Colab_Tutorial.ipynb

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    "kernelspec": {
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/BritneyMuller/colab-notebooks/blob/master/Colab_Tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmghR5eUr6Y0",
        "colab_type": "text"
      },
      "source": [
        "# ML in Cybersecurity: Tutorial I\n",
        "\n",
        "Contents in this tutorial\n",
        "  1. Jupyter\n",
        "  1. Python\n",
        "  1. Numpy\n",
        "  1. Pandas\n",
        "  1. Matplotlib\n",
        "\n",
        "The content in this notebook is a compilation of many amazing existing tutorials. References below:\n",
        "  - http://cs231n.github.io/python-numpy-tutorial/   ($\\leftarrow$ Justin Johnson's tutorial; Python/Numpy content adapted from here)\n",
        "  - https://github.com/kuleshov/cs228-material/blob/master/tutorials/python/cs228-python-tutorial.ipynb (Exhaustive version of this tutorial)\n",
        "  - https://docs.scipy.org/doc/numpy-1.15.1/user/quickstart.html\n",
        "  - https://www.dataquest.io/blog/jupyter-notebook-tutorial/\n",
        "  - https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks#machine-learning-statistics-and-probability\n",
        "  - https://pandas.pydata.org/pandas-docs/stable/10min.html\n",
        "  \n",
        "\n",
        "Course web-page: https://cms.cispa.saarland/mlcysec/\n",
        "\n",
        "Author: [Tribhuvanesh Orekondy](http://people.mpi-inf.mpg.de/~orekondy)   \n",
        "Contact: orekondy@mpi-inf.mpg.de"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rY3DQCEYr6Y2",
        "colab_type": "text"
      },
      "source": [
        "### Header\n",
        "\n",
        "This is a block that will probably remain common in all your notebooks. It imports the necessary packages and provides some additional conveniences. For instance, `%matplotlib inline` allows plotting graphs within the notebook and `%autoreload` allows you to install packages and use them immediately (without having to restart the notebook kernel)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiP8mQfkr6Y4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time \n",
        " \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "import json \n",
        "import time \n",
        "import pickle \n",
        "import sys \n",
        "import csv \n",
        "import os \n",
        "import os.path as osp \n",
        "import shutil \n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "from IPython.display import display, HTML\n",
        " \n",
        "%matplotlib inline \n",
        "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots \n",
        "plt.rcParams['image.interpolation'] = 'nearest' \n",
        "plt.rcParams['image.cmap'] = 'gray' \n",
        " \n",
        "# for auto-reloading external modules \n",
        "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython \n",
        "%load_ext autoreload\n",
        "%autoreload 2 "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJvMsZk0r6Y9",
        "colab_type": "text"
      },
      "source": [
        "___"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zf0F1CE-r6Y-",
        "colab_type": "text"
      },
      "source": [
        "# Jupyter\n",
        "\n",
        "Two main ingredients in a notebook are:\n",
        "  1. **cells**: A container for code or text (e.g., this is written within a markdown cell)\n",
        "  1. **kernels**: The \"computational engine\" which executes code blocks of the notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMGWZzQnr6Y_",
        "colab_type": "text"
      },
      "source": [
        "### Cells\n",
        "\n",
        "Cells can contain either **code** or **markdown**\n",
        "\n",
        "Check out keyboard shortcuts via `Cmd/Ctrl + Shift + P`.\n",
        "\n",
        "Few important ones:\n",
        " * `Shift + Enter`: Executes the current cell and moves to the next\n",
        " * `Esc`: Enter vim-like command mode. Allows you to navigate cells via up/down arrow keys.\n",
        " * `Tab`: Autocompletes\n",
        " * `Shift + Tab`: Brings up documentation. Try this after entering `np.ones(`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6Nipc8Xr6ZC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "15263e01-b4a0-453a-bbd8-7d9a8f72f9f6"
      },
      "source": [
        "def say_hello():\n",
        "    print('This is a code cell')\n",
        "\n",
        "say_hello()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This is a code cell\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9O94quMr6ZI",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "or it can be a **markdown** cell, like this one.\n",
        "\n",
        "If you're unfamiliar with Markdown syntax, check our this [cheat sheet](https://www.markdownguide.org/cheat-sheet).\n",
        "\n",
        "Some things you can do with Markdown:\n",
        "\n",
        "## This is a level 2 heading\n",
        "### This is a level 3 heading\n",
        "\n",
        "**Syntax**\n",
        "\n",
        "This is some plain text that forms a paragraph.\n",
        "Add emphasis via **bold** and __bold__, or *italic* and _italic_.\n",
        "\n",
        "Paragraphs must be separated by an empty line."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atCO7hSYr6ZI",
        "colab_type": "text"
      },
      "source": [
        "**Lists**\n",
        "\n",
        "* Sometimes we want to include lists.\n",
        " * Which can be indented.   \n",
        "\n",
        "\n",
        " 1. Lists can also be numbered.\n",
        " 2. For ordered lists.\n",
        "\n",
        "[It is possible to include hyperlinks](https://www.example.com)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qu3-Cnxr6ZJ",
        "colab_type": "text"
      },
      "source": [
        "**Code blocks**\n",
        "\n",
        "Inline code uses single backticks: `foo()`, and code blocks use triple backticks:\n",
        "\n",
        "```\n",
        "bar()\n",
        "```\n",
        "\n",
        "Or can be intented by 4 spaces:\n",
        "\n",
        "    foo()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzEDtFKFr6ZL",
        "colab_type": "text"
      },
      "source": [
        "**Latex Code**\n",
        "\n",
        ">$y=x^2$\n",
        "\n",
        ">$e^{i/pi} + 1 = 0$\n",
        "\n",
        ">$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$\n",
        "\n",
        ">$\\frac{n!}{k!(n-k)!} = {n \\choose k}$\n",
        "\n",
        ">$A_{m,n} =\n",
        " \\begin{pmatrix}\n",
        "  a_{1,1} & a_{1,2} & \\cdots & a_{1,n} \\\\\n",
        "  a_{2,1} & a_{2,2} & \\cdots & a_{2,n} \\\\\n",
        "  \\vdots  & \\vdots  & \\ddots & \\vdots  \\\\\n",
        "  a_{m,1} & a_{m,2} & \\cdots & a_{m,n}\n",
        " \\end{pmatrix}$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFVIqSAir6ZN",
        "colab_type": "text"
      },
      "source": [
        "**Tables**\n",
        "\n",
        ">```\n",
        "First column name | Second column name\n",
        "--- | ---\n",
        "Row 1, Col 1 | Row 1, Col 2\n",
        "Row 2, Col 1 | Row 2, Col 2\n",
        "```\n",
        "\n",
        "becomes:\n",
        "\n",
        ">First column name | Second column name\n",
        ">--- | ---\n",
        ">Row 1, Col 1 | Row 1, Col 2\n",
        ">Row 2, Col 1 | Row 2, Col 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acT4R5oTr6ZO",
        "colab_type": "text"
      },
      "source": [
        "___"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ocj05pSEr6ZQ",
        "colab_type": "text"
      },
      "source": [
        "# Python\n",
        "\n",
        "### Python Versions\n",
        "\n",
        "There are two supported/popular versions of Python: 2.7 and 3.x. Note that Python 3.x is not backward-compatible (i.e., Python 2 code is incompatible with 3.x). For thhis course, we'll rely specifically on Python 3.7. \n",
        "\n",
        "Note that that Python 2.7 was once really popular. This will be deprecated in the near future. If you're using this (even elsewhere in your personal/work life) -- migrate!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMbhLYH3r6ZR",
        "colab_type": "text"
      },
      "source": [
        "### Basic Data Types\n",
        "\n",
        "**Numbers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnCdwFJjr6ZT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "1f64cbaa-1950-4a33-94b7-a70f27158d75"
      },
      "source": [
        "x = 3\n",
        "print(type(x)) # Prints \"<class 'int'>\"\n",
        "print(x)       # Prints \"3\"\n",
        "print(x + 1)   # Addition; prints \"4\"\n",
        "print(x - 1)   # Subtraction; prints \"2\"\n",
        "print(x * 2)   # Multiplication; prints \"6\"\n",
        "print(x ** 2)  # Exponentiation; prints \"9\"\n",
        "x += 1\n",
        "print(x)  # Prints \"4\"\n",
        "x *= 2\n",
        "print(x)  # Prints \"8\"\n",
        "y = 2.5\n",
        "print(type(y)) # Prints \"<class 'float'>\"\n",
        "print(y, y + 1, y * 2, y ** 2) # Prints \"2.5 3.5 5.0 6.25\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'int'>\n",
            "3\n",
            "4\n",
            "2\n",
            "6\n",
            "9\n",
            "4\n",
            "8\n",
            "<class 'float'>\n",
            "2.5 3.5 5.0 6.25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsqkumfRr6ZY",
        "colab_type": "text"
      },
      "source": [
        "**Boolean**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWjczYkIr6Za",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "8665402d-55c7-4ca9-dbf7-ff2b06be1548"
      },
      "source": [
        "t = True\n",
        "f = False\n",
        "print(type(t)) # Prints \"<class 'bool'>\"\n",
        "print(t and f) # Logical AND; prints \"False\"\n",
        "print(t or f)  # Logical OR; prints \"True\"\n",
        "print(not t)   # Logical NOT; prints \"False\"\n",
        "print(t != f)  # Logical XOR; prints \"True\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'bool'>\n",
            "False\n",
            "True\n",
            "False\n",
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKekP5e3r6Zd",
