coffee_analysis_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Introduction to data analytics with pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quentin Caudron\n",
    "\n",
    "#### PyData Seattle, July 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Systems check\n",
    "\n",
    "Do you have a working Python installation, with the `pandas` package ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** This cell should run without raising a traceback. Assuming it runs, you can also try printing the value of `pd.__version__` to see what version of `pandas` you have installed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## A little about me\n",
    "\n",
    "- Lapsed computational physicist\n",
    "- PhD computational neuroscience, postdoc statistical epidemiology\n",
    "- Data Scientist at CBRE - `www.cbredev.com`\n",
    "- ATOM at Seattle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## A little about the hero of this story\n",
    "\n",
    "<center><img src=\"images/coffee_machine.jpg\" width=\"400px\" /></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We'll be analysing a real-world dataset together. It's about my favourite thing in the world : **coffee**. This dataset was collected at the Mathematics Institute at the University of Warwick. It's a time-series dataset, describing the **total number of coffees made by our espresso machine** by a certain date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## A little about this workshop\n",
    "\n",
    "We'll be running through an analysis of this dataset as a way to expose you to the `pandas` API. The aim is to develop a little familiarity with how to work with `pandas`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Slides are available at https://github.com/QCaudron/pydata_pandas. One notebook contains solutions; **beware of spoilers**.\n",
    "\n",
    "The notebooks contain **notes** about what we're doing that I'll skip during this workshop, but try to explain on the way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The `pandas` API is **enormous**. The [documentation](http://pandas.pydata.org/pandas-docs/stable/) is excellent, don't hesitate to look things up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Key questions\n",
    "\n",
    "1. Who are the main contributors to this dataset, and when are contributions generally made ?\n",
    "2. What are the department's weekday coffee habits ?\n",
    "3. How much coffee are people drinking ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Let's begin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** The second line here tells `matplotlib` to plot directly under the cell where any plotting code is called. `pandas` uses `matplotlib` to generate graphs, and without this, the graphs would appear outside the Jupyter notebook when you called `plt.show()` - but we just want them to appear without having to do this.\n",
    "\n",
    "http://ipython.readthedocs.io/en/stable/interactive/plotting.html#id1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Importing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import the coffee data from CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read data from data/coffees.csv\n",
    "data = pd.read_csv(\"data/coffees.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `pandas` can read from many data formats : CSV, JSON, Excel, HDF5, SQL, and more.\n",
    "\n",
    "http://pandas.pydata.org/pandas-docs/version/0.20/io.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### What does this data look like ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2013-09-12 15:38:00</td>\n",
       "      <td>24450.0</td>\n",
       "      <td>Quentin</td>\n",
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       "      <td>24463.0</td>\n",
       "      <td>Quentin</td>\n",
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      "text/plain": [
       "               timestamp  coffees contributor\n",
       "0    2011-10-03 08:22:00    397.0     Quentin\n",
       "1    2011-10-04 11:48:00    410.0     Quentin\n",
       "2    2011-10-05 07:02:00  testing     Anthony\n",
       "3    2011-10-05 08:25:00      NaN     Quentin\n",
       "4    2011-10-05 10:47:00    464.0     Quentin\n",
       "5    2011-10-05 13:15:00    481.0     Quentin\n",
       "6    2011-10-06 07:21:00    503.0     Anthony\n",
       "7    2011-10-06 10:04:00    513.0     Quentin\n",
       "8    2011-10-06 12:14:00    539.0        Mike\n",
       "9    2011-10-06 12:49:00    540.0     Quentin\n",
       "10   2011-10-06 14:52:00    563.0         Ben\n",
       "11   2011-10-07 07:34:00    581.0     Anthony\n",
       "12   2011-10-07 08:37:00    587.0     Quentin\n",
       "13   2011-10-07 11:09:00    605.0     Quentin\n",
       "14   2011-10-07 13:14:00    616.0        Mike\n",
       "15   2011-10-07 14:10:00      NaN         Ben\n",
       "16   2011-10-07 15:20:00    626.0      Mike M\n",
       "17   2011-10-07 16:50:00    635.0      Mike M\n",
       "18   2011-10-09 16:53:00    650.0        Colm\n",
       "19   2011-10-10 07:29:00    656.0     Anthony\n",
       "20   2011-10-10 10:13:00    673.0     Quentin\n",
       "21   2011-10-10 13:41:00    694.0      Mike M\n",
       "22   2011-10-10 14:02:00    699.0     Quentin\n",
       "23   2011-10-10 15:23:00    713.0     Quentin\n",
       "24   2011-10-11 14:09:00    770.0      Mike M\n",
       "25   2011-10-12 08:11:00    790.0     Quentin\n",
       "26   2011-10-12 09:57:00    799.0      Mike M\n",
       "27   2011-10-12 10:06:00    805.0      Sergio\n",
       "28   2011-10-12 12:01:00    818.0      Mike M\n",
       "29   2011-10-12 12:30:00    819.0     Quentin\n",
       "..                   ...      ...         ...\n",
       "641  2013-01-28 10:43:00      NaN      Sergio\n",
       "642  2013-01-28 13:13:00      NaN     Quentin\n",
       "643  2013-01-28 14:01:00  16195.0     Quentin\n",
       "644  2013-01-29 13:43:00  16237.0     Quentin\n",
       "645  2013-01-29 15:06:00  16257.0     Quentin\n",
       "646  2013-02-04 13:25:00  16513.0      Sergio\n",
       "647  2013-02-06 17:33:00  16659.0     Quentin\n",
       "648  2013-02-07 13:30:00  16714.0      Sergio\n",
       "649  2013-02-12 08:36:00  16891.0      Sergio\n",
       "650  2013-02-12 11:39:00  16909.0     Quentin\n",
       "651  2013-02-13 13:58:00  16977.0     Quentin\n",
       "652  2013-02-16 11:55:00  17104.0     Quentin\n",
       "653  2013-02-18 12:04:00      NaN     Quentin\n",
       "654  2013-02-18 13:46:00  17165.0     Quentin\n",
       "655  2013-02-21 13:44:00  17345.0     Quentin\n",
       "656  2013-02-21 15:02:00  17354.0     Quentin\n",
       "657  2013-02-25 13:33:00  17468.0     Quentin\n",
       "658  2013-02-25 17:25:00  17489.0     Quentin\n",
       "659  2013-02-27 09:33:00  17564.0     Quentin\n",
       "660  2013-03-04 10:46:00  17789.0      Sergio\n",
       "661  2013-03-04 11:12:00  17793.0     Quentin\n",
       "662  2013-03-04 16:43:00  17824.0     Quentin\n",
       "663  2013-03-05 10:42:00  17852.0     Quentin\n",
       "664  2013-03-05 13:29:00  17868.0     Quentin\n",
       "665  2013-03-08 10:28:00  18062.0     Quentin\n",
       "666  2013-03-12 08:28:00  18235.0      Sergio\n",
       "667  2013-04-05 11:20:00  18942.0      Sergio\n",
       "668  2013-04-27 11:04:00  19698.0      Sergio\n",
       "669  2013-09-12 15:38:00  24450.0     Quentin\n",
       "670  2013-09-13 10:28:00  24463.0     Quentin\n",
       "\n",
       "[671 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's just look at the first few rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-03 08:22:00</td>\n",
       "      <td>397.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-04 11:48:00</td>\n",
       "      <td>410.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-10-05 07:02:00</td>\n",
       "      <td>testing</td>\n",
       "      <td>Anthony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-10-05 08:25:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-10-05 10:47:00</td>\n",
       "      <td>464.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp  coffees contributor\n",
       "0  2011-10-03 08:22:00    397.0     Quentin\n",
       "1  2011-10-04 11:48:00    410.0     Quentin\n",
       "2  2011-10-05 07:02:00  testing     Anthony\n",
       "3  2011-10-05 08:25:00      NaN     Quentin\n",
       "4  2011-10-05 10:47:00    464.0     Quentin"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .head()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We have an index, and three columns : `timestamp`, `coffees`, and `contributor`.\n",
    "\n",
    "Uh-oh. Why is there a string of text, `testing`, in our coffee numbers ? What's going on in the `coffees` column in the row after that ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `df.head(n=10)` would show the first ten rows. The default is `n=5`.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.head.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's look at that string in the third row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp      2011-10-05 07:02:00\n",
       "coffees                    testing\n",
       "contributor                Anthony\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .loc or .iloc\n",
