks the output array immediately\n",
        "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
        "ax1.plot(x, y)\n",
        "ax1.set_title('Sharing Y axis')\n",
        "ax2.scatter(x, y)\n",
        "\n",
        "# Creates four polar axes, and accesses them through the returned array\n",
        "fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))\n",
        "axes[0, 0].plot(x, y)\n",
        "axes[1, 1].scatter(x, y)\n",
        "\n",
        "# Share a X axis with each column of subplots\n",
        "plt.subplots(2, 2, sharex='col')\n",
        "\n",
        "# Share a Y axis with each row of subplots\n",
        "plt.subplots(2, 2, sharey='row')\n",
        "\n",
        "# Share both X and Y axes with all subplots\n",
        "plt.subplots(2, 2, sharex='all', sharey='all')\n",
        "\n",
        "# Note that this is the same as\n",
        "plt.subplots(2, 2, sharex=True, sharey=True)\n",
        "\n",
        "# Creates figure number 10 with a single subplot\n",
        "# and clears it if it already exists.\n",
        "fig, ax=plt.subplots(num=10, clear=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "busFsclDBBFP",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "# Grouped bar chart with labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZmZ7bBK-gUu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "b1d6b26d-9e7d-48f7-c445-2655bad19e3e"
      },
      "source": [
        "#\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "labels = ['G1', 'G2', 'G3', 'G4', 'G5']\n",
        "men_means = [20, 34, 30, 35, 27]\n",
        "women_means = [25, 32, 34, 20, 25]\n",
        "\n",
        "x = np.arange(len(labels))  # the label locations\n",
        "width = 0.35  # the width of the bars\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "rects1 = ax.bar(x - width/2, men_means, width, label='Men')\n",
        "    # Rectangle(xy=(19.6, 0), width=0.8, height=0.35, angle=0)\n",
        "    # Rectangle(xy=(33.6, 0), width=0.8, height=0.35, angle=0)\n",
        "    # Rectangle(xy=(29.6, 0), width=0.8, height=0.35, angle=0)\n",
        "    # Rectangle(xy=(34.6, 0), width=0.8, height=0.35, angle=0)\n",
        "    # Rectangle(xy=(26.6, 0), width=0.8, height=0.35, angle=0)\n",
        "\n",
        "rects2 = ax.bar(x + width/2, women_means, width, label='Women')\n",
        "\n",
        "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_title('Scores by group and gender')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.legend()\n",
        "ax.set_ylim(0,40)\n",
        "\n",
        "def autolabel(rects):\n",
        "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
        "    for rect in rects:\n",
        "        height = rect.get_height()\n",
        "        ax.annotate('{}'.format(height),\n",
        "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
        "                    xytext=(0, 3),  # 3 points vertical offset\n",
        "                    textcoords=\"offset points\",\n",
        "                    ha='center', va='bottom')\n",
        "\n",
        "\n",
        "autolabel(rects1)\n",
        "autolabel(rects2)\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WSyojGaHCzMxsK40dSeJIkqGNrgN+DvxN0hENPEckt+VYFBE/Llh1LzA+nR4P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\n/w+7FV6Rg7CTzwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8eu_oRXDcVCm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "fa9a69c9-a77e-4e0b-bd14-476eb185fca3"
      },
      "source": [
        "print(*rects1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rectangle(xy=(19.6, 0), width=0.8, height=0.35, angle=0) Rectangle(xy=(33.6, 0), width=0.8, height=0.35, angle=0) Rectangle(xy=(29.6, 0), width=0.8, height=0.35, angle=0) Rectangle(xy=(34.6, 0), width=0.8, height=0.35, angle=0) Rectangle(xy=(26.6, 0), width=0.8, height=0.35, angle=0),"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEeJlaKFhvyI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "19bd10f6-9314-40da-f4b0-8436162bf16b"
      },
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "labels = ['G1', 'G2', 'G3', 'G4', 'G5']\n",
        "men_means = [20, 34, 30, 35, 27]\n",
        "women_means = [25, 32, 34, 20, 25]\n",
        "x = range(len(men_means))\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "men_means_fig = ax.bar(x=x,\n",
        "       height=men_means\n",
        ")\n",
        "\n",
        "women_means_fig = ax.bar(x=x,\n",
        "       height=women_means\n",
        ")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFXCXjTGzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6H_y9VQwUTYS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from math import *\n",
        "from PIL import Image\n",
        "im=Image.open('t1.jpg')\n",
        "m=im.size[0]\n",
        "n=im.size[0]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTvukUa7dIVn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f3a25645-d221-4047-896b-8035e7a59594"
      },
      "source": [
        "print(m)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5OncBpmXt0v4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a38e2474-fbf6-436f-d725-dd880f5fbe01"
      },
      "source": [
        "print(n)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCIes9LIt2EO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "6bf37780-696c-4193-b396-6def68479065"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "import mlxtend\n",
        "import tensorflow as tf\n",
        "\n",
        "print(np.__version__)\n",
        "print(pd.__version__)\n",
        "print(matplotlib.__version__)\n",
        "print(sns.__version__)\n",
        "print(sklearn.__version__)\n",
        "print(mlxtend.__version__)\n",
        "print(tf.__version__)\n",
        "import sys\n",
        "print(sys.version)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1.18.4\n",
            "1.0.3\n",
            "3.2.1\n",
            "0.10.1\n",
            "0.22.2.post1\n",
            "0.14.0\n",
            "2.2.0\n",
            "3.6.9 (default, Apr 18 2020, 01:56:04) \n",
            "[GCC 8.4.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ss6lTqaFmoVV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "e1633896-d314-4b7a-eb17-64fe2668cc67"
      },
      "source": [
        "# Titanic\n",
        "titanic_url = \"https://kaggle-getting-started.s3-ap-northeast-1.amazonaws.com/titanic/train.csv\"\n",
        "# Housing\n",
        "housing_url = \"https://kaggle-getting-started.s3-ap-northeast-1.amazonaws.com/house-prices/train.csv\"\n",
        "# MNIST\n",
        "mnist_url = \"https://kaggle-getting-started.s3-ap-northeast-1.amazonaws.com/mnist/train.csv\"\n",
        "import pandas as pd\n",
        "\n",
        "def get_y_x(data_url,target_label):\n",
        "    df = pd.read_csv(data_url)\n",
        "    y = df[target_label].values\n",
        "    x = df.drop(target_label,axis=1).values\n",
        "    return y,x\n",
        "\n",
        "titanic_y,titanic_x = get_y_x(titanic_url,'Survived')\n",
        "titanic_y[:5],titanic_x[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 1, 1, 0]),\n",
              " array([[1, 3, 'Braund, Mr. Owen Harris', 'male', 22.0, 1, 0, 'A/5 21171',\n",
              "         7.25, nan, 'S'],\n",
              "        [2, 1, 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',\n",
              "         'female', 38.0, 1, 0, 'PC 17599', 71.2833, 'C85', 'C'],\n",
              "        [3, 3, 'Heikkinen, Miss. Laina', 'female', 26.0, 0, 0,\n",
              "         'STON/O2. 3101282', 7.925, nan, 'S'],\n",
              "        [4, 1, 'Futrelle, Mrs. Jacques Heath (Lily May Peel)', 'female',\n",
              "         35.0, 1, 0, '113803', 53.1, 'C123', 'S'],\n",
              "        [5, 3, 'Allen, Mr. William Henry', 'male', 35.0, 0, 0, '373450',\n",
              "         8.05, nan, 'S']], dtype=object))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2i2rhfwmqpe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import glob\n",
        "import math\n",
        "import random\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.python import keras\n",
        "from tensorflow.python.keras import backend as K\n",
        "from tensorflow.python.keras.models import Model, Sequential\n",
        "from tensorflow.python.keras.layers import Conv2D, Dense, Input, MaxPooling2D, UpSampling2D, Lambda\n",
        "from tensorflow.python.keras.preprocessing.image import load_img, img_to_array, array_to_img, ImageDataGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuDW1qwLt56A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_path = 'img/colorize'\n",
        "data_lists = glob.glob(os.path.join(data_path, '*.jpg'))\n",
        "\n",
        "val_n_sample = math.floor(len(data_lists)*0.1)\n",
        "test_n_sample = math.floor(len(data_lists)*0.1)\n",
        "train_n_sample = len(data_lists) - val_n_sample - test_n_sample\n",
        "\n",
        "val_lists = data_lists[:val_n_sample]\n",
        "test_lists = data_lists[val_n_sample:val_n_sample + test_n_sample]\n",
        "train_lists = data_lists[val_n_sample + test_n_sample:train_n_sample + val_n_sample + test_n_sample]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqTUx9eIt6aT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "\n",
        "\n",
        "img_size = 224\n",
        "def rgb2lab(rgb):\n",
        "    assert rgb.dtype == 'uint8'\n",
        "    return cv2.cvtColor(rgb, cv2.COLOR_RGB2Lab)\n",
        "\n",
        "def lab2rgb(lab):\n",
        "    assert lab.dtype == 'uint8'\n",
        "    return cv2.cvtColor(lab, cv2.COLOR_Lab2RGB)\n",
        "\n",
        "def get_lab_from_data_list(data_list):\n",
        "    x_lab = []\n",
        "    for f in data_list:\n",
        "        rgb = img_to_array(\n",
        "            load_img(\n",
        "                f, \n",
        "                target_size=(img_size, img_size)\n",
        "            )\n",
        "        ).astype(np.uint8)\n",
        "        lab = rgb2lab(rgb)\n",
        "        x_lab.append(lab)\n",
        "    return np.stack(x_lab)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "coe3eOA3t8yr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "48310f27-a1d9-4e01-887f-54d030fca52b"
      },
      "source": [
        "from tensorflow.python.keras.layers import Conv2DTranspose\n",
        "\n",
        "\n",
        "autoencoder = Sequential()\n",
        "# Encoder\n",
        "autoencoder.add(\n",
        "    Conv2D(\n",
        "        32, \n",
        "        (3, 3), \n",
        "        (1, 1), \n",
        "        activation='relu', \n",
        "        padding='same', \n",
        "        input_shape=(224, 224, 1)\n",
        "    )\n",
        ")\n",
        "autoencoder.add(\n",
        "    Conv2D(\n",
        "        64,\n",
        "        (3, 3),\n",
        "        (2, 2),\n",
        "        activation='relu',\n",
        "        padding='same'\n",
        "    )\n",
        ")\n",
        "autoencoder.add(\n",
        "    Conv2D(\n",
        "        128,\n",
        "        (3, 3), \n",
        "        (2, 2), \n",
        "        activation='relu', \n",
        "        padding='same'\n",
        "    )\n",
        ")\n",
        "autoencoder.add(\n",
        "    Conv2D(\n",
        "        256, \n",
        "        (3, 3), \n",
        "        (2, 2), \n",
        "        activation='relu', \n",
        "        padding='same')\n",
        ")\n",
        "# Decoder\n",
        "autoencoder.add(\n",
        "    Conv2DTranspose(\n",
        "        128, \n",
        "        (3, 3), \n",
        "        (2, 2), \n",
        "        activation='relu', \n",
        "        padding='same'\n",
        "    )\n",
        ")\n",
        "autoencoder.add(\n",
        "    Conv2DTranspose(\n",
        "        64,\n",
        "        (3, 3), \n",
        "        (2, 2), \n",
        "        activation='relu',\n",
        "        padding='same'\n",
        "    )\n",
        ")\n",
        "autoencoder.add(\n",
        "    Conv2DTranspose(\n",
        "        32,\n",
        "        (3, 3), \n",
        "        (2, 2), \n",
        "        activation='relu', \n",
        "        padding='same'\n",
        "    )\n",
        ")\n",
        "autoencoder.add(\n",
        "    Conv2D(\n",
        "        2, \n",
        "        (1, 1), \n",
        "        (1, 1), \n",
        "        activation='relu', \n",
        "        padding='same'\n",
        "    )\n",
        ")\n",
        "autoencoder.compile(optimizer='adam', loss='mse')\n",
        "autoencoder.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 224, 224, 32)      320       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 112, 112, 64)      18496     \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 56, 56, 128)       73856     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 28, 28, 256)       295168    \n",
            "_________________________________________________________________\n",
            "conv2d_transpose (Conv2DTran (None, 56, 56, 128)       295040    \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTr (None, 112, 112, 64)      73792     \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTr (None, 224, 224, 32)      18464     \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 224, 224, 2)       66        \n",
            "=================================================================\n",
            "Total params: 775,202\n",
            "Trainable params: 775,202\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7SYY0Ujt_hO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generator_with_preprocessing(data_list, batch_size, shuffle=False):\n",
        "    while True:\n",
        "        if shuffle:\n",
        "            np.random.shuffle(data_list)\n",
        "        for i in range(0, len(data_list), batch_size):\n",
        "            batch_list = data_list[i:i + batch_size]\n",
        "            batch_lab = get_lab_from_data_list(batch_list)\n",
        "            batch_l = batch_lab[:, :, :, 0:1]\n",
        "            batch_ab = batch_lab[:, :, :, 1:]\n",
        "            yield (batch_l, batch_ab)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUChsFrouDda",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 30\n",
        "\n",
        "train_gen = generator_with_preprocessing(train_lists, batch_size, shuffle=True)\n",
        "val_gen = generator_with_preprocessing(val_lists, batch_size)\n",
        "test_gen = generator_with_preprocessing(test_lists, batch_size)\n",
        "\n",
        "train_steps = math.ceil(len(train_lists)/batch_size)\n",