        "colab_type": "text"
      },
      "source": [
        "**Strings**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zk5coifGr6Zd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a109bc1d-7b06-4284-c75c-c7983da60583"
      },
      "source": [
        "hello = 'hello'    # String literals can use single quotes\n",
        "world = \"world\"    # or double quotes; it does not matter.\n",
        "print(hello)       # Prints \"hello\"\n",
        "print(len(hello))  # String length; prints \"5\"\n",
        "hw = hello + ' ' + world  # String concatenation\n",
        "print(hw)  # prints \"hello world\"\n",
        "hw12 = '%s %s %d' % (hello, world, 12)  # sprintf style string formatting\n",
        "print(hw12)  # prints \"hello world 12\"\n",
        "# Recommended printing style\n",
        "hw12 = '{} {} {}'.format(hello, world, 12)\n",
        "print(hw12)\n",
        "\n",
        "s = \"hello\"\n",
        "print(s.capitalize())  # Capitalize a string; prints \"Hello\"\n",
        "print(s.upper())       # Convert a string to uppercase; prints \"HELLO\"\n",
        "print(s.rjust(7))      # Right-justify a string, padding with spaces; prints \"  hello\"\n",
        "print(s.center(7))     # Center a string, padding with spaces; prints \" hello \"\n",
        "print(s.replace('l', '(ell)'))  # Replace all instances of one substring with another;\n",
        "                                # prints \"he(ell)(ell)o\"\n",
        "print('  world '.strip())  # Strip leading and trailing whitespace; prints \"world\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello\n",
            "5\n",
            "hello world\n",
            "hello world 12\n",
            "hello world 12\n",
            "Hello\n",
            "HELLO\n",
            "  hello\n",
            " hello \n",
            "he(ell)(ell)o\n",
            "world\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FsAycd7Vr6Zh",
        "colab_type": "text"
      },
      "source": [
        "## Containers\n",
        "\n",
        "Python extensively relies on four types of containers:\n",
        " 1. Lists\n",
        " 1. Dictionaries\n",
        " 1. Sets\n",
        " 1. Tuples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ZR3C8ZHr6Zi",
        "colab_type": "text"
      },
      "source": [
        "**Lists**  : resizable arrays, which can contain heteregenous elements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGFvuz2Jr6Zk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "7d100d32-4970-4757-cdc1-27cf2148f465"
      },
      "source": [
        "xs = [3, 1, 2, 'foo']    # Create a list\n",
        "print(xs, xs[2])  # Prints \"[3, 1, 2] 2\"\n",
        "print(xs[-1])     # Negative indices count from the end of the list; prints \"2\"\n",
        "xs[2] = 'foo'     # Lists can contain elements of different types\n",
        "print(xs)         # Prints \"[3, 1, 'foo']\"\n",
        "xs.append('bar')  # Add a new element to the end of the list\n",
        "print(xs)         # Prints \"[3, 1, 'foo', 'bar']\"\n",
        "x = xs.pop()      # Remove and return the last element of the list\n",
        "print(x, xs)      # Prints \"bar [3, 1, 'foo']\"\n",
        "\n",
        "# ----- Loops\n",
        "animals = ['cat', 'dog', 'monkey']\n",
        "for animal in animals:\n",
        "    print(animal)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3, 1, 2, 'foo'] 2\n",
            "foo\n",
            "[3, 1, 'foo', 'foo']\n",
            "[3, 1, 'foo', 'foo', 'bar']\n",
            "bar [3, 1, 'foo', 'foo']\n",
            "cat\n",
            "dog\n",
            "monkey\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkfemiY6r6Zo",
        "colab_type": "text"
      },
      "source": [
        "**Dictionaries**: Key-value pairs (similar to `Map`)\n",
        "\n",
        "Also check-out `collections.defaultdict`, which is convenient at certain times."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sf4Uc2eyr6Zp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "104d3ad5-06ef-48f0-9e00-1174cbb3a43b"
      },
      "source": [
        "d = {'cat': 'cute', 'dog': 'furry'}  # Create a new dictionary with some data\n",
        "print(d['cat'])       # Get an entry from a dictionary; prints \"cute\"\n",
        "print('cat' in d)     # Check if a dictionary has a given key; prints \"True\"\n",
        "print(d)\n",
        "d['fish'] = 'wet'     # Set an entry in a dictionary\n",
        "print(d)\n",
        "print(d['fish'])      # Prints \"wet\"\n",
        "# print(d['monkey'])  # KeyError: 'monkey' not a key of d\n",
        "print(d.get('monkey', 'N/A'))  # Get an element with a default; prints \"N/A\"\n",
        "print(d.get('fish', 'N/A'))    # Get an element with a default; prints \"wet\"\n",
        "del d['fish']         # Remove an element from a dictionary\n",
        "print(d.get('fish', 'N/A')) # \"fish\" is no longer a key; prints \"N/A\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cute\n",
            "True\n",
            "{'cat': 'cute', 'dog': 'furry'}\n",
            "{'cat': 'cute', 'dog': 'furry', 'fish': 'wet'}\n",
            "wet\n",
            "N/A\n",
            "wet\n",
            "N/A\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1F5X9Fy9r6Zt",
        "colab_type": "text"
      },
      "source": [
        "**Sets**: Unordered collection of elements\n",
        "\n",
        "PS: Both dictionaries and sets use a `{...}` convention. Be cautious."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPzsxL4dr6Zw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "0c19d2c1-ffc8-498c-dc06-6e770210c896"
      },
      "source": [
        "animals = {'cat', 'dog'}\n",
        "print('cat' in animals)   # Check if an element is in a set; prints \"True\"\n",
        "print('fish' in animals)  # prints \"False\"\n",
        "animals.add('fish')       # Add an element to a set\n",
        "print('fish' in animals)  # Prints \"True\"\n",
        "print(len(animals))       # Number of elements in a set; prints \"3\"\n",
        "animals.add('cat')        # Adding an element that is already in the set does nothing\n",
        "print(len(animals))       # Prints \"3\"\n",
        "animals.remove('cat')     # Remove an element from a set\n",
        "print(len(animals))       # Prints \"2\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n",
            "False\n",
            "True\n",
            "3\n",
            "3\n",
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlAJJ9ror6Z1",
        "colab_type": "text"
      },
      "source": [
        "**Tuples**: Immutable lists"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rsjHUgur6Z6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "94a436a9-a984-46c1-df39-b3593a395bb1"
      },
      "source": [
        "d = {(x, x + 1): x for x in range(10)}  # Create a dictionary with tuple keys\n",
        "t = (5, 6)        # Create a tuple\n",
        "print(type(t))    # Prints \"<class 'tuple'>\"\n",
        "print(d[t])       # Prints \"5\"\n",
        "print(d[(1, 2)])  # Prints \"1\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'tuple'>\n",
            "5\n",
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IfWvQQdLr6Z-",
        "colab_type": "text"
      },
      "source": [
        "### Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DKVpig-r6Z-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "8ca1521c-f9a7-45b0-969d-24d30085b893"
      },
      "source": [
        "def sign(x):\n",
        "    if x > 0:\n",
        "        return 'positive'\n",
        "    elif x < 0:\n",
        "        return 'negative'\n",
        "    else:\n",
        "        return 'zero'\n",
        "\n",
        "for x in [-1, 0, 1]:\n",
        "    print(sign(x))\n",
        "# Prints \"negative\", \"zero\", \"positive\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "negative\n",
            "zero\n",
            "positive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKvDgdKlr6aC",
        "colab_type": "text"
      },
      "source": [
        "___"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mak1I18Sr6aG",
        "colab_type": "text"
      },
      "source": [
        "# Numpy\n",
        "\n",
        "\"NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more\"\n",
        "-https://docs.scipy.org/doc/numpy-1.10.1/user/whatisnumpy.html."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OOCUaPOMr6aI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as  np    # Almost everyone (at least the sane ones) import it this way"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5erTBwlVr6aM",
        "colab_type": "text"
      },
      "source": [
        "## Motivation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUm26YiFr6aN",
        "colab_type": "text"
      },
      "source": [
        "### Faster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EKYpHh4Lr6aO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def matrixmult (A, B):\n",
        "    rows_A = len(A)\n",
        "    cols_A = len(A[0])\n",
        "    rows_B = len(B)\n",
        "    cols_B = len(B[0])\n",
        "\n",
        "    if cols_A != rows_B:\n",
        "      print(\"Cannot multiply the two matrices. Incorrect dimensions.\")\n",
        "      return\n",
        "\n",
        "    # Create the result matrix\n",
        "    # Dimensions would be rows_A x cols_B\n",
        "    C = [[0 for row in range(cols_B)] for col in range(rows_A)]\n",
        "\n",
        "    for i in range(rows_A):\n",
        "        for j in range(cols_B):\n",
        "            for k in range(cols_A):\n",
        "                C[i][j] += A[i][k] * B[k][j]\n",
        "    return C"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWl-mMThr6aS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0ec978aa-3785-404e-a36e-0cf32effaef0"
      },
      "source": [
        "A = np.random.random((10**2, 10**2))\n",
        "B = np.random.random((10**2, 10**2))\n",
        "print(A.shape, B.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 100) (100, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXMdFMqYr6aX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "d170bc3d-5436-4166-b536-f864c95ae6e1"