    "data.loc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Definitely a string. We'll note this as something to fix after we finish looking around."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `.loc` uses a label-based lookup, which means that the value you pass into the square brackets must be in the index. Another method, `.iloc`, is integer-location-based, so `.iloc[2]` would return the third row. In this case, they're the same, but had we changed our index, as we'll see later, things would work differently.\n",
    "\n",
    "Indexing a dataframe with `[]` directly returns a `pd.Series` or `pd.DataFrame` by searching over *columns*, not rows. Indexing a `pd.Series` with `[]` is like indexing a dataframe with `.iloc`.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/indexing.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We should also take a look at that NaN. In fact, let's look at the first five values in `coffees`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      397.0\n",
       "1      410.0\n",
       "2    testing\n",
       "3        NaN\n",
       "4      464.0\n",
       "Name: coffees, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [] indexing on a series\n",
    "data.coffees[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** here, we're indexing a *series* ( a `pd.Series` object ). From a `pd.DataFrame` ( here, `data` ), when you access a single column ( `data.coffees` or `data[\"coffees\"]` ), the object returned is a `pd.Series`. From that, indexing directly with `[]` works in an integer-location-based manner, and like with numpy arrays, you can take slices ( `[:5]` ).\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### How long is the dataset ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset length :\n",
      "671\n"
     ]
    }
   ],
   "source": [
    "print(\"Dataset length :\")\n",
    "\n",
    "# len()\n",
    "print(len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### What else can we find out ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>671</td>\n",
       "      <td>658</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>671</td>\n",
       "      <td>654</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2012-08-02 09:16:00</td>\n",
       "      <td>9134.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  timestamp coffees contributor\n",
       "count                   671     658         671\n",
       "unique                  671     654           9\n",
       "top     2012-08-02 09:16:00  9134.0     Quentin\n",
       "freq                      1       2         367"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .describe()\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Looks like we also have some missing data - we have 671 rows, but the `coffees` column only has 658 entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `.describe()` returns different things based on what's in the dataframe, as we'll see later. For numerical columns, it will return things like the mean, standard deviation, and percentiles. For object columns ( strings or datetimes ), it will return the most frequent entry and the first and last items. For all columns, `.describe()` will return the count of objects in that column ( not counting NaNs ) and the unique number of entries. You can determine what's returned using `.describe()`'s keyword arguments.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's look at the dataframe where `coffees` is null."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-10-05 08:25:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2011-10-07 14:10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ben</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2011-10-28 10:53:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mike M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2011-11-11 11:13:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2012-06-10 16:10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sergio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>2012-07-13 13:59:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2012-08-03 14:35:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sergio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>2012-09-21 10:15:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sergio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>2012-11-01 09:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2012-11-30 13:11:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>2013-01-28 10:43:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sergio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>2013-01-28 13:13:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>2013-02-18 12:04:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               timestamp coffees contributor\n",
       "3    2011-10-05 08:25:00     NaN     Quentin\n",
       "15   2011-10-07 14:10:00     NaN         Ben\n",
       "72   2011-10-28 10:53:00     NaN      Mike M\n",
       "95   2011-11-11 11:13:00     NaN     Quentin\n",
       "323  2012-06-10 16:10:00     NaN      Sergio\n",
       "370  2012-07-13 13:59:00     NaN        Mike\n",
       "394  2012-08-03 14:35:00     NaN      Sergio\n",
       "479  2012-09-21 10:15:00     NaN      Sergio\n",
       "562  2012-11-01 09:45:00     NaN     Quentin\n",
       "606  2012-11-30 13:11:00     NaN     Quentin\n",
       "641  2013-01-28 10:43:00     NaN      Sergio\n",
       "642  2013-01-28 13:13:00     NaN     Quentin\n",
       "653  2013-02-18 12:04:00     NaN     Quentin"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .isnull() and boolean indexing with []\n",
    "data[data.coffees.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `.isnull()` returns a boolean array ( an array of `True`s and `False`s ), that you can then use to index the dataframe directly. Here, our boolean array tells us which entries in the `coffees` column are null, and we use that to index against the full dataframe - so we get back every column in the dataframe, but only those rows where `coffees` is null.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.isnull.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### What type of Python objects are the columns ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp      object\n",
       "coffees        object\n",
       "contributor    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .dtypes\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The `contributor` column makes sense as `object`, because we expect strings there; but surely the `timestamp` should be a timestamp-type, and `coffees` should be numerical ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's inspect what's in the `timestamp` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-10-03 08:22:00\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# print the first element of the series with [] indexing\n",
    "print(data.timestamp[0])\n",
    "\n",
    "# print its type()\n",
    "print(type(data.timestamp[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "It looks like the `timestamp` field was read from CSV as a string. That makes sense - CSV files are very basic. We'll have `pandas` interpret these strings as datetimes for us automatically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** here's an example of using direct `[]` indexing on a `pd.Series`. We're accessing the first entry, just to see what type of object we have there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### On our first pass, what problems did we find ?\n",
    "\n",
    "- The `timestamp` column contains strings; these need to be datetimes\n",
    "- The `coffees` column contains some null values and at least one string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Cleaning the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### The `coffees` column should only contain numerical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-03 08:22:00</td>\n",
       "      <td>397.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-04 11:48:00</td>\n",
       "      <td>410.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-10-05 07:02:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Anthony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-10-05 08:25:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-10-05 10:47:00</td>\n",
       "      <td>464.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp  coffees contributor\n",
       "0  2011-10-03 08:22:00    397.0     Quentin\n",
       "1  2011-10-04 11:48:00    410.0     Quentin\n",
       "2  2011-10-05 07:02:00      NaN     Anthony\n",
       "3  2011-10-05 08:25:00      NaN     Quentin\n",
       "4  2011-10-05 10:47:00    464.0     Quentin"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cast the coffees column using pd.to_numeric, and coerce errors\n",
    "data.coffees = pd.to_numeric(data.coffees, errors=\"coerce\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    " #### The `coffees` column contains NaNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-03 08:22:00</td>\n",
       "      <td>397.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-04 11:48:00</td>\n",
       "      <td>410.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-10-05 10:47:00</td>\n",
       "      <td>464.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-10-05 13:15:00</td>\n",
       "      <td>481.0</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-10-06 07:21:00</td>\n",
       "      <td>503.0</td>\n",
       "      <td>Anthony</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp  coffees contributor\n",
       "0  2011-10-03 08:22:00    397.0     Quentin\n",
