{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Selecting and Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "> “Every second of every day, our senses bring in way too much data than we can possibly process in our brains.”\n",
    ">\n",
    "> \\- Peter Diamandis, Founder of the X-Prize for human-AI collaboration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Applied Review"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Data Structures and the DataFrame Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* Data is frequently represented inside a **DataFrame** - a class from the `pandas` library that is similar to a *table* or *dataset*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* Each DataFrame object has rows and columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* The DataFrame class has methods (built-in operations) for common tasks and attributes (stored data) of common information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* Other structures exist, too - lists, dicts, tuples, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Importing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* Tabular data can be imported into DataFrames using the `pd.read_csv()` function - there are parameters for different options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "planes_df = pd.read_csv('../data/planes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Other data formats like JSON (key-value pairs) and Pickle (native Python) can be imported using the `with` statement and respective functions:\n",
    "  * JSON files use the `load()` function from the `json` library\n",
    "  * Pickle files use the `load()` function from the `pickle` library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## General Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Subsetting Dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* We don't always want all of the data in a DataFrame, so we need to take subsets of the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* In general, **subsetting** is extracting a small portion of a DataFrame -- making the DataFrame smaller."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* Since the DataFrame is two-dimensional, there are two dimensions on which to subset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Dimension 1:** We may only want to consider certain *variables*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "For example, we may only care about the `year` and `engines` variables:\n",
    "\n",
    "![](./images/selecting_columns.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    },
    "tags": []
   },
   "source": [
    "We call this **selecting** columns/variables -- this is similar to SQL's `SELECT` or R's dplyr package's `select()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "**Dimension 2:** We may only want to consider certain *cases*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "For example, we may only care about the cases where the manufacturer is Embraer.\n",
    "\n",
    "![](./images/selecting_rows.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We call this **filtering** or **slicing** -- this is similar to SQL's `WHERE` or R's dplyr package's `filter()` or `slice()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "And we can combine these two options to subset in both dimensions -- the `year` and `engines` variables where the manufacturer is Embraer:\n",
    "\n",
    "![](./images/selecting_rows_columns.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Subsetting into a New DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In the previous example, we want to do two things using `planes_df`:\n",
    "\n",
    "  1. **select** the `year` and `engines` variables\n",
    "  2. **filter** to cases where the manufacturer is Embraer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "But we also want to return a new DataFrame -- not just highlight certain cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In other words, we want to turn this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "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>tailnum</th>\n",
       "      <th>year</th>\n",
       "      <th>type</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>engines</th>\n",
       "      <th>seats</th>\n",
       "      <th>speed</th>\n",
       "      <th>engine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N10156</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>EMBRAER</td>\n",
       "      <td>EMB-145XR</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N102UW</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>AIRBUS INDUSTRIE</td>\n",
       "      <td>A320-214</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N103US</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>AIRBUS INDUSTRIE</td>\n",
       "      <td>A320-214</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N104UW</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>AIRBUS INDUSTRIE</td>\n",
       "      <td>A320-214</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N10575</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>EMBRAER</td>\n",
       "      <td>EMB-145LR</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tailnum    year                     type      manufacturer      model  \\\n",
       "0  N10156  2004.0  Fixed wing multi engine           EMBRAER  EMB-145XR   \n",
       "1  N102UW  1998.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "2  N103US  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "3  N104UW  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "4  N10575  2002.0  Fixed wing multi engine           EMBRAER  EMB-145LR   \n",
       "\n",
       "   engines  seats  speed     engine  \n",
       "0        2     55    NaN  Turbo-fan  \n",
       "1        2    182    NaN  Turbo-fan  \n",
       "2        2    182    NaN  Turbo-fan  \n",
       "3        2    182    NaN  Turbo-fan  \n",
       "4        2     55    NaN  Turbo-fan  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Into this:\n",
    "\n",
    "![](./images/subsetting_result.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "So we really have a third need: return the resulting DataFrame so we can continue our analysis:\n",
    "\n",
    "  1. **select** the `year` and `engines` variables\n",
    "  2. **filter** to cases where the manufacturer is Embraer\n",
    "  3. Return a DataFrame to continue the analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Subsetting Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Recall that the subsetting of variables/columns is called **selecting** variables/columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In a simple example, we can select a single variable using bracket subsetting notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2004.0\n",
       "1    1998.0\n",
       "2    1999.0\n",
       "3    1999.0\n",
       "4    2002.0\n",