      },
      "source": [
        "%%time\n",
        "C = matrixmult(A, B)\n",
        "print(np.sum(C))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "250564.80507326918\n",
            "CPU times: user 767 ms, sys: 1.26 ms, total: 768 ms\n",
            "Wall time: 768 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwFdom_Dr6ac",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "c7e93f46-efde-4567-b571-ba2e4d492567"
      },
      "source": [
        "%%time\n",
        "C = A.dot(B)    # Note: A*B performs element-wise multiplication\n",
        "print(np.sum(C))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "250564.80507326918\n",
            "CPU times: user 447 µs, sys: 1.69 ms, total: 2.14 ms\n",
            "Wall time: 1.21 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViSGYTU6r6ag",
        "colab_type": "text"
      },
      "source": [
        "### Stronger"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1NfUc1ur6ah",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "902906a9-cc52-4c07-f17b-e60a41923e16"
      },
      "source": [
        "a = np.array([\n",
        "    [1, 2, 3, 1],\n",
        "    [5, 7, 9, 10],\n",
        "    [4, 6, 8, 2],\n",
        "])\n",
        "\n",
        "print(a.shape)\n",
        "print(a[2, 2])\n",
        "print(a[1:2, 2:3])\n",
        "print(a[:-1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3, 4)\n",
            "8\n",
            "[[9]]\n",
            "[[ 1  2  3  1]\n",
            " [ 5  7  9 10]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuQPNLuLr6al",
        "colab_type": "text"
      },
      "source": [
        "Other ways of creating arrays"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7t-sToQr6am",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "53959c18-6123-4a3d-84cd-a1afa337f9e2"
      },
      "source": [
        "a = np.zeros((2,2))   # Create an array of all zeros\n",
        "print(a)              # Prints \"[[ 0.  0.]\n",
        "                      #          [ 0.  0.]]\"\n",
        "\n",
        "b = np.ones((1,2))    # Create an array of all ones\n",
        "print(b)              # Prints \"[[ 1.  1.]]\"\n",
        "\n",
        "c = np.full((2,2), 7)  # Create a constant array\n",
        "print(c)               # Prints \"[[ 7.  7.]\n",
        "                       #          [ 7.  7.]]\"\n",
        "\n",
        "d = np.eye(2)         # Create a 2x2 identity matrix\n",
        "print(d)              # Prints \"[[ 1.  0.]\n",
        "                      #          [ 0.  1.]]\"\n",
        "\n",
        "e = np.random.random((2,2))  # Create an array filled with random values\n",
        "print(e)                     # Might print \"[[ 0.91940167  0.08143941]\n",
        "                             #               [ 0.68744134  0.87236687]]\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0.]\n",
            " [0. 0.]]\n",
            "[[1. 1.]]\n",
            "[[7 7]\n",
            " [7 7]]\n",
            "[[1. 0.]\n",
            " [0. 1.]]\n",
            "[[0.50488863 0.65921167]\n",
            " [0.19178881 0.5312908 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtaUGkMKr6aq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9d38f5ec-d386-4949-ff59-fbfe7b333885"
      },
      "source": [
        "print(e > 0.5)\n",
        "print(e[e > 0.5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ True  True]\n",
            " [False  True]]\n",
            "[0.50488863 0.65921167 0.5312908 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYKvnp8tr6as",
        "colab_type": "text"
      },
      "source": [
        "**Numpy Operations**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1toq8yh8r6at",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "3e968c36-4c23-4c0e-a962-38964e826f95"
      },
      "source": [
        "x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
        "y = np.array([[5,6],[7,8]], dtype=np.float64)\n",
        "\n",
        "# Elementwise sum; both produce the array\n",
        "# [[ 6.0  8.0]\n",
        "#  [10.0 12.0]]\n",
        "print(x + y)\n",
        "print(np.add(x, y))\n",
        "\n",
        "# Elementwise difference; both produce the array\n",
        "# [[-4.0 -4.0]\n",
        "#  [-4.0 -4.0]]\n",
        "print(x - y)\n",
        "print(np.subtract(x, y))\n",
        "\n",
        "# Elementwise product; both produce the array\n",
        "# [[ 5.0 12.0]\n",
        "#  [21.0 32.0]]\n",
        "print(x * y)\n",
        "print(np.multiply(x, y))\n",
        "\n",
        "# Elementwise division; both produce the array\n",
        "# [[ 0.2         0.33333333]\n",
        "#  [ 0.42857143  0.5       ]]\n",
        "print(x / y)\n",
        "print(np.divide(x, y))\n",
        "\n",
        "# Elementwise square root; produces the array\n",
        "# [[ 1.          1.41421356]\n",
        "#  [ 1.73205081  2.        ]]\n",
        "print(np.sqrt(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 6.  8.]\n",
            " [10. 12.]]\n",
            "[[ 6.  8.]\n",
            " [10. 12.]]\n",
            "[[-4. -4.]\n",
            " [-4. -4.]]\n",
            "[[-4. -4.]\n",
            " [-4. -4.]]\n",
            "[[ 5. 12.]\n",
            " [21. 32.]]\n",
            "[[ 5. 12.]\n",
            " [21. 32.]]\n",
            "[[0.2        0.33333333]\n",
            " [0.42857143 0.5       ]]\n",
            "[[0.2        0.33333333]\n",
            " [0.42857143 0.5       ]]\n",
            "[[1.         1.41421356]\n",
            " [1.73205081 2.        ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZ0u3LApr6ay",
        "colab_type": "text"
      },
      "source": [
        "# Pandas\n",
        "\n",
        "\"pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.\" - [Pandas](http://pandas.pydata.org/pandas-docs/stable/)\n",
        "\n",
        "\n",
        "\"Python has long been great for data munging and preparation, but less so for data analysis and modeling. pandas helps fill this gap, enabling you to carry out your entire data analysis workflow in Python without having to switch to a more domain specific language like R.\" - [Why Pandas?](https://pandas.pydata.org/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY2QTSEzr6az",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REy3ldRcr6a3",
        "colab_type": "text"
      },
      "source": [
        "Often you'll be dealing with $d$-dim datapoints, or $d$-features. A **dataframe** provides a nice way to encapsulate this data and represent it in a tabular structure."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujknwEhWr6a4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "3241973e-20c5-4f7a-9682-e979433171fc"
      },
      "source": [
        "df = pd.DataFrame({ 'A' : [1., 2., 3., 4.],\n",
        "                     'B' : pd.Timestamp('20130102'),\n",
        "                     'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
        "                     'D' : np.array([3] * 4,dtype='int32'),\n",
        "                     'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
        "                     'F' : ['foo', 'bar', 'foo', 'bar'] })\n",
        "display(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>F</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2013-01-02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>test</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2013-01-02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>train</td>\n",
              "      <td>bar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2013-01-02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>test</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>2013-01-02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>train</td>\n",
              "      <td>bar</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     A          B    C  D      E    F\n",
              "0  1.0 2013-01-02  1.0  3   test  foo\n",
              "1  2.0 2013-01-02  1.0  3  train  bar\n",
              "2  3.0 2013-01-02  1.0  3   test  foo\n",
              "3  4.0 2013-01-02  1.0  3  train  bar"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZH1VOyz_r6a8",
        "colab_type": "text"
      },
      "source": [
        "### Load some existing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4jqZdv3lr6a9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "18a73268-b5c1-4706-cd8c-d9374e595a45"
      },
      "source": [
        "! if [ ! -f iris.csv ]; then wget https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv; fi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-10-22 12:12:29--  https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv\n",
            "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3858 (3.8K) [text/plain]\n",
            "Saving to: ‘iris.csv’\n",
            "\n",
            "\riris.csv              0%[                    ]       0  --.-KB/s               \riris.csv            100%[===================>]   3.77K  --.-KB/s    in 0s      \n",
            "\n",
            "2018-10-22 12:12:29 (79.5 MB/s) - ‘iris.csv’ saved [3858/3858]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QTmlx5Ir6a_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "1146627d-73d1-4719-b68b-1ab69612adf1"
      },
      "source": [
        "! head iris.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sepal_length,sepal_width,petal_length,petal_width,species\n",
            "5.1,3.5,1.4,0.2,setosa\n",
            "4.9,3.0,1.4,0.2,setosa\n",
            "4.7,3.2,1.3,0.2,setosa\n",
            "4.6,3.1,1.5,0.2,setosa\n",
            "5.0,3.6,1.4,0.2,setosa\n",
            "5.4,3.9,1.7,0.4,setosa\n",
            "4.6,3.4,1.4,0.3,setosa\n",
            "5.0,3.4,1.5,0.2,setosa\n",
            "4.4,2.9,1.4,0.2,setosa\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "pDWbP-eUr6bF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1986
        },
        "outputId": "7422fa6b-f379-49f7-ed40-b70e95ce2d6f"
      },
      "source": [
        "iris = pd.read_csv('iris.csv')\n",
        "print(type(iris))   # Iris is a dataframe\n",
        "iris"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ],
          "name": "stdout"
