Bakery Transactions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transactions from a bakery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data belongs to a bakery called \"The Bread Basket\", located in the historic center of Edinburgh. This bakery presents a refreshing offer of Argentine and Spanish products.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Content Data set containing 15 010 observations and more than 6 000 transactions from a bakery. The data set contains the following columns:\n",
    "\n",
    "- **Date**. Categorical variable that tells us the date of the transactions (YYYY-MM-DD format). The column includes dates from 30/10/2016 to 09/04/2017.\n",
    "\n",
    "- **Time**. Categorical variable that tells us the time of the transactions (HH:MM:SS format).\n",
    "\n",
    "- **Transaction**. Quantitative variable that allows us to differentiate the transactions. The rows that share the same value in this field belong to the same transaction, that's why the data set has less transactions than observations.\n",
    "\n",
    "You can find the original dataset [here](https://www.kaggle.com/aboliveira/bakery-market-basket-analysis/data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./dataset-cover.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be using the python programming language to help us look at the data in more detail. Before we continue, we should ensure that the correct version of python is installed."
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 2.7.15\r\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are some of the libraries that we will use along the way. \n",
    "\n",
    "- Pandas\n",
    "- Numpy\n",
    "- Scipy\n",
    "- Bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import bokeh\n",
    "\n",
    "from math import pi\n",
    "\n",
    "from bokeh.io import output_notebook, show\n",
    "from bokeh.palettes import inferno\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.transform import cumsum"
   ]
  },
  {
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   "source": [
    "Bokeh usually outputs to a file. By setting this, we ensure that the output gets set inline."
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   "execution_count": 3,
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "output_notebook() "
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have imported all the tools that we need, let's start by loading the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date           object\n",
       "Time           object\n",
       "Transaction     int64\n",
       "Item           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df = pd.read_csv('BreadBasket_DMS.csv')\n",
    "raw_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a glimps of what the dataset looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Transaction           Item\n",
       "0  2016-10-30  09:58:11            1          Bread\n",
       "1  2016-10-30  10:05:34            2   Scandinavian\n",
       "2  2016-10-30  10:05:34            2   Scandinavian\n",
       "3  2016-10-30  10:07:57            3  Hot chocolate\n",
       "4  2016-10-30  10:07:57            3            Jam"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we continue, it is probably a good idea to get the data into a workable format. In this dataset, some of the Item information is missing. To make our lives easier, we can discard these data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Transaction           Item\n",
       "0  2016-10-30  09:58:11            1          Bread\n",
       "1  2016-10-30  10:05:34            2   Scandinavian\n",
       "2  2016-10-30  10:05:34            2   Scandinavian\n",
       "3  2016-10-30  10:07:57            3  Hot chocolate\n",
       "4  2016-10-30  10:07:57            3            Jam"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cleanup_dataset(df):\n",
    "    # Returns new dataset without NONE values in specified c\n",
    "    df_none_entries = df.loc[df['Item']=='NONE',:]\n",
    "    return df.drop(df_none_entries.index)\n",
    "\n",