       "Name: year, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df['year'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Notice the `head()` method also works on `planes_df['year']` to return the first five elements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"admonition tip alert alert-warning\">\n",
    "    <b><p class=\"first admonition-title\" style=\"font-weight: bold\">Question</p></b>\n",
    "    <p>What is the data type of <tt class=\\\"docutils literal\\\">planes_df['year']</tt>?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "This returns `pandas.core.series.Series`, referred to simply as a \"Series\", rather than a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(planes_df['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "This is okay -- the Series is a popular data structure in Python. Recall:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* A Series is a one-dimensional data structure -- this is similar to a Python `list`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* Note that all objects in a Series must be of the same type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "* Each DataFrame can be thought of as a list of equal-length Series (plus an Index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "This visual of a Series and DataFrame may be helpful:\n",
    "\n",
    "<center>\n",
    "<img src=\"images/dataframe-series.png\" alt=\"dataframe-series.png\" width=\"300\" height=\"300\">\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Series can be useful for other things, and we will cover that later -- but for now, we are interested in *returning a DataFrame* rather than a series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can select a single variable and return a DataFrame by still using bracket subsetting notation, but this time we will pass a `list` of variables names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "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>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year\n",
       "0  2004.0\n",
       "1  1998.0\n",
       "2  1999.0\n",
       "3  1999.0\n",
       "4  2002.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df[['year']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "And we can see that we've returned a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(planes_df[['year']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"admonition tip alert alert-warning\">\n",
    "    <b><p class=\"first admonition-title\" style=\"font-weight: bold\">Question</p></b>\n",
    "    <p>What's another advantage of this passing a <tt class=\\\"docutils literal\\\">list</tt>?</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Passing a list into the bracket subsetting notation allows us to select multiple variables at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>engines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>4</th>\n",
       "      <td>2002.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  engines\n",
       "0  2004.0        2\n",
       "1  1998.0        2\n",
       "2  1999.0        2\n",
       "3  1999.0        2\n",
       "4  2002.0        2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df[['year', 'engines']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In another example, assume we are interested in the `model` of plane, number of `seats` and `engine` type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>seats</th>\n",
       "      <th>engine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EMB-145XR</td>\n",
       "      <td>55</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A320-214</td>\n",
       "      <td>182</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A320-214</td>\n",
       "      <td>182</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A320-214</td>\n",
       "      <td>182</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EMB-145LR</td>\n",
       "      <td>55</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       model  seats     engine\n",
       "0  EMB-145XR     55  Turbo-fan\n",
       "1   A320-214    182  Turbo-fan\n",
       "2   A320-214    182  Turbo-fan\n",
       "3   A320-214    182  Turbo-fan\n",
       "4  EMB-145LR     55  Turbo-fan"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df[['model', 'seats', 'engine']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Your Turn\n",
    "\n",
    "1\\. ______ is a common term for subsetting DataFrame variables.\n",
    "\n",
    "2\\. What type of object is a DataFrame column?\n",
    "\n",
    "3\\. What will be returned by the following code?\n",
    "\n",
    "   ```python\n",
    "   planes_df['type', 'model']\n",
    "   ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Subsetting Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "When we subset cases/records/rows we primarily use two names: **slicing** and **filtering**, but *these are not the same*:\n",
    "\n",
    "  * **slicing**, similar to row **indexing**, subsets cases by the value of the Index\n",
    "  * **filtering** subsets cases using a conditional test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Slicing Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Remember that all DataFrames have an Index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>tailnum</th>\n",
       "      <th>year</th>\n",
       "      <th>type</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>engines</th>\n",
       "      <th>seats</th>\n",
       "      <th>speed</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>N102UW</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>AIRBUS INDUSTRIE</td>\n",
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       "      <td>1999.0</td>\n",
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       "      <td>AIRBUS INDUSTRIE</td>\n",
       "      <td>A320-214</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
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       "      <td>Turbo-fan</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N104UW</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>AIRBUS INDUSTRIE</td>\n",
       "      <td>A320-214</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N10575</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>EMBRAER</td>\n",
       "      <td>EMB-145LR</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tailnum    year                     type      manufacturer      model  \\\n",
       "0  N10156  2004.0  Fixed wing multi engine           EMBRAER  EMB-145XR   \n",
       "1  N102UW  1998.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "2  N103US  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "3  N104UW  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "4  N10575  2002.0  Fixed wing multi engine           EMBRAER  EMB-145LR   \n",
       "\n",
       "   engines  seats  speed     engine  \n",
       "0        2     55    NaN  Turbo-fan  \n",
       "1        2    182    NaN  Turbo-fan  \n",
       "2        2    182    NaN  Turbo-fan  \n",
       "3        2    182    NaN  Turbo-fan  \n",