        },
        {
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>7.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>6.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>7.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>6.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "0             5.1          3.5           1.4          0.2     setosa\n",
              "1             4.9          3.0           1.4          0.2     setosa\n",
              "2             4.7          3.2           1.3          0.2     setosa\n",
              "3             4.6          3.1           1.5          0.2     setosa\n",
              "4             5.0          3.6           1.4          0.2     setosa\n",
              "5             5.4          3.9           1.7          0.4     setosa\n",
              "6             4.6          3.4           1.4          0.3     setosa\n",
              "7             5.0          3.4           1.5          0.2     setosa\n",
              "8             4.4          2.9           1.4          0.2     setosa\n",
              "9             4.9          3.1           1.5          0.1     setosa\n",
              "10            5.4          3.7           1.5          0.2     setosa\n",
              "11            4.8          3.4           1.6          0.2     setosa\n",
              "12            4.8          3.0           1.4          0.1     setosa\n",
              "13            4.3          3.0           1.1          0.1     setosa\n",
              "14            5.8          4.0           1.2          0.2     setosa\n",
              "15            5.7          4.4           1.5          0.4     setosa\n",
              "16            5.4          3.9           1.3          0.4     setosa\n",
              "17            5.1          3.5           1.4          0.3     setosa\n",
              "18            5.7          3.8           1.7          0.3     setosa\n",
              "19            5.1          3.8           1.5          0.3     setosa\n",
              "20            5.4          3.4           1.7          0.2     setosa\n",
              "21            5.1          3.7           1.5          0.4     setosa\n",
              "22            4.6          3.6           1.0          0.2     setosa\n",
              "23            5.1          3.3           1.7          0.5     setosa\n",
              "24            4.8          3.4           1.9          0.2     setosa\n",
              "25            5.0          3.0           1.6          0.2     setosa\n",
              "26            5.0          3.4           1.6          0.4     setosa\n",
              "27            5.2          3.5           1.5          0.2     setosa\n",
              "28            5.2          3.4           1.4          0.2     setosa\n",
              "29            4.7          3.2           1.6          0.2     setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "120           6.9          3.2           5.7          2.3  virginica\n",
              "121           5.6          2.8           4.9          2.0  virginica\n",
              "122           7.7          2.8           6.7          2.0  virginica\n",
              "123           6.3          2.7           4.9          1.8  virginica\n",
              "124           6.7          3.3           5.7          2.1  virginica\n",
              "125           7.2          3.2           6.0          1.8  virginica\n",
              "126           6.2          2.8           4.8          1.8  virginica\n",
              "127           6.1          3.0           4.9          1.8  virginica\n",
              "128           6.4          2.8           5.6          2.1  virginica\n",
              "129           7.2          3.0           5.8          1.6  virginica\n",
              "130           7.4          2.8           6.1          1.9  virginica\n",
              "131           7.9          3.8           6.4          2.0  virginica\n",
              "132           6.4          2.8           5.6          2.2  virginica\n",
              "133           6.3          2.8           5.1          1.5  virginica\n",
              "134           6.1          2.6           5.6          1.4  virginica\n",
              "135           7.7          3.0           6.1          2.3  virginica\n",
              "136           6.3          3.4           5.6          2.4  virginica\n",
              "137           6.4          3.1           5.5          1.8  virginica\n",
              "138           6.0          3.0           4.8          1.8  virginica\n",
              "139           6.9          3.1           5.4          2.1  virginica\n",
              "140           6.7          3.1           5.6          2.4  virginica\n",
              "141           6.9          3.1           5.1          2.3  virginica\n",
              "142           5.8          2.7           5.1          1.9  virginica\n",
              "143           6.8          3.2           5.9          2.3  virginica\n",
              "144           6.7          3.3           5.7          2.5  virginica\n",
              "145           6.7          3.0           5.2          2.3  virginica\n",
              "146           6.3          2.5           5.0          1.9  virginica\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24rPHcBCr6bL",
        "colab_type": "text"
      },
      "source": [
        "### Viewing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TMbh5zcr6bU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "outputId": "d8e03626-18f2-4769-d932-4226921f4a44"
      },
      "source": [
        "# Some exploratory functions\n",
        "print('Head')\n",
        "display(iris.head(n=5))   # Show just the first few rows\n",
        "\n",
        "print('Tail')\n",
        "display(iris.tail(n=3))\n",
        "\n",
        "print('Random sample')\n",
        "display(iris.sample(n=5))\n",
        "\n",
        "display(iris.columns)\n",
        "\n",
        "# Simple stats\n",
        "display(iris.describe())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Head\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Tail\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Random sample\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.3</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>5.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>versicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width     species\n",
              "16            5.4          3.9           1.3          0.4      setosa\n",
              "87            6.3          2.3           4.4          1.3  versicolor\n",
              "66            5.6          3.0           4.5          1.5  versicolor\n",
              "103           6.3          2.9           5.6          1.8   virginica\n",
              "31            5.4          3.4           1.5          0.4      setosa"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
              "       'species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count    150.000000   150.000000    150.000000   150.000000\n",
              "mean       5.843333     3.054000      3.758667     1.198667\n",
              "std        0.828066     0.433594      1.764420     0.763161\n",
              "min        4.300000     2.000000      1.000000     0.100000\n",
              "25%        5.100000     2.800000      1.600000     0.300000\n",
              "50%        5.800000     3.000000      4.350000     1.300000\n",
              "75%        6.400000     3.300000      5.100000     1.800000\n",
              "max        7.900000     4.400000      6.900000     2.500000"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eV91Qn27r6bX",
        "colab_type": "text"
      },
      "source": [
        "### Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "1ONLlFbkr6bZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "193e3928-d82f-483e-f782-b042fc83b994"
      },
      "source": [
        "# sample = iris.sample(n=5)\n",
        "\n",
        "# # Select columns\n",
        "# print('Selecting columns')\n",
        "# display(sample[['sepal_length', 'species']])\n",
        "\n",
        "# # Select rows\n",
        "# print('Selecting rows')\n",
        "# display(sample[:3])\n",
        "\n",
        "print('Filter rows based on some criteria')\n",
        "# Using a single filter\n",
        "display(iris[iris['petal_length'] > 6.0])\n",
        "# Multiple filters\n",
        "display(iris[(iris['petal_length'] > 6.0) & (iris['petal_width'] < 2.0)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Filter rows based on some criteria\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>7.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>7.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>6.3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>7.7</td>\n",
              "      <td>2.6</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>7.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>7.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "105           7.6          3.0           6.6          2.1  virginica\n",
              "107           7.3          2.9           6.3          1.8  virginica\n",
              "109           7.2          3.6           6.1          2.5  virginica\n",
              "117           7.7          3.8           6.7          2.2  virginica\n",
              "118           7.7          2.6           6.9          2.3  virginica\n",
              "122           7.7          2.8           6.7          2.0  virginica\n",
              "130           7.4          2.8           6.1          1.9  virginica\n",
              "131           7.9          3.8           6.4          2.0  virginica\n",
              "135           7.7          3.0           6.1          2.3  virginica"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>7.3</td>\n",
              "      <td>2.9</td>\n",
              "      <td>6.3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>7.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "107           7.3          2.9           6.3          1.8  virginica\n",
              "130           7.4          2.8           6.1          1.9  virginica"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95TIrmTdr6bc",