        "val_steps = math.ceil(len(val_lists)/batch_size)\n",
        "test_steps = math.ceil(len(test_lists)/batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWw5bq-GuFAn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fc815373-e934-4574-ad50-61550c6ae955"
      },
      "source": [
        "from tensorflow.python.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "valid_ck = ModelCheckpoint('{epoch:02d}-{val_loss:.2f}.hdf5', monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
        "callbacks = [valid_ck]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qlvv_gW7uHJf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b47e1bbb-f0a6-43bb-dc04-77fa77fe0478"
      },
      "source": [
        "epochs= 150\n",
        "    \n",
        "autoencoder.fit_generator(\n",
        "    generator=train_gen,\n",
        "    steps_per_epoch=train_steps,\n",
        "    epochs=epochs,\n",
        "    validation_data=val_gen,\n",
        "    validation_steps=val_steps,\n",
        "    # callbacks=callbacks, # コールバックを使用する場合はコメントアウトを外す\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-8-d81ad99b7897>:8: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7D7gIaNKuPG_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "import matplotlib.animation as animation \n",
        "import sympy as sym\n",
        "from matplotlib import rc\n",
        "from IPython.display import HTML\n",
        "\n",
        "fig = plt.figure() \n",
        "plt.axis([0, 2, -2, 2])\n",
        "x = np.linspace(0, 2, 100)  \n",
        "y = np.sin(2 * np.pi * x )  \n",
        "xy = [[x,y],[0,0]]\n",
        " \n",
        "line, = plt.plot([], [], lw=8)\n",
        "def animate(i):\n",
        "#     y = np.sin(2 * np.pi * x )  \n",
        "    num = i%2\n",
        "    line.set_data(xy[num][0], xy[num][1])  \n",
        "\n",
        "interval = 1000/30\n",
        "anim1=animation.FuncAnimation(fig,\n",
        "                              animate,\n",
        "                              frames=1000,\n",
        "                              interval=interval,\n",
        "                              blit = False)  \n",
        "\n",
        "# Note: below is the part which makes it work on Colab\n",
        "rc('animation', html='jshtml')\n",
        "anim1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fjX2LdwsGSa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "ea43fa0a-0894-4a6c-c250-d49290cea465"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "#pd.date_range\n",
        "\n",
        "d = pd.date_range(start='2010-1-1',\n",
        "                  end='2020-1-1',\n",
        "                #   periods=10,\n",
        "                  freq='Y')\n",
        "d\n",
        "# m = pd.date_range('2020-01','2020-12')\n",
        "# y = pd.date_range('2010','2020')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2010-12-31', '2011-12-31', '2012-12-31', '2013-12-31',\n",
              "               '2014-12-31', '2015-12-31', '2016-12-31', '2017-12-31',\n",
              "               '2018-12-31', '2019-12-31'],\n",
              "              dtype='datetime64[ns]', freq='A-DEC')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAWaqaGwC3km",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "448d9ac5-1961-4189-afdf-9ed6a9d77b99"
      },
      "source": [
        "df = pd.read_csv('goo.csv',sep='\\t')\n",
        "df"
      ],
      "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>date</th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-12-19</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-12-20</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-12-21</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-12-22</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  open  close\n",
              "0  2016-12-19    12     13\n",
              "1  2016-12-20    13     14\n",
              "2  2016-12-21    14     15\n",
              "3  2016-12-22    15     16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMmWMFCCWldb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "57251f7f-5690-481c-887c-95eb1f4bdcf6"
      },
      "source": [
        "df = pd.read_csv('goo.csv',sep='\\t',index_col='date',parse_dates=['date'])\n",
        "df.index\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2016-12-19', '2016-12-20', '2016-12-21', '2016-12-22'], dtype='datetime64[ns]', name='date', freq=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHYQLVQKW6YP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "2a9b840b-2671-4742-bd46-bb383796f656"
      },
      "source": [
        "df.open.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f2bd1ac50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sXR0sYCpsE5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "c8a6711f-1438-44cc-f9d5-1bf993639ce8"
      },
      "source": [
        "df = pd.read_csv('goo.csv',sep='\\t')\n",
        "df.iloc[:3,0:3]"
      ],
      "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>date</th>\n",
              "      <th>open</th>\n",
              "      <th>close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-12-19</th>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-20</th>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-21</th>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            date  open  close\n",
              "2016-12-19    12    13     12\n",
              "2016-12-20    13    14     12\n",
              "2016-12-21    14    15     12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6bnzBgwuDdc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7ce60502-af95-4aca-ac26-46220d39ba99"
      },
      "source": [
        "x = 100\n",
        "eval(\"x==100\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JitcmpY9ukHm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e81a7124-d3c4-40ba-94f7-12a1e3b95d2b"
      },
      "source": [
        "exp = \"sum([1,2,3])\"\n",
        "print(exp,eval(exp))\n",
        "# eval(exp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sum([1,2,3]) 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzRFa1Vr4qF0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "40188877-c96d-428b-de15-1917f93c46e8"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "s = np.random.randint(100,size=50)\n",
        "bins = [0,60,80,90,99,100]\n",
        "grade_bin = list(\"EDCBA\")\n",
        "bins2 = np.arange(0,101,10)\n",
        "s"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([60, 49, 99, 89, 89, 65, 55, 94, 94, 84,  5, 79, 58, 96, 83, 74, 93,\n",
              "       86, 20, 25, 10, 69, 16,  2, 47,  6, 92,  0, 28, 54, 82, 95, 84, 38,\n",
              "       49, 91, 37, 40, 10, 37, 93, 90, 56, 51, 56, 48,  1, 32, 71, 38])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6ceAaKx5dVI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "34eee61b-2811-4629-ba92-52a983c5a929"
      },
      "source": [
        "grade_bin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['E', 'D', 'C', 'B', 'A']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFUaC8Cz55tX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5dfe1293-795e-4962-9c34-1fccb32f9775"
      },
      "source": [
        "score = pd.DataFrame({\"score\":s})\n",
        "score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
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              "      <th>1</th>\n",
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              "      <td>65</td>\n",
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              "      <td>55</td>\n",
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              "      <th>8</th>\n",
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              "      <th>9</th>\n",
              "      <td>84</td>\n",
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              "      <th>10</th>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>79</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>58</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>96</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>83</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>74</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>51</td>\n",
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              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>56</td>\n",
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              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>48</td>\n",
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              "      <td>1</td>\n",
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              "      <td>32</td>\n",
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              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>38</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    score\n",
              "0      60\n",
              "1      49\n",
              "2      99\n",
              "3      89\n",
              "4      89\n",
              "5      65\n",
              "6      55\n",
              "7      94\n",
              "8      94\n",
              "9      84\n",
              "10      5\n",
              "11     79\n",
              "12     58\n",
              "13     96\n",
              "14     83\n",
              "15     74\n",
              "16     93\n",
              "17     86\n",
              "18     20\n",
              "19     25\n",
              "20     10\n",
              "21     69\n",
              "22     16\n",
              "23      2\n",
              "24     47\n",
              "25      6\n",
              "26     92\n",
              "27      0\n",
              "28     28\n",
              "29     54\n",
              "30     82\n",
              "31     95\n",
              "32     84\n",
              "33     38\n",
              "34     49\n",
              "35     91\n",
              "36     37\n",
              "37     40\n",
              "38     10\n",
              "39     37\n",
              "40     93\n",
              "41     90\n",
              "42     56\n",
              "43     51\n",
              "44     56\n",
              "45     48\n",
              "46      1\n",
              "47     32\n",
              "48     71\n",
              "49     38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjvNpxjA6ODj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "afb2144c-dcdf-40ab-9291-0820df9ccdab"
      },
      "source": [
        "letter = pd.cut(score.score,bins=bins,labels=grade_bin)\n",
        "score[\"Letter\"] = letter\n",
        "score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <th>Letter</th>\n",
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              "      <td>E</td>\n",
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              "      <th>19</th>\n",
              "      <td>25</td>\n",
              "      <td>E</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>10</td>\n",
              "      <td>E</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>69</td>\n",
              "      <td>D</td>\n",
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              "      <th>22</th>\n",
              "      <td>16</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2</td>\n",
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              "      <th>24</th>\n",
              "      <td>47</td>\n",
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              "      <td>6</td>\n",
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              "      <th>28</th>\n",
              "      <td>28</td>\n",
              "      <td>E</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>54</td>\n",
              "      <td>E</td>\n",
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              "      <th>30</th>\n",
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              "      <td>C</td>\n",
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              "      <td>C</td>\n",
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              "      <th>38</th>\n",
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              "      <td>E</td>\n",
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              "      <th>39</th>\n",
              "      <td>37</td>\n",
              "      <td>E</td>\n",
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              "      <td>93</td>\n",
              "      <td>B</td>\n",
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              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>90</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>56</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>51</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>56</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>48</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>32</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>71</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>38</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    score Letter\n",
              "0      60      E\n",
              "1      49      E\n",
              "2      99      B\n",
              "3      89      C\n",
              "4      89      C\n",
              "5      65      D\n",
              "6      55      E\n",
              "7      94      B\n",
              "8      94      B\n",
              "9      84      C\n",
              "10      5      E\n",
              "11     79      D\n",
              "12     58      E\n",
              "13     96      B\n",
              "14     83      C\n",
              "15     74      D\n",
              "16     93      B\n",
              "17     86      C\n",
              "18     20      E\n",
              "19     25      E\n",
              "20     10      E\n",
              "21     69      D\n",
              "22     16      E\n",
              "23      2      E\n",
              "24     47      E\n",
              "25      6      E\n",
              "26     92      B\n",
              "27      0    NaN\n",
              "28     28      E\n",
              "29     54      E\n",
              "30     82      C\n",
              "31     95      B\n",
              "32     84      C\n",
              "33     38      E\n",
              "34     49      E\n",
              "35     91      B\n",
              "36     37      E\n",
              "37     40      E\n",
              "38     10      E\n",
              "39     37      E\n",
              "40     93      B\n",
              "41     90      C\n",
              "42     56      E\n",
              "43     51      E\n",