       "1  2011-10-04 11:48:00    410.0     Quentin\n",
       "4  2011-10-05 10:47:00    464.0     Quentin\n",
       "5  2011-10-05 13:15:00    481.0     Quentin\n",
       "6  2011-10-06 07:21:00    503.0     Anthony"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .dropna() using a subset, and pass inplace\n",
    "data.dropna(subset=[\"coffees\"], inplace=True)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### The `coffees` column is of type `float`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-03 08:22:00</td>\n",
       "      <td>397</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-04 11:48:00</td>\n",
       "      <td>410</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-10-05 10:47:00</td>\n",
       "      <td>464</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-10-05 13:15:00</td>\n",
       "      <td>481</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-10-06 07:21:00</td>\n",
       "      <td>503</td>\n",
       "      <td>Anthony</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp  coffees contributor\n",
       "0  2011-10-03 08:22:00      397     Quentin\n",
       "1  2011-10-04 11:48:00      410     Quentin\n",
       "4  2011-10-05 10:47:00      464     Quentin\n",
       "5  2011-10-05 13:15:00      481     Quentin\n",
       "6  2011-10-06 07:21:00      503     Anthony"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cast to int using .astype()\n",
    "data.coffees = data.coffees.astype(int)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's have pandas parse the `timestamp` strings to datetime objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp      datetime64[ns]\n",
       "coffees                 int64\n",
       "contributor            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.to_datetime()\n",
    "data.timestamp = pd.to_datetime(data.timestamp)\n",
    "\n",
    "# Confirm dtypes\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### So where do we stand ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>657</td>\n",
       "      <td>657.000000</td>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2011-10-24 14:32:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>2011-10-03 08:22:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>2013-09-13 10:28:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8568.471842</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4600.215049</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4986.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9172.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11562.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24463.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  timestamp       coffees contributor\n",
       "count                   657    657.000000         657\n",
       "unique                  657           NaN           9\n",
       "top     2011-10-24 14:32:00           NaN     Quentin\n",
       "freq                      1           NaN         361\n",
       "first   2011-10-03 08:22:00           NaN         NaN\n",
       "last    2013-09-13 10:28:00           NaN         NaN\n",
       "mean                    NaN   8568.471842         NaN\n",
       "std                     NaN   4600.215049         NaN\n",
       "min                     NaN    397.000000         NaN\n",
       "25%                     NaN   4986.000000         NaN\n",
       "50%                     NaN   9172.000000         NaN\n",
       "75%                     NaN  11562.000000         NaN\n",
       "max                     NaN  24463.000000         NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .describe(), passing the include kwarg to see all information\n",
    "data.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-03 08:22:00</td>\n",
       "      <td>397</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-04 11:48:00</td>\n",
       "      <td>410</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-10-05 10:47:00</td>\n",
       "      <td>464</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-10-05 13:15:00</td>\n",
       "      <td>481</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-10-06 07:21:00</td>\n",
       "      <td>503</td>\n",
       "      <td>Anthony</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp  coffees contributor\n",
       "0 2011-10-03 08:22:00      397     Quentin\n",
       "1 2011-10-04 11:48:00      410     Quentin\n",
       "4 2011-10-05 10:47:00      464     Quentin\n",
       "5 2011-10-05 13:15:00      481     Quentin\n",
       "6 2011-10-06 07:21:00      503     Anthony"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What do the first few rows look like ?\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `.describe(include=\"all\")` is describing all attributes of all columns, but some don't make sense based on the column's `dtype`. For example, the contributor column has no `first` and `last` attributes, because those describe the first and last entries in an ordered series. That makes sense for the timestamp - those have an intuitive definition of sorting - but not so much for strings ( alphabetical order doesn't really matter when they're arbitrary strings ). Similary, the timestamp column has no mean or other numerical traits. What does it mean to calculate the mean timestamp ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The time-series at a glance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's begin by visualising the coffee counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112a251d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b08x3X9xGad0Rlk5L547LZzNtQjJJI4bHukQRGQIUDgNEQ2sHP3l5F/e/UUJ7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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129fc7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .plot() on the coffees series\n",
    "data.coffees.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "`pandas` is plotting the coffees against the index, which is just a series of integers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `.plot()` on a `pd.Series` will plot the data against the index. On a `pd.DataFrame`, the `.plot()` method allows plotting of one column against another.\n",
    "\n",
    "By default, `.plot()` renders a line graph, but you can specify which type of plot you'd like - bar, line, histogram, area, scatter, etc..\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's use the dataframe's `plot()` method rather than that of the series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11500e400>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8UHkwGHDXuUMVDCLSZBpjtNKp7j7C3dPD328C3nT3QcCb4e+Y2ZHARGAIMB74\ns5klh+tMA64CBoWP8Y1QV2Sdcf/bVQ5T7d+9Pc/95wnqXxCRJtUUQ1nPBZ4Mp58EJsS0z3L3Pe6+\nAlgGjDKzXkBnd1/g7g7MiFmnVZmZuZLDb82o8rabXfdL4e3Jp2qPQUSaXEPDwYE3zCzbzK4O2w50\n97LLgq4HDgyn+wCrYtZdHbb1CafLt1dgZlebWZaZZW3atKmBpSeWKRk53PLCYnYXV3IciSAYFt0+\nrtJ5IiKNraEd0mPcfY2ZHQC8bmafxs50dzezyj/t6sHdpwPTAdLT0xvteeNtSkYOD81bXum89qlJ\n3HrOEB1GEpFm1aBwcPc14c+NZvYCMArYYGa93H1deMhoY7j4GuDgmNXTwrY14XT59lbhounv897y\ngkrnDepZ/T0VRESaSr0PK5lZBzPrVDYNnAl8ArwEXBYudhnwYjj9EjDRzNqa2QCCjucPwkNQ281s\ndDhK6dKYdVq00+6bW2UwjEjromAQkbhpyJ7DgcAL4ajTFGCmu79iZguBZ81sEpAPXADg7kvM7Flg\nKVAMXOvuZTctvgZ4AtgPmBM+WqyZC/K559VP2fZVcaXzJ4zo/Y3bcoqINDcLBghFT3p6umdlZcW7\njDqr7jaeXduncsO4w9W/ICJNxsyyY049qJLOkG5ie4pL+PfqbXywooDZH60hd+OXlS6n/gURSSQK\nh0aQnV/IguVbGH1Id47o1YkP87fywYotZK4o4KNVW9lbXApA+zbJFdbdLzWZcUMO1GEkEUkoCocG\nmJKRw+xFa9i4Yw/ugAU9/CUOSQZD+3Th0tH9GDWgG8f278acT9ZzywuL962vvgURSVQKh3qYkpHD\n4++uYE/5Cx85HHtIN/7zlIGM7Lc/Hdt+8+0t60uY88k6zhraS30LIpKwFA519K375rJs865K57VL\nTWLyuMOrvbzFRcf1VSiISMJTONRSdn4hlz+WyY49JZXO79O1HQ98/xhd90hEWgSFQw2y8wt56F9f\n8PrSDVUuY6BgEJEWReFQjeputgPQNtk4+bAD+NHJhyoYRKRFUThUoapbc5ZJ69qO+Ted3owViYg0\nH4VDOdn5hUx68gO27qr80hY6L0FEWgOFQyg7v5Apc3JYmFdY6fwzjzxQh49EpNVQOFD9/RQAThrU\ng+mX1ngpEhGRFqNVh0N2fiG3vrCYnPU7qlxmUM8OzJh0XDNWJSISf602HLLzC/l/096rdpm0ru10\nMTwRaZVBi1LKAAANQElEQVQaeg/pyLrqyYVVzjOCQ0kajSQirVWr23O4++WlPJ25kl17Kz/TWZfO\nFhFpReEwM3Mlt724mPDq2RWkJhmTxgzgprOPaN7CREQSUKsIh5pGI2lvQUTkm1p8OIz45atsreJe\nzWld23HNqYN0lVQRkXJadDgcevPLVV4XqWfHNupwFhGpQosMhykZOfxl3nKqyAVdF0lEpAYtLhzG\nTHmT1Vt3VzovJcm489yhOowkIlKDFhMOZ9z/NrmbdlY5P9lg2a/PbsaKRESiq0WEw+BfZLC3qs4F\ngv6Fhbee0YwViYhEW+TPkB562yvVBsOItC4KBhGROor0nkN1o5EguASGLponIlJ3kQ2HxWu20auK\nYND5CyIiDRPZcKiK9hZERBouYfoczGy8mX1mZsvM7Kb6PMevzztKwSAi0ggSYs/BzJKBB4EzgNXA\nQjN7yd2X1mb9lCRY9utvN2WJIiKtSqLsOYwClrn7cnffC8wCzq1uhXYpyXRsm8yEEb0VDCIijSwh\n9hyAPsCqmN9XAxWOD5nZ1cDVAH379uWTX45vnupERFqZRNlzqBV3n+7u6e6e3rNnz3iXIyLSYiVK\nOKwBDo75PS1sExGROEiUcFgIDDKzAWbWBpgIvBTnmkREWq2E6HNw92Iz+wnwKpAMPObuS+JclohI\nq5UQ4QDg7hlARrzrEBERMPdqLk6UwMxsE5Af7zpqoQewOd5F1JJqbRpRqhWiU29U6oTEqrWfu9c4\noiey4RAVZpbl7unxrqM2VGvTiFKtEJ16o1InRKvWMonSIS0iIglE4SAiIhUoHJre9HgXUAeqtWlE\nqVaITr1RqROiVSugPgcREamE9hxERKQChYOIiFSgcGgEZmbxrqG2zOyIeNdQG2Z2vZmdGU4n/Ptr\nZl1iphO63kSvr0xUtlWI3vZaGwqHBjCzc83sSWB4vGupDTN7AJhjZv3jXEqVzOxMM3sVuBG4FMAT\nuGPMzE4zs0XANDO7BRK33ihtr1HYViF622tdJMzlM6LCzMzd3cxOBe4CioDjzSzf3QvjXN43lNUa\n09QdKADOMLMZ7r4nTqV9Q/hNKxW4DTgZ+A3QBjjWzFKB4kT8D2dmHYFbCLaDD4Anzay9u98a38q+\nFpXtNSrbKkR3e60r7TnUQbkNeAUwDphMcGOiYXErrBKxtYa3YQVYAPwZuAgYFK/aYpXVGd4B8EV3\nHxteZ6sQmOjuRYn4H83MkoCOBDep+sjdVwFXAhcmyuGQqGyvUdlWIbrba30oHGopvGrs82b2X2Z2\nkLvnufs6d38L2ACcbGZ94lwm8I1arzOz3u5eEl4KfTwwG5gLTDSz75pZ3O6aVO497eXuC8P2VHf/\nF7DczM6KV33lmdk1Zvb/ANy9FHCgJ0FI4O7LgReAO8Pl43bsOSrba1S21XK1RmJ7bSiFQy2Y2XnA\nZcADBN+4bjWzETGLPA0MptytTePx4VCu1uHALWY2Mvymk+Xum4Fc4GfA3UBcPsAqeU9/YWZlx8KL\nzawbwYUVS+JRXywz62RmDxEcRnjSzFIA3H0DkANcF7P4TcAoMxsSr2+QUdleo7KtVlJrQm+vjUXh\nUDvHAX9297nAHQS76D8rm+nu/ya4YdHQsIPyxrA9Hh8OldX6n+G8s83sHYLOs9kEu+7b41AjVF7n\nzyF439y9ANgPOBX2HcaJC3ffAfzL3Q8C/gk8GDP7l8AIMzvbzNqGexT/JDgmHS9R2V6jsq1ChLbX\nxhL5F9CYyn9zivl9OXAxgLvnE/zn72Bm58Ys/jeCY87PEFyet0m/idWx1v3N7HiCbz3vufsId78U\nOAho0uPjDXxPnyb4Ft4u/NBtctXUW3ZnwuuA75vZIAB3/xL4LcHdC28xszuBscC6ONaaUNtrVLbV\netQa9+21KSkcvukb70fMN6nngF0xG8J64G3gCAt0BP4ILAaGufvkcuvHu9Y3CT6wnnb3G2NWO8/d\nP2rCGuta59uE72nY1g6YRfPuqldar7vvNLMkd19P0FH6SMwys4BfExz26AmcFR5yamrf2DtJ4O21\nLnXGc1uFer6nYVs8ttcmo3AAzGyUmT0F/MbMjirbJbRw5ES4y/g8cI2ZmbtvI+iEbBduPLuBn7v7\nt929Sb8x1rPWDsB+7l5qZsll67j77gSrsyPQNuY/5Ivu/rC7FzVVnbWoN6n8IQJ3vwkYYGbHm9lB\nZnacu38K3O7u/+nua5q41nQz+ztwr5mNKXtPY/5dE2J7rWedzb6tNqDWuG2vzaFVh0P4H/92gm+B\ncwjO+7iW8CQhdy8Jl2sPvAasBaabWW/gaIIx47h7sbtvTPBai8uWa8pd3saqM3bZplSLekvDD6qO\nQJeYVe8B3gXmEXxjbPJj9uG3/inAQwSHNTYAPwH6ltUaLhfX7bUR6myWbbUxay2rtylrbXbu3qof\nwBXAMeF0d4IPiPSY+XcArxJsCN2AXxHsTv4ZSFat0a2zDvXeDrwCjA1/Pwv4FLgPSG3mWs8C9g+n\nexH0HXSMmX9nIry3UakzarU26/sS7wKa/QUHZzQeF/N7O4I9qLbh788C3wmnDwBmAoeWe472qjV6\ndTZWvcCRwMHxqDWmfSzB0Mn3gT8QjJLpGNY6MN7bQKLWGbVa4/mIewHN9kKhE8ExwwLgMaBb2G4x\ny6QC7wGDK1k/SbVGs85GrLc5v3mXr7Xsm21S+HMIcGo4fTkwAxiQANtAQtYZtVoT4dGa+hz2Am8B\nPyA4bng+VDhOfDiwwd0/t+DEp1Gw75T55hyaFpVao1JnY9XbnMeUy9f6vbDW0vDnEg/G3EPQ79GJ\nsE8hHFUVr20gUeuMWq1x16LDwcwuNbOTzayrBxfuegR4A/gcSDezweFyZRcg7E4wXO1ygm+PR4Uf\nCk1+clBUao1KnVGstw61lj8f4QyC/8s74OsPu9ZeZ9RqTTQtLhzC0Qe9zGwuwenuFwMPmlkPd9/t\nwan57wMbgQsgGL0Rrj4O+D5wEnCxuz/alB8KUak1KnVGsd561upm1tbMvmVm2cDZwI0eDK9s1XVG\nrdZE1qLCwcySw//InYA17n46wen4BcTc4Nvdc4FsoLeZDbRgmBrA/wHfd/cfenCJgVZfa1TqjGK9\nDai1LVBKMOzydnc/14PzLFp1nVGrNdG1iPs5WHDCyl1AspllAJ0Jz1L04CqPPwfWmtnJHlw9EXd/\nwYJLK78CdDSzU939PdUarTqjWG9j1ErQcbqY4CznVl1n1GqNDE+AXvGGPAiGpS0CpgFXEXQkjQdW\nAqNilvsxMDfm9+8BO4GHgQNUa/TqjGK9Uak1KnVGrdYoPeJeQCNsGGOBS2J+/zPBbuTlQHbYlkRw\n4a5nCYemheuNVa3RrTOK9Ual1qjUGbVao/RoCX0O2cCz9vUdpN4F+rr7EwS7mD/1YKRBGsHt+1YA\nuPs77v6Oao10nVGsNyq1RqXOqNUaGZEPB3ff5e57/Osx6GcAm8LpKwiumvhPglPim+OqjlWKSq1R\nqbNMlOqNSq1RqROiVWuUtIgOadjXIeXAgXx97f0dBDeAHwqs8Ca+WmZtRaXWqNRZJkr1RqXWqNQJ\n0ao1CiK/5xCjlODSB5uBYeE3hf8FSt19foJtFFGpNSp1lolSvVGpNSp1QrRqTXzx7vRozAcwmmAD\nmQ9Minc9LaHWqNQZxXqjUmtU6oxarYn+sPANbRHMLA24BPidB6fKJ6yo1BqVOstEqd6o1BqVOiFa\ntSa6FhUOIiLSOFpSn4OIiDQShYOIiFSgcBARkQoUDiIiUoHCQUREKlA4SKthZl3N7JpwureZPdeE\nf2uEmZ3dVM8v0tQUDtKadAWuAXD3te5+fhP+rREEdxMTiSSd5yCthpnNAs4FPgNygSPcfagF94ue\nAHQABgH3AW0ITqbaA5zt7gVmdijwINAT2AVc5e6fmtn3gNsJbi6zDfgWsAzYD1gD/AZYAfwRaAd8\nBVzh7p/V4W+/DXxMcO+CFOCH7v5B07xTIrSsy2fooUd1D6A/8Ekl05cTfJh3Ivjg3wb8OJz3e+C6\ncPpNYFA4fRzwVji9GOgTTneNec6pMX+7M5ASTn8L+Ecd//bbwMPh9EllteuhR1M9WsxVWUUaaK67\n7wB2mNk2gntJQ/DBP8zMOgInAH83s7J12oY/3wWeMLNngeereP4uwJNmNojgyqGptf3bMcv9DcDd\n55lZZzPr6u5b6/l6RaqlcBAJxF6HpzTm91KC/ydJwFZ3H1F+RXf/sZkdB3wbyDazkZU8/10EIXCe\nmfUn2BOo7d/e96fK/+lqXo9Ig6hDWlqTHQSHb+rM3bcDK8L+BSwwPJw+1N0z3f02gpvMHFzJ3+pC\n0P8AwaGk+rgw/HtjgG3uvq2ezyNSI4WDtBruvgV418w+Ae6tx1NcDEwys4+BJQSd2wD3mtni8Hnf\nI+g4ngscaWaLzOxC4LfAb8zsI+q/x747XP8hYFI9n0OkVjRaSSQCwtFK/+PuWfGuRVoH7TmIiEgF\n2nMQEZEKtOcgIiIVKBxERKQChYOIiFSgcBARkQoUDiIiUoHCQUREKvj/+LBEJ9I7CF0AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150b7c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .plot() on the dataframe, setting x to the timestamp, with dot-dash style\n",
    "data.plot(x=data.timestamp, style=\".-\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We have some very uneven spacing in places. We might start by cutting off the last few points of this time-series, which is missing a lot of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We'll inspect the last few points of this time-series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>2013-03-04 11:12:00</td>\n",
       "      <td>17793</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>2013-03-04 16:43:00</td>\n",
       "      <td>17824</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2013-03-05 10:42:00</td>\n",
       "      <td>17852</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>2013-03-05 13:29:00</td>\n",
       "      <td>17868</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>2013-03-08 10:28:00</td>\n",
       "      <td>18062</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>2013-03-12 08:28:00</td>\n",
       "      <td>18235</td>\n",
       "      <td>Sergio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>2013-04-05 11:20:00</td>\n",
       "      <td>18942</td>\n",
       "      <td>Sergio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>2013-04-27 11:04:00</td>\n",
       "      <td>19698</td>\n",
       "      <td>Sergio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>2013-09-12 15:38:00</td>\n",
       "      <td>24450</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>2013-09-13 10:28:00</td>\n",
       "      <td>24463</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              timestamp  coffees contributor\n",
       "661 2013-03-04 11:12:00    17793     Quentin\n",
       "662 2013-03-04 16:43:00    17824     Quentin\n",
       "663 2013-03-05 10:42:00    17852     Quentin\n",
       "664 2013-03-05 13:29:00    17868     Quentin\n",
       "665 2013-03-08 10:28:00    18062     Quentin\n",
       "666 2013-03-12 08:28:00    18235      Sergio\n",
       "667 2013-04-05 11:20:00    18942      Sergio\n",
       "668 2013-04-27 11:04:00    19698      Sergio\n",
       "669 2013-09-12 15:38:00    24450     Quentin\n",
       "670 2013-09-13 10:28:00    24463     Quentin"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .tail() with ten rows\n",
    "data.tail(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "After mid-March, things start getting spaced rather erratically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's cut off the tail of the time-series, anything after 2013-03-01."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2013-02-21 13:44:00</td>\n",
       "      <td>17345</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>2013-02-21 15:02:00</td>\n",
       "      <td>17354</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>2013-02-25 13:33:00</td>\n",
       "      <td>17468</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>2013-02-25 17:25:00</td>\n",
       "      <td>17489</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>2013-02-27 09:33:00</td>\n",
       "      <td>17564</td>\n",
       "      <td>Quentin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              timestamp  coffees contributor\n",
       "655 2013-02-21 13:44:00    17345     Quentin\n",
       "656 2013-02-21 15:02:00    17354     Quentin\n",
       "657 2013-02-25 13:33:00    17468     Quentin\n",
       "658 2013-02-25 17:25:00    17489     Quentin\n",
       "659 2013-02-27 09:33:00    17564     Quentin"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use conditional indexing against the timestamp\n",
    "data = data[data.timestamp < \"2013-03-01\"]\n",
    "\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** this is another example of boolean indexing. `data.timestamp < \"2013-03-01\"` is a boolean array, and can be passed into the dataframe immediately in `[]`, much like with a `np.ndarray`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### One final look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1150ef6d8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LgGJgITCeYB7pqUDM3Q83s8nAL4BJjXgtItKKXDP3df72xgdU1ThbyIkYt04YTmznZ5p3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\n1KLkICIitSg5iIhILUoOIiJSy/8PmrbljbRYH2oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150eb208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Once again, plot the data against the timestamp\n",
    "data.plot(x=data.timestamp, style=\".-\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 1. Contributions to the time-series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Who are our main contributors ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Quentin    354\n",
       "Mike       152\n",
       "Sergio      89\n",
       "Mike M      29\n",
       "Colm        12\n",
       "Anthony      4\n",
       "Ben          3\n",
       "Stefano      2\n",
       "Arturo       1\n",
       "Name: contributor, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .value_counts()\n",
    "data.contributor.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `.value_counts()` counts the unique values in a series. It's similar to doing a `.groupby()` followed by a `.count()`, as we'll see soon.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's plot this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1151046d8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1152dc4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .plot() a bar chart from the value counts\n",
    "data.contributor.value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### On which weekdays were contributions made ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "      <th>weekdays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-03 08:22:00</td>\n",
       "      <td>397</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-04 11:48:00</td>\n",
       "      <td>410</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-10-05 10:47:00</td>\n",
       "      <td>464</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-10-05 13:15:00</td>\n",
       "      <td>481</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-10-06 07:21:00</td>\n",
       "      <td>503</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp  coffees contributor  weekdays\n",
       "0 2011-10-03 08:22:00      397     Quentin         0\n",