        "colab_type": "text"
      },
      "source": [
        "### Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "vRYNS5NNr6bd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "bfc30ef5-38f1-4ad0-80ed-fc0c6e738b69"
      },
      "source": [
        "display(iris.sample(n=5).apply(np.cumsum))   # Map function f to each row in sequence\n",
        "display(iris.mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>11.6</td>\n",
              "      <td>5.5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>virginicaversicolor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>17.3</td>\n",
              "      <td>9.9</td>\n",
              "      <td>11.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>virginicaversicolorsetosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.4</td>\n",
              "      <td>13.4</td>\n",
              "      <td>12.9</td>\n",
              "      <td>4.2</td>\n",
              "      <td>virginicaversicolorsetosasetosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>29.1</td>\n",
              "      <td>16.7</td>\n",
              "      <td>18.6</td>\n",
              "      <td>6.7</td>\n",
              "      <td>virginicaversicolorsetosasetosavirginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width  \\\n",
              "121           5.6          2.8           4.9          2.0   \n",
              "83           11.6          5.5          10.0          3.6   \n",
              "15           17.3          9.9          11.5          4.0   \n",
              "0            22.4         13.4          12.9          4.2   \n",
              "144          29.1         16.7          18.6          6.7   \n",
              "\n",
              "                                      species  \n",
              "121                                 virginica  \n",
              "83                        virginicaversicolor  \n",
              "15                  virginicaversicolorsetosa  \n",
              "0             virginicaversicolorsetosasetosa  \n",
              "144  virginicaversicolorsetosasetosavirginica  "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sepal_length    5.843333\n",
              "sepal_width     3.054000\n",
              "petal_length    3.758667\n",
              "petal_width     1.198667\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HgFyCmgDr6bj",
        "colab_type": "text"
      },
      "source": [
        "# Matplotlib\n",
        "\n",
        "\"Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.\" -[Matplotlib](https://matplotlib.org/index.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Xuy9VLvr6bk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# To display plots inline, use this special Jupyter command\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKvYAK2Ar6bm",
        "colab_type": "text"
      },
      "source": [
        "### Barebones example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8x45UP4r6bn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "02a1d42c-6ae3-496f-9f48-4d591a8f66c3"
      },
      "source": [
        "t = np.arange(0, np.pi * 3, 0.01)\n",
        "y1 = np.sin(t)\n",
        "y2 = np.cos(t)\n",
        "\n",
        "plt.plot(t, y1)\n",
        "plt.plot(t, y2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NvNnzLivrKw895krbKIOOOY5WpSs+IbUVer2OL54oQpbh3z66r7Q4GrtgbHGCi8NXSYlK\nDonJ2614Ov8MZoOZt3rfZ2nt4a6RxnYbA/Y5GirSKcgIYV05VYRMeOmKZvR3SEJEPKdzHmN6ZZYL\nA5d+Yf/aupNXLvZgNOj4peOFCki4c2qKkynOTuDmvTG6BqeVFkdjh7zZ8x5O2cmzBU8GtYjJbokz\nx3Im5wSzK3NcGLz4C/vXnU5e+bgHg17HLz0WGpO3W1FZkERpTiKt3eN0DkwpLY5f8MnoCyG+I4S4\nIoS4LIQ4uGF7lhDiwoa/fiHEC0KIrwohBjZs/4++30LweDLvJDHGaN7pv8DC6qeLUly8PcLY9BIn\na7JITohUSMKdodPp+NIpV9K3ly50BbyGgIbvjM7buTbaTEZMGgfSapUWZ1vO5J4gxhTNe/0fMr9J\nVy7fHsU2uciJmkysuygPqgQ6ne5BgsSfftgdFrritdEXQpwEStzFz38L+CvPPkmShiRJOiVJ0ing\nCaAfVylFgB979kmS9Mfeix58ooxRnM0/zeLaIu/3f/Rg++qak59f7sVk1PPsUXVU4SnJTqS2OIXO\nwWnu9k4qLY7GNrze8w4yMs8VPhUSIZrbEWWM5Km8x1lcW+LdvgsPtnvqOpuMep47mq+YfLuhODuB\n2uIU7g1Oc/u++hc3+vL2nAFeBpAkqR2wCCEe5pz7Kq4auWExa3giq4FYUwwXBi+zsLoIwEe3hpmY\nWebx+iwSYyMUlnDnnDueD8DPL/cqKofGoxmaG+GGvZXcuCxqUiqUFmfHnMhqIMEcx0dDlx/09j9u\nHWZ8ZpnTdVlY4tSjK58/4XLZ/vxyr+p7+744BtOB5g3fHe5tM5uO+wZwdsP3k0KItwAT8PuSJN18\n1EUslmiMRu/jd61W/6+GPVf+JP/S+jJNk02cE0/x5rV+Is0Gfv3ZChJC0Ohv9Qys1jgOlA/Q1G7D\nPrtCRWFykCULDoF4B4LJP3f9KwAv1H6O1FTvJj2Vegaf23eW77f8lMaJ63yh/Bnevj6A2eTSlcQg\nGn1f799qjePQvnQa745im1mhqjjFT5IFH3/OBv1CzJUQogHokCTJ80NwFXBIkvS6e9/3gapHnXRy\n0vuCzoEqir0/sZ6XjW/z8473WRvOYWJmiacO5bCyuIJj8eGRPUqx3TM4eyCbpnYbP3jjLr/3K6Hv\nK94tai+M7lgY50p/M1mxGWQb87y6FyWfQW1CHT81vckb0gfoRvNxTC7yxP5sVpdWcCwFR1f8df9P\nHsii8e4oP3jjLv/+y3V+kCywbPVD54t7ZxhXz95DJjCy6ZjngAcZmCRJ6pAk6XX35yuAVQgROsvw\ndkikMZLTOceZX1vg9c6PMOh1nD2Yq7RYXlGclUB5noW2ngnuD28epGkozXsDHyIjczbvdEjGsm9H\nhMHM6ezHWFhb5OedH2LQ63jqkDp1pSgzgX35Ftr7JukeUm/Umy9G/x3geQAhRD0wLEnS5p/Tg8At\nzxchxB8IIb7s/lyJq9evyvpkp7KPYdKZWYzv5HBliqr8k5vxTKi9fqVXSTE0NjG9PMvVkSZSIpOo\nsz5yQBzSnMw+iklnZiG+k0P7kkM+uu1RPNeQD6h7Hsxroy9J0mWgWQhxGVfkzrfdIZmf33BYBrAx\n5d6/AL8thPgQ+DtcUT+qJMoYhXm6AJ15haxi9f7qA5TlJlKYGU/LvTFsE9670zT8y/mBj1lzrvFE\n3ikMetUNiB8QZYzEPF2IzrRCeom6I8VEbiLFWQnc6h5neEydhdR98ulLkvSHmzbd2rS/atP3QeC0\nL9cMFTr6pxjrziCqtpPmyWs8LR9T5fAbXLHIZw/m8LevtPFu0wC/dlYoLdKeZ3FtkY+HrhJvjuNI\n+n6lxfGJzoEpxrrTiart5ObkdZ6VH1NF2OnD0Ol0PHUoh66fTfNu0wC/+XSZ0iLtGnU++RDgrWv9\nsBpJWcI+RuZtdEyqu9LOfmElOT6Ci7dHmFsM31JxauHy8HWW1pc4nX0ckyF4tWIDwduNA7Aayb6E\nCmwLdtonOpUWySfqSqykJERy+c4oswuhFbixEzSj7wWjEwvcvj9OcXYC50ofB+CD/o8Vlso3DHo9\nTxzIYWXVyYctQ0qLs6dxyk4+HLyMSW/iWNZhpcXxCfvUIre6xijMjOecOAOoX1f0eh1PHshhdc3J\nhZZhpcXZNZrR94L3m11FSJ7Yn01efA7FiQXcnZAYnhtVWDLfeKw6kwizgfebB7XqWgpyZ6yd8aUJ\nDqXXEWOKVlocn/igeRAZl67kxGVSmlhEx+Q9huY2B/qpi+PVGURFGPigeVB11bU0o79LFpfXuHR7\nBEtcBPWlrnz5j+ecAOD8wC8ml1IT0ZFGTlRnMjW3QmO7TWlx9iwXBl0J/U5lH1dYEt9YWlnj49YR\nEmLMHChLBeDx3McA9etKVISREzWZTM+rT1c0o79LLt0eYWllnVN1WQ8KOFellJMSlUyj7cYvJJdS\nG08cyEan+2Q0oxFchudGkSa7KLUUkxmbvn2DEObKnVEWl9c4vUFXKpLLSI1K4froDWZX1J2Z5cx+\nl6682zSgqtQMmtHfBU5Z5v0bQxgNOk7WfFKmTq/T81jWEdaca1wdaVJQQt+xJkZRU5RCz8gsvaPa\nYq1g8+GDXv4xhSXxDacs817zIAa9jpN1WQ+263V6TmYfY01e58ojChKpgZSEKOpLrPTb5lS1sFEz\n+rugrWcC28QCh8vTiI8xf2rfkYwDGPVGPh66glNWl49vM6fcSnr+hjahG0wWVhe4NnqD5EgLVSnl\nSovjE3d7JxgZX+BQeRoJm3TlUHo9Zr2Ji0PX1K8r9S5duXBTPbqiGf1d4HF5nDmQ/Qv7Yk0x7E+t\nwbE4jjSp7pqalYVJpCREcu2ujfklLXwzWFweuc6qc5UT2UdVG8fu4f0md7DDQ3Ql2hTFgbRaxpcm\nVB++WZ5nIc0SRWOHXTWhzup+s4LI2NQit7vHKcqM37IU4mNZDQB8PHQ1mKL5Hb1Ox+m6LFbWnFy+\nre6IJLUgyzKXhq9h1BtpyDi4fYMQZmJmidb74xRkxG1ZCjGcdOVUXRara04u3VZHRJJm9HfIR60j\nyMDJ2qwtj8mPzyEnNpNWRxuTS+ourXasOgOjQcf5m0OqmqRSK11T97EvjFFnrVJ9mObF1hFk+dG6\nkhufTW5cNnfG2plYUndqhmNVGRgNei6oRFc0o78D1p1OLrYOExVh4KA79Oxh6HQ6HstqQEbm0nBj\nECX0P/HRrjC70YkFOvrUrZRqwPO+HMtU92Isp1Pmo9ZhIswGDpVvrStA2OhKbJSJQ+Wp2CYXaVeB\nrmhGfwe0do8zNbfCkYp0IsyPTnx1IL2OSEMkl4evse5UZQLRBzxe5/LHnlfRJJUamVud56bjNmnR\nVooTQ7tQ+Hbc6ZlgYmaZI/vSiDQ/OrXX/rQaooyRXB5uVL2unPYEP6hAVzSjvwM+ci+13himuRUR\nBjOHM+qZXpnlznhHoEULKEVZ8WRbY7h5b4wZFeYYUQuNozdYc65xLPOwapP2efCk8DhZu0NdSd/P\nzMosrWN3Ay1aQCnMjCc3NZabnWNMzy0rLc4j0Yz+NngmpfLT48hN21nJtYaMQwCqj9nX6XQ8Vp3J\nulPm6h1tQjcQuCZwGzHqDBxWeTbNqbllbnWNk5cWt2Www2Y87iy1x+zrdDpO1GbilGUut4W2rmhG\nfxsu3nZNSp3YQc/FQ05cJjmxmdwZb2d6Wb2l+gCOVKRh0Ov4uHVEFZNUauP+dB+j8zZqrJXEmmOU\nFscnLraO4JTlXelKZmw6eXE53B2XmFpWd12Kw/vSMBr07ons0NUVzeg/Aqcs8/GtESJMBg6Xp+2q\n7ZHMgzhlJ9dtNwIkXXCIizZTV5LC0Ng8vaPq/gELRS4NXwPguMqzaTplmY9uDWM26Tmyb3e60pB5\nABmZxhF160pMpIn60hRGxhdCeoWu10ZfCPEdIcQVIcRlIcTBTft6hRAfCyEuuP+ytmsTitztmWB8\nZolD5alEReyu3szBtDqMOgNXhq+H9K/+Tjhe7eq5fdyqjjhktbC4tsgNeyvWqGRKEouUFscn2vsm\nGZte4lB52q51ZX9qLSa9kSsj6teVx1SgK14ZfSHESaBEkqQGXCUP/+ohh31GkqRT7r+hHbYJKS66\nF1uc2MEE7mZiTNHUWCsZXbDTO9Pvb9GCSmVBEpa4CK7dHWV5Vd1RFqHEDXsrq85VjmQcVP0E7mW3\nrjxWnbHrttGmKGqsldgXx7g/3edv0YJKeZ6F5PgIGtttLK+Epq5429M/A7wMIElSO2ARQmw3c+NN\nG8VYWFrlRucY6UnRFGZ6J6ZnZaXaJ6n0eh1HK9NZXF7nRqdDaXHChmsjzejQcSi9TmlRfGJxeY3m\nTgepliiKsxK8Okd46UoGSyvrNHfat2+gAN7WyE0Hmjd8d7i3bXRk/a0QIh+4CPzRDtv8AhZLNEaj\n90WhrdadRdxs5u2rvaytOzl7JI/UVO+MfnJyHT+6Z+GGvZVvNrxApDHCq/P4irfPYCPnThXz+pU+\nrrXbOXeqxA9SBQ9/3L+/GZ1z0D3dS2WqQOTkBvx6gXwG7zX2sbLq5MnDPuhKSi0vdiZx09HKtxpe\nINIU6VcZg/kOnDtVzGuXe7nW7uBzp0uDdt2d4lNh9A1sHpv+Z+AtYAJX7/6LO2jzUCYnvc9Pb7XG\n4XB4N/n49pVedEB1vsXrcwAcSq3nzd73ee/uFQ5nBD8kz5dnsBETUJqTSGvXGHfv2bEmRvkuXBDw\n1/37m7fufwRAfXJtwOUL9DN463IvADU+6srB1Hre6H2Pd9qv0JBxwE/SBf8dMABluYnc7h6j7Z6d\nVIV0ZasfOm/dO8O4eukeMoEHMxeSJH1fkiS7JElrwBtA1XZtQgn75AL3Bqcpy7OQFO9bj+OI++VV\n+7AV4HiVy197WYvZ9wmn7OTa6A3MBjM11kqlxfGJsalFpIEpRE4iKT4atwe6MhwGuuKe27gUghO6\n3hr9d4DnAYQQ9cCwJEmz7u8JQoi3hRCeJNongTuPahNqeIza0UrfKxelRCVTlFBA11SP6pOw7RdW\nzEY9V9pGVR9loSTdU72ML01QZ61SzOXnLzwLkY5W+a4ryVFJlCYW0T3dw9jihM/nU5L9IpUIsyEk\ndcUroy9J0mWgWQhxGVcUzreFEF8VQnxekqRpXL37q0KIS7h89y89rI1/bsG/OGWZy3dGiTAZ2C+s\nfjnnofQ6ZGSabC1+OZ9SREUYqS+1Yp9c5P5I6MYhhzqNo66pLbWvwJXdumI26TkgHp1cbaccSq8H\noMl20y/nU4oIk4H9pVbGppfoGgqtRWde+/QlSfrDTZtubdj3l8Bf7qBNyNE1OM3Y9BJHK9O3TRi1\nU+pTq/lJ5ys0jt7gidyTqg7PO1KRztW7Nq7esVGU6V2kxl5mZX2FG/ZWLBGJlFgKlRbHJ7qGprFP\nLtJQsfvY/K2oTa3kx50/o3H0Jk/lPa5qXWmoSOfynVGutNkoyU5UWpwHaCtyN3H5jssH5w/Xjodo\nUzQVKeUMz48yNBd6Pr7dUFFgIS7axLV2G2vr6i51pwS3HG0srS9zOL1e9dWxPnGD7j42fyuijFFU\npezDtmBnYDb0M1Y+ivI8CwkxZq6HmK6o+63zMyur61zvsJMUH0FZnsWv5z6U5orFblR5WgaDXs/h\n8jTmFle506Nuv6sSXHO7dg4pEMnlT1bX1mlst2OJi6Dc37ridvFcV7mLR6/XcXhfGvNLa9zuHlda\nnAdoRn8DLV1jLC6v01CRjt7Pw8qKlHKijFE0jbaovhh0g3sUdDXEswmGGtPLs3RM3CM/Ppe0aP/M\nFynFra5xFpfXOFKRhl7vX10pTyolxhhNk61F9Xn2GypcunIlhHRFM/obuNpmA1x+a39j0hupT61m\nemWGzsluv58/mOSnx5GeFM3Ne2MsLq8pLY5quGlvRUbmQFqt0qL4zLW7bl3Z539dMeqN1KfVMLMy\nq3pdyU2LJTMlhpaucRaWQqNwumb03cwvrXL7/jjZ1liyUgKT4vbBsHVU3cNWnU5HQ0Uaq2tOmiUt\nLcNOabK1oENHfWqN0qL4xMLSGre6x8lMiSHbGihdCQ93qEdX1tadNIWIrmhG302z5GDdKXN4n39C\nzx5GYUIeSZEWWhy3WVlXdyWqIyE4bA1lxhYn6JnpQ1iKSYgIvbQQu+HmPQdr604Ol6cGLLqmID6P\n5MgkWhx3WFa5rhx2p5oOFXfuyvIhAAAgAElEQVSoZvTdeIaru82bvxv0Oj0H0+pYWl9WfXk4a2IU\nJdkJdPRNMjGzpLQ4IU+ze43G/jBy7RzaZd783aDTuRLRrayv0OpoC9h1gkFKQhSlOYl09E8xPq28\nrmhGH5ieW6ajf5KirHifl5Jvh2fYqvbFJ+Dq7cvA9Y7QzCYYSjTZWjDqDNSqPO3CzPwKd3snKciI\nJ80SHdBrHQyTiDdwVaADuNZuU1gSzegD0NhhR5YD28v3kB6TRlZsBnfHO1lY9T6ZXChwQFjR63Q0\nhsCLHMoMz40yPD/KvuQyok3qSFS3Fdc77Dhl+YHLIpCkxaSSG5dFx8Q95lbnA369QHJApGLQh4au\naEYfaLxrQ6eDg0Ew+gD7U2tYl9e5pfJha1y0mX35FnpGZrFPLSotTsjice2ERdROuw0dcLAscHNf\nG6lPrcEpO7llvxOU6wWK2CgTFQVJ9NvmGJ1QtrO3542