              "44     56      E\n",
              "45     48      E\n",
              "46      1      E\n",
              "47     32      E\n",
              "48     71      D\n",
              "49     38      E"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQnXgOHI6gzx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "70933f64-fbb0-4d64-8ce4-f2ac39d0e869"
      },
      "source": [
        "score.Letter.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "E    27\n",
              "B     9\n",
              "C     8\n",
              "D     5\n",
              "A     0\n",
              "Name: Letter, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qoj4DUet7G1c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "55461119-0178-4e7e-bfaf-51e809320186"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "s = np.random.randint(100,size=50)\n",
        "bins = [0,60,80,90,99,100]\n",
        "grade_bin = list(\"EDCBA\")\n",
        "bins2 = np.arange(0,101,10)\n",
        "\n",
        "df_score = pd.DataFrame({\"score\":s})\n",
        "df_score\n"
      ],
      "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>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    score\n",
              "0      14\n",
              "1      53\n",
              "2      47\n",
              "3      26\n",
              "4      75\n",
              "5      96\n",
              "6       6\n",
              "7      26\n",
              "8      98\n",
              "9      27\n",
              "10     11\n",
              "11     75\n",
              "12     85\n",
              "13     42\n",
              "14     48\n",
              "15      2\n",
              "16     77\n",
              "17     40\n",
              "18     32\n",
              "19     15\n",
              "20     66\n",
              "21     88\n",
              "22     80\n",
              "23     74\n",
              "24     31\n",
              "25      2\n",
              "26     44\n",
              "27     80\n",
              "28     20\n",
              "29     46\n",
              "30     16\n",
              "31     73\n",
              "32     76\n",
              "33     62\n",
              "34     24\n",
              "35     17\n",
              "36     11\n",
              "37     36\n",
              "38     20\n",
              "39     98\n",
              "40     11\n",
              "41     50\n",
              "42     50\n",
              "43     87\n",
              "44     14\n",
              "45     26\n",
              "46     93\n",
              "47     35\n",
              "48     51\n",
              "49     67"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnpqebMl7hyx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "fccaf2c6-ac52-4e86-fe3c-5d6a214f43ce"
      },
      "source": [
        "print(type(bins))\n",
        "type(list(bins2))\n",
        "\n",
        "print(bins2)\n",
        "grade_bin = [str(g)+\"+\" for g in range(0,91,10)]\n",
        "grade_bin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'list'>\n",
            "[  0  10  20  30  40  50  60  70  80  90 100]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['0+', '10+', '20+', '30+', '40+', '50+', '60+', '70+', '80+', '90+']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGNDf_kP7SbK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 932
        },
        "outputId": "1bf1351e-bbc2-46e1-b625-02bd39f50e5a"
      },
      "source": [
        "\n",
        "letter = pd.cut(score.score,bins=list(bins2),labels=grade_bin)\n",
        "letter"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     10+\n",
              "1     30+\n",
              "2     40+\n",
              "3     20+\n",
              "4     40+\n",
              "5     60+\n",
              "6     50+\n",
              "7     10+\n",
              "8     60+\n",
              "9     50+\n",
              "10    50+\n",
              "11    50+\n",
              "12    10+\n",
              "13    30+\n",
              "14    80+\n",
              "15    20+\n",
              "16    50+\n",
              "17    50+\n",
              "18    90+\n",
              "19    80+\n",
              "20    50+\n",
              "21    30+\n",
              "22    80+\n",
              "23    50+\n",
              "24    70+\n",
              "25    50+\n",
              "26    30+\n",
              "27    20+\n",
              "28    40+\n",
              "29     0+\n",
              "30    70+\n",
              "31    30+\n",
              "32    30+\n",
              "33    10+\n",
              "34    20+\n",
              "35    50+\n",
              "36    60+\n",
              "37    50+\n",
              "38    50+\n",
              "39    50+\n",
              "40    40+\n",
              "41    60+\n",
              "42    30+\n",
              "43    80+\n",
              "44    60+\n",
              "45    20+\n",
              "46    50+\n",
              "47    20+\n",
              "48    70+\n",
              "49    50+\n",
              "Name: score, dtype: category\n",
              "Categories (10, object): [0+ < 10+ < 20+ < 30+ ... 60+ < 70+ < 80+ < 90+]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8xsep_c8eah",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "073f35eb-1948-41a4-d1c1-b591a197f7f6"
      },
      "source": [
        "score[\"Level\"] = letter\n",
        "score.Level.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50+    15\n",
              "30+     7\n",
              "20+     6\n",
              "60+     5\n",
              "80+     4\n",
              "40+     4\n",
              "10+     4\n",
              "70+     3\n",
              "90+     1\n",
              "0+      1\n",
              "Name: Level, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTokr7mS-qr6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "1417c508-0c9f-49ed-fd9e-9e3568c659ef"
      },
      "source": [
        "score.nsmallest(3,[\"score\"])"
      ],
      "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>score</th>\n",
              "      <th>Letter</th>\n",
              "      <th>Level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>10</td>\n",
              "      <td>E</td>\n",
              "      <td>0+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14</td>\n",
              "      <td>E</td>\n",
              "      <td>10+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>15</td>\n",
              "      <td>E</td>\n",
              "      <td>10+</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    score Letter Level\n",
              "29     10      E    0+\n",
              "0      14      E   10+\n",
              "12     15      E   10+"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "462zzwpY-yJc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.html.widgets import interact\n",
        "from dautil import data\n",
        "from dautil import ts"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJt_a3wtSdrN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "df = data.Weather.load()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4pMV6s8rSwl4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_data(x='TEMP', y='RAIN', z='WIND_SPEED', f='A', size=10,cmap='Blues'):\n",
        "    dfx = df[x].resample(f)\n",
        "    dfy = df[y].resample(f).mean()\n",
        "    dfz = df[z].resample(f).mean()\n",
        "\n",
        "    bubbles = (dfz - dfz.min())/(dfz.max() - dfz.min())\n",
        "    years = dfz.index.year\n",
        "    sc = plt.scatter(dfx, dfy, s= size * bubbles + 9, c = years, \n",
        "                    cmap=cmap, label=data.Weather.get_header(z), \n",
        "                    alpha=0.5)\n",
        "    plt.colorbar(sc, label='Year')\n",
        "\n",
        "    freqs = {'A': 'Annual', 'M': 'Monthly', 'D': 'Daily'}\n",
        "    plt.title(freqs[f] + ' Averages')\n",
        "    plt.xlabel(data.Weather.get_header(x))\n",
        "    plt.ylabel(data.Weather.get_header(y))\n",
        "    plt.legend(loc='best')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smeWiE0LTULl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "4bbf6fb3-1ed3-433a-f035-ff07efaa25ac"
      },
      "source": [
        "vars = df.columns.tolist()\n",
        "freqs = ('A', 'M', 'D')\n",
        "cmaps = [cmap for cmap in plt.cm.datad if not cmap.endswith(\"_r\")]\n",
        "cmaps.sort()\n",
        "interact(plot_data, x=vars, y=vars, z=vars, f=freqs,size=(100,700), cmap=cmaps)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-a68f83ef3fd3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcmaps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcmap\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcmap\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatad\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"_r\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mcmaps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfreqs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m700\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmaps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, _InteractFactory__interact_f, **kwargs)\u001b[0m\n\u001b[1;32m    526\u001b[0m         \u001b[0;31m#    def f(*args, **kwargs):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;31m#        ...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m             \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mwidget\u001b[0;34m(self, f)\u001b[0m\n\u001b[1;32m    442\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0mto\u001b[0m \u001b[0mwhich\u001b[0m \u001b[0mthe\u001b[0m \u001b[0minteractive\u001b[0m \u001b[0mwidgets\u001b[0m \u001b[0mare\u001b[0m \u001b[0mtied\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m         \"\"\"\n\u001b[0;32m--> 444\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m__interact_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, _interactive__interact_f, _interactive__options, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m             \u001b[0mgetcallargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnew_kwargs\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0;31m# Now build the widgets from the abbreviations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs_widgets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidgets_from_abbreviations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0;31m# This has to be done as an assignment, not using self.children.append,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mwidgets_from_abbreviations\u001b[0;34m(self, seq)\u001b[0m\n\u001b[1;32m    298\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueWidget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfixed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mwidget\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{!r} cannot be transformed to a widget\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabbrev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    301\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{!r} is not a ValueWidget\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: ('A', 'M', 'D') cannot be transformed to a widget"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfiJUVqDv_cX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "f7b7d376-3c26-4998-c0b0-692b2e327946"
      },
      "source": [
        "# IntSlider\n",
        "wmm_count = 2 #@param {type:'slider', min:0, max:6, step:1}\n",
        "wmm_msg = \"I've conquered {} World Marathon Major(s).\".format(wmm_count)\n",
        "print(wmm_msg)\n",
        "\n",
        "# FloatSlider\n",
        "my_height = 174 #@param {type:'slider', min:170, max:180, step:0.1}\n",
        "my_weight = 63 #@param {type: 'slider', min:60, max:70, step:0.1}\n",
        "bmi = my_weight / (my_height*0.01)**2\n",
        "bmi_msg = \"I am {:.1f} cm tall and weigh {:.1f} kg with a BMI of {:.2f}.\".format(my_height, my_weight, bmi)\n",
        "print(bmi_msg)\n",
        "\n",
        "# IntText\n",
        "wmm_count = 1 #@param {type:'integer', min:0, max:6, step:1}\n",
        "wmm_msg = \"I've conquered {} World Marathon Major(s).\".format(wmm_count)\n",
        "print(wmm_msg)\n",
        "\n",
        "# FloatText\n",
        "my_height = 172 #@param {type:'number'}\n",
        "my_weight = 63 #@param {type: 'number'}\n",
        "bmi = my_weight / (my_height*0.01)**2\n",
        "bmi_msg = \"I am {:.1f} cm tall and weigh {:.1f} kg with a BMI of {:.2f}.\".format(my_height, my_weight, bmi)\n",
        "print(bmi_msg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I've conquered 1 World Marathon Major(s).\n",
            "I am 172.0 cm tall and weigh 63.0 kg with a BMI of 21.30.\n",
            "I've conquered 1 World Marathon Major(s).\n",
            "I am 172.0 cm tall and weigh 63.0 kg with a BMI of 21.30.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "REa3Du7DwAFG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6cdcebdd-32df-4166-9b04-53c9d3dc8a7d"
      },
      "source": [
        "# Dropdown\n",
        "next_wmm = 'New York Marathon' #@param ['Tokyo Marathon', 'Boston Marathon', 'New York Marathon', 'Chicago Marathon', 'London Marathon', 'Berlin Marathon']\n",
        "wmm_msg = \"{} will be my next World Marathon Major!\".format(next_wmm)\n",
        "print(wmm_msg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "New York Marathon will be my next World Marathon Major!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJjlA557wask",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "30d9608b-b779-4c51-83b9-3bae7cfbebb1"
      },
      "source": [
        "# DatePicker\n",
        "next_wmm_date = '2020-03-01' #@param {type:'date'}\n",
        "wmm_msg = \"2020 Tokyo Marathon will be hosted on {}.\".format(next_wmm_date)\n",
        "print(wmm_msg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020 Tokyo Marathon will be hosted on 2020-03-01.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Js82HBY5wlwu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Checkbox\n",
        "sub_245 = False #@param {type: 'boolean'}\n",
        "if sub_245:\n",
        "    print(\"I am eligible to register 2020 Tokyo Marathon as a semi-elite.\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a860S78wwscz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xa1IVpJumJs6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CaErZYtumsFb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "438fb058-4cb4-40bd-d27f-4d4348615f37"
      },
      "source": [
        "arr = np.zeros(10000)\n",
        "df = pd.DataFrame({'test':arr})\n",
        "df"