       "1 2011-10-04 11:48:00      410     Quentin         1\n",
       "4 2011-10-05 10:47:00      464     Quentin         2\n",
       "5 2011-10-05 13:15:00      481     Quentin         2\n",
       "6 2011-10-06 07:21:00      503     Anthony         3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series of the weekdays for each entry using .dt.weekday\n",
    "weekdays = data.timestamp.dt.weekday\n",
    "\n",
    "# assign() it to our dataframe\n",
    "data = data.assign(weekdays=weekdays)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Can we replace these integers with actual weekdays ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "      <th>weekdays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-10-03 08:22:00</td>\n",
       "      <td>397</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-10-04 11:48:00</td>\n",
       "      <td>410</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-10-05 10:47:00</td>\n",
       "      <td>464</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-10-05 13:15:00</td>\n",
       "      <td>481</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011-10-06 07:21:00</td>\n",
       "      <td>503</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp  coffees contributor   weekdays\n",
       "0 2011-10-03 08:22:00      397     Quentin     Monday\n",
       "1 2011-10-04 11:48:00      410     Quentin    Tuesday\n",
       "4 2011-10-05 10:47:00      464     Quentin  Wednesday\n",
       "5 2011-10-05 13:15:00      481     Quentin  Wednesday\n",
       "6 2011-10-06 07:21:00      503     Anthony   Thursday"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekday_names = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
    "weekday_dict = {key: weekday_names[key] for key in range(7)}\n",
    "\n",
    "# Use .apply() to apply a custom function to the weekdays column\n",
    "data.weekdays = data.weekdays.apply(lambda x: weekday_dict[x])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's group by these weekdays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekdays</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           timestamp  coffees  contributor\n",
       "weekdays                                  \n",
       "Monday           145      145          145\n",
       "Tuesday           97       97           97\n",
       "Wednesday        131      131          131\n",
       "Thursday         117      117          117\n",
       "Friday           114      114          114\n",
       "Saturday          29       29           29\n",
       "Sunday            13       13           13"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .groupby() the weekdays and then .count() rows in each group\n",
    "weekday_counts = data.groupby(\"weekdays\").count()\n",
    "\n",
    "# We can reorder this dataframe by our weekday_names list\n",
    "weekday_counts = weekday_counts.loc[weekday_names]\n",
    "\n",
    "weekday_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** this first line could be replaced by `weekday_counts = data.coffees.value_counts()`, with the only difference being that that would return a series to us, and here, we got back a dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We can now visualise these weekday counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115380898>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G+Aq1Cior74j9OPDniLgiB+V2UYHrieeQ/EOFQxJ4Yz/PRyKiqwJnUdCN2VPn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\n0JuZ1ZyD3sys5v4PB3v6HwCIDT4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150f74a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .plot() a bar chart of data in weekday_counts\n",
    "weekday_counts.timestamp.plot(kind=\"bar\", title=\"Datapoints added on each weekday\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 2. Weekday trends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### First, we'll set our timestamps to the dataframe's index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "      <th>weekdays</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-03 08:22:00</th>\n",
       "      <td>397</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04 11:48:00</th>\n",
       "      <td>410</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05 10:47:00</th>\n",
       "      <td>464</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05 13:15:00</th>\n",
       "      <td>481</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06 07:21:00</th>\n",
       "      <td>503</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     coffees contributor   weekdays\n",
       "timestamp                                          \n",
       "2011-10-03 08:22:00      397     Quentin     Monday\n",
       "2011-10-04 11:48:00      410     Quentin    Tuesday\n",
       "2011-10-05 10:47:00      464     Quentin  Wednesday\n",
       "2011-10-05 13:15:00      481     Quentin  Wednesday\n",
       "2011-10-06 07:21:00      503     Anthony   Thursday"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the dataframe's .index property\n",
    "data.index = data.timestamp\n",
    "\n",
    "data.drop([\"timestamp\"], axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's add some rows at midnight on every day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-10-03', '2011-10-04', '2011-10-05', '2011-10-06',\n",
       "               '2011-10-07', '2011-10-08', '2011-10-09', '2011-10-10',\n",
       "               '2011-10-11', '2011-10-12',\n",
       "               ...\n",
       "               '2013-02-18', '2013-02-19', '2013-02-20', '2013-02-21',\n",
       "               '2013-02-22', '2013-02-23', '2013-02-24', '2013-02-25',\n",
       "               '2013-02-26', '2013-02-27'],\n",
       "              dtype='datetime64[ns]', length=514, freq='D')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.date_range, with daily frequency, and normalisation\n",
    "midnights = pd.date_range(data.index[0], data.index[-1], freq=\"D\", normalize=True)\n",
    "\n",
    "midnights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `pd.date_range` creates a fixed-frequency DatetimeIndex. `normalize=True` ensures these datetimes are at midnight, and not at whatever time the starting point is.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.date_range.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's take the union of this index and our dataset's index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-10-03 00:00:00', '2011-10-03 08:22:00',\n",
       "               '2011-10-04 00:00:00', '2011-10-04 11:48:00',\n",
       "               '2011-10-05 00:00:00', '2011-10-05 10:47:00',\n",
       "               '2011-10-05 13:15:00', '2011-10-06 00:00:00',\n",
       "               '2011-10-06 07:21:00', '2011-10-06 10:04:00',\n",
       "               ...\n",
       "               '2013-02-21 15:02:00', '2013-02-22 00:00:00',\n",
       "               '2013-02-23 00:00:00', '2013-02-24 00:00:00',\n",
       "               '2013-02-25 00:00:00', '2013-02-25 13:33:00',\n",
       "               '2013-02-25 17:25:00', '2013-02-26 00:00:00',\n",
       "               '2013-02-27 00:00:00', '2013-02-27 09:33:00'],\n",
       "              dtype='datetime64[ns]', length=1160, freq=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the union of the existing and new indices\n",
    "new_index = midnights.union(data.index)\n",
    "\n",
    "new_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** the union of these indices is just a new index where entries from both indices are present. It's sorted by time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Now we can reindex our dataframe with this new index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "      <th>weekdays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-03 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03 08:22:00</th>\n",
       "      <td>397.0</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04 11:48:00</th>\n",
       "      <td>410.0</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05 10:47:00</th>\n",
       "      <td>464.0</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05 13:15:00</th>\n",
       "      <td>481.0</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06 07:21:00</th>\n",
       "      <td>503.0</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06 10:04:00</th>\n",
       "      <td>513.0</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     coffees contributor   weekdays\n",
       "2011-10-03 00:00:00      NaN         NaN        NaN\n",
       "2011-10-03 08:22:00    397.0     Quentin     Monday\n",
       "2011-10-04 00:00:00      NaN         NaN        NaN\n",
       "2011-10-04 11:48:00    410.0     Quentin    Tuesday\n",
       "2011-10-05 00:00:00      NaN         NaN        NaN\n",
       "2011-10-05 10:47:00    464.0     Quentin  Wednesday\n",
       "2011-10-05 13:15:00    481.0     Quentin  Wednesday\n",
       "2011-10-06 00:00:00      NaN         NaN        NaN\n",
       "2011-10-06 07:21:00    503.0     Anthony   Thursday\n",
       "2011-10-06 10:04:00    513.0     Quentin   Thursday"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .reindex() the dataframe\n",
    "upsampled_data = data.reindex(new_index)\n",
    "\n",
    "upsampled_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** `.reindex()` keeps any values that conform to the new index, and inserts `NaN`s where we have no values.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We can fill in these `NaN`s using interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coffees</th>\n",