+Y2qR7uGZB6vngsH+NFf0RrP91jZHhj4H\ny13Kfz0EejChiCy7ci5FGMxUpZQrLY5PjE8v0TU4jchNxBIXnERxnkinpjDQlUNuXVG6t7/njb7n\nPyAYrh0PKVHJ5MXlIE12MbsyF7TrBoL9pVYMeh3X7mp+/YfROzPA2NIE1SkVmA3B6VQEige6EgTX\njofkKAsF8Xncm+xmZiUkk/LumLoSK0aDnsZ2ZXVlzxv9q3dtGA06v2XU3Cn1adU4ZSctjttBva6/\niY40UVWYzKBjjuExdftdA0FYuXbu2jDodez3U0bNnbI/rQYZmZt2detKVISR6qJkhsfmGXQo19nb\n00Z/0DHHkGOeqsJkoiNNQb32fvewtdmmDVvDFafspNl+ixhTNOVJoVc2bzcMj83Tb5+jqjCZ2Kjg\n6kpdahU6dA9+QNVMKOjKnjb6D2Lzgzhc9WCJTKQoIZ+uqR6mlkMr3/ZuqSlOwWTUc73DHnIFI5Sk\n0+2SqLNWYdB7X+c5FPgkNj+4vXyAxIgEihML6J7uVX0hopqiFMwml4tHKV3xOgm2EOI7wBFABn5X\nkqTrG/adBv4EWAck4BvACeAngCdk5bYkSb/j7fV9RZZlGtttRJgM1BSnKCJDfVoN3dO93LTf5nTO\ncUVk8AdREUZqipJpkhwM2OfITVP3ilN/ES6uHVmWudZuw2zSU1esTKK4/Wk13Ju6z017K4/nnlBE\nBn8QYTZQW5xCY7udftsceenB1xWvevpCiJNAiSRJDcBv4aqEtZG/B56XJOkYEAc87d7+oSRJp9x/\nihl8gJ6RWRxTS9SVpBBhUqYXVmetdg9bw8HF4xotKT1JFSqsO10huQnmeIoSC5QWxyf6bXPYJxep\nLU4hwqyMrtRaq9Dr9GESxaPsQi1v3TtngJcBJElqByxCiPgN+/dLkjTo/uwAkr0XMTA0uVeRekIO\nlSAhIo4SSxE9M32ML04qJoc/qC5KJsJsoLHdprl4AGmyi/m1BWpTq1RfLKVJcutKkGLzH0acOZbS\nxCL6ZgZUXz+3qjCJqAgD1xXSFW/dO+lA84bvDve2GQBJkmYAhBAZwFngPwFVwD4hxKtAEvBfJEl6\nd7sLWSzRGI3e9y6s1l8cPsmyzI17DqIijJw6mIdZoZ4+wKmiw3Q2ddG50MG53LMBucbDnkEgaKjM\n4MKNQaaW1inN9W9hDV8I1v1vpL2nHYDHSw8rcv3NeCuDS1fGiDAbOHUoz28lRL3hVPEROq7fQ5rv\noDz3qV21DYX/g400VGXyQdMAEwtrlOUnBfXa/vof/IVUe0KIVOA14H+RJGlcCHEP+C/AvwKFwHkh\nRLEkSY9MoTc56f3qNas1DofjF2N7e0ZmsE8ucqQijekpZVfHFUUVo9fp+fB+Iw3JDX4//1bPIBBU\nFyRx4cYgb1/uwRKlnHHYSDDv38O6c51rAy0kmONIklODfv3N+PIM+m2zjIzNc7AsldnpRZS8k6LI\nIgw6Ax/db+RYytEdt1PiHdiO6gILHzQN8PaVHpJjAhMNtdUPnbfjzmFcPXsPmcCD4q9uV8+bwP8u\nSdI7AJIkDUmS9GNJkmRJkrqBUSDLy+v7xAPXTpDjjR9GrCmGsqQSBmaHsC+ERr5tb6koSCI6wvgg\nP8tepXOyO2xcO55kekq6djxEu0NfB+eGsc2re+5oX34SMZFuXXEGV1e8fSPfAZ4HEELUA8OSJG38\nKf0L4DuSJL3l2SCE+IoQ4vfdn9OBNCDolY9lWeZ6h50Is4GKguAOq7biQKoruuOGvVVhSXzDZNRT\nX2plcnaZrkF1h6H6wk2H6/+xzlqtsCS+IcsyTR12zCY9VUWhMS0XLilMjAY9+4WV6bkV7g0GNwzV\nK6MvSdJloFkIcRlX5M63hRBfFUJ8XggRDfwG8A0hxAX3328DrwInhRAfA68A39rOtRMI+myzjE0v\nUVucoqgvfyNVKfsw6AyqX3EIcMDdI/RM/u01PFE78eY4ihLzlRbHJwYd89gmF6kuUi7CbTNVKfsw\n6o1hoSueBI/BTk3uteNVkqQ/3LRp40/vVtmYPuvt9fxFU4fLhXIgBFw7HqJNUZQlldA23oF9YYzU\naGXWDfiDffkWoiOMNEsOfvVMid8LzIc696buM7c6z4mso5prJwBEGSPZlyRoHWtjdN5OekzoyLZb\nRE4isVEmmiUHLzxZGjRdUfdbuUs8w9UIk4GqwtBw7XioS3W5AlpU3oMxGvTUlaQwObtMz/CM0uIE\nHY+Lrj61SmFJfOOBa8eop7owNFw7Hurcz1bteas8ujI9vxJUd+ieMvr9tjnsU4vUFCeHjGvHQ3XK\nPvQ6/QN/sJrZ7+4Z7rWKWi7Xzh3izLGqX5A15JhndGKBKvf6i1CiKqU8bNyhnlFUUxB1ZU8ZfY+f\nOZRcOx5iTNEISzH9s0OMLY4rLY5PVOS7Fp80S3srF4/HtVNn1aJ2AkmUMYrypBIG54axL4wpLY5P\nlOVZiIk00tzpCFrEm7rfzF0QipEIm6l3u3jU3oMxGfXUFqcwPrNMz0hoxUcHkptu106dyl074Oog\nmYx6qkNUV8LJHVrrdofeHwqOO3TPGP0B+1zIRSJspjqlwu3iUfeLDHsvimfduU6L4w5xpliKEwuV\nFscnhhxzjIwvUF2YrOgK3EcRTu7Qg0HWlT1j9EMhf8h2xJpjHuQXUXsunsqCJCLNBpr2SLrlrqke\n5lbnqUmtDBvXzoEQ1pVoUzRllpKwcIeW5yURFWGkKUjuUHW/nTvEtSDLEZKRCJsJl8gEk9GVQnZs\neok+W/i7eG64e5z1Kl+QBdAkOULatePBoyvh4g6dmFnm/kjgXTx7wugPOeaxhWgkwmZqrJXo0Kn+\nRQYelNXzrI0IV5yyk1v2O8SaYihWe9TO2DzDY/NUFrh6n6FMtTV83KEeD0RzEHRlTxj9UI5E2Eyc\nOfZBumW1VwmqKkwiwmQI2rBVKbqm7jO7OkettVL1FbKaVKQrsabwcYdWFFhc7tAg6ErYG31ZlkM+\nEmEzdVaPi+eOwpL4htlkoKY4GfvkIgN25QpBBxrPqMwTUaJmmiQ7RoNesWpyuyUc3aG9o4F1h4a9\n0R8em2dkfIGqEI5E2ExtqsvFo/YEbPDJmohwjeJxyk5uOm4Ta4qhROVROyPj8ww55t1FPtShK+Hk\nDg1WxFvYG/1PIhGUqe3pDfHmOIoTC7g/3av6oulVRcmYTXqudzjC0sXTPdXD7MocNWHg2lFD1M5m\nwskdWlngcoc2B1hXwt7oN0kO13C1SB3DVQ+1qeHh4okwGaguTMY2scCQY15pcfzODXcPsz4cXDsd\ndowGHbUqce14CDt36NQi/bbAuUPD2uj3j84wPKau4aqH2gfD1jBw8YTpQi2n7KQljFw7g455KguS\nVacrNeGkK0Fwh4a10b/U6irmpabhqofEiAQKE/Lonuplelndce7VRcmYjPqwS8DWPdXLzMosNdYK\n1bt2mlToBvWQEOFxh/aFjTs0kIsaw9vo3xpS5XDVQ11qNTIyt1Q+bI00G6kqTGZkfIGhsfBx8Tyo\nkBUOrh3J4dYV9Rl9cLlDXbrSprQoPvHAHTq5yGCA3KFeG30hxHeEEFeEEJeFEAc37XtCCNHo3v+f\ndtLG34yMz9M3OqvK4aqHWmslQHgMW909yOYw6e07ZSct9tvEGKMpTSxSWhyfsE0sMGCfoyI/iehI\n9epK2Lh4Apxu2SujL4Q4CZRIktQA/Baukokb+Svgi8Ax4KwQYt8O2vgVNQ9XPVgiEymIz+Pe1H1m\nV9Qd515TlILRoOd6mPj1e6b7mQ4T144ao3Y243GHdk31MLMSHu7QQPn1ve3pnwFeBpAkqR2wCCHi\nAYQQhcCEJEkDkiQ5gTfcx2/ZJhA0q3y46qHePWxVe2RCVISRqsIkhhzzjIyr38Xj6VHWhoVrx45B\nr6OuRJ1uUA/h5A6tdrtDZxf8X0bc27FcOtC84bvDvW3G/e/GBBJ2oAhIeUSbLbFYojEad9+TykqL\no1akkpdj2XXbUOJMdAM/7fo5bVN3+ULtk16dw2qN87NU3nH6YC43743RPjBNdVl60K7r7/t3yk5a\nr7QRY47meGkdRhX09Ld6BiNj8/Tb5jhQnkZeTmiVEN0tj8cc5qV7r3Jn8i5fqD37qX2hogM75du/\nXEd77zgFuUno/Fw7118OvEdJtdW+Hd3J5OTC7qUB/t2z5VitcTgc6h7qgYn8+Fza7J30DI0Sa47Z\nVetQegaFqbEYDTouNA/yeG1mUK4ZiPvvme5jfHGSI+kHmBz37v0MJo96Bu9c7QOgqsASMu+J95go\ncOvK/aER4syxQGjpwG4oz05gbMx7t+5WP3TeuneGcfXSPWQCI1vsy3Jve1QbjUdQl1rlyuSo8mFr\ndKSRivwkBh1zjE6EvrHcik9y7YRBhawOj2tH3W5QD+Hi4gkk3hr9d4DnAYQQ9cCwJEmzAJIk9QLx\nQoh8IYQReM59/JZtNB6NZ8VhWOTiUaAQtD+RZZmbjttEGiIRSSVKi+MTY1OL9I7OUp5nITbKpLQ4\nfiFccuwHEq+MviRJl4FmIcRlXFE43xZCfFUI8Xn3Id8CfgR8DPxYkqTOh7XxXfy9QXJUEnlxOXRO\ndTO3ou5J0LqSFAx6nWqNfv/sIBNLk1Sl7MOkV2d4o4cmyTX1puaonc0kRVrIiw8PXQkUXr+1kiT9\n4aZNtzbs+who2EEbjR1Sl1pF3+wArWNtHM08pLQ4XhMdaaKiIInW7nFskwukWaKVFmlX3HyQaycM\nXDuSHb1O/VE7m6lPraZvZoBbjjscyzqstDghR1ivyA0nPKs+w8LFI9Tp4pFlmZv2ViIMZsqTSpUW\nxyfGp5e4PzxDWV4icdFmpcXxKx53aDhU1AoEmtFXCSlRSeTGZSFNdjG/qt5JUIC6UpeLR225eAbm\nhhhbmnC5dgzq9oE3d7pdOyJ8XDsePO5QabKLuVXNxbMZzeiriLrUancUj7rzi8REmtiXn0S/bQ67\nlyG5SvAgascaBq6dDjs6HdSVhkfUzmY8EW+tKteVQKAZfRXhydnuSfSlZjzpMTyTiaGOx7VjNpjZ\nl1ymtDg+MTm7TNfQNCInkYSY8HLteAgnd6i/0Yy+ikiJSiYnLgtpoosFtbt4SqyqcvEMzY3gWByn\nMrkMs9pdO5L6c+1sx0Z36Nyy5uLZiGb0VUadtYp1eZ1bY3eVFsUnYqNMlOdZ6BudxT61qLQ42+KZ\nFAyLNModdnRAfZi6djx43KGNQ7e2P3gPoRl9leExOuGUQjbU0y17XDsmvYkKlbt2puaWuTc4TUl2\nAomxEUqLE1A87tBrgzcUliS00Iy+ykiNTiE7NpOOiXssrIZ+D/lR1Jda0et0IV9GcWTehm3BQUVy\nGREGdfvAb3Q6kAlv144Hjzu01daheneoP9GMvgqpS61mXV7ndli4eBLpGZllLIRdPJ5RVbjk2gHY\nH4ahmg+j3lrNulP97lB/ohl9FeIxPuEQmfBJ0fTQjeK56biNUW+kUuWunen5FaSBKYqzErDEhbdr\nx0Ptg1w86tcVf6EZfRWSFm0lKzaDjolOFtdCt4e8EzwunlCN4hmdtzEyb2NfkiDSGKm0OD5xs9OB\nLMMBEd4TuBtJjU4hPzE7LNyh/kIz+iqlzlrNmrxOq0Pdw9a4aDNleYn0jMwwNh16SnnT7krRGw6u\nnet7zLXj4UhOPevyOq1j2kIt0Iy+avEk/AqH/CKeVADNIejiueloxagzUJVSrrQoPjGzsILUP0Vh\nZjzJCeoeseyWhpz9gJZu2YNm9FVKWkwqmTHptE90sri2pLQ4PlFfakWnC70EbLYFB0NzI5QllRJl\njFJaHJ9ouTeGU5bDMtfOdmTEpYaNO9QfaEZfxdSlVrHmXFN9FE98jJmyXAvdwzNMzITOD1hLGFXI\n+sS1s3f8+RupTw0Pd6g/0Iy+inmQiycMhq2hWFGr2X4Lg85Adco+pUXxiem5Zdp7J8lPj8OaqO4R\ni7d8km5Zi+LxqoiKEMIE/COQB6wDX5Mk6f6mY34F+D3ACbwvSdJ/FEJ8FfivQLf7sHclSfpj70TX\nSI9JIyMmjbsTEktrS6qOLqkvtfLDdySuS3bOHspVWhxG5+0MzY1QlVJOtEldhV42c/n2CE5Z5lB5\nmtKiKMYDd+i4y8WjdnedL3jb038BmJIk6Tjwx8CfbNwphIgG/gw4g6uC1hNCCE936ceSJJ1y/2kG\n30fqrC4Xz52xdqVF8YmEGDMiJ5HuodBw8TTbXfla6lNrFJbEdy62DAFwqHzv+fM34nHx3Fa5rviK\nt0b/DPAz9+f3gGMbd0qStABUSZI0K0mSDIwDyV5LqbElD1LIhkMUT1loRPHIskyz7RYmvVH1rp2p\nuWVud49RnJ1AUrx6R4L+oC6M3KG+4G2N3HTAASBJklMIIQshzJIkrXgOkCRpFkAIUQXkA1eBIuCk\nEOItwAT8viRJNx91IYslGqPR4KWYYLXGed1WDVitcWR1pHN3QiIu0USk6RcVWy3P4GxDAf/8bict\n3eO88Iz/jO1u779vahDbgp3D2XXkZKh74vNqh2tB1uMHclXzHgQCqzUOqzWOnI5M2ickYhKNRJv2\npotnW6MvhPgG8I1NmzdXG9Zt0bYE+BfgBUmSVoUQVwGHJEmvCyEagO8DjwyNmPShspLVGofDMet1\ne7VQnVTJmzPvcV66zoG02k/tU9szKMlOpL13gs77Y35JFeDN/b/XfQWAysQKVT27h/FBUz86HZRl\nx6v+Xrxl4ztQlVTBwPQwFzquczC9TmHJAstWP/LbunckSfquJElHNv4B/4Srt++Z1NVt7OW7t2cD\nLwO/KUlSi/tcHZIkve7+fAWwCiG878ZrAJ9E8dywqT9v+EG3i0eptAwu104LZoNZ9bl2JmaW6Bqc\npqooJezTKO+U/W5daba3KCyJcnjr038H+JL782eB8w855h+Ab0mS9CCZtRDiD4QQX3Z/rsTV61/3\nUgYNN5mx6WTEpNE23qH6/CIHylLR6eDaXZsi1++fHWRsaYLqlH2YVZ5GubHd9cN5vDZLYUlCh/SY\nNLJiM7g73sn8Hk237K3R/zFgEEJcBL4N/BGAEOIPhRANQohS4DHg/xJCXHD/ncPl6vltIcSHwN8B\nv+X7LWgAHEirY01e55bjjtKi+ERCjJl9+Un0jMwoUjQ9nKJ2rnfY0Ot0HK3KUFqUkOJgWh3r8vqD\nxXd7Da8mct298689ZPufbvi6VXDzaW+uqfFoDqTV8Nr9t2iytdCQeVBpcXzicHkabT0TXLtr47PH\nCoJ2Xafs5IatlShjJPuSRdCuGwjsU4v0jMxSWZBEQmwEjsWV7RvtEfan1fBy9xs02Vo4lrV5ejL8\n0VbkhgkpUcnkx+ciTXYxvazuCbv6UitGg55r7XZkWQ7adXtn+plcnqI6pQKT3tvAttDgervLPXZw\nj8fmP4ykSAuFCfncm7rP1PK00uIEHc3ohxEH0mqRkVVfMCI60kh1UTLDY/MMOuaDdt1m90T4/jT1\nu3Ya2+0Y9Dr2h3nxc2/x6Eo4FCLaLZrRDyPqU2vQoaPJpv7IhCP7XCkDgjWh65Sd3LC3EmOMpsxS\nEpRrBoqR8XkG7HNUFSYTHWlSWpyQpD61Gr1OT9Oo+nVlt2hGP4xIiIij1FJEz0wfY4sTSovjE9VF\nyUSaDVy7awuKi6drqoeZlVlqUysx6NUdReyJ2tnraRceRZw5FmEppm92APvCmNLiBBXN6IcZB9Jc\nC06aVd7bN5sM1JVYGZ9ZontoJuDXa7K5FobvT63d5sjQRpZlrt21YTbqqSlOUVqckMazkLE5DNa3\n7AbN6IcZtdZKjDpDeLh4KoLj4lldX+WGvZXEiARKLIUBvVag6R2dZXRigdqSFKIi1D0ZHWhqrJUY\n9UaabDeDGjCgNJrRDzOiTVHsSy5jeH6U4blRpcXxifI8C7FRJq532Fh3OgN2nbbxDhbXljiQVote\np26VuNLm+j9vqEhXWJLQJ8oYSWVyGaMLrjTaewV1v+EaD8UzbFV7b99o0HOwLJWZhVXa+yYDdp1G\nt2vnYJq6c7GsO5003rURG2WioiBJaXFUgccdqnZd2Q2a0Q9DqlLKiTCYabK1qH7YejjAUTwLqwu0\njbWTGZNOVqy6V67e7Z1kZmGVQ+WpGA2aau+EiuQyIg0RNNtvqV5Xdor2ZoQhZoOZ6pQKxpcmuDfe\no7Q4PuHKAx/BjU4Hq2v+T9N0036bNXmdg+l16HQPTRarGjTXzu4xG0zUWCuZWJrk/nSf0uIEBc3o\nhykeF89HfdcUlsQ39Dodh8vTWFxep6Vr3O/nb7S58gGq3bWztLLGjU4HqYlRFGbGKy2OqvD83zeO\nNissSXDQjH6YUp5USpw5lsv9zaw615QWxyeOVrp6rpdu+3eybXxxkq6pHkoSC7FEJvr13MHmZucY\nK6tOjlSkqX7EEmxEUjEJ5nia7a2srq8qLU7A0Yx+mGLQGziYVsfcyjxtKq8JmmWNJS89jjv3J5ie\n91/iME9sfjgU0/C4do5orp1do9fpOZRez+LaIq1jd5UWJ+BoRj+MOZJxAICrYTBsPVaZjlOWudrm\nnzBUWZZptN3EqDNQZ632yzmVYnpumbbeCQoy4klP2iq5rcajOJReD+wNF49m9MOYrNgM8hOzaRvv\nYHZlTmlxfOLwvjQMeh2XbvvH6A/ODTM6b6MyZZ/qa6W6spFCg3sxm8buyYxNJzcui7sTncysqDtL\n7XZ4tWTPXSLxH4E8YB34miRJ9zcdswpc2rDpDK4fmUe20/AvJ/OP8E8tL9Fka+F0znGlxfGauGgz\n1UXJ3Lw3Rr9tltw034p8Xx1pAj7p4akVWZa52DqCQa/jULlm9H3hcPoB+u+9QtPoTR7PPaG0OAHD\n257+C8CUJEnHgT8G/uQhx0xLknRqw9/6Dttp+JFjeQfR6/Rccxs5NXPMXQHq8h3fevurzjWu224S\nZ4pVfR3cftscg445qouSiY9Rd3lHpfGsyA4Hd+ij8NbonwF+5v78HnAswO00vCQxMp59SYKBuWHV\nLzWvLkomNsrE1bZR1ta9T8twe+wu86sLHEqvV31GzYutrv/T49XqXlgWCsSaY6hMLmdoboTB2WGl\nxQkY3mZkSgccAJIkOYUQshDCLEnSxtCKSCHEv+By5fxUkqT/Z4ftPoXFEo3R6L1iWq2+uQHCgbPi\nOHcut9M6fZvaglKlxfGJU/uz+fnFHgYmFjm0b2eRKpvfgRvtriX3z1ScxJqg3vdjZXWda+02EuMi\nePxw/iNX4e51Pdjp/Z8Vx2kda+P29G3qCtVdMnMrtjX6QohvAN/YtHlzYcmHBQb/PvBDQAY+EkJ8\n9JBjtg0onvShOLbVGofDEd6TMtthtcaRay4g2hjFRz3XOJtxRtW927qiZH5+sYc3L96nwBqz7fGb\n34Gp5WlaRtrIi88hckXd70dju425xVWePpzL5MTWFcb2uh7s5v5zTHnEGKP5sPcaZzOfULWubPVD\nt63RlyTpu8B3N24TQvwjrl77Lfekrm5zb12SpL/dcPz7QBUwvF07Df9j0hvZn1bLx0NXaJ/opDKl\nXGmRvCY/PY6slBhausaYW1wlNmp3laEaR24gI9PgDmdVMw9cO1Waa8dfGPVGDqTX8uHgZdrGO6i2\nVigtkt/x1qf/DvAl9+fPAuc37hQu/kUIoRNCGHH57tu2a6cRODxG7tJwo8KS+IZOp+Ox6gzW1uVd\nT+jKssyVkeuuH0GVF0uZmFmirWeCosx4MlO2H/Fo7JyjGYcA9evKVnhr9H8MGIQQF4FvA38EIIT4\nQyFEgyRJEjAANOIK23xDkqTGrdppBJ7cuGxyYjO5M97O1PK00uL4RENlOkaDjg9bhnaVGfH+dB/2\nxTFqrJWqj82/dGcUGTimTeD6ney4TPLicmgb72ByaUppcfyOVxO57vDLrz1k+59u+PwfdtpOI/Do\ndDqOZR3mRelnXB1p4un8M0qL5DVx0Wb2i1Su3bVxb3Ca0pyd5c25OnIdgIaMg4EUL+DIssyl1hHM\nRj2HyrTY/EBwLOsQfR0DXBm5zjMFTyotjl/RVuTuIQ6k1WHWm7g83IhTDlwlqmBwsiYTgA9bdhZa\nt7i2RJP9FkmRFkotRYEULeB09E9hn1pkv7ASHamVRAwE+1NriTCYuTx8XfW6shnN6O8hooyRHEir\nZXxpEmmiS2lxfELkJpJmiaJJsjO/tH1mxOujN1hZX+FY5mHVl0Q8f3MIgFN1WQpLEr5EGiM4kFbH\n5PIU7ROdSovjV9T99mvsmqOZrmjbi8PqzrOv0+k4UZvJ6pqTK9tM6MqyzMdDV9Hr9BzNVLdrZ2pu\nmZudDrKsMRRnJSgtTlhzLNM9oTukbl3ZjGb09xj58TlkxWbQOtam+sRSxyozMOh1fHhr+JETut3T\nvQzPj1JrrSTerO5FSh+3jrDulDldl6XlzQ8wnuCH2+PtTC/PKC2O39CM/h5Dp9NxLPMwTtn5IOmY\nWomPMVNXamXIMU/38C7IoUIAABTESURBVNZKeXHoKgCPZTUES7SA4HTKfNQyRITJoJVEDAI6nY6j\nbl25onJd2Yhm9PcgB9PqMOlNXBy6pvpJqpO1rgnd8zeGHrp/ZmmWm/ZW0qJTKUksDKZofqf1/jjj\nM8scqUgjKkKbwA0GB9NrMetNXBy6yrrT/zWalUAz+nuQaFMUB9PqGF+aoG28Q2lxfKI8z0J6UjTX\nO2wPrap1ofcKa/I6j2UdUb075IJnArdWm8ANFlHGKA5nHGByeYrb4+quQOdBM/p7lFM5rgSn5wcu\nKiyJb+h1Os7sz2ZtXebDlk/39p2yk3e7L2LSmzicvl8hCf3D2NQit7vHKcyMJy9d3fMSauNk9lEA\nLqhcVzxoRn+PkhWbQWliEdJkF8Nz/qlGpRRHK9OJijBw/ubQp1Iut413YJtzcCCtVvUrcD+4MYQM\nnNbCNINORkwaZZYS7k3dV316ctCM/p7G09v/cPDSNkeGNlERRo5XZTI9t0KTZH+w/YP+jwFUXTEM\nYHF5jQ9vDREfY9aqYymER1cuDKhbV0Az+nuaqpR9JEdauDZ6g/lV71NYhwJn9mehA95rGgRgYHaY\nzqluqtIEWbHqzk9z8fYIi8vrPF6fhcmoqawSVCSXkRKVzHXbDeZWt05jrQa0N2gPo9fpOZF9lFXn\nKpdVnlEw1RJNTXEK94dn6B6e5vyAq5f/bKl6cwyBK0zzvaYBjAa9tgJXQfQ6PSezj7LqXFO9rmhG\nf49zNOMgZr2JDwcvqz4k7cyBbADear5Hs62FtGgrtRnqzofe0jWGY2qJo5XpxEdrNXCVpCHjAGaD\nWfW6ohn9PU60KZoj7pC0G/ZWpcXxiX15FrJSYmidamZNXudU9nHV59l55/oAAE8ezFFYEo0oYxRH\nMw4ytTxNk61FaXG8Rt0aoeEXzuSeQIeOd/rO7yo/faih0+l48nAGhtR+jHIEhzPUHabZMzJD58AU\nlQVJZGmFUkKCx3NOoNfpeaf/gmoXNmpGX4OUqGT2p9UwPD+q+sVaK/G96EyrrIzmsLyktDS+8fqV\nPgCeOpyrsCQaHpKjLBxMq2N03sadMXUu1vJqLbe7vu0/AnnAOvA1SZLub9i/H/iLDU32Ab8EnAW+\nAnhW0fxAkqR/8EYGDf9yNu80TbYW3u47r9oauqvONc4PfowBI4sjubzXPEBRfrLSYnnFkGOOG50O\nCjPj2ZdnUVocjQ08kXuSa6PNvNN3nqqUfapb6e1tT/8FYEqSpOPAHwN/snGnJEnNkiSdkiTpFC5j\n3w5cde/+S88+zeCHDlmxGVQml3F/upeuqR6lxfGKxpFmppanOZ51hDhzDO83D7Gwg1z7ocjrV129\n/Oca8lVnVMKdzNh0qlL20TPTT9fU/e0bhBjeGv0zwM/cn9/DVfh8K34f+H8lSVKnA2wPcTbvcQDe\n7vtAYUl2z7pznXf6zmPUGTibf5InDuSwuLzGm5d7lRZt19gmF7h210a2NZaaYnWOVMKdp/JOA/B2\n33mFJdk93qbqSwccAJIkOYUQshDCLEnSpzJeCSGigKeA/7xh85eEEJ8DloHfkSTpkd1KiyUao9Hg\npZhgtWp5Snb6DKzWKsoHirnrkJjWj1OcnB9YwfzIx72NjC1N8GTRY5RkZ5ORnMbbjf387MMunjlW\noKqslC+e70aW4YWnykhNjffLOfe6Hvj7/q3WSioGSmmzdzKhsyNS1FOCc1tNEEJ8A/jGps2HN33f\navz5S8DrG3r5bwAfSJL0kRDiV4G/Bp571PUnJ71fKWq1xuFwqLtQiK/s9hmczT5Du6OL7zf/G79T\n9+8CKJn/WHeu82Lrq+h1eo6nHntwv08eyOGViz386K12Pns0X1khd4htcoH3r/eTlhRNaaZ/3t+9\nrgeBuv+z2Wdos3fyg+af8bv13/T7+X1lqx+6bY2+JEnfBb67cZsQ4h9x9fZvuSd1dZt7+W6eA/5m\nw7k2LmV7Ffiz7a6vEVxKLUWUWUromLzHvcluSlRQRPzKyHUci+OcyGogJSrpwfazB3P44MYQb13r\n5/H/v707j46qyhM4/q0tW2UlqSwkBkISLpCwqCACIpuyKBHFKO2gHkXb03Oc6fbMdM+xzyzaZ/7o\naft0j932jMugMt2tNrQLosBhcWERAWWRBMINkYQEAiEL2QNJpWr+qOggJiSpVNUrUr/PX6mXqvd+\n96XuL6/uu3V/N6Rjj7AZGOXArN9VTrfLzT2zszCbZSw/mOXEZzFhhOJYg0Y3lKFG5Bgd0oB4O6a/\nFbiv5+cCoK+BrWnAV988UEr9Tik1u+fhXKDYy+MLPyrIXgTAhpNbgn7efmd3F5vKt2Mz21g8+rtL\nLkSGWymcn0PHJSdb9lcaFOHAVda0sO9YDZkp0Uwdl2x0OGIACsZ4+soH10Bf+Ya3SX8tYFFK7Qae\nBH4OoJR6Wil1eU26eK315Z+rVgO/UkrtAH4G/MTL4ws/Gh2bycSkCZxsquBYgzY6nKvaeWYPTZ3N\nzLvuFuLCvz/+fcesLOLsYWz74jTNvRRZCSbv7vTMBCmck41ZZuxcEzJjM5jsyKe8+RTF10iRFa/u\nbmmtu4FHe9n+H1c8Tr7icREw05tjisAqGLOI4roS3ivbyLiEXCxm72+m+0t7VwdbKz4h0hrJ7Zlz\nen1ORJiVpTNH88a2UtbvOsnDi8cFOMqBKa1q5MjX9ajr4snLGtH/C0TQWJq1kCO1R3mvbBMTRqig\n7CuXk2/kil6lR6cxI20aZ9tq2F29z+hwerWpYhttznYWjppLlC2qz+fNmTKStMQodhyuprIm+G5o\nulxu3vroBAD3zs2WefnXmJHRqcxKn05N+3l2nvnc6HD6JUlf9KkgexERlgg+PLkl6NYQP9tWw47T\ne0iKTGTedbOv+lyrxcwDC3JxA29tPxF0Y6+7i85y6lwLM/JSyEmPMzoc4YWlWQuJtEaysXwbrZ3B\n1VeuJElf9Ck2LIYlWQtod3awqXyb0eF8y+1283bpBlxuF4W5BdjM/Y9S5o9JZEpOErqqkQO6NgBR\nDkz7xS7e/vRrwm0WCudeG7M/xPfFhEVzR9ZtdDg72Fi+1ehwrkqSvriquRmzSI5MYteZvVS1VBsd\nDgCHa4s5fuEEE0Yo8hMHvk7Qivk5WMwm1n58go5LTj9GOHDv7SqntaOLglmjSYgJNzocMQRz0meS\nEuVg15m9nGquMjqcPknSF1dlNVu5f+zduNwu3ihZZ3jxiLaudtaWvofVbKUwt2BQ498pI6JYcnMm\n9c2XeHeH8WumnDjdyMcHTpM6Iorbp8p6+dc6i9nCirH34MbNG8ffNryv9EWSvujX+MSx3Jw6larW\naj6q3GloLO+c+ICWzlbuzLqdFPvg57IXzBxNWmIUHx88TWlVox8iHJguZzevb/IsY/3oHeOk9u0w\noUbkMDPtJs60nmVb5adGh9MreaeJAVmeu5SYsGg2VmzjXNt5Q2I4Wn+cfecOkBmTzoLrbvVqHzar\nhUeWeKZtrtl8nM4uY67G3t9dwbmGdhZMzSA3I96QGIR/3JNzJ3FhMWwu30516zmjw/keSfpiQOy2\nKH6gluN0OXnt6Bt0dQd2yeLmzhb+VLIOs8nMg+PvH9Jc6NyMeBbcmMG5hvZvp0oGkq68wOZ9p0iK\ni+DeW4N/mQsxOFG2SE9fcXfz+tE36QxwX+mPJH0xYFMc+cwaOZ0zrWd5p+zDgB3X5Xbxx2Nraels\nZVn2EtKj04a8z/vmZZPhiGbH4Wr2l9T4IMqBaW7v5OUNRzFh4om78ggPC+4v8gjvTHLkcWv6DKrb\nzvHOiQ1Gh/MdkvTFoBTm3sVIeyq7znzOgZqv+n+BD2yp+ISShlLyEscxv585+QNls1r427vzCLdZ\nWLP5ODUN3q/mOlAul5vVHxyjsbWTe+eMkTn5w9zynKWkR6exu3pfUBVSl6QvBiXMYuOx/JWEWcL4\nU8k6v09NO3S+iA/Lt5AQHs9D4+/HbPLdWzYt0c7DixQXO7t5/u0jtPm5ytbaj8soLm9gUnai1L0N\nATaLjVV5Kwm3hPHnknVUNAfHon+S9MWgpdpTeHTCAzhdTl46soaGixf8cpxTzVX877G/EGYJ40eT\nHiEmLNrnx5iRn8ri6ZnUNLTzX+8W4ez2T4G3Tw6eZtuXVaQn2XmiIE8WVAsRqfZkVuWtxOnq5qUj\na6jv8E9fGQxJ+sIrkxx5LM9dSnNnCy8c+h8aLzX5dP9VLdX84fBqnC4nq/L+hoyYkT7d/+UK52Zz\n41gHxysbeXF9sc8T/2dFZ/nz1lJiomz8uHASURHXThUvMXT5SeO5N7eAls5Wfn/4FS5cNG6qMEjS\nF0MwL+MWFo6ax/mOOp4/+JLP3sxVLWd44fArdDgv8tD4+5mYNMEn++2L2WTi8YIJjB+VwKETdfz3\ne8V0OX0zlfOzorO8trGEqAgr/7hiCo74SJ/sV1xb5mbMYsnoBdR11PP8oZf99ul4ICTpC6+ZTCbu\nGrOYxaPmU9tRz6+/fIHypqGNWxbVHeO3B1+kvauDleMKmZ52o4+ivbpwm4UfF05iwugEDpfV8dyb\nh2hsveT1/txuN+/vLufVjSVEhlv56Q+uJzMltOvUhjKTycSdWQtZ3JP4n/vyBcqbThkSi+XZZ581\n5MAD1d7e+ay3r7Xbw2lvD+7CGf7m73NgMpkYm5BNpDWCw7VH2V9zkEhLBJmxGYNaIqHL5WTD15v5\na6mn1u2q/JVMS71+yPENpv1Wi5mpKpm6posUnWxgf8l5MpKjSR7k1XlT6yVefP8oO7+qJikugp89\ncD3XJfv+fsRAhXo/CJb2m0wmxsZnY7fZOVxbzP6ag0RYwhk1yL4yUHZ7+C96jcPbZWaVUnOAvwKr\ntNbfm7StlFoJPAW4gFe01q/21NNdA4wCuoFHtdZXXQSltrbF63VwQ70gNAT2HByr16w5+hZtznbG\nxI1mWfYScuKzrvoat9tNcb2nWEtNey2OyERW5a0kMzbDJzF50363283mfZW8u+MkLrebmfmpLLsl\nq9+hmS6ni08OnuaDPRW0XXSSlzWCHy6dQKw9bChNGLJQ7wfB2P6S+lJeP/YmbV3tZMWO4u6cO/rt\nK4PlcMT0+p/Eq6SvlMoGfosnob96ZdJXStmBg8BNQCfwBXArnnq6N2mtn1RKLQQe01qvuNqxJOkP\nTaDPQdOlFtaVrudwbREAWbGZTE25npz4LFLsydjMVi46L1LTXotuKGPvuS+paa/FbDJzy8jpLMu+\ngwir71abHEr7T51r4bVNJVSdb8ViNjEpO5Fp45IZkx5HYmw4ZpOJ1o4uKmtaKTpZz57ic7R2dBEZ\nbmX5rWOYd0N6UMzSCfV+EKztb+lsZW3peg6dPwJ4ypROTZlCTvwYUnv6ylD0lfS93etZYDnwah+/\nnw58obVuAlBKfQbMAhYAf+x5znbgNS+PL4JUXHgMP5z4ECebKthc8REl9aWUXzY/2Wwy43L//+wY\nq8nCtJTrWTR6Pmn2FCNC7tOo1BieeWQa+4/XsOnzSg6dqOPQibpvf28yweXXTNGRNpZMz2TJzaOI\njrQZELG4lsSERfN4/oOUN51ic8VHHKvX35nLnxyZxNM3PUW4xbefFL2tkdsOoJTq6ympwOWVKs4D\naZdv11q7lFJupVSY1rrPAbeEhCisVu+/qu5wyM0zI86BwzGR6TkTaeho5GB1EWUNp6hra+Ci8xL2\nsEgcUYmMc+QwKXU8seH+He8eavsLUmIpmJNLeXUTh3QtX59upLH1Ep1d3cRFh5PuiGZiThKTc5Ow\nDeG96k+h3g+Cuf0ORz435eRzoaOJA9VFlDVUUNtWh91mJ9URh9Xi2ym+/e5NKfU48PgVm5/RWm8Z\nxHH6+ozb72ffCxe8/3p8sH6sCyTjz4GFybFTmBw7pdffXmp2U4v/4vNl+6NtZmbnpzA7v/dPJI1D\neK/6k/HvAWNdO+03Mzl2MpNjJ3+75UJDh9d76+sfXb9JX2u9Glg9yONV47mq/0Y6sPey7V/13NQ1\nXe0qXwghhG/566uB+4DVSql4wIlnPP8pIBa4D9iC56buJ346vhBCiF549eUspdSdSqlPgcXAL5VS\nW3u2P62UmqG17gCexpPctwO/6LmpuxawKKV2A08CP/dBG4QQQgyQ1/P0A0WmbA5NqJ+DUG8/yDkI\n1fb3NWVTlmEQQogQIklfCCFCiCR9IYQIIZL0hRAihAT9jVwhhBC+I1f6QggRQiTpCyFECJGkL4QQ\nIUSSvhBChBBJ+kIIEUIk6QshRAiRpC+EECHEX0srG0op9Z/AzYAb+InW+guDQwo4pdRzwGw8f+Nf\naq3fNTgkQyilIoFi4N+11msMDieglFIrgX/Cs7z5v2mtNxocUkAppaLxlGdNAMLxrPY7mOJPw9Kw\nu9JXSs0BcrXWM4DHgN8bHFLAKaXmAfk952Ax8LzBIRnpX4AGo4MINKVUIvAMcAuwFFhmbESGeATQ\nWut5QCHwO2PDCQ7DLunjKb6+HkBrXQIkKKVijQ0p4HbiKVYD0AjYlVLBWbzVj5RS44AJQEhd4fa4\nDdiutW7RWp/VWj9hdEAGqAMSe35O6Hkc8oZj0r+yKHst3y3dOOxprbu11m09Dx8DNmmtu42MySC/\nAf7B6CAMMhqIUkptUErtUkotMDqgQNNa/wXIVEqV4bkQ+qnBIQWF4Zj0r9Rv8fXhSim1DE/S/zuj\nYwk0pdTDwOda63KjYzGICc9V7nI8wxyvK6VCqi8opR4EKrXWOcB84A8GhxQUhmPSv7Io+0jgrEGx\nGEYptQj4Z2BJT6nKUHMnsEwptRd4HPhXpdRtBscUSDXAHq21U2v9NdACOAyOKdBm4SnZitb6K2Bk\nKA5zXmk4zt7ZCvwCeFkpdQNQrbUOqVppSqk44NfAbVrrkLuJCaC1XvHNz0qpZ4EKrfV24yIKuK3A\nGqXUr/CMZ0cTemPaZcB04B2l1CigNUSHOb9j2CV9rfUepdQBpdQewIWnAHuoWQEkAeuUUt9se1hr\nXWlcSCKQtNZnlFJvA3t7Nv291tplZEwGeBl4TSm1A0+u+5HB8QQFWU9fCCFCyHAc0xdCCNEHSfpC\nCBFCJOkLIUQIkaQvhBAhRJK+EEKEEEn6QggRQiTpCyFECPk/WD80BZMxYSgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f923d0a6630>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KWETrUcFr6bu",
        "colab_type": "text"
      },
      "source": [
        "### Let's beautify this"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJbQO5mor6bv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "c90f438f-331d-4c93-f3a9-b25aaebef89e"
      },
      "source": [
        "fig, ax = plt.subplots(nrows=1, ncols=1)\n",
        "\n",
        "# Plot them\n",
        "ax.plot(t, y1, label='$\\sin(x)$', linewidth=3.0)\n",
        "ax.plot(t, y2, label='$\\cos(x)$', linewidth=3.0)\n",
        "\n",
        "# Set the title (shown above the plot)\n",
        "ax.set_title('Sine and Cosine functions', fontsize=16)\n",
        "\n",
        "# Mark axes\n",
        "ax.set_xlabel('$x$', fontsize=16)\n",
        "ax.set_ylabel('$f(x)$', fontsize=16)\n",
        "\n",
        "# Legend\n",
        "ax.legend(loc='best', fancybox=True, framealpha=0.5, fontsize=16)\n",
        "\n",
        "# Grid\n",
        "ax.grid(True)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jKNX8sdus3L3Vl63y0uGriyaXfjFQ13VBbYlrt9rZvWJHlCWaP3et2aN+tqQr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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f923a165400>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ydd9Zl2pr6b2",
        "colab_type": "text"
      },
      "source": [
        "Another helpful library to clean-up your plots is [Seaborn](). This can be installed via `conda install seaborn` (make sure you've activated your environment before executing this command).\n",
        "\n",
        "With this library installed, you can simply import it and set the style. The library will take plotting, setting the fontsizes, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OkidFgFr6b3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "99302acb-dc3f-406a-c606-2797c778b1e8"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set(style=\"darkgrid\")   # or \"whitegrid\"\n",
        "sns.set_context(\"talk\")     # or \"paper\", \"notebook\", \"poster\"\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=1)\n",
        "\n",
        "# Plot them\n",
        "ax.plot(t, y1, label='$\\sin(x)$', linewidth=3.0)\n",
        "ax.plot(t, y2, label='$\\cos(x)$', linewidth=3.0)\n",
        "\n",
        "# Set the title (shown above the plot)\n",
        "ax.set_title('Sine and Cosine functions')\n",
        "\n",
        "# Mark axes\n",
        "ax.set_xlabel('$x$')\n",
        "ax.set_ylabel('$f(x)$')\n",
        "\n",