      ],
      "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>test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      test\n",
              "0      0.0\n",
              "1      0.0\n",
              "2      0.0\n",
              "3      0.0\n",
              "4      0.0\n",
              "...    ...\n",
              "9995   0.0\n",
              "9996   0.0\n",
              "9997   0.0\n",
              "9998   0.0\n",
              "9999   0.0\n",
              "\n",
              "[10000 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDXJ1QySmxct",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "df1 = df\n",
        "for i in range(len(df)):\n",
        "    if df.iloc[i]['test'] != 1:\n",
        "        df1.iloc[i]['test'] = 1\n",
        "#3.098s"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9vNAUb1MoojE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "318083cd-9e79-42e0-caa9-43f86479b07f"
      },
      "source": [
        "df1.head()"
      ],
      "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>test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   test\n",
              "0   1.0\n",
              "1   1.0\n",
              "2   1.0\n",
              "3   1.0\n",
              "4   1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMEmG6fwowLR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i = 0\n",
        "for ind, row in df.iterrows():\n",
        "    if row['test'] != 1:\n",
        "        df1.iloc[i]['test'] = 1\n",
        "    i += 1\n",
        "# 1.818s"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsNF4KeUo2tb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1['test'] = df['test'].apply(lambda x: x if x == 1 else 0)\n",
        "# 1.198s"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2BZNLL2pP-q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "519d755a-5a0a-4a8c-862e-3d1f6a376ab9"
      },
      "source": [
        "for i ,r in df.iterrows():\n",
        "    print(i,r)\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 test    1.0\n",
            "Name: 0, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPBcJCMEpmW2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "1abe2b36-cc15-483a-c97e-a469796fede2"
      },
      "source": [
        "N = 10\n",
        "A_list = np.random.randint(1, 100, N)\n",
        "B_list = np.random.randint(1, 100, N)\n",
        "df = pd.DataFrame({'A': A_list, 'B': B_list})\n",
        "df.head()"
      ],
      "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>A</th>\n",
              "      <th>B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>42</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>93</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    A   B\n",
              "0  35  93\n",
              "1  33  89\n",
              "2  35  63\n",
              "3  42  68\n",
              "4  93  67"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RczLejn7rEwT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9f3223fb-d080-4b24-d3b3-18c37877d821"
      },
      "source": [
        "def xp2(x):\n",
        "    return x**2\n",
        "\n",
        "xp2(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWLQiGucrdcN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "out = lambda x:x**2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-L0rzHErmbr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bb44f09a-394e-492f-a871-a657ac5c6084"
      },
      "source": [
        "out(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dwsupx3BsO55",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def divide(a, b):\n",
        "    if b == 0:\n",
        "        return 0.0\n",
        "    return float(a)/b"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hFdRdF-rnir",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "80d738b6-14fc-4e46-fa06-55c17320b906"
      },
      "source": [
        "df['result_lambda'] = df.apply(lambda df:divide(df['A'],df['B']),axis = 1)\n",
        "df\n",
        "#2.21s"
      ],
      "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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>result_lambda</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35</td>\n",
              "      <td>93</td>\n",
              "      <td>0.376344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33</td>\n",
              "      <td>89</td>\n",
              "      <td>0.370787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35</td>\n",
              "      <td>63</td>\n",
              "      <td>0.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>42</td>\n",
              "      <td>68</td>\n",
              "      <td>0.617647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>93</td>\n",
              "      <td>67</td>\n",
              "      <td>1.388060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>61</td>\n",
              "      <td>54</td>\n",
              "      <td>1.129630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>81</td>\n",
              "      <td>92</td>\n",
              "      <td>0.880435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4</td>\n",
              "      <td>57</td>\n",
              "      <td>0.070175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>37</td>\n",
              "      <td>34</td>\n",
              "      <td>1.088235</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    A   B  result_lambda\n",
              "0  35  93       0.376344\n",
              "1  33  89       0.370787\n",
              "2  35  63       0.555556\n",
              "3  42  68       0.617647\n",
              "4  93  67       1.388060\n",
              "5  61  54       1.129630\n",
              "6  12   1      12.000000\n",
              "7  81  92       0.880435\n",
              "8   4  57       0.070175\n",
              "9  37  34       1.088235"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpyFQ1ACsmPe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np_divide = np.vectorize(divide)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YD3bnMesbLF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "185df772-870d-460b-f584-f3822c009f89"
      },
      "source": [
        "df['result2'] = np_divide(df['A'], df['B'])\n",
        "df\n",
        "#0.974"
      ],
      "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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>result_lambda</th>\n",
              "      <th>result2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
              "      <td>96</td>\n",
              "      <td>0.114583</td>\n",
              "      <td>0.114583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67</td>\n",
              "      <td>10</td>\n",
              "      <td>6.700000</td>\n",
              "      <td>6.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36</td>\n",
              "      <td>53</td>\n",
              "      <td>0.679245</td>\n",
              "      <td>0.679245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35</td>\n",
              "      <td>94</td>\n",
              "      <td>0.372340</td>\n",
              "      <td>0.372340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>76</td>\n",
              "      <td>36</td>\n",
              "      <td>2.111111</td>\n",
              "      <td>2.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>19</td>\n",
              "      <td>18</td>\n",
              "      <td>1.055556</td>\n",
              "      <td>1.055556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>28</td>\n",
              "      <td>44</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>0.636364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>66</td>\n",
              "      <td>58</td>\n",
              "      <td>1.137931</td>\n",
              "      <td>1.137931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>58</td>\n",
              "      <td>99</td>\n",
              "      <td>0.585859</td>\n",
              "      <td>0.585859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>49</td>\n",
              "      <td>35</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>1.400000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    A   B  result_lambda   result2\n",
              "0  11  96       0.114583  0.114583\n",
              "1  67  10       6.700000  6.700000\n",
              "2  36  53       0.679245  0.679245\n",
              "3  35  94       0.372340  0.372340\n",
              "4  76  36       2.111111  2.111111\n",
              "5  19  18       1.055556  1.055556\n",
              "6  28  44       0.636364  0.636364\n",
              "7  66  58       1.137931  1.137931\n",
              "8  58  99       0.585859  0.585859\n",
              "9  49  35       1.400000  1.400000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I40IxBTdtivE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "a64561a6-08ab-4b40-dcce-b9ede500e6dd"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import time\n",
        "\n",
        "def divide(a, b):\n",
        "    if b == 0:\n",
        "        return 0.0\n",
        "    return float(a)/b\n",
        "\n",
        "for N in [1000, 10000, 100000, 1000000, 5000000]:    \n",
        "\n",
        "    print ('')\n",
        "    A_list = np.random.randint(1, 100, N)\n",
        "    B_list = np.random.randint(1, 100, N)\n",
        "    df = pd.DataFrame({'A': A_list, 'B': B_list})\n",
        "\n",
        "    start_epoch_sec = int(time.time())\n",
        "    df['result'] = df.apply(lambda row: divide(row['A'], row['B']), axis=1)\n",
        "    end_epoch_sec = int(time.time())\n",
        "    result_apply = end_epoch_sec - start_epoch_sec\n",
        "\n",
        "    start_epoch_sec = int(time.time())\n",
        "    df['result2'] = np.vectorize(divide)(df['A'], df['B'])\n",
        "    end_epoch_sec = int(time.time())\n",
        "    result_vectorize = end_epoch_sec - start_epoch_sec\n",
        "\n",
        "\n",
        "    print ('N=%d, df.apply: %d sec, np.vectorize: %d sec' % (N, result_apply, result_vectorize))\n",
        "\n",
        "    # Make sure results from df.apply and np.vectorize match.\n",
        "    assert(df['result'].equals(df['result2']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "N=1000, df.apply: 0 sec, np.vectorize: 0 sec\n",
            "\n",
            "N=10000, df.apply: 0 sec, np.vectorize: 0 sec\n",
            "\n",
            "N=100000, df.apply: 3 sec, np.vectorize: 0 sec\n",
            "\n",
            "N=1000000, df.apply: 27 sec, np.vectorize: 1 sec\n",
            "\n",
            "N=5000000, df.apply: 137 sec, np.vectorize: 1 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sAbrMCytlTX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "06492025-4662-4744-85eb-b85a5f741417"
      },
      "source": [
        "df['result_map'] = list(map(divide, df['A'], df['B']))\n",
        "df"
      ],
      "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>A</th>\n",
              "      <th>B</th>\n",
              "      <th>result_lambda</th>\n",
              "      <th>result_map</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35</td>\n",
              "      <td>93</td>\n",
              "      <td>0.376344</td>\n",
              "      <td>0.376344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33</td>\n",
              "      <td>89</td>\n",
              "      <td>0.370787</td>\n",
              "      <td>0.370787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35</td>\n",
              "      <td>63</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>42</td>\n",
              "      <td>68</td>\n",
              "      <td>0.617647</td>\n",
              "      <td>0.617647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>93</td>\n",
              "      <td>67</td>\n",
              "      <td>1.388060</td>\n",
              "      <td>1.388060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>61</td>\n",
              "      <td>54</td>\n",
              "      <td>1.129630</td>\n",
              "      <td>1.129630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>81</td>\n",
              "      <td>92</td>\n",
              "      <td>0.880435</td>\n",
              "      <td>0.880435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4</td>\n",
              "      <td>57</td>\n",
              "      <td>0.070175</td>\n",
              "      <td>0.070175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>37</td>\n",
              "      <td>34</td>\n",
              "      <td>1.088235</td>\n",
              "      <td>1.088235</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    A   B  result_lambda  result_map\n",
              "0  35  93       0.376344    0.376344\n",
              "1  33  89       0.370787    0.370787\n",
              "2  35  63       0.555556    0.555556\n",
              "3  42  68       0.617647    0.617647\n",
              "4  93  67       1.388060    1.388060\n",
              "5  61  54       1.129630    1.129630\n",
              "6  12   1      12.000000   12.000000\n",
              "7  81  92       0.880435    0.880435\n",
              "8   4  57       0.070175    0.070175\n",
              "9  37  34       1.088235    1.088235"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUOjcL8Hvpiw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "a806c951-9fe3-4319-95ec-3d306d8ea3f2"
      },
      "source": [
        "df.info"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of     A   B  result_lambda  result_map\n",
              "0  35  93       0.376344    0.376344\n",
              "1  33  89       0.370787    0.370787\n",
              "2  35  63       0.555556    0.555556\n",
              "3  42  68       0.617647    0.617647\n",
              "4  93  67       1.388060    1.388060\n",
              "5  61  54       1.129630    1.129630\n",
              "6  12   1      12.000000   12.000000\n",
              "7  81  92       0.880435    0.880435\n",
              "8   4  57       0.070175    0.070175\n",
              "9  37  34       1.088235    1.088235>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMMv5BuFv5jn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vj3hdXk37GbG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({'num1':np.random.randint(1,10,1000),\n",
        "                   \"num2\":np.random.randint(300,500,1000),\n",
        "                   \"num3\":np.random.randint(50,100,1000)})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uC6Luu698Jj6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "4b825ca0-64c7-4a82-de9b-6b2dd62a7ded"