       "4        2     55    NaN  Turbo-fan  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can **slice** cases/rows using the values in the Index and bracket subsetting notation. It's common practice to use `.loc` to slice cases/rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>N102UW</td>\n",
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       "      <td>A320-214</td>\n",
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       "      <td>Turbo-fan</td>\n",
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       "      <th>4</th>\n",
       "      <td>N10575</td>\n",
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       "      <td>Fixed wing multi engine</td>\n",
       "      <td>EMBRAER</td>\n",
       "      <td>EMB-145LR</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>N105UW</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>AIRBUS INDUSTRIE</td>\n",
       "      <td>A320-214</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tailnum    year                     type      manufacturer      model  \\\n",
       "0  N10156  2004.0  Fixed wing multi engine           EMBRAER  EMB-145XR   \n",
       "1  N102UW  1998.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "2  N103US  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "3  N104UW  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "4  N10575  2002.0  Fixed wing multi engine           EMBRAER  EMB-145LR   \n",
       "5  N105UW  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "\n",
       "   engines  seats  speed     engine  \n",
       "0        2     55    NaN  Turbo-fan  \n",
       "1        2    182    NaN  Turbo-fan  \n",
       "2        2    182    NaN  Turbo-fan  \n",
       "3        2    182    NaN  Turbo-fan  \n",
       "4        2     55    NaN  Turbo-fan  \n",
       "5        2    182    NaN  Turbo-fan  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df.loc[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"admonition note alert alert-info\">\n",
    "    <b><p class=\"first admonition-title\" style=\"font-weight: bold\">Note</p></b>\n",
    "    <p>Note that since this is <u><b><em>not</em></b></u> \"indexing\", the last element is inclusive.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can also pass a `list` of Index values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tailnum</th>\n",
       "      <th>year</th>\n",
       "      <th>type</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>engines</th>\n",
       "      <th>seats</th>\n",
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       "      <td>Turbo-fan</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Turbo-fan</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N10575</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>EMBRAER</td>\n",
       "      <td>EMB-145LR</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>N107US</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>AIRBUS INDUSTRIE</td>\n",
       "      <td>A320-214</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>N109UW</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>AIRBUS INDUSTRIE</td>\n",
       "      <td>A320-214</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tailnum    year                     type      manufacturer      model  \\\n",
       "0  N10156  2004.0  Fixed wing multi engine           EMBRAER  EMB-145XR   \n",
       "2  N103US  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "4  N10575  2002.0  Fixed wing multi engine           EMBRAER  EMB-145LR   \n",
       "6  N107US  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "8  N109UW  1999.0  Fixed wing multi engine  AIRBUS INDUSTRIE   A320-214   \n",
       "\n",
       "   engines  seats  speed     engine  \n",
       "0        2     55    NaN  Turbo-fan  \n",
       "2        2    182    NaN  Turbo-fan  \n",
       "4        2     55    NaN  Turbo-fan  \n",
       "6        2    182    NaN  Turbo-fan  \n",
       "8        2    182    NaN  Turbo-fan  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df.loc[[0, 2, 4, 6, 8]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Filtering Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We can **filter** cases/rows using a logical sequence equal in length to the number of rows in the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Continuing our example, assume we want to determine whether each case's `manufacturer` is Embraer. We can use the `manufacturer` Series and a logical equivalency test to find the result for each row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4        True\n",
       "        ...  \n",
       "3317    False\n",
       "3318    False\n",
       "3319    False\n",
       "3320    False\n",
       "3321    False\n",
       "Name: manufacturer, Length: 3322, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df['manufacturer'] == 'EMBRAER'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can use this resulting logical sequence to test **filter** cases -- rows that are `True` will be returned while those that are `False` will be removed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
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       "      <td>Turbo-fan</td>\n",
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       "      <td>2002.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>EMBRAER</td>\n",
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       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   tailnum    year                     type manufacturer      model  engines  \\\n",
       "0   N10156  2004.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "4   N10575  2002.0  Fixed wing multi engine      EMBRAER  EMB-145LR        2   \n",
       "10  N11106  2002.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "11  N11107  2002.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "12  N11109  2002.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "\n",
       "    seats  speed     engine  \n",
       "0      55    NaN  Turbo-fan  \n",
       "4      55    NaN  Turbo-fan  \n",
       "10     55    NaN  Turbo-fan  \n",
       "11     55    NaN  Turbo-fan  \n",
       "12     55    NaN  Turbo-fan  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df[planes_df['manufacturer'] == 'EMBRAER'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "This also works with `.loc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
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       "      <td>Turbo-fan</td>\n",
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       "      <td>N11109</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>EMBRAER</td>\n",
       "      <td>EMB-145XR</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   tailnum    year                     type manufacturer      model  engines  \\\n",
       "0   N10156  2004.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "4   N10575  2002.0  Fixed wing multi engine      EMBRAER  EMB-145LR        2   \n",
       "10  N11106  2002.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "11  N11107  2002.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "12  N11109  2002.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "\n",