        "# Legend\n",
        "ax.legend()\n",
        "\n",
        "plt.show()\n",
        "\n",
        "sns.reset_orig()   # Seaborn always overrides plotting styles. This \"unsets\" seaborn from doing it."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Bv78/tmzZgrVr1wIAVq1ahe+++w4bN27E888/D3d3d7P9B3rvBsLn8/Hhhx/itddew/r1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\nKe3JI+JueCqOWhzx8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r28vPDy8qKurvGIoMFgwNu7af+jXrxY3eQRyJvh4qIhIMCbsrJq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\n/D8NzImiHJWTlAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f92533ef828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIZ5dV4hr6b9",
        "colab_type": "text"
      },
      "source": [
        "## Subplots\n",
        "\n",
        "When you want to display multiple plots simultaneously"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0426sAFr6cG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "cd0eb657-541f-426e-c9c1-23810ca7c478"
      },
      "source": [
        "dt = 0.01\n",
        "t = np.arange(0, 30, dt)\n",
        "nse1 = np.random.randn(len(t))                 # white noise 1\n",
        "nse2 = np.random.randn(len(t))                 # white noise 2\n",
        "\n",
        "# Two signals with a coherent part at 10Hz and a random part\n",
        "s1 = np.sin(2 * np.pi * 10 * t) + nse1\n",
        "s2 = np.sin(2 * np.pi * 10 * t) + nse2\n",
        "\n",
        "fig, axs = plt.subplots(2, 1, figsize=(10.0, 6.0))\n",
        "# axs is an array. axs[0] = top-plot, axs[1] = bottom-plot\n",
        "\n",
        "# plotting on top\n",
        "axs[0].plot(t, s1, t, s2)\n",
        "axs[0].set_xlim(0, 2)\n",
        "axs[0].set_xlabel('time', fontsize=16)\n",
        "axs[0].set_ylabel('s1 and s2', fontsize=16)\n",
        "axs[0].grid(True)\n",
        "\n",
        "# plotting on bottom\n",
        "cxy, f = axs[1].cohere(s1, s2, 256, 1. / dt)\n",
        "axs[1].set_xlabel('frequency', fontsize=16)\n",
        "axs[1].set_ylabel('coherence', fontsize=16)\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Z59xIlN2KEAFSiod9ybU77s1GSs2x3TIGugZtu4tPeNsWLMpCHkfDCiZNwttz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CQ0atRQ/VaZmeEIHVLlmcrB23jNNYiDXoSYjQk5cUwZU+sp5XzE2htsPEMZeU4dWiWnKTIlg8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\n9WecNLA7Pz1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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9239ff58d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZyY89gVr6cN",
        "colab_type": "text"
      },
      "source": [
        "## Scatter plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "DyN5Nxesr6cP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        },
        "outputId": "12c8e23e-aada-4b55-d724-f82dbc799838"
      },
      "source": [
        "iris"
      ],
      "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5.7</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.5</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>7.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.2</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>6.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>7.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>1.6</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>7.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>6.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>7.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>6.3</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.5</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.1</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>6.8</td>\n",
              "      <td>3.2</td>\n",
              "      <td>5.9</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.3</td>\n",
              "      <td>5.7</td>\n",
              "      <td>2.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "0             5.1          3.5           1.4          0.2     setosa\n",
              "1             4.9          3.0           1.4          0.2     setosa\n",
              "2             4.7          3.2           1.3          0.2     setosa\n",
              "3             4.6          3.1           1.5          0.2     setosa\n",
              "4             5.0          3.6           1.4          0.2     setosa\n",
              "5             5.4          3.9           1.7          0.4     setosa\n",
              "6             4.6          3.4           1.4          0.3     setosa\n",
              "7             5.0          3.4           1.5          0.2     setosa\n",
              "8             4.4          2.9           1.4          0.2     setosa\n",
              "9             4.9          3.1           1.5          0.1     setosa\n",
              "10            5.4          3.7           1.5          0.2     setosa\n",
              "11            4.8          3.4           1.6          0.2     setosa\n",
              "12            4.8          3.0           1.4          0.1     setosa\n",
              "13            4.3          3.0           1.1          0.1     setosa\n",
              "14            5.8          4.0           1.2          0.2     setosa\n",
              "15            5.7          4.4           1.5          0.4     setosa\n",
              "16            5.4          3.9           1.3          0.4     setosa\n",
              "17            5.1          3.5           1.4          0.3     setosa\n",
              "18            5.7          3.8           1.7          0.3     setosa\n",
              "19            5.1          3.8           1.5          0.3     setosa\n",
              "20            5.4          3.4           1.7          0.2     setosa\n",
              "21            5.1          3.7           1.5          0.4     setosa\n",
              "22            4.6          3.6           1.0          0.2     setosa\n",
              "23            5.1          3.3           1.7          0.5     setosa\n",
              "24            4.8          3.4           1.9          0.2     setosa\n",
              "25            5.0          3.0           1.6          0.2     setosa\n",
              "26            5.0          3.4           1.6          0.4     setosa\n",
              "27            5.2          3.5           1.5          0.2     setosa\n",
              "28            5.2          3.4           1.4          0.2     setosa\n",
              "29            4.7          3.2           1.6          0.2     setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "120           6.9          3.2           5.7          2.3  virginica\n",
              "121           5.6          2.8           4.9          2.0  virginica\n",
              "122           7.7          2.8           6.7          2.0  virginica\n",
              "123           6.3          2.7           4.9          1.8  virginica\n",
              "124           6.7          3.3           5.7          2.1  virginica\n",
              "125           7.2          3.2           6.0          1.8  virginica\n",
              "126           6.2          2.8           4.8          1.8  virginica\n",
              "127           6.1          3.0           4.9          1.8  virginica\n",
              "128           6.4          2.8           5.6          2.1  virginica\n",
              "129           7.2          3.0           5.8          1.6  virginica\n",
              "130           7.4          2.8           6.1          1.9  virginica\n",
              "131           7.9          3.8           6.4          2.0  virginica\n",
              "132           6.4          2.8           5.6          2.2  virginica\n",
              "133           6.3          2.8           5.1          1.5  virginica\n",
              "134           6.1          2.6           5.6          1.4  virginica\n",
              "135           7.7          3.0           6.1          2.3  virginica\n",
              "136           6.3          3.4           5.6          2.4  virginica\n",
              "137           6.4          3.1           5.5          1.8  virginica\n",
              "138           6.0          3.0           4.8          1.8  virginica\n",
              "139           6.9          3.1           5.4          2.1  virginica\n",
              "140           6.7          3.1           5.6          2.4  virginica\n",
              "141           6.9          3.1           5.1          2.3  virginica\n",
              "142           5.8          2.7           5.1          1.9  virginica\n",
              "143           6.8          3.2           5.9          2.3  virginica\n",
              "144           6.7          3.3           5.7          2.5  virginica\n",
              "145           6.7          3.0           5.2          2.3  virginica\n",
              "146           6.3          2.5           5.0          1.9  virginica\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7kPPtKar6cW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "outputId": "e995a9de-1a3b-4f8e-a578-cca9be4315ca"
      },
      "source": [
        "x_label = 'sepal_length'\n",
        "y_label = 'sepal_width'\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5.0, 5.0))\n",
        "\n",
        "for spec in ['setosa', 'versicolor', 'virginica']:\n",
        "    df = iris[iris['species'] == spec]\n",
        "    ax.scatter(df[x_label], df[y_label], label=spec)\n",
        "\n",
        "# Set the title (shown above the plot)\n",
        "ax.set_title('Iris dataset', fontsize=16)\n",
        "\n",
        "# Mark axes\n",
        "ax.set_xlabel(x_label, fontsize=16)\n",
        "ax.set_ylabel(y_label, fontsize=16)\n",
        "\n",
        "# Legend\n",
        "ax.legend(loc='best', fancybox=True, framealpha=0.5, fontsize=16)\n",
        "\n",
        "# Grid\n",
        "ax.grid()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xcnJyeOutt3jooYf44Ycf6NixI+eeey7Tp0+Pe3cpV/U1ywk52sxpFj4oZDx1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\nqIZhGC7x/wPRUSbTMlU6hgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f923a0e6908>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cubdQAuUr6cZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}