0_prep.ipynb

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      "source": [
        "# Introduction to Python\n",
        "\n",
        "## Setting up Python\n",
        "\n",
        "Make yourself acquainted with the notebook environment. It's basically a webpage with executable code. Code is run by clicking the \"run cell\" button (eighth from the left).\n",
        "\n",
        "There are many great keyboard shortcuts. Press 'H' to see a cheat sheet."
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      "source": [
        "### Output\n",
        "\n",
        "Jupyter Notebook outputs the output of last line:"
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        "1+1"
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        "1+1\n",
        "\"Hello!\""
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      "source": [
        "Using 'print' explictly allows you multiple print-outs per cell. Output by using Python's print function:"
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        "print(1+1)\n",
        "print(\"Hello!\")"
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      "source": [
        "### Imports\n",
        "\n",
        "A central building block of Python, and especially the distribution of Anaconda you should have installed, is the ability to import additional modules, packages or libraries into your current script with the 'import' command."
      ]
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      "source": [
        "import math"
      ]
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      "source": [
        "math.log(4)"
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      "source": [
        "math.cos(math.pi)"
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      "source": [
        "Sometimes you will want to use a short name for a library:"
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      "source": [
        "import math as mt"
      ]
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      "source": [
        "mt.log(4)"
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      "source": [
        "Note that you have to type the module name (\"math\" or \"mt\") before each function call.\n",
        "\n",
        "You can also import a specific function of a module. Then the explicit call is not necessary:"
      ]
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      "source": [
        "from statistics import mean"
      ]
    },
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      "source": [
        "mean([2, 5, 6, 100])"
      ]
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      "source": [
        "Now that we know the basics of importing, make yourself comfortable with using multiple libraries. NumPy and Pandas are some of the ones we will be using in the course.\n"
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      "source": [
        "### NumPy\n",
        "\n",
        "NumPy is the fundamental package for scientific computing with Python. More information and tutorials at:\n",
        "\n",
        "http://www.numpy.org/"
      ]
    },
    {
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      "source": [
        "import numpy as np"
      ]
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      "source": [
        "An example command:"
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      "source": [
        "x = [2, 5, 6, 100]\n",
        "np.mean(x)"
      ]
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      "source": [
        "### Pandas\n",
        "\n",
        "Pandas provides data structures and data analysis tools. More information and tutorials at:\n",
        "\n",
        "http://pandas.pydata.org/\n",
        "\n",
        "Import the pandas module as alias pd:"
      ]
    },
    {
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      "execution_count": null,
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        "id": "J9PQf5dRpR6X"
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      "source": [
        "import pandas as pd"
      ]
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      "source": [
        "An example command:"
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      "source": [
        "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n",
        "s"
      ]
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