      },
      "source": [
        "df"
      ],
      "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",
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              "    }\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>num1</th>\n",
              "      <th>num2</th>\n",
              "      <th>num3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>364</td>\n",
              "      <td>73</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>405</td>\n",
              "      <td>96</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>398</td>\n",
              "      <td>71</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>390</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9</td>\n",
              "      <td>373</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>6</td>\n",
              "      <td>347</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>9</td>\n",
              "      <td>481</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>4</td>\n",
              "      <td>378</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>5</td>\n",
              "      <td>392</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>9</td>\n",
              "      <td>401</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     num1  num2  num3\n",
              "0       9   364    73\n",
              "1       8   405    96\n",
              "2       9   398    71\n",
              "3       4   390    74\n",
              "4       9   373    72\n",
              "..    ...   ...   ...\n",
              "995     6   347    62\n",
              "996     9   481    61\n",
              "997     4   378    92\n",
              "998     5   392    74\n",
              "999     9   401    69\n",
              "\n",
              "[1000 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXZrZ2yh9b3N",
        "colab_type": "code",
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          "height": 411
        },
        "outputId": "564aad75-8823-41c5-c20c-6bc5a06ee60f"
      },
      "source": [
        "df[df.num1 ==1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>num1</th>\n",
              "      <th>num2</th>\n",
              "      <th>num3</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>359</td>\n",
              "      <td>53</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>481</td>\n",
              "      <td>71</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>488</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>412</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>1</td>\n",
              "      <td>309</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>982</th>\n",
              "      <td>1</td>\n",
              "      <td>479</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>983</th>\n",
              "      <td>1</td>\n",
              "      <td>407</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>989</th>\n",
              "      <td>1</td>\n",
              "      <td>335</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>993</th>\n",
              "      <td>1</td>\n",
              "      <td>404</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994</th>\n",
              "      <td>1</td>\n",
              "      <td>368</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     num1  num2  num3\n",
              "6       1   359    53\n",
              "7       1   481    71\n",
              "15      1   488    76\n",
              "22      1   412    70\n",
              "41      1   309    68\n",
              "..    ...   ...   ...\n",
              "982     1   479    71\n",
              "983     1   407    93\n",
              "989     1   335    60\n",
              "993     1   404    80\n",
              "994     1   368    72\n",
              "\n",
              "[119 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVda63veCT6U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "e713809a-fb25-400f-b308-dd8508a9a197"
      },
      "source": [
        "df.groupby(['num1']).size()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num1\n",
              "1    109\n",
              "2    110\n",
              "3    121\n",
              "4    101\n",
              "5     98\n",
              "6    111\n",
              "7    116\n",
              "8     98\n",
              "9    136\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2VL0wXa8y6I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "2354afd7-2875-439d-b50f-00f405521770"
      },
      "source": [
        "df.groupby(['num1','num2']).size()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "num1  num2\n",
              "1     301     2\n",
              "      302     1\n",
              "      304     1\n",
              "      305     1\n",
              "      307     1\n",
              "             ..\n",
              "9     490     1\n",
              "      491     1\n",
              "      497     1\n",
              "      498     1\n",
              "      499     1\n",
              "Length: 777, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Py3fVl7CC_BW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8eee6133-87b9-4a67-bb4e-36076187f14f"
      },
      "source": [
        "df.groupby(['num1','num2'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f8ecb583c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRe4Fpk7D2tX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "9f3cf832-1a67-488f-bacd-f752cd0ab259"
      },
      "source": [
        "df_sum = df.groupby(['num1','num2']).sum()\n",
        "df_sum"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
              "      <th>300</th>\n",
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              "      <td>148</td>\n",
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              "      <td>71</td>\n",
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              "      <th>306</th>\n",
              "      <td>96</td>\n",
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              "      <th>310</th>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
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              "      <th rowspan=\"5\" valign=\"top\">9</th>\n",
              "      <th>490</th>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>54</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>743 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           num3\n",
              "num1 num2      \n",
              "1    300     94\n",
              "     301    148\n",
              "     304     71\n",
              "     306     96\n",
              "     310     51\n",
              "...         ...\n",
              "9    490     92\n",
              "     494     79\n",
              "     495    183\n",
              "     496    174\n",
              "     499     54\n",
              "\n",
              "[743 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGMNMIgjEBh0",
        "colab_type": "code",
        "colab": {
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          "height": 441
        },
        "outputId": "7f9d9e4a-8a7c-40e6-c6aa-c4922fa515f1"
      },
      "source": [
        "df_sum[df_sum.num3>100]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>num3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num1</th>\n",
              "      <th>num2</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
              "      <th>301</th>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>334</th>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>346</th>\n",
              "      <td>195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">9</th>\n",
              "      <th>480</th>\n",
              "      <td>243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>481</th>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>488</th>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>211 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           num3\n",
              "num1 num2      \n",
              "1    301    148\n",
              "     334    356\n",
              "     339    192\n",
              "     346    195\n",
              "     352    312\n",
              "...         ...\n",
              "9    480    243\n",
              "     481    128\n",
              "     488    117\n",
              "     495    183\n",
              "     496    174\n",
              "\n",
              "[211 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHnI3ykuEXFD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "fc30dbf1-9060-4a78-879c-eaabcc0b4eb9"
      },
      "source": [
        "df.num1 > 5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       True\n",
              "1       True\n",
              "2       True\n",
              "3      False\n",
              "4       True\n",
              "       ...  \n",
              "995     True\n",
              "996     True\n",
              "997    False\n",
              "998    False\n",
              "999     True\n",
              "Name: num1, Length: 1000, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdtv7lKJEyWG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "e4b5ae6c-3780-4362-dc0a-67ba2b42404e"
      },
      "source": [
        "df.loc[lambda df:df.num1 >5]"
      ],
      "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",
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              "\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>num1</th>\n",
              "      <th>num2</th>\n",
              "      <th>num3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>364</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>405</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>398</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9</td>\n",
              "      <td>373</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>9</td>\n",
              "      <td>480</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>993</th>\n",
              "      <td>6</td>\n",
              "      <td>420</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994</th>\n",
              "      <td>6</td>\n",
              "      <td>489</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>6</td>\n",
              "      <td>347</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>9</td>\n",
              "      <td>481</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>9</td>\n",
              "      <td>401</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>461 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     num1  num2  num3\n",
              "0       9   364    73\n",
              "1       8   405    96\n",
              "2       9   398    71\n",
              "4       9   373    72\n",
              "5       9   480    71\n",
              "..    ...   ...   ...\n",
              "993     6   420    67\n",
              "994     6   489    99\n",
              "995     6   347    62\n",
              "996     9   481    61\n",
              "999     9   401    69\n",
              "\n",
              "[461 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtGrGUkI9LBC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "682ec759-de93-4440-ca94-13b625e9a0f1"
      },
      "source": [
        "df.groupby(['num1','num2']).sum().loc[lambda df: df['num3'] > 300]"
      ],
      "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></th>\n",
              "      <th>num3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num1</th>\n",
              "      <th>num2</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>334</th>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <th>438</th>\n",
              "      <td>370</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           num3\n",
              "num1 num2      \n",
              "1    334    356\n",
              "     352    312\n",
              "9    438    370"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWjLEmdy8Q3e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "1d28db44-cee1-4ce5-eac4-e86f620b58c6"
      },
      "source": [
        "bins = [0, 6,7,8,9,10]\n",
        "pd.cut(df.num1,bins=bins,labels=list('abcde'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      d\n",
              "1      c\n",
              "2      d\n",
              "3      a\n",
              "4      d\n",
              "      ..\n",
              "995    a\n",
              "996    d\n",
              "997    a\n",
              "998    a\n",
              "999    d\n",
              "Name: num1, Length: 1000, dtype: category\n",
              "Categories (5, object): [a < b < c < d < e]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlC560oiJcyI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.insert(1, 'Rank', pd.cut(df.num1,bins=bins,labels=list('abcde')))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Doq0DHqhIoKS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "bd58f5e0-00e1-4f82-8812-4c76b0df8b8f"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num1</th>\n",
              "      <th>Rank</th>\n",
              "      <th>num2</th>\n",
              "      <th>num3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>d</td>\n",
              "      <td>364</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>c</td>\n",
              "      <td>405</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>d</td>\n",
              "      <td>398</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>a</td>\n",
              "      <td>390</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9</td>\n",
              "      <td>d</td>\n",
              "      <td>373</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>6</td>\n",
              "      <td>a</td>\n",
              "      <td>347</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>9</td>\n",
              "      <td>d</td>\n",
              "      <td>481</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>4</td>\n",
              "      <td>a</td>\n",
              "      <td>378</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>5</td>\n",
              "      <td>a</td>\n",
              "      <td>392</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>9</td>\n",
              "      <td>d</td>\n",
              "      <td>401</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     num1 Rank  num2  num3\n",
              "0       9    d   364    73\n",
              "1       8    c   405    96\n",
              "2       9    d   398    71\n",
              "3       4    a   390    74\n",