       "      <th>contributor</th>\n",
       "      <th>weekdays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-03 00:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03 08:22:00</th>\n",
       "      <td>397.000000</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04 00:00:00</th>\n",
       "      <td>404.408262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04 11:48:00</th>\n",
       "      <td>410.000000</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05 00:00:00</th>\n",
       "      <td>438.664249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05 10:47:00</th>\n",
       "      <td>464.000000</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05 13:15:00</th>\n",
       "      <td>481.000000</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06 00:00:00</th>\n",
       "      <td>494.066298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06 07:21:00</th>\n",
       "      <td>503.000000</td>\n",
       "      <td>Anthony</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06 10:04:00</th>\n",
       "      <td>513.000000</td>\n",
       "      <td>Quentin</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        coffees contributor   weekdays\n",
       "2011-10-03 00:00:00         NaN         NaN        NaN\n",
       "2011-10-03 08:22:00  397.000000     Quentin     Monday\n",
       "2011-10-04 00:00:00  404.408262         NaN        NaN\n",
       "2011-10-04 11:48:00  410.000000     Quentin    Tuesday\n",
       "2011-10-05 00:00:00  438.664249         NaN        NaN\n",
       "2011-10-05 10:47:00  464.000000     Quentin  Wednesday\n",
       "2011-10-05 13:15:00  481.000000     Quentin  Wednesday\n",
       "2011-10-06 00:00:00  494.066298         NaN        NaN\n",
       "2011-10-06 07:21:00  503.000000     Anthony   Thursday\n",
       "2011-10-06 10:04:00  513.000000     Quentin   Thursday"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .interpolate the upsampled_data using the time method\n",
    "upsampled_data = upsampled_data.interpolate(method=\"time\")\n",
    "\n",
    "upsampled_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We're now ready to resample the time-series at a daily frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coffees</th>\n",
       "      <th>weekdays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>404.408262</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>438.664249</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>494.066298</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>572.844311</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coffees   weekdays\n",
       "2011-10-03         NaN     Monday\n",
       "2011-10-04  404.408262    Tuesday\n",
       "2011-10-05  438.664249  Wednesday\n",
       "2011-10-06  494.066298   Thursday\n",
       "2011-10-07  572.844311     Friday"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .resample() followed by .asfreq()\n",
    "daily_data = upsampled_data.resample(\"D\").asfreq()\n",
    "\n",
    "# Drop the contributor column, we no longer need it\n",
    "daily_data = daily_data.drop([\"contributor\"], axis=1)\n",
    "\n",
    "# Generate a column of weekday names\n",
    "daily_data[\"weekdays\"] = daily_data.index.weekday_name  # ?!\n",
    "daily_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115512da0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sM5+NiMOA9cXl64D5pafPK8bWFbdrx6Vppxr0ntu2nfse31C3KEvtSl+1p55F\nWSRJklTPuENfRLwUmJGZ24rbbwYuB24DLgA+V3y9tXjKbcD1EfFlKoVcjgIeyMyhiNgaEadQKeRy\nPnDFeOcldYryOb3q+bx7HlvP4FD9tbzemvN5q57ZQoBhT5IkSSOayErfocB3I6L6Otdn5r9GxIPA\nTRFxIfAU8DaAzHw4Im4Cfg4MAh8oKncCvJ89LRvuwCIu6mKjtVcoq/bSO8eVPEmSJI1TdGo7vL6+\nvuzv72/1NKQxaaS9QpXbNiVJkjSSiFiZmX2jXTcZLRskFWq3cNYWZanV2xMsOvrlzJ09y7AnSZKk\npjD0SU1WDXrbfreTq+9/cthWC+X2Cp7PkyRJ0mQx9EkTUK8Yy3BVN/equGl7BUmSJE0RQ59URznM\nbX5+x17VMquNz2tX8kYzIyphz6IskiRJmkqGPk1LI4W62bN6R9yWyTBj9QTQU2q1cMor5xj2JEmS\nNKUMfeoq41mhGy3A1XtspOurVTer38+gJ0mSpFYy9KljVPvblQNcM1boRluxG+l1yit5W7cPWoxF\nkiRJbcfQp7ZUG/BWPbOFm/rXMji0J341a4VuuFBX3pZZb8WwuproSp4kSZLamaFPbaMa9J7btp17\nHls/asBr1grdcKHOMCdJkqRuYOjTlKptcTDSSl6ZK3SSJEnS+Bj6NOnKK3jD9bAbbatmtd3BaUe/\nnLmzZ9U90+cKnSRJkvRihj5NmmrYG2kFr6r20d6eYFFNwDPMSZIkSWNn6FPTVcPezSsH6q7q1VO7\nkmcFTEmSJKk5DH2asHKlzdq2CbXKPezclilJkiRNPkOfxqy2GMtI2zcDmNnjCp4kSZLUKoY+jcn1\nK57msltXjdgAvaonYMnJCwx6kiRJUgsZ+rSXek3Ry7dvfHAtQ7v2xLzhWin0zAguX3wc73jdgima\nuSRJkqR6DH0CGqu0WW9Vb7hWCp7PkyRJktqDoW+aG0ulzfJjvaUG6AY8SZIkqX0Z+qax2vN5o6mu\n6p3TN99zepIkSVKHGHfoi4j5wLXAoVQWgZZl5t9HxGeA9wAbiks/kZm3F8+5FLgQGAIuzsw7i/GT\ngGuA/YDbgQ9lZiM5RKOorbQ50vm82kqbtlWQJEmSOt9EVvoGgY9k5kMRMRtYGRE/KB77u8z8Yvni\niDgGWAIcCxwO/DAiXpWZQ8BVVILiCiqh70zgjgnMbdobbdtm7fk8K21KkiRJ3WncoS8znwWeLW5v\ni4hHgCMABL4SAAALPklEQVRGeMpi4IbM3A48GRFPACdHxBrggMxcDhAR1wJnY+gbt0a2bdaez7PS\npiRJktSdmnKmLyIWAq+hslL3BuCDEXE+0E9lNXAzlUC4vPS0gWJsZ3G7drze91kKLAVYsMCAAi/e\nvvnctu3c/ej6vbZt1uP5PEmSJGl6mHDoi4j9gW8DH87MrRFxFfBZKotJnwW+BLx7ot8HIDOXAcsA\n+vr6pu2Zv+rWzee2bee+xzeMWHWzum3T83mSJEnS9DSh0BcRM6kEvusy8zsAmfnr0uNfA75X3F0H\nzC89fV4xtq64XTuuGo300itz26YkSZKkiVTvDODrwCOZ+eXS+GHFeT+AtwCritu3AddHxJepFHI5\nCnggM4ciYmtEnEJle+j5wBXjnVe3qLdtc7RVvarenuBct21KkiRJYmIrfW8AzgN+FhE/KcY+Abw9\nIk6ksr1zDfBegMx8OCJuAn5OpfLnB4rKnQDvZ0/LhjuYpkVcqkFv2+92cvX9TzbUP68a8MrbNw17\nkiRJkqqiU9vh9fX1ZX9/f6un0RSjtVcoq+2lZ8CTJEmSpqeIWJmZfaNd15TqnRq7iazqGfQkSZIk\nNcrQN4XGUnUzgJ4ZwUWnHsnW7YNu25QkSZI0Loa+SVINeNUWCaue2TJq1c1yewVbKkiSJElqBkNf\nkw3XViFg1FU92ytIkiRJajZDXxNdv+JpLrt1Vd3zebX3y1U3XdWTJEmSNFkMfU1QXd278cG1DO2q\nv543IyrN0q26KUmSJGkqGfoaVG6Wvvn5HaM2Te8JOP3VhzJ39ixX8yRJkiS1jKGvAbXbNkc6nweV\nFT3P50mSJElqB4a+EQy3bbNe4Ks2TT/HPnqSJEmS2oihr0YjTdNrV/psmi5JkiSpXU3r0Fc+pzfS\n+byq3qJZ+uz9Zu5+jk3TJUmSJLWzaRn6qts2b145MGzAK6s2TTfcSZIkSeo00y70jdRLr5ZN0yVJ\nkiR1uq4PfbVbOEfqpQc2TZckSZLUXbou9FW3bgYwe1bvXsVYaguwVLdtHnv4gZ7PkyRJktSVuib0\nVcPeTf1rGRyqv5K3V8VNt21KkiRJmgY6OvRVg95oVTerZkQl7NlLT5IkSdJ00bGh7/kdQ7x92Y/Z\nMcyqXlW1GEu11YJn9CRJkiRNJx0b+n6zfZCemsAXwMye4LSjX87c2bMsxiJJkiRp2uvY0Lf/rF4G\ne2L3Sl+16qbbNiVJkiRpj7YJfRFxJvD3QA9wdWZ+bqTrX7JPD19d+vrdlToNe5IkSZL0Ym0R+iKi\nB/gH4L8AA8CDEXFbZv58pOed9IqDDHqSJEmSNIIZrZ5A4WTgicz8ZWbuAG4AFrd4TpIkSZLU8dol\n9B0BrC3dHyjG9hIRSyOiPyL6N2zYMGWTkyRJkqRO1S6hryGZuSwz+zKzb+7cua2ejiRJkiS1vXYJ\nfeuA+aX784oxSZIkSdIEtEvoexA4KiKOjIh9gCXAbS2ekyRJkiR1vLao3pmZgxHxfwJ3UmnZ8I3M\nfLjF05IkSZKkjheZ2eo5jEtEbAMea/U8xulAYEurJzFB3fAeqhYAT7d6Ek3Q6X8nnT7/qm74PHXL\n3wV0x3vphs8UdMffRVU3vJdu+VxB5/99dPr8y/xcTb2jM3P2aBd1cujrz8y+Vs9jPCJiWWYubfU8\nJqIb3kNVRGzIzI6vDNTpfyedPv+qbvg8dcvfBXTHe+mGzxR0x99FVTe8l275XEHn/310+vzL/FxN\nvUYzUbuc6Ztu/t9WT6AJuuE9VP1HqyfQJJ3+d9Lp86/qhs9Tt/xdQHe8l274TEF3/F1UdcN76ZbP\nFXT+30enz7/Mz1WbcqVP056fJTWTnyc1m58pTQY/V5oMfq6m3nRY6VvW6gmoa/hZUjP5eVKz+ZnS\nZPBzpcng52rqNfTfvGNX+iRJkiRJo+vklT5JkiRJ0igMfZIkSZLUxQx9mjYi4jetnoO6R0ScHREZ\nEf+51XNR9xjt51RE3BcRFklQQyJiXkTcGhGrI+IXEfH3EbHPCNd/OCJeMpVzVGfyd6rOY+iTpPF5\nO3B/8bVhEdEzOdORpD0iIoDvALdk5lHAq4D9gf81wtM+DBj6pC5k6NO0EhH7R8TdEfFQRPwsIhYX\n4wsj4pGI+FpEPBwRd0XEfq2er9pTROwPnApcCCwpxk6LiB9FxPcj4rGI+MeImFE89puI+FJE/G/g\n9a2buTpB8Vn6Xun+lRHxly2ckjrTIuCFzPwngMwcAv4H8O6IeGlEfDEiVkXETyPigxFxMXA4cG9E\n3NvCeatD+DtVZzH0abp5AXhLZr4WeBPwpeJfQwGOAv4hM4+l0lz0v7dojmp/i4F/zczHgY0RcVIx\nfjLwQeAY4D8Bf16MvxRYkZknZOb9Uz5bSdPRscDK8kBmbgWeBi4CFgInZubxwHWZ+RXgGeBNmfmm\nKZ6rOpO/U3UQQ5+mmwD+r4j4KfBD4Ajg0OKxJzPzJ8XtlVT+hyjV83bghuL2DezZ4vlAZv6y+Bf1\nb1FZDQQYAr49tVOUpGGdBnw1MwcBMnNTa6ejDuXvVB2kt9UTkKbYO4G5wEmZuTMi1gD7Fo9tL103\nBLgVQS8SEQdT2Tb1BxGRQA+QwPeLr2XV+y8UQVBqxCB7/6PsvsNdKI3g58BbywMRcQCwAFjTigmp\n6/g7VQdxpU/TzYHA+uKH05uAV7R6Quo4bwW+mZmvyMyFmTkfeBJ4I3ByRBxZnOU7l0qhF2msngKO\niYhZEfEy4PRWT0gd6W7gJRFxPuwuIvUl4BrgTuC9EdFbPHZw8ZxtwOypn6o6lL9TdRBDn6aF4n9s\n24HrgL6I+BlwPvBoSyemTvR24Ls1Y98uxh8ErgQeoRIEa6+ThlX9OZWZa4GbgFXF1/+vpRNTR8rM\nBN4CnBMRq4HHqZzB+gRwNZWzfT8tCky9o3jaMuBfLeSikfg7VWeKys8EqbtFxAnA1zLz5FbPRd0p\nIk4D/mdmntXquagz+XNKUifwZ1VncqVPXS8i3kelqMYnWz0XSarHn1OSOoE/qzqXK32SJEmS1MVc\n6ZMkSZKkLmboU9eJiPkRcW9E/DwiHo6IDxXjB0fEDyJidfH1oGJ8TnH9byLiytLrvCQivh8Rjxav\n87lWvSdJkqSp1qzfqYrH/jUi/nfxOv9YVJTVFDH0qRsNAh/JzGOAU4APRMQxwMeBuzPzKCqlrD9e\nXP8C8Cngf9Z5rS9m5n8GXgO8ISL+66TPXpIkqT0083eqt2XmCcBxVPr7nTPZk9cehj51ncx8NjMf\nKm5vo1I+/whgMfDPxWX/DJxdXPPbzLyfyg+q8us8n5n3Frd3AA8B86bkTUiSJLVYs36nKh7bWtzs\nBfYBLCwyhQx96moRsZDKKt0K4NDMfLZ46FfAoWN4nZcB/43Kv2ZJkiRNK834nSoi7gTWA9uAm5s/\nSw3H0KeuFRH7U2ma/eHSvy4Bu5vWNvQvTEUT0m8BX8nMXzZ9opIkSW2sWb9TZeYZwGHALGBRs+ep\n4Rn61JUiYiaVH07XZeZ3iuFfR8RhxeOHUfmXpkYsA1Zn5v/T/JlKkiS1ryb/TkVmvgDcSmWLqKaI\noU9dJyIC+DrwSGZ+ufTQbcAFxe0LqPzAGe21/hY4EPhws+cpSZLUzpr1O1VE7F8Kib3AnwKPNn/G\nGo7N2dV1IuJU4N+AnwG7iuFPUNmDfhOwAHiKShWpTcVz1gAHUDlY/B/Am4GtwFoqP5S2F69zZWZe\nPSVvRJIkqYWa+DvVRuB7VLZ1zgDuBf5HZg5O1XuZ7gx9kiRJktTF3N4pSZIkSV3M0CdJkiRJXczQ\nJ0mSJEldzNAnSZIkSV3M0CdJkiRJXczQJ0mSJEldzNAnSZIkSV3s/we1z716GsFDDgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154afac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the data once more, to see how we're doing\n",
    "daily_data.plot(figsize=(15, 4), style=\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's begin by figuring out how many coffees are made on any given day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coffees</th>\n",
       "      <th>weekdays</th>\n",
       "      <th>coffees_made_today</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>404.408262</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>34.255987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>438.664249</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>55.402049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>494.066298</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>78.778013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>572.844311</td>\n",