       "    seats  speed     engine  \n",
       "0      55    NaN  Turbo-fan  \n",
       "4      55    NaN  Turbo-fan  \n",
       "10     55    NaN  Turbo-fan  \n",
       "11     55    NaN  Turbo-fan  \n",
       "12     55    NaN  Turbo-fan  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df.loc[planes_df['manufacturer'] == 'EMBRAER'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Any conditional test can be used to **filter** DataFrame rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>Fixed wing multi engine</td>\n",
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       "      <td>EMB-145XR</td>\n",
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       "      <td>Turbo-fan</td>\n",
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       "      <td>N11140</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>EMBRAER</td>\n",
       "      <td>EMB-145XR</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   tailnum    year                     type manufacturer      model  engines  \\\n",
       "0   N10156  2004.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "15  N11121  2003.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "16  N11127  2003.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "17  N11137  2003.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "18  N11140  2003.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "\n",
       "    seats  speed     engine  \n",
       "0      55    NaN  Turbo-fan  \n",
       "15     55    NaN  Turbo-fan  \n",
       "16     55    NaN  Turbo-fan  \n",
       "17     55    NaN  Turbo-fan  \n",
       "18     55    NaN  Turbo-fan  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df.loc[planes_df['year'] > 2002].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "And multiple conditional tests can be combined using logical operators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>tailnum</th>\n",
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       "      <td>Turbo-fan</td>\n",
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       "      <th>19</th>\n",
       "      <td>N11150</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Fixed wing multi engine</td>\n",
       "      <td>EMBRAER</td>\n",
       "      <td>EMB-145XR</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turbo-fan</td>\n",
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      ],
      "text/plain": [
       "   tailnum    year                     type manufacturer      model  engines  \\\n",
       "15  N11121  2003.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "16  N11127  2003.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "17  N11137  2003.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "18  N11140  2003.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "19  N11150  2003.0  Fixed wing multi engine      EMBRAER  EMB-145XR        2   \n",
       "\n",
       "    seats  speed     engine  \n",
       "15     55    NaN  Turbo-fan  \n",
       "16     55    NaN  Turbo-fan  \n",
       "17     55    NaN  Turbo-fan  \n",
       "18     55    NaN  Turbo-fan  \n",
       "19     55    NaN  Turbo-fan  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planes_df.loc[(planes_df['year'] > 2002) & (planes_df['year'] < 2004)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"admonition note alert alert-info\">\n",
    "    <b><p class=\"first admonition-title\" style=\"font-weight: bold\">Note</p></b>\n",
    "    <p>Note that each condition is wrapped in parentheses -- this is <b><em>required</em></b>.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Your Turn\n",
    "\n",
    "1. What's the difference between **slicing** cases and **filtering** cases?\n",
    "2. Fill in the blanks to fix the following code to find planes that have more than three engines:\n",
    "\n",
    "   ```python\n",
    "    planes_df.loc[______['______'] > 3]\n",
    "   ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Selecting Variables and Filtering Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "If we want to select variables and filter cases at the same time, we have a few options:\n",
    "\n",
    "1. Sequential operations\n",
    "2. Simultaneous operations"
   ]
  },
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   "source": [
    "### Sequential Operations\n",
    "\n",
    "We can use what we've previously learned to select variables and filter cases in multiple steps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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       "      <th></th>\n",
       "      <th>year</th>\n",
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      "text/plain": [
       "      year  engines\n",
       "0   2004.0        2\n",
       "4   2002.0        2\n",
       "10  2002.0        2\n",
       "11  2002.0        2\n",
       "12  2002.0        2"
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   "source": [
    "planes_df_filtered = planes_df.loc[planes_df['manufacturer'] == 'EMBRAER']\n",
    "planes_df_filtered_and_selected = planes_df_filtered[['year', 'engines']]\n",
    "planes_df_filtered_and_selected.head()"
   ]
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   "source": [
    "This is a good way to learn how to select and filter independently, and it also reads very clearly."
   ]
  },
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   "metadata": {
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   "source": [
    "### Simultaneous Operations\n",
    "\n",
    "However, we can also do both selecting and filtering in a single step with `.loc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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     "slide_type": "-"
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       "<style scoped>\n",
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       "      <td>2</td>\n",
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       "      <td>2002.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2002.0</td>\n",
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      "text/plain": [
       "      year  engines\n",
       "0   2004.0        2\n",
       "4   2002.0        2\n",
       "10  2002.0        2\n",
       "11  2002.0        2\n",
       "12  2002.0        2"
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   "source": [
    "planes_df.loc[planes_df['manufacturer'] == 'EMBRAER', ['year', 'engines']].head()"
   ]
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   "source": [
    "This option is more succinct and also reduces programming time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Your Turn\n",
    "\n",
    "Subset `planes_df` to only include planes made by Boeing and the `seats` and `model` variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Questions\n",
    "\n",
    "Are there any questions before we move on?"
   ]
  }
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