    "dataset = cleanup_dataset(raw_df)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in the table above represents in item in a transaction. We might want to focus in on each of these aspects seperatly. So let's make a list of transactions and a seperate list of items!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:08:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:13:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction        Date      Time\n",
       "0            1  2016-10-30  09:58:11\n",
       "1            2  2016-10-30  10:05:34\n",
       "3            3  2016-10-30  10:07:57\n",
       "6            4  2016-10-30  10:08:41\n",
       "7            5  2016-10-30  10:13:03"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_transactions = dataset[['Transaction', 'Date', 'Time']].drop_duplicates()\n",
    "list_of_transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bread</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scandinavian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hot chocolate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cookies</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Item\n",
       "0          Bread\n",
       "1   Scandinavian\n",
       "3  Hot chocolate\n",
       "4            Jam\n",
       "5        Cookies"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bakery_items = dataset[['Item']].drop_duplicates()\n",
    "bakery_items.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up some auxillary functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are a few functions that will make it easier for us to interact with our dataframes:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **Date** and **Time** headings encode many different pieces of information. Splitting up this information is gonna make it easier for us to group data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Transaction           Item  Year  Month  Day  Weekday\n",
       "0  2016-10-30  09:58:11            1          Bread  2016     10   30        6\n",
       "1  2016-10-30  10:05:34            2   Scandinavian  2016     10   30        6\n",
       "2  2016-10-30  10:05:34            2   Scandinavian  2016     10   30        6\n",
       "3  2016-10-30  10:07:57            3  Hot chocolate  2016     10   30        6\n",
       "4  2016-10-30  10:07:57            3            Jam  2016     10   30        6"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def split_date_field(df_orig):\n",
    "    \"\"\"\n",
    "    Converts the Date Column into three sepreate columns\n",
    "    (YYYY-MM-DD) -> (YYYY, MM, DD)\n",
    "    \"\"\"\n",
    "    \n",
    "    df = df_orig.copy()\n",
    "    date = pd.to_datetime(df['Date'])\n",
    "    \n",
    "    df['Year'] = date.dt.year\n",
    "    df['Month'] = date.dt.month\n",
    "    df['Day'] = date.dt.day\n",
    "    \n",
    "    weekday_map = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "    df['Weekday'] = date.dt.weekday #.apply(lambda x: weekday_map[x])\n",
    "    \n",
    "    return df\n",
    "\n",
    "split_date_field(dataset).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>Hours</th>\n",
       "      <th>Mins</th>\n",
       "      <th>Secs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "      <td>09</td>\n",
       "      <td>58</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>10</td>\n",
       "      <td>05</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>10</td>\n",
       "      <td>05</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "      <td>10</td>\n",
       "      <td>07</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "      <td>10</td>\n",
       "      <td>07</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Transaction           Item Hours Mins Secs\n",
       "0  2016-10-30  09:58:11            1          Bread    09   58   11\n",
       "1  2016-10-30  10:05:34            2   Scandinavian    10   05   34\n",
       "2  2016-10-30  10:05:34            2   Scandinavian    10   05   34\n",
       "3  2016-10-30  10:07:57            3  Hot chocolate    10   07   57\n",
       "4  2016-10-30  10:07:57            3            Jam    10   07   57"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def split_time_field(df_orig):\n",
    "    \"\"\"\n",
    "    Converts the Date Column into three sepreate columns\n",
    "    \n",
    "    (HH-MM-SS) -> (HH, MM, SS)\n",
    "    \"\"\"\n",
    "    df = df_orig.copy()\n",
    "    df['Hours'], df['Mins'], df['Secs'] = df['Time'].str.split(':').str\n",