       "      <td>Friday</td>\n",
       "      <td>64.392941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coffees   weekdays  coffees_made_today\n",
       "2011-10-03         NaN     Monday                 NaN\n",
       "2011-10-04  404.408262    Tuesday           34.255987\n",
       "2011-10-05  438.664249  Wednesday           55.402049\n",
       "2011-10-06  494.066298   Thursday           78.778013\n",
       "2011-10-07  572.844311     Friday           64.392941"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use .diff() on the coffees column; follow up with .shift()\n",
    "coffees_made = daily_data.coffees.diff().shift(-1)\n",
    "\n",
    "# Add this as a column to the dataframe\n",
    "daily_data[\"coffees_made_today\"] = coffees_made\n",
    "\n",
    "daily_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** we use `.shift()` here because if we look at the `.diff()` between a Monday and a Tuesday, those coffees are attributed to the Tuesday. However, what we want to say is \"this many coffees were made at some point on the Monday\", so we shift the entire series up one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Now we can group this by weekday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekdays\n",
       "Friday       34.138034\n",
       "Monday       37.802404\n",
       "Saturday     16.299858\n",
       "Sunday       15.768215\n",
       "Thursday     43.500824\n",
       "Tuesday      39.132964\n",
       "Wednesday    47.640057\n",
       "Name: coffees_made_today, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .groupby weekdays, take the mean, and grab the coffees_made_today column\n",
    "coffees_by_day = daily_data.groupby(\"weekdays\").mean().coffees_made_today\n",
    "\n",
    "coffees_by_day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's order this series and then plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1156a3c18>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115308390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort coffees_by_day by our list of weekend names\n",
    "coffees_by_day = coffees_by_day[weekday_names]\n",
    "\n",
    "# Plot a bar chart\n",
    "coffees_by_day.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Wednesdays was seminar day..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 3. Coffee per person"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We can now pull in data on how many people were in the department."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-30</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-31</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-30</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            members\n",
       "date               \n",
       "2011-08-31       12\n",
       "2011-09-30       14\n",
       "2011-10-31       14\n",
       "2011-11-30       14\n",
       "2011-12-31       15"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bring in data/department_members.csv;\n",
    "# have the first column be the index, and parse the dates\n",
    "people = pd.read_csv(\"data/department_members.csv\", index_col=[0], parse_dates=True)\n",
    "\n",
    "people.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's join the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coffees</th>\n",
       "      <th>weekdays</th>\n",
       "      <th>coffees_made_today</th>\n",
       "      <th>members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>404.408262</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>34.255987</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>438.664249</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>55.402049</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coffees   weekdays  coffees_made_today  members\n",
       "2011-08-31         NaN        NaN                 NaN     12.0\n",
       "2011-09-30         NaN        NaN                 NaN     14.0\n",
       "2011-10-03         NaN     Monday                 NaN     14.0\n",
       "2011-10-04  404.408262    Tuesday           34.255987     14.0\n",
       "2011-10-05  438.664249  Wednesday           55.402049     14.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use an outer join, then interpolate over missing values using nearest values\n",
    "daily_data = daily_data.join(people, how=\"outer\").interpolate(method=\"nearest\")\n",
    "\n",
    "daily_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** by default, inner joins are performed. That is, if a row from one of the datasets has an index that isn't in the other dataset, that row is dropped. You can specify whether you want outer, left, or right joins, as well plenty of other useful options. The `pandas` API for joining or merging datasets is very developed.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/merging.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's create a column for the number of coffees consumed per person."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coffees</th>\n",
       "      <th>weekdays</th>\n",
       "      <th>coffees_made_today</th>\n",
       "      <th>members</th>\n",
       "      <th>coffees_per_person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>404.408262</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>34.255987</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.446856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>438.664249</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>55.402049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.957289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>494.066298</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>78.778013</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.627001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>572.844311</td>\n",
       "      <td>Friday</td>\n",
       "      <td>64.392941</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.599496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-08</th>\n",
       "      <td>637.237253</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>7.492196</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.535157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coffees   weekdays  coffees_made_today  members  \\\n",
       "2011-10-04  404.408262    Tuesday           34.255987     14.0   \n",
       "2011-10-05  438.664249  Wednesday           55.402049     14.0   \n",
       "2011-10-06  494.066298   Thursday           78.778013     14.0   \n",
       "2011-10-07  572.844311     Friday           64.392941     14.0   \n",
       "2011-10-08  637.237253   Saturday            7.492196     14.0   \n",
       "\n",
       "            coffees_per_person  \n",
       "2011-10-04            2.446856  \n",
       "2011-10-05            3.957289  \n",
       "2011-10-06            5.627001  \n",
       "2011-10-07            4.599496  \n",
       "2011-10-08            0.535157  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New column is the ratio of coffees made on a given day to number of members in the department\n",
    "daily_data[\"coffees_per_person\"] = daily_data.coffees_made_today / daily_data.members\n",
    "\n",
    "# Let's drop those remaining NaNs while we're at it\n",
    "daily_data.dropna(inplace=True)\n",
    "\n",
    "daily_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We can now plot this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116fc3a58>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ca0GV+I7bnFsjUShldUZpNsi15bhlnaNZyAFzrdFJXIB0VFKYH6sqAKSmZnjL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\nMPVBWbaZK034eaRyJeakYzODTkuVn4nl9ntTPJu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XB0MDZSHidlTJ1d886LXTqy+ea6RJ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\nittgMBhqDKO4DQaDocb4f26B73MQ3j39AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1156d46d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the coffees_per_person column\n",
    "daily_data.coffees_per_person.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Those are strange plateaus. We'll pull in another dataset, telling us when the machine was broken."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-09-16</th>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-17</th>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-18</th>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-19</th>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-20</th>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           status\n",
       "date             \n",
       "2011-09-16     OK\n",
       "2011-09-17     OK\n",
       "2011-09-18     OK\n",
       "2011-09-19     OK\n",
       "2011-09-20     OK"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.read_csv(); try using data/coffee_status.csv\n",
    "# parse_dates as kwarg; also pass index_col\n",
    "machine_status = pd.read_csv(\"data/coffee_status.csv\", parse_dates=[\"date\"], index_col=\"date\")\n",
    "\n",
    "machine_status.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** the `parse_dates` keyword argument takes several values. By passing in a list of strings, we're telling `pandas` to attempt to parse the dates in columns with those names.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### What values are in the `status` column ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OK        514\n",