              "4       9    d   373    72\n",
              "..    ...  ...   ...   ...\n",
              "995     6    a   347    62\n",
              "996     9    d   481    61\n",
              "997     4    a   378    92\n",
              "998     5    a   392    74\n",
              "999     9    d   401    69\n",
              "\n",
              "[1000 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qfLOS1PbJiUT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "3ca65d6e-1e02-40ce-90a9-68078842dfd0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "# from dautil import plotting\n",
        "\n",
        "fig, axes = plt.subplots(4, 4)\n",
        "cmaps = ['autumn', 'spring', 'summer', 'winter',\n",
        "         'Reds', 'Blues', 'Greens', 'Purples',\n",
        "         'Oranges', 'pink', 'Greys', 'gray',\n",
        "         'binary', 'bone', 'hot', 'cool']\n",
        "\n",
        "for ax, cm in zip(axes.ravel(), cmaps):\n",
        "    cmap = plt.cm.get_cmap(cm)\n",
        "    cb = mpl.colorbar.ColorbarBase(ax, cmap=cmap,\n",
        "                                    orientation='horizontal')\n",
        "    cb.set_label(cm)\n",
        "    ax.xaxis.set_ticklabels([])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZvgCfuUw1hEU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import jieba\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "giyPN9V71iO8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from bs4 import BeautifulSoup  \n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "import math\n",
        "import requests  \n",
        "import lxml\n",
        "import re\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQO9s4CHoWxV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "area_dic = {'西湖区':'xihu',\n",
        "            '钱塘新区':'qiantangxinqu',\n",
        "            '下城区':'xiacheng',\n",
        "            '江干区':'jianggan',\n",
        "            '拱墅区':'gongshu',\n",
        "            '上城区':'shangcheng',\n",
        "            '滨江区':'binjiang',\n",
        "            '余杭区':'yuhang',\n",
        "           '萧山区':'xiaoshan',\n",
        "           '淳安区':'chunan1',\n",
        "           '富阳区':'fuyang',\n",
        "           '临安区':'linan'}\n",
        "\n",
        "\n",
        "# 加个header以示尊敬\n",
        "headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.146 Safari/537.36',\n",
        "           'Referer': 'https://hz.lianjia.com/ershoufang/'}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PguWrsn9og15",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 新建一个会话\n",
        "sess = requests.session()\n",
        "sess.get('https://hz.lianjia.com/ershoufang/', headers=headers)\n",
        "\n",
        "# url示例：https://sz.lianjia.com/ershoufang/xihu/pg2/\n",
        "url = 'https://hz.lianjia.com/ershoufang/{}/pg{}/'\n",
        "\n",
        "# 当正则表达式匹配失败时，返回默认值（errif）\n",
        "def re_match(re_pattern, string, errif=None):\n",
        "    try:\n",
        "        return re.findall(re_pattern, string)[0].strip()\n",
        "    except IndexError:\n",
        "        return errif\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beITvsSaorXj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "outputId": "ddceec09-fe8b-4841-c924-428dc269f0c3"
      },
      "source": [
        "# 新建一个DataFrame存储信息\n",
        "data = pd.DataFrame()\n",
        "\n",
        "for key_, value_ in area_dic.items():\n",
        "    # 获取该行政区下房源记录数\n",
        "    start_url = 'https://hz.lianjia.com/ershoufang/{}/'.format(value_)\n",
        "    html = sess.get(start_url).text\n",
        "    house_num = re.findall('共找到<span> (.*?) </span>套.*二手房', html)[0].strip()\n",
        "    print('正在获取{}数据: 二手房源共计「{}」套'.format(key_, house_num))\n",
        "    time.sleep(1)\n",
        "    # 由于页面限制， 所以每个行政区只能获取最多100页共计3000条房源信息\n",
        "    total_page = int(math.ceil(min(3000, int(house_num)) / 30.0))\n",
        "    for i in tqdm(range(total_page), desc=key_):\n",
        "        html = sess.get(url.format(value_, i+1)).text\n",
        "        soup = BeautifulSoup(html, 'lxml')\n",
        "        info_collect = soup.find_all(class_=\"info clear\")\n",
        "        \n",
        "        for info in info_collect:\n",
        "            info_dic = {}\n",
        "            # 行政区\n",
        "            info_dic['area'] = key_\n",
        "            # 房源的标题\n",
        "            info_dic['title'] = re_match('target=\"_blank\">(.*?)</a><!--', str(info))\n",
        "            # 小区名\n",
        "            info_dic['community'] = re_match('xiaoqu.*?target=\"_blank\">(.*?)</a>', str(info))\n",
        "            # 位置\n",
        "            info_dic['position'] = re_match('<a href.*?target=\"_blank\">(.*?)</a>.*?class=\"address\">', str(info))\n",
        "            # 税相关，如房本满5年\n",
        "            info_dic['tax'] = re_match('class=\"taxfree\">(.*?)</span>', str(info))\n",
        "            # 总价\n",
        "            info_dic['total_price'] = float(re_match('class=\"totalPrice\"><span>(.*?)</span>万', str(info)))\n",
        "            # 单价\n",
        "            info_dic['unit_price'] = float(re_match('data-price=\"(.*?)\"', str(info)))\n",
        "            \n",
        "            # 匹配房源标签信息，通过|切割\n",
        "            # 包括面积，朝向，装修等信息\n",
        "            icons = re.findall('class=\"houseIcon\"></span>(.*?)</div>', str(info))[0].strip().split('|')\n",
        "            info_dic['hourseType'] = icons[0].strip()\n",
        "            info_dic['hourseSize'] = float(icons[1].replace('平米', ''))\n",
        "            info_dic['direction'] = icons[2].strip()\n",
        "            info_dic['fitment'] = icons[3].strip()\n",
        "            \n",
        "            # 存入DataFrame\n",
        "            if data.empty:\n",
        "                data = pd.DataFrame(info_dic,index=[0])\n",
        "            else:\n",
        "                data = data.append(info_dic,ignore_index=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "正在获取西湖区数据: 二手房源共计「7240」套\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "西湖区: 100%|██████████| 100/100 [04:52<00:00,  2.92s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "正在获取钱塘新区数据: 二手房源共计「4147」套\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "钱塘新区: 100%|██████████| 100/100 [04:54<00:00,  2.95s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "正在获取下城区数据: 二手房源共计「4490」套\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "下城区: 100%|██████████| 100/100 [05:19<00:00,  3.19s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "正在获取江干区数据: 二手房源共计「7299」套\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "江干区: 100%|██████████| 100/100 [05:00<00:00,  3.00s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "正在获取拱墅区数据: 二手房源共计「5338」套\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "拱墅区: 100%|██████████| 100/100 [05:06<00:00,  3.07s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "正在获取上城区数据: 二手房源共计「2827」套\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "上城区: 100%|██████████| 95/95 [04:56<00:00,  3.12s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "正在获取滨江区数据: 二手房源共计「4203」套\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "滨江区: 100%|██████████| 100/100 [05:01<00:00,  3.02s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "正在获取余杭区数据: 二手房源共计「20242」套\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "余杭区: 100%|██████████| 100/100 [05:13<00:00,  3.14s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "正在获取萧山区数据: 二手房源共计「7107」套\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "萧山区: 100%|██████████| 100/100 [04:51<00:00,  2.91s/it]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-0176cd0cdd5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mstart_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'https://hz.lianjia.com/ershoufang/{}/'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mhtml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mhouse_num\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'共找到<span> (.*?) </span>套.*二手房'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'正在获取{}数据: 二手房源共计「{}」套'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhouse_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X8QCX7lBotGC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "outputfile='./hangzhou_lianjia.csv'\n",
        "data.to_csv(outputfile,index=False,encoding='utf_8_sig',header=True) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HythQSRJ2AsJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "f6c3dad9-09b2-4a3d-faa8-7dda903b0cde"
      },
      "source": [
        "data.head()"
      ],
      "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>area</th>\n",
              "      <th>title</th>\n",
              "      <th>community</th>\n",
              "      <th>position</th>\n",
              "      <th>tax</th>\n",
              "      <th>total_price</th>\n",
              "      <th>unit_price</th>\n",
              "      <th>hourseType</th>\n",
              "      <th>hourseSize</th>\n",
              "      <th>direction</th>\n",
              "      <th>fitment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>西湖区</td>\n",
              "      <td>保小一楼带大院子 自住装修 小胡环境好</td>\n",
              "      <td>西溪路22-97号</td>\n",
              "      <td>黄龙</td>\n",
              "      <td>None</td>\n",
              "      <td>435.0</td>\n",
              "      <td>62925.0</td>\n",
              "      <td>3室2厅</td>\n",
              "      <td>69.13</td>\n",
              "      <td>南</td>\n",
              "      <td>精装</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>西湖区</td>\n",
              "      <td>自住精装！无增值税，2号地铁白洋站直线370米</td>\n",
              "      <td>三墩景溪北苑</td>\n",
              "      <td>良渚</td>\n",
              "      <td>None</td>\n",
              "      <td>192.0</td>\n",
              "      <td>31491.0</td>\n",
              "      <td>2室1厅</td>\n",
              "      <td>60.97</td>\n",
              "      <td>南</td>\n",
              "      <td>精装</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>西湖区</td>\n",
              "      <td>远离绕城高速，东边套，满两年，诚心出售</td>\n",
              "      <td>三墩景溪北苑</td>\n",
              "      <td>良渚</td>\n",
              "      <td>None</td>\n",
              "      <td>180.0</td>\n",
              "      <td>29861.0</td>\n",
              "      <td>2室1厅</td>\n",
              "      <td>60.28</td>\n",
              "      <td>东南</td>\n",
              "      <td>简装</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>西湖区</td>\n",
              "      <td>中间楼层，采光视野都没有影响，价格低，成交快。</td>\n",
              "      <td>万科融信西雅图</td>\n",
              "      <td>良渚</td>\n",
              "      <td>None</td>\n",
              "      <td>360.0</td>\n",
              "      <td>40255.0</td>\n",
              "      <td>3室2厅</td>\n",
              "      <td>89.43</td>\n",
              "      <td>南</td>\n",
              "      <td>精装</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>西湖区</td>\n",
              "      <td>中间楼层 全明户型 两房朝南 可直接入住 满二唯一</td>\n",
              "      <td>翠苑三区</td>\n",
              "      <td>翠苑</td>\n",
              "      <td>None</td>\n",
              "      <td>255.0</td>\n",
              "      <td>41756.0</td>\n",
              "      <td>2室1厅</td>\n",
              "      <td>61.07</td>\n",
              "      <td>南</td>\n",
              "      <td>精装</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  area                      title  community  ... hourseSize direction  fitment\n",
              "0  西湖区        保小一楼带大院子 自住装修 小胡环境好  西溪路22-97号  ...      69.13         南       精装\n",
              "1  西湖区    自住精装！无增值税，2号地铁白洋站直线370米     三墩景溪北苑  ...      60.97         南       精装\n",
              "2  西湖区        远离绕城高速，东边套，满两年，诚心出售     三墩景溪北苑  ...      60.28        东南       简装\n",
              "3  西湖区    中间楼层，采光视野都没有影响，价格低，成交快。    万科融信西雅图  ...      89.43         南       精装\n",
              "4  西湖区  中间楼层 全明户型 两房朝南 可直接入住 满二唯一       翠苑三区  ...      61.07         南       精装\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMGtH7NIa1Xy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "58ac9949-716d-4dfb-f179-c5e532850572"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.linspace(-np.pi,np.pi,100)\n",
        "y = np.sin(3*x)\n",
        "\n",
        "plt.plot(x,y)\n",
        "\n",
        "plt.fill_between(x,y,color = 'green',alpha = 0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PolyCollection at 0x7fad647c5860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfU41sewbVo2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "9b16e1ea-4472-4e83-d6aa-97f4531c1dc1"
      },
      "source": [
        "plt.scatter(x,y,c = x,cmap = 'binary')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fad61d8fa90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1GGz57Jm3p9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "63b805a4-ec37-401a-97b9-fde95f8fe4a3"
      },
      "source": [
        "x = np.linspace(0,1,)\n",
        "y = x\n",
        "y2 = np.sqrt(x)\n",
        "plt.plot(x,y,)\n",
        "plt.plot(0.5,0.5,'ro')\n",
        "plt.plot([0,0.5],[0.5,0.5],'r--' )\n",
        "plt.plot([0.5,0.5],[0,0.5],'r--' )\n",
        "plt.xticks([0,0.5,1],)\n",
        "plt.yticks([0,0.5,1],)\n",
        "\n",
        "plt.plot(x,y2,)\n",
        "plt.plot(0.5,np.sqrt(0.5),'ro')\n",
        "plt.plot([0,0.5],[np.sqrt(0.5),np.sqrt(0.5)],'r--' )\n",
        "plt.plot([0.5,0.5],[0,np.sqrt(0.5)],'r--' )\n",
        "plt.xticks([0,0.5,1],)\n",
        "plt.yticks([0,np.sqrt(0.5),0.5,1],)\n",
        "plt.axis([0,1,0,1])\n",
        "# plt.axis('square')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0, 0.0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTzuVPsUrOIO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "05653a9b-f510-4ba6-bcab-cffeeab279bd"
      },
      "source": [
        "red_arr = np.array([\n",
        "            [[0,0,0],[1,0,0],[2,0,0],[3,0,0],[4,0,0],[50,0,0]]\n",
        "])\n",
        "print(red_arr.shape)\n",
        "plt.yticks([])\n",
        "plt.imshow(red_arr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 6, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fad61822dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTJOFQZixoD1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9277b73e-e6ad-443f-be5a-627bf7078e5b"
      },
      "source": [
        "li = np.array([0,0,0])\n",
        "li2 =np.array(np.stack((li,li)))\n",
        "li2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoQsx-iQ02QW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9e04eeb2-8b0d-47e9-9594-627240bc0b28"
