{
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  {
   "cell_type": "code",
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   "source": [
    "from datascience import *\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review and continuation of functions ##\n",
    "\n",
    "Functions (also refered to as `call expressions`) take in input values (called `arguments`) and return an output value. \n",
    "\n",
    "A few example functions are: \n",
    "\n",
    "- `abs()`: Takes the absolute value of a number\n",
    "- `min(x, y, ..., z)`: Takes the minimum value of x, y, ..., z\n",
    "- `round()`: round a number, potentially to a given number of decimal places\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take the absolute value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take the absolute value of a difference of numbers\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the minimum value\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# round a number\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# round a number to 2 decimal places\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review and continuation of numbers ##\n",
    "\n",
    "Python (and most programming languages) have different ways to represent numbers. Two import numerical representation of numbers are: \n",
    "\n",
    "- `ints`: These represent integers (whole numbers)\n",
    "- `floats`: These represent (approximate) real numbers (although still with finite precision)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
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  {
   "cell_type": "code",
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  {
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  {
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  {
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  {
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  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Strings ##\n",
    "\n",
    "A `string` is the term used to describe data that is text. There are a number of operations (functions) that can be applied to strings as shown below. We can also convert numbers (ints and floats) into string representations, and strings back to number representations. \n"
   ]
  },
  {
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   "source": []
  },
  {
   "cell_type": "code",
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
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  {
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types ##\n",
    "\n",
    "As we have seen there are different `types` of data; e.g., ints, floats, strings, etc. Different functions operate on particular types of data. For example, one can't take the absolute value of a string (try it and you will see you will get a `TypeError`). \n",
    "\n",
    "We can use the `type()` function to take what type of value a given name is holding. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arrays ##\n",
    "\n",
    "Arrays are a data structure that holds a sequence of values of the same type. For example, a squence of all numbers, or a squence of all strings, etc. \n",
    "\n",
    "We can use use the `make_array` function from the `datascience` package to create what are called `ndarray` that are array implemented by the `NumPy` package. One can perform a range of operations on these arrays in a very efficient manner. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ranges ##\n",
    "\n",
    "We can use use the `numpy` `np.arange()` function to create `ndarrays` that contain useful sequences of numbers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tables ##\n",
    "\n",
    "Tables stored structured data. We can use the `datascience` package to create `Table` objects that we can perform data manipulation operations on (the `Table` object is a simplified version of a Pandas DataFrame). \n",
    "\n",
    "Some methods we can perform on `Table` objects are:\n",
    "- `tb.show(k)`: show the first k rows of the table\n",
    "- `tb.select('col1', 'col2')`: select `col1` and `col2` from the table\n",
    "- `tb.drop('col')`: remove `col` from the table\n",
    "- `tb.sort('col')`: sort the rows in the table based on the values in `col`\n",
    "- `tb.where('col', value)`: reduce the table to rows where `col` is equal to `value` \n",
    "\n",
    "These methods all return Table objects that have been modified based on the methods that have been called. \n",
    "\n",
    "\n",
    "Let's look at data on ice cream cones that is described in the class textbook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the ice cream data. Each row represents one ice cream cone.\n",
    "cones = Table.read_table('cones.csv')\n",
    "cones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show the first 2 rows of the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only the Flavor column\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the original cones Table is not modified\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the Flavor and Price columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the Color column\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sort by price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sort by price highest to loweset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only the chocolate cones\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can combine mulitple method called. Let's drop the color and then sort by price\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Columns of Tables are Arrays ##\n",
    "\n",
    "We can extract columns from a `Table` as either:\n",
    "\n",
    "- A new `Table` with fewer columns using `tb.select()`\n",
    "- An `ndarray` using `tb.column()` \n"
   ]
  },
  {
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  {
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