       "BROKEN     88\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .value_counts()\n",
    "machine_status.status.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### A quick trick to plot this as a time-series..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1170fbc88>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fUm4302VimrftPtxOpEkLh3VnmcM2mwDsMrPdAEDyOgDnAbinsc15AD5r1RX+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\n/wNLG+Sdry/ExAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11565aa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a pd.Series from the status series where things are OK\n",
    "numerical_status = machine_status.status == \"OK\"\n",
    "\n",
    "numerical_status.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "**Note :** the first line here creates a boolean `pd.Series`, holding the value `True` when `machine_status.status` is `\"OK\"`, and `False` otherwise. Because it's a `pd.Series`, its index stays the same as that of `machine_status`, which was a `DatetimeIndex`. Then, we can plot the boolean series ( `True` appearing as `1`, and `False` appearing as `0` ), and just quickly scan to see that there are long areas where the coffee machine was operations, with short bouts ( thankfully ! ) of the machine being broken."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's join the datasets on the date field !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coffees</th>\n",
       "      <th>weekdays</th>\n",
       "      <th>coffees_made_today</th>\n",
       "      <th>members</th>\n",
       "      <th>coffees_per_person</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>404.408262</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>34.255987</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.446856</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>438.664249</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>55.402049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.957289</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>494.066298</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>78.778013</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.627001</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>572.844311</td>\n",
       "      <td>Friday</td>\n",
       "      <td>64.392941</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.599496</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-08</th>\n",
       "      <td>637.237253</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>7.492196</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.535157</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coffees   weekdays  coffees_made_today  members  \\\n",
       "2011-10-04  404.408262    Tuesday           34.255987     14.0   \n",
       "2011-10-05  438.664249  Wednesday           55.402049     14.0   \n",
       "2011-10-06  494.066298   Thursday           78.778013     14.0   \n",
       "2011-10-07  572.844311     Friday           64.392941     14.0   \n",
       "2011-10-08  637.237253   Saturday            7.492196     14.0   \n",
       "\n",
       "            coffees_per_person status  \n",
       "2011-10-04            2.446856     OK  \n",
       "2011-10-05            3.957289     OK  \n",
       "2011-10-06            5.627001     OK  \n",
       "2011-10-07            4.599496     OK  \n",
       "2011-10-08            0.535157     OK  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .join()\n",
    "daily_data = daily_data.join(machine_status)\n",
    "\n",
    "daily_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We'll bring in this numerical representation of status column into our dataframe too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coffees</th>\n",
       "      <th>weekdays</th>\n",
       "      <th>coffees_made_today</th>\n",
       "      <th>members</th>\n",
       "      <th>coffees_per_person</th>\n",
       "      <th>status</th>\n",
       "      <th>numerical_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-04</th>\n",
       "      <td>404.408262</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>34.255987</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.446856</td>\n",
       "      <td>OK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-05</th>\n",
       "      <td>438.664249</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>55.402049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.957289</td>\n",
       "      <td>OK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-06</th>\n",
       "      <td>494.066298</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>78.778013</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.627001</td>\n",
       "      <td>OK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-07</th>\n",
       "      <td>572.844311</td>\n",
       "      <td>Friday</td>\n",
       "      <td>64.392941</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.599496</td>\n",
       "      <td>OK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-08</th>\n",
       "      <td>637.237253</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>7.492196</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.535157</td>\n",
       "      <td>OK</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coffees   weekdays  coffees_made_today  members  \\\n",
       "2011-10-04  404.408262    Tuesday           34.255987     14.0   \n",
       "2011-10-05  438.664249  Wednesday           55.402049     14.0   \n",
       "2011-10-06  494.066298   Thursday           78.778013     14.0   \n",
       "2011-10-07  572.844311     Friday           64.392941     14.0   \n",
       "2011-10-08  637.237253   Saturday            7.492196     14.0   \n",
       "\n",
       "            coffees_per_person status  numerical_status  \n",
       "2011-10-04            2.446856     OK                 1  \n",
       "2011-10-05            3.957289     OK                 1  \n",
       "2011-10-06            5.627001     OK                 1  \n",
       "2011-10-07            4.599496     OK                 1  \n",
       "2011-10-08            0.535157     OK                 1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column depicting when the status was \"OK\"\n",
    "# Cast the series to ints before as you create a new column in the dataframe\n",
    "daily_data[\"numerical_status\"] = (daily_data.status == \"OK\").astype(int)\n",
    "\n",
    "daily_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Let's plot both the coffees per person and the numerical status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117286b00>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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13CTu57zZO1lPXoplqlYC3x+3LLIyzaeNEpeggpOVH1ouECjF0hp1VBP++1Le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5WoFFXyDAsZc1/LGCvXyTGWrncsXSsw9q4XqL5rLqU/ldK4b65Qu9WGsXKdHGPpesfStQJj73qB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\nIRbcMWLEiFFj+C/22y/DaEpqYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117298e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot both columns on the same graph, using default args\n",
    "daily_data[[\"coffees_per_person\", \"numerical_status\"]].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### We see a strong weekday-weekend effect. Resampling weekly will fix that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1173dc5f8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AldP8HNbNN0CrkoWaccwOD255fj8e/6gdly8vQ+O8mbW38wvVyJFLQ/4TFEok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\nqZSa/oWmb3/kydWMe36O+i0Eilm3VEpJVyg9fVMpNf0LTd+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11711c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resample weekly, taking the mean of each day to get a weekly value\n",
    "weekly_data = daily_data.resample(\"W\").mean()\n",
    "\n",
    "weekly_data[[\"coffees_per_person\", \"numerical_status\"]].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What have we achieved ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Cleaning\n",
    "\n",
    "- Cast columns to the correct dtypes\n",
    "- Dropped rows with no data\n",
    "- Truncated the time-series when things got sparse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Exploring the contributions\n",
    "\n",
    "- Discovered who contributed to the dataset and how much\n",
    "- Established how contributions varied by day of the week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Exploring the time-series\n",
    "\n",
    "- Resampled the uneven time-series to regular daily intervals\n",
    "- Interpolated over missing data using our time index\n",
    "- Discovered on what days of the week coffee was especially popular\n",
    "- Joined the dataset with others to enrich the information available\n",
    "- Derived new columns from existing ones\n",
    "- Smoothed the time-series by weekly downsampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Insights\n",
    "\n",
    "- A small number of people contributed most of the data\n",
    "- Contributions were lacking at the weekends, and Mondays had the most contributions\n",
    "- Seminar Wednesdays is a strong driver of coffee consumption\n",
    "- Periods of low coffee drinking correlated strongly with periods where the machine was broken\n",
    "- A significant dip in consumption occurred in the summer months"
   ]
  }
 ],
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