      },
      "source": [
        "# red = [0,0,0]\n",
        "red[0] = 3\n",
        "red"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 0, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elmvF4DG2Yfj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fsad"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Or6vHFFOz4ZD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "4fa26670-91c2-40f4-9b2d-9d6491b3423e"
      },
      "source": [
        "red = [0,0,0]\n",
        "red_arr = []\n",
        "green_arr = []\n",
        "blue_arr = []\n",
        "\n",
        "for i in range(0,256,10):\n",
        "    red_arr.append([i,0,0])\n",
        "    green_arr.append([0,i,0])\n",
        "    blue_arr.append([0,0,i])\n",
        "colorbar = [red_arr,green_arr,blue_arr]\n",
        "plt.imshow(colorbar)\n",
        "plt.yticks([])\n",
        "_ = plt.xticks([i for i in range(0,26,5)],[str(i*10) for i in range(0,26,5)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_l50hWpU_P0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "70106039-2ff5-4fed-874c-d9eb30c39ea2"
      },
      "source": [
        "[str(i*10) for i in range(0,26,5)]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 5, 10, 15, 20, 25]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcDYlLPrTce2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "6f83ca64-2087-40e2-bc99-ef57bd950a1b"
      },
      "source": [
        "import numpy as np\n",
        "x = np.array([\n",
        "              [1,2],\n",
        "              [3,4]\n",
        "])\n",
        "y = np.array([\n",
        "              [2,3],\n",
        "              [4,5]\n",
        "])\n",
        "x+y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3, 5],\n",
              "       [7, 9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yem-qPN5ipTW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "ee9f5bf4-e611-44cb-f8de-f3e4c3e0e764"
      },
      "source": [
        "x.dot(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10, 13],\n",
              "       [22, 29]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYVePIDLjjQm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "a08b68b0-d61c-4929-9a8c-c5cea12c14e4"
      },
      "source": [
        "xT = x.T\n",
        "xT"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 3],\n",
              "       [2, 4]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQ2sds6vjlpB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d1a8c50c-3072-4561-9969-1529527bda60"
      },
      "source": [
        "yT = y.T\n",
        "yT"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 4],\n",
              "       [3, 5]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1jpox8kyj0DC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "0f48fdbc-76a3-46ef-8edd-370fc35b7971"
      },
      "source": [
        "xT.dot(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14, 18],\n",
              "       [20, 26]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P1h44UQ_j3JZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "cc9a9244-4d01-4062-a4c9-2e3e297ebfca"
      },
      "source": [
        "yT.dot(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14, 20],\n",
              "       [18, 26]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jk4KiVKjkSic",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cec9abf2-9697-4e7e-e25b-0a2fae3b9548"
      },
      "source": [
        "a = np.array([1,2,3])\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r12EacaYsKgL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "30643234-e9a7-45c8-d4ce-2e06dfd468f6"
      },
      "source": [
        "A = a.T\n",
        "A"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZ8DC7NxsMeH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d58e3100-9015-41f4-8b86-c2891f1d1a9e"
      },
      "source": [
        "a.dot(A)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkN938PBsX_a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0b8be800-cc40-4933-9eef-32fb78a8c161"
      },
      "source": [
        "A.dot(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20wxPPmlsaEJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "d927269e-0060-4bf8-ca20-8ad6165f7d1c"
      },
      "source": [
        "a = a.reshape(-1,1)\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1],\n",
              "       [2],\n",
              "       [3]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dA43LcVDshWh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5f78e0b7-e0a0-4480-9e26-d16c44c1b60e"
      },
      "source": [
        "A = a.T\n",
        "A"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQgk6POvsjkb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "759ef502-6527-422b-b9d4-bda717d09c8a"
      },
      "source": [
        "a.dot(A)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [2, 4, 6],\n",
              "       [3, 6, 9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVpXj9jzsmx7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b75984ba-8c1a-495e-c90e-1fac477f7dfc"
      },
      "source": [
        "A.dot(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Ti6VCDVspbS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "286f7ca8-504d-483e-fad9-c9783b89dab1"
      },
      "source": [
        "A = (0.0,0.0)\n",
        "B = (150.0,150.0)\n",
        "C = (0.0,150.0)\n",
        "# D = (0.0,240.0)\n",
        "\n",
        "points = {0:A,1:B,2:C}\n",
        "for i in points.values():\n",
        "    print(i)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0.0, 0.0)\n",
            "(150.0, 150.0)\n",
            "(0.0, 150.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftchMceoydZw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "d53833ae-ba16-4553-95ac-ad11816f20e8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "x = 50\n",
        "y = 100\n",
        "result_arr = []\n",
        "for i in range(1000):\n",
        "    result = (x+i)/(y+i)\n",
        "    result_arr.append(result)\n",
        "    if result == 1:\n",
        "        print(i,x+i)\n",
        "print(result)\n",
        "plt.plot(result_arr,)\n",
        "# plt.axis('equal')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9545040946314831\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-49.95, 1048.95, 0.47727479526842587, 0.9772292993630572)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2Xy7PDI8iBj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "425191a8-f06c-4a22-c91d-8f3be3eaefa1"
      },
      "source": [
        "import numpy as np\n",
        "diff = np.diff(result_arr)\n",
        "plt.plot(diff)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f95878a1550>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3pT-YbM_jGW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "884c4bf2-8f91-44f5-d9c2-cbf998957f30"
      },
      "source": [
        "diff\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.95049505e-03, 4.85342652e-03, 4.75918523e-03, 4.66766243e-03,\n",
              "       4.57875458e-03, 4.49236298e-03, 4.40839358e-03, 4.32675666e-03,\n",
              "       4.24736663e-03, 4.17014178e-03, 4.09500410e-03, 4.02187902e-03,\n",
              "       3.95069532e-03, 3.88138488e-03, 3.81388253e-03, 3.74812594e-03,\n",
              "       3.68405541e-03, 3.62161379e-03, 3.56074633e-03, 3.50140056e-03,\n",
              "       3.44352617e-03, 3.38707492e-03, 3.33200053e-03, 3.27825859e-03,\n",
              "       3.22580645e-03, 3.17460317e-03, 3.12460942e-03, 3.07578740e-03,\n",
              "       3.02810078e-03, 2.98151461e-03, 2.93599530e-03, 2.89151053e-03,\n",
              "       2.84802916e-03, 2.80552127e-03, 2.76395799e-03, 2.72331155e-03,\n",
              "       2.68355517e-03, 2.64466307e-03, 2.60661036e-03, 2.56937307e-03,\n",
              "       2.53292806e-03, 2.49725302e-03, 2.46232641e-03, 2.42812743e-03,\n",
              "       2.39463602e-03, 2.36183278e-03, 2.32969900e-03, 2.29821658e-03,\n",
              "       2.26736804e-03, 2.23713647e-03, 2.20750552e-03, 2.17845939e-03,\n",
              "       2.14998280e-03, 2.12206095e-03, 2.09467951e-03, 2.06782465e-03,\n",
              "       2.04148293e-03, 2.01564138e-03, 1.99028740e-03, 1.96540881e-03,\n",
              "       1.94099379e-03, 1.91703090e-03, 1.89350905e-03, 1.87041748e-03,\n",
              "       1.84774575e-03, 1.82548375e-03, 1.80362167e-03, 1.78214999e-03,\n",
              "       1.76105945e-03, 1.74034111e-03, 1.71998624e-03, 1.69998640e-03,\n",
              "       1.68033338e-03, 1.66101920e-03, 1.64203612e-03, 1.62337662e-03,\n",
              "       1.60503338e-03, 1.58699930e-03, 1.56926747e-03, 1.55183116e-03,\n",
              "       1.53468386e-03, 1.51781920e-03, 1.50123101e-03, 1.48491328e-03,\n",
              "       1.46886016e-03, 1.45306597e-03, 1.43752516e-03, 1.42223234e-03,\n",
              "       1.40718226e-03, 1.39236981e-03, 1.37779002e-03, 1.36343805e-03,\n",
              "       1.34930915e-03, 1.33539875e-03, 1.32170235e-03, 1.30821559e-03,\n",
              "       1.29493422e-03, 1.28185407e-03, 1.26897112e-03, 1.25628141e-03,\n",
              "       1.24378109e-03, 1.23146643e-03, 1.21933376e-03, 1.20737950e-03,\n",
              "       1.19560019e-03, 1.18399242e-03, 1.17255288e-03, 1.16127834e-03,\n",
              "       1.15016562e-03, 1.13921167e-03, 1.12841345e-03, 1.11776804e-03,\n",
              "       1.10727257e-03, 1.09692422e-03, 1.08672028e-03, 1.07665805e-03,\n",
              "       1.06673494e-03, 1.05694838e-03, 1.04729588e-03, 1.03777501e-03,\n",
              "       1.02838338e-03, 1.01911867e-03, 1.00997859e-03, 1.00096092e-03,\n",
              "       9.92063492e-04, 9.83284169e-04, 9.74620872e-04, 9.66071567e-04,\n",
              "       9.57634260e-04, 9.49307006e-04, 9.41087898e-04, 9.32975071e-04,\n",
              "       9.24966701e-04, 9.17061003e-04, 9.09256228e-04, 9.01550667e-04,\n",
              "       8.93942645e-04, 8.86430522e-04, 8.79012693e-04, 8.71687587e-04,\n",
              "       8.64453665e-04, 8.57309420e-04, 8.50253376e-04, 8.43284086e-04,\n",
              "       8.36400134e-04, 8.29600133e-04, 8.22882723e-04, 8.16246572e-04,\n",
              "       8.09690374e-04, 8.03212851e-04, 7.96812749e-04, 7.90488838e-04,\n",
              "       7.84239915e-04, 7.78064797e-04, 7.71962328e-04, 7.65931373e-04,\n",
              "       7.59970817e-04, 7.54079570e-04, 7.48256562e-04, 7.42500743e-04,\n",
              "       7.36811082e-04, 7.31186570e-04, 7.25626215e-04, 7.20129047e-04,\n",
              "       7.14694111e-04, 7.09320471e-04, 7.04007209e-04, 6.98753424e-04,\n",
              "       6.93558231e-04, 6.88420763e-04, 6.83340167e-04, 6.78315607e-04,\n",
              "       6.73346262e-04, 6.68431325e-04, 6.63570007e-04, 6.58761528e-04,\n",
              "       6.54005127e-04, 6.49300055e-04, 6.44645574e-04, 6.40040963e-04,\n",
              "       6.35485511e-04, 6.30978521e-04, 6.26519309e-04, 6.22107202e-04,\n",
              "       6.17741537e-04, 6.13421666e-04, 6.09146951e-04, 6.04916763e-04,\n",
              "       6.00730488e-04, 5.96587519e-04, 5.92487262e-04, 5.88429130e-04,\n",
              "       5.84412549e-04, 5.80436953e-04, 5.76501787e-04, 5.72606505e-04,\n",
              "       5.68750569e-04, 5.64933451e-04, 5.61154632e-04, 5.57413601e-04,\n",
              "       5.53709856e-04, 5.50042903e-04, 5.46412257e-04, 5.42817440e-04,\n",
              "       5.39257981e-04, 5.35733419e-04, 5.32243299e-04, 5.28787174e-04,\n",
              "       5.25364603e-04, 5.21975154e-04, 5.18618401e-04, 5.15293924e-04,\n",
              "       5.12001311e-04, 5.08740156e-04, 5.05510060e-04, 5.02310629e-04,\n",
              "       4.99141477e-04, 4.96002222e-04, 4.92892490e-04, 4.89811912e-04,\n",
              "       4.86760125e-04, 4.83736770e-04, 4.80741496e-04, 4.77773956e-04,\n",
              "       4.74833808e-04, 4.71920717e-04, 4.69034352e-04, 4.66174387e-04,\n",
              "       4.63340500e-04, 4.60532375e-04, 4.57749702e-04, 4.54992174e-04,\n",
              "       4.52259488e-04, 4.49551348e-04, 4.46867459e-04, 4.44207534e-04,\n",
              "       4.41571287e-04, 4.38958439e-04, 4.36368714e-04, 4.33801839e-04,\n",
              "       4.31257547e-04, 4.28735573e-04, 4.26235657e-04, 4.23757543e-04,\n",
              "       4.21300977e-04, 4.18865712e-04, 4.16451500e-04, 4.14058101e-04,\n",
              "       4.11685275e-04, 4.09332788e-04, 4.07000407e-04, 4.04687905e-04,\n",
              "       4.02395055e-04, 4.00121637e-04, 3.97867431e-04, 3.95632220e-04,\n",
              "       3.93415793e-04, 3.91217940e-04, 3.89038453e-04, 3.86877128e-04,\n",
              "       3.84733764e-04, 3.82608163e-04, 3.80500129e-04, 3.78409469e-04,\n",
              "       3.76335993e-04, 3.74279512e-04, 3.72239842e-04, 3.70216799e-04,\n",
              "       3.68210204e-04, 3.66219878e-04, 3.64245647e-04, 3.62287337e-04,\n",
              "       3.60344778e-04, 3.58417800e-04, 3.56506239e-04, 3.54609929e-04,\n",
              "       3.52728709e-04, 3.50862420e-04, 3.49010903e-04, 3.47174004e-04,\n",
              "       3.45351568e-04, 3.43543445e-04, 3.41749484e-04, 3.39969539e-04,\n",
              "       3.38203463e-04, 3.36451114e-04, 3.34712348e-04, 3.32987027e-04,\n",
              "       3.31275011e-04, 3.29576165e-04, 3.27890353e-04, 3.26217443e-04,\n",
              "       3.24557304e-04, 3.22909805e-04, 3.21274818e-04, 3.19652218e-04,\n",
              "       3.18041880e-04, 3.16443679e-04, 3.14857495e-04, 3.13283208e-04,\n",
              "       3.11720698e-04, 3.10169849e-04, 3.08630545e-04, 3.07102671e-04,\n",
              "       3.05586114e-04, 3.04080764e-04, 3.02586509e-04, 3.01103242e-04,\n",
              "       2.99630855e-04, 2.98169241e-04, 2.96718296e-04, 2.95277916e-04,\n",
              "       2.93847998e-04, 2.92428443e-04, 2.91019149e-04, 2.89620019e-04,\n",
              "       2.88230954e-04, 2.86851858e-04, 2.85482637e-04, 2.84123196e-04,\n",
              "       2.82773442e-04, 2.81433283e-04, 2.80102630e-04, 2.78781391e-04,\n",
              "       2.77469478e-04, 2.76166805e-04, 2.74873283e-04, 2.73588829e-04,\n",
              "       2.72313356e-04, 2.71046783e-04, 2.69789025e-04, 2.68540002e-04,\n",
              "       2.67299632e-04, 2.66067837e-04, 2.64844536e-04, 2.63629653e-04,\n",
              "       2.62423110e-04, 2.61224831e-04, 2.60034741e-04, 2.58852765e-04,\n",
              "       2.57678829e-04, 2.56512862e-04, 2.55354790e-04, 2.54204543e-04,\n",