    "    return df\n",
    "\n",
    "split_time_field(dataset).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The exact time of day is sometimes too specific to an individual transaction. If we want to look for trends, it might make more sense to look at the approximate time of day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>Daytime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Transaction           Item  Daytime\n",
       "0  2016-10-30  09:58:11            1          Bread  Morning\n",
       "1  2016-10-30  10:05:34            2   Scandinavian  Morning\n",
       "2  2016-10-30  10:05:34            2   Scandinavian  Morning\n",
       "3  2016-10-30  10:07:57            3  Hot chocolate  Morning\n",
       "4  2016-10-30  10:07:57            3            Jam  Morning"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_time_of_day(df_orig):\n",
    "    df = df_orig.copy()\n",
    "    \n",
    "    time = df['Time']\n",
    "    \n",
    "    time_of_day = ['Morning', 'Afternoon', 'Evening', 'Night']\n",
    "    \n",
    "    df.loc[(time <'12:00:00'),'Daytime']= time_of_day[0]\n",
    "    df.loc[(time>='12:00:00')&(time <'17:00:00'),'Daytime'] = time_of_day[1]\n",
    "    df.loc[(time>='17:00:00')&(time <'21:00:00'),'Daytime'] = time_of_day[2]\n",
    "    df.loc[(time>='21:00:00')&(time <'23:50:00'),'Daytime'] = time_of_day[3]\n",
    "    \n",
    "    return df\n",
    "\n",
    "extract_time_of_day(dataset).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>date</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>09:58:11</td>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:05:34</td>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10:07:57</td>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>Fall</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Transaction           Item       date Season\n",
       "0  2016-10-30  09:58:11            1          Bread 2016-10-30   Fall\n",
       "1  2016-10-30  10:05:34            2   Scandinavian 2016-10-30   Fall\n",
       "2  2016-10-30  10:05:34            2   Scandinavian 2016-10-30   Fall\n",
       "3  2016-10-30  10:07:57            3  Hot chocolate 2016-10-30   Fall\n",
       "4  2016-10-30  10:07:57            3            Jam 2016-10-30   Fall"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@TODO:\n",
    "def extract_season(df_orig):\n",
    "    df = df_orig.copy()\n",
    "    \n",
    "    date = df['Date']\n",
    "    \n",
    "    df['date'] = pd.to_datetime(df['Date'])\n",
    "    month = df['date'].dt.month\n",
    "    day = df['date'].dt.day\n",
    "    \n",
    "    time_of_year = ['Winter', 'Spring', 'Summer', 'Fall']\n",
    "    \n",
    "    df.loc[(month < 3), 'Season']= time_of_year[0]\n",
    "    df.loc[(month >=11)&(month >=11),'Season']= time_of_year[0]\n",
    "    df.loc[(month>=3)&(month <6),'Season'] = time_of_year[1]\n",
    "    df.loc[(month>=6)&(month <9),'Season'] = time_of_year[2]\n",
    "    df.loc[(month>=9)&(month <11),'Season'] = time_of_year[3]\n",
    "    \n",
    "    return df\n",
    "\n",
    "extract_season(dataset).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataset, we can see that a transaction is defined by a date and time. We can now look more closely at all the transactions that were made during this time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use the data to answer some real questions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many transactions took place?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9465"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_transactions['Transaction'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many items are in the bakery?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_items = dataset['Item'].nunique()\n",
    "number_of_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item    94\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bakery_items.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the most popular items?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Coffee</th>\n",
       "      <td>5471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bread</th>\n",
       "      <td>3325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tea</th>\n",
       "      <td>1435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cake</th>\n",