              "       2.53062051e-04, 2.51927243e-04, 2.50800052e-04, 2.49680409e-04,\n",
              "       2.48568247e-04, 2.47463499e-04, 2.46366100e-04, 2.45275985e-04,\n",
              "       2.44193088e-04, 2.43117348e-04, 2.42048700e-04, 2.40987083e-04,\n",
              "       2.39932435e-04, 2.38884695e-04, 2.37843803e-04, 2.36809700e-04,\n",
              "       2.35782326e-04, 2.34761623e-04, 2.33747534e-04, 2.32740001e-04,\n",
              "       2.31738969e-04, 2.30744381e-04, 2.29756183e-04, 2.28774319e-04,\n",
              "       2.27798735e-04, 2.26829379e-04, 2.25866197e-04, 2.24909137e-04,\n",
              "       2.23958147e-04, 2.23013176e-04, 2.22074173e-04, 2.21141088e-04,\n",
              "       2.20213872e-04, 2.19292475e-04, 2.18376849e-04, 2.17466945e-04,\n",
              "       2.16562717e-04, 2.15664116e-04, 2.14771097e-04, 2.13883613e-04,\n",
              "       2.13001619e-04, 2.12125069e-04, 2.11253919e-04, 2.10388124e-04,\n",
              "       2.09527641e-04, 2.08672426e-04, 2.07822437e-04, 2.06977630e-04,\n",
              "       2.06137964e-04, 2.05303397e-04, 2.04473889e-04, 2.03649397e-04,\n",
              "       2.02829883e-04, 2.02015305e-04, 2.01205624e-04, 2.00400802e-04,\n",
              "       1.99600798e-04, 1.98805576e-04, 1.98015097e-04, 1.97229322e-04,\n",
              "       1.96448216e-04, 1.95671741e-04, 1.94899860e-04, 1.94132538e-04,\n",
              "       1.93369738e-04, 1.92611426e-04, 1.91857565e-04, 1.91108121e-04,\n",
              "       1.90363060e-04, 1.89622348e-04, 1.88885951e-04, 1.88153835e-04,\n",
              "       1.87425967e-04, 1.86702314e-04, 1.85982845e-04, 1.85267526e-04,\n",
              "       1.84556327e-04, 1.83849214e-04, 1.83146158e-04, 1.82447127e-04,\n",
              "       1.81752090e-04, 1.81061018e-04, 1.80373879e-04, 1.79690645e-04,\n",
              "       1.79011285e-04, 1.78335771e-04, 1.77664073e-04, 1.76996163e-04,\n",
              "       1.76332012e-04, 1.75671592e-04, 1.75014876e-04, 1.74361836e-04,\n",
              "       1.73712443e-04, 1.73066672e-04, 1.72424495e-04, 1.71785886e-04,\n",
              "       1.71150818e-04, 1.70519265e-04, 1.69891202e-04, 1.69266602e-04,\n",
              "       1.68645440e-04, 1.68027691e-04, 1.67413330e-04, 1.66802333e-04,\n",
              "       1.66194674e-04, 1.65590330e-04, 1.64989276e-04, 1.64391488e-04,\n",
              "       1.63796944e-04, 1.63205619e-04, 1.62617491e-04, 1.62032536e-04,\n",
              "       1.61450732e-04, 1.60872055e-04, 1.60296484e-04, 1.59723997e-04,\n",
              "       1.59154571e-04, 1.58588185e-04, 1.58024816e-04, 1.57464445e-04,\n",
              "       1.56907048e-04, 1.56352606e-04, 1.55801098e-04, 1.55252503e-04,\n",
              "       1.54706800e-04, 1.54163969e-04, 1.53623990e-04, 1.53086843e-04,\n",
              "       1.52552509e-04, 1.52020967e-04, 1.51492198e-04, 1.50966184e-04,\n",
              "       1.50442904e-04, 1.49922340e-04, 1.49404474e-04, 1.48889286e-04,\n",
              "       1.48376758e-04, 1.47866872e-04, 1.47359610e-04, 1.46854954e-04,\n",
              "       1.46352886e-04, 1.45853388e-04, 1.45356443e-04, 1.44862033e-04,\n",
              "       1.44370142e-04, 1.43880752e-04, 1.43393846e-04, 1.42909407e-04,\n",
              "       1.42427419e-04, 1.41947865e-04, 1.41470730e-04, 1.40995996e-04,\n",
              "       1.40523647e-04, 1.40053669e-04, 1.39586044e-04, 1.39120757e-04,\n",
              "       1.38657793e-04, 1.38197135e-04, 1.37738770e-04, 1.37282682e-04,\n",
              "       1.36828854e-04, 1.36377274e-04, 1.35927926e-04, 1.35480794e-04,\n",
              "       1.35035866e-04, 1.34593125e-04, 1.34152558e-04, 1.33714151e-04,\n",
              "       1.33277890e-04, 1.32843760e-04, 1.32411748e-04, 1.31981839e-04,\n",
              "       1.31554021e-04, 1.31128280e-04, 1.30704602e-04, 1.30282975e-04,\n",
              "       1.29863384e-04, 1.29445817e-04, 1.29030260e-04, 1.28616702e-04,\n",
              "       1.28205128e-04, 1.27795527e-04, 1.27387886e-04, 1.26982192e-04,\n",
              "       1.26578433e-04, 1.26176597e-04, 1.25776671e-04, 1.25378644e-04,\n",
              "       1.24982502e-04, 1.24588236e-04, 1.24195832e-04, 1.23805279e-04,\n",
              "       1.23416565e-04, 1.23029680e-04, 1.22644610e-04, 1.22261346e-04,\n",
              "       1.21879875e-04, 1.21500187e-04, 1.21122271e-04, 1.20746114e-04,\n",
              "       1.20371708e-04, 1.19999040e-04, 1.19628100e-04, 1.19258878e-04,\n",
              "       1.18891362e-04, 1.18525542e-04, 1.18161408e-04, 1.17798950e-04,\n",
              "       1.17438157e-04, 1.17079019e-04, 1.16721526e-04, 1.16365667e-04,\n",
              "       1.16011434e-04, 1.15658816e-04, 1.15307803e-04, 1.14958385e-04,\n",
              "       1.14610553e-04, 1.14264298e-04, 1.13919609e-04, 1.13576478e-04,\n",
              "       1.13234894e-04, 1.12894850e-04, 1.12556334e-04, 1.12219339e-04,\n",
              "       1.11883856e-04, 1.11549874e-04, 1.11217386e-04, 1.10886381e-04,\n",
              "       1.10556853e-04, 1.10228791e-04, 1.09902187e-04, 1.09577033e-04,\n",
              "       1.09253319e-04, 1.08931038e-04, 1.08610181e-04, 1.08290739e-04,\n",
              "       1.07972705e-04, 1.07656069e-04, 1.07340824e-04, 1.07026962e-04,\n",
              "       1.06714475e-04, 1.06403354e-04, 1.06093592e-04, 1.05785180e-04,\n",
              "       1.05478111e-04, 1.05172378e-04, 1.04867971e-04, 1.04564885e-04,\n",
              "       1.04263110e-04, 1.03962640e-04, 1.03663467e-04, 1.03365583e-04,\n",
              "       1.03068982e-04, 1.02773655e-04, 1.02479596e-04, 1.02186797e-04,\n",
              "       1.01895252e-04, 1.01604952e-04, 1.01315891e-04, 1.01028062e-04,\n",
              "       1.00741457e-04, 1.00456071e-04, 1.00171895e-04, 9.98889235e-05,\n",
              "       9.96071494e-05, 9.93265659e-05, 9.90471663e-05, 9.87689439e-05,\n",
              "       9.84918921e-05, 9.82160045e-05, 9.79412744e-05, 9.76676954e-05,\n",
              "       9.73952611e-05, 9.71239651e-05, 9.68538011e-05, 9.65847628e-05,\n",
              "       9.63168439e-05, 9.60500382e-05, 9.57843396e-05, 9.55197420e-05,\n",
              "       9.52562393e-05, 9.49938254e-05, 9.47324944e-05, 9.44722403e-05,\n",
              "       9.42130572e-05, 9.39549392e-05, 9.36978806e-05, 9.34418754e-05,\n",
              "       9.31869180e-05, 9.29330027e-05, 9.26801238e-05, 9.24282757e-05,\n",
              "       9.21774527e-05, 9.19276493e-05, 9.16788600e-05, 9.14310793e-05,\n",
              "       9.11843017e-05, 9.09385219e-05, 9.06937345e-05, 9.04499342e-05,\n",
              "       9.02071155e-05, 8.99652734e-05, 8.97244025e-05, 8.94844977e-05,\n",
              "       8.92455538e-05, 8.90075656e-05, 8.87705282e-05, 8.85344364e-05,\n",
              "       8.82992851e-05, 8.80650695e-05, 8.78317846e-05, 8.75994253e-05,\n",
              "       8.73679870e-05, 8.71374646e-05, 8.69078533e-05, 8.66791485e-05,\n",
              "       8.64513452e-05, 8.62244388e-05, 8.59984245e-05, 8.57732977e-05,\n",
              "       8.55490538e-05, 8.53256882e-05, 8.51031961e-05, 8.48815732e-05,\n",
              "       8.46608149e-05, 8.44409167e-05, 8.42218741e-05, 8.40036827e-05,\n",
              "       8.37863381e-05, 8.35698360e-05, 8.33541719e-05, 8.31393415e-05,\n",
              "       8.29253407e-05, 8.27121650e-05, 8.24998103e-05, 8.22882723e-05,\n",
              "       8.20775469e-05, 8.18676299e-05, 8.16585171e-05, 8.14502046e-05,\n",
              "       8.12426882e-05, 8.10359638e-05, 8.08300274e-05, 8.06248750e-05,\n",
              "       8.04205027e-05, 8.02169065e-05, 8.00140825e-05, 7.98120267e-05,\n",
              "       7.96107353e-05, 7.94102045e-05, 7.92104304e-05, 7.90114092e-05,\n",
              "       7.88131372e-05, 7.86156105e-05, 7.84188255e-05, 7.82227785e-05,\n",
              "       7.80274657e-05, 7.78328835e-05, 7.76390282e-05, 7.74458963e-05,\n",
              "       7.72534841e-05, 7.70617881e-05, 7.68708048e-05, 7.66805305e-05,\n",
              "       7.64909618e-05, 7.63020953e-05, 7.61139273e-05, 7.59264546e-05,\n",
              "       7.57396737e-05, 7.55535811e-05, 7.53681735e-05, 7.51834476e-05,\n",
              "       7.49994000e-05, 7.48160274e-05, 7.46333265e-05, 7.44512940e-05,\n",
              "       7.42699266e-05, 7.40892212e-05, 7.39091745e-05, 7.37297833e-05,\n",
              "       7.35510444e-05, 7.33729547e-05, 7.31955111e-05, 7.30187103e-05,\n",
              "       7.28425494e-05, 7.26670252e-05, 7.24921346e-05, 7.23178747e-05,\n",
              "       7.21442423e-05, 7.19712345e-05, 7.17988483e-05, 7.16270808e-05,\n",
              "       7.14559288e-05, 7.12853896e-05, 7.11154602e-05, 7.09461377e-05,\n",
              "       7.07774192e-05, 7.06093018e-05, 7.04417827e-05, 7.02748590e-05,\n",
              "       7.01085280e-05, 6.99427868e-05, 6.97776326e-05, 6.96130628e-05,\n",
              "       6.94490744e-05, 6.92856648e-05, 6.91228313e-05, 6.89605711e-05,\n",
              "       6.87988816e-05, 6.86377601e-05, 6.84772039e-05, 6.83172105e-05,\n",
              "       6.81577771e-05, 6.79989011e-05, 6.78405801e-05, 6.76828113e-05,\n",
              "       6.75255922e-05, 6.73689203e-05, 6.72127930e-05, 6.70572078e-05,\n",
              "       6.69021623e-05, 6.67476538e-05, 6.65936800e-05, 6.64402383e-05,\n",
              "       6.62873264e-05, 6.61349417e-05, 6.59830819e-05, 6.58317446e-05,\n",
              "       6.56809273e-05, 6.55306277e-05, 6.53808434e-05, 6.52315721e-05,\n",
              "       6.50828114e-05, 6.49345590e-05, 6.47868125e-05, 6.46395698e-05,\n",
              "       6.44928284e-05, 6.43465862e-05, 6.42008408e-05, 6.40555900e-05,\n",
              "       6.39108316e-05, 6.37665634e-05, 6.36227831e-05, 6.34794885e-05,\n",
              "       6.33366775e-05, 6.31943479e-05, 6.30524975e-05, 6.29111242e-05,\n",
              "       6.27702258e-05, 6.26298003e-05, 6.24898454e-05, 6.23503591e-05,\n",
              "       6.22113394e-05, 6.20727841e-05, 6.19346911e-05, 6.17970585e-05,\n",
              "       6.16598841e-05, 6.15231659e-05, 6.13869020e-05, 6.12510903e-05,\n",
              "       6.11157287e-05, 6.09808154e-05, 6.08463484e-05, 6.07123256e-05,\n",
              "       6.05787451e-05, 6.04456050e-05, 6.03129033e-05, 6.01806382e-05,\n",
              "       6.00488077e-05, 5.99174098e-05, 5.97864428e-05, 5.96559047e-05,\n",
              "       5.95257937e-05, 5.93961079e-05, 5.92668454e-05, 5.91380044e-05,\n",
              "       5.90095832e-05, 5.88815797e-05, 5.87539923e-05, 5.86268192e-05,\n",
              "       5.85000585e-05, 5.83737085e-05, 5.82477674e-05, 5.81222334e-05,\n",
              "       5.79971048e-05, 5.78723798e-05, 5.77480568e-05, 5.76241339e-05,\n",
              "       5.75006095e-05, 5.73774819e-05, 5.72547493e-05, 5.71324101e-05,\n",
              "       5.70104626e-05, 5.68889051e-05, 5.67677359e-05, 5.66469535e-05,\n",
              "       5.65265562e-05, 5.64065423e-05, 5.62869101e-05, 5.61676582e-05,\n",
              "       5.60487849e-05, 5.59302885e-05, 5.58121675e-05, 5.56944203e-05,\n",
              "       5.55770453e-05, 5.54600410e-05, 5.53434058e-05, 5.52271382e-05,\n",
              "       5.51112365e-05, 5.49956993e-05, 5.48805251e-05, 5.47657123e-05,\n",
              "       5.46512594e-05, 5.45371649e-05, 5.44234273e-05, 5.43100452e-05,\n",
              "       5.41970170e-05, 5.40843413e-05, 5.39720166e-05, 5.38600415e-05,\n",
              "       5.37484144e-05, 5.36371341e-05, 5.35261989e-05, 5.34156076e-05,\n",
              "       5.33053587e-05, 5.31954507e-05, 5.30858823e-05, 5.29766521e-05,\n",
              "       5.28677587e-05, 5.27592007e-05, 5.26509767e-05, 5.25430853e-05,\n",
              "       5.24355253e-05, 5.23282952e-05, 5.22213936e-05, 5.21148194e-05,\n",
              "       5.20085710e-05, 5.19026472e-05, 5.17970467e-05, 5.16917682e-05,\n",
              "       5.15868103e-05, 5.14821717e-05, 5.13778512e-05, 5.12738475e-05,\n",
              "       5.11701592e-05, 5.10667851e-05, 5.09637240e-05, 5.08609746e-05,\n",
              "       5.07585356e-05, 5.06564057e-05, 5.05545838e-05, 5.04530686e-05,\n",
              "       5.03518588e-05, 5.02509533e-05, 5.01503508e-05, 5.00500500e-05,\n",
              "       4.99500500e-05, 4.98503493e-05, 4.97509468e-05, 4.96518413e-05,\n",
              "       4.95530317e-05, 4.94545167e-05, 4.93562952e-05, 4.92583660e-05,\n",
              "       4.91607281e-05, 4.90633801e-05, 4.89663210e-05, 4.88695496e-05,\n",
              "       4.87730648e-05, 4.86768654e-05, 4.85809504e-05, 4.84853186e-05,\n",
              "       4.83899690e-05, 4.82949003e-05, 4.82001114e-05, 4.81056014e-05,\n",
              "       4.80113691e-05, 4.79174134e-05, 4.78237332e-05, 4.77303275e-05,\n",
              "       4.76371951e-05, 4.75443351e-05, 4.74517463e-05, 4.73594277e-05,\n",
              "       4.72673783e-05, 4.71755970e-05, 4.70840828e-05, 4.69928345e-05,\n",
              "       4.69018513e-05, 4.68111321e-05, 4.67206758e-05, 4.66304814e-05,\n",
              "       4.65405480e-05, 4.64508745e-05, 4.63614599e-05, 4.62723033e-05,\n",
              "       4.61834035e-05, 4.60947598e-05, 4.60063710e-05, 4.59182362e-05,\n",
              "       4.58303544e-05, 4.57427246e-05, 4.56553460e-05, 4.55682174e-05,\n",
              "       4.54813381e-05, 4.53947070e-05, 4.53083231e-05, 4.52221856e-05,\n",
              "       4.51362936e-05, 4.50506459e-05, 4.49652419e-05, 4.48800804e-05,\n",
              "       4.47951607e-05, 4.47104817e-05, 4.46260427e-05, 4.45418426e-05,\n",
              "       4.44578806e-05, 4.43741558e-05, 4.42906672e-05, 4.42074141e-05,\n",
              "       4.41243955e-05, 4.40416105e-05, 4.39590583e-05, 4.38767380e-05,\n",
              "       4.37946486e-05, 4.37127895e-05, 4.36311596e-05, 4.35497582e-05,\n",
              "       4.34685844e-05, 4.33876373e-05, 4.33069161e-05, 4.32264200e-05,\n",
              "       4.31461481e-05, 4.30660996e-05, 4.29862736e-05, 4.29066694e-05,\n",
              "       4.28272861e-05, 4.27481229e-05, 4.26691790e-05, 4.25904536e-05,\n",
              "       4.25119459e-05, 4.24336550e-05, 4.23555802e-05, 4.22777207e-05,\n",
              "       4.22000756e-05, 4.21226443e-05, 4.20454259e-05, 4.19684196e-05,\n",
              "       4.18916247e-05, 4.18150404e-05, 4.17386659e-05, 4.16625004e-05,\n",
              "       4.15865433e-05, 4.15107936e-05, 4.14352508e-05])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    }
  ]
}