       "      <td>1025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pastry</th>\n",
       "      <td>856</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "        Transaction\n",
       "Item               \n",
       "Coffee         5471\n",
       "Bread          3325\n",
       "Tea            1435\n",
       "Cake           1025\n",
       "Pastry          856"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_count = dataset.groupby(by='Item')[['Transaction']].count().sort_values(by='Transaction', ascending=False)\n",
    "transaction_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Coffee</th>\n",
       "      <td>26.678695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bread</th>\n",
       "      <td>16.213976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tea</th>\n",
       "      <td>6.997611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cake</th>\n",
       "      <td>4.998293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pastry</th>\n",
       "      <td>4.174184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Transaction\n",
       "Item               \n",
       "Coffee    26.678695\n",
       "Bread     16.213976\n",
       "Tea        6.997611\n",
       "Cake       4.998293\n",
       "Pastry     4.174184"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_to_percentage(x):\n",
    "    return 100 * x / float(x.sum())\n",
    "\n",
    "transaction_percentage = transaction_count.apply(convert_to_percentage)\n",
    "transaction_percentage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"905d51a8-ea64-4505-8953-cbd489b0d2e9\"></div>\n"
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     },
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     "output_type": "display_data"
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Items\":[\"Adjustment\",\"Afternoon with the baker\",\"Alfajores\",\"Argentina Night\",\"Art Tray\",\"Bacon\",\"Baguette\",\"Bakewell\",\"Bare Popcorn\",\"Basket\",\"Bowl Nic Pitt\",\"Bread\",\"Bread Pudding\",\"Brioche and salami\",\"Brownie\",\"Cake\",\"Caramel bites\",\"Cherry me Dried fruit\",\"Chicken Stew\",\"Chicken sand\",\"Chimichurri Oil\",\"Chocolates\",\"Christmas common\",\"Coffee\",\"Coffee granules \",\"Coke\",\"Cookies\",\"Crepes\",\"Crisps\",\"Drinking chocolate spoons \",\"Duck egg\",\"Dulce de Leche\",\"Eggs\",\"Ella's Kitchen Pouches\",\"Empanadas\",\"Extra Salami or Feta\",\"Fairy Doors\",\"Farm House\",\"Focaccia\",\"Frittata\",\"Fudge\",\"Gift voucher\",\"Gingerbread syrup\",\"Granola\",\"Hack the stack\",\"Half slice Monster \",\"Hearty & Seasonal\",\"Honey\",\"Hot chocolate\",\"Jam\",\"Jammie Dodgers\",\"Juice\",\"Keeping It Local\",\"Kids biscuit\",\"Lemon and coconut\",\"Medialuna\",\"Mighty Protein\",\"Mineral water\",\"Mortimer\",\"Muesli\",\"Muffin\",\"My-5 Fruit Shoot\",\"Nomad bag\",\"Olum & polenta\",\"Panatone\",\"Pastry\",\"Pick and Mix Bowls\",\"Pintxos\",\"Polenta\",\"Postcard\",\"Raspberry shortbread sandwich\",\"Raw bars\",\"Salad\",\"Sandwich\",\"Scandinavian\",\"Scone\",\"Siblings\",\"Smoothies\",\"Soup\",\"Spanish Brunch\",\"Spread\",\"Tacos/Fajita\",\"Tartine\",\"Tea\",\"The BART\",\"The Nomad\",\"Tiffin\",\"Toast\",\"Truffles\",\"Tshirt\",\"Valentine's card\",\"Vegan Feast\",\"Vegan mincepie\",\"Victorian Sponge\"],\"angle\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[94]},\"color\":[\"#000003\",\"#000006\",\"#01010B\",\"#030212\",\"#040316\",\"#07051D\",\"#0A0723\",\"#0E082A\",\"#10092F\",\"#140B36\",\"#190B3E\",\"#1D0C45\",\"#200C4A\",\"#260B50\",\"#2B0A56\",\"#300A5C\",\"#34095F\",\"#390962\",\"#3E0966\",\"#430A68\",\"#460A69\",\"#4B0C6B\",\"#500D6C\",\"#550F6D\",\"#58106D\",\"#5D126E\",\"#62146E\",\"#66156E\",\"#6A176E\",\"#6E186E\",\"#731A6D\",\"#781C6D\",\"#7B1D6C\",\"#801F6B\",\"#85206A\",\"#88216A\",\"#8D2369\",\"#912567\",\"#962666\",\"#992864\",\"#9E2963\",\"#A32B61\",\"#A72D5F\",\"#AB2E5D\",\"#AF315B\",\"#B43358\",\"#B83556\",\"#BB3754\",\"#BF3951\",\"#C43C4E\",\"#C83E4B\",\"#CB4049\",\"#CF4446\",\"#D24742\",\"#D64A3F\",\"#D94D3D\",\"#DC5039\",\"#DF5436\",\"#E35832\",\"#E55B30\",\"#E75F2C\",\"#EA6428\",\"#ED6825\",\"#EE6C22\",\"#F0701E\",\"#F2751A\",\"#F37918\",\"#F57E14\",\"#F78310\",\"#F8880C\",\"#F98C09\",\"#FA9107\",\"#FA9706\",\"#FB9D06\",\"#FBA007\",\"#FBA60B\",\"#FBAC10\",\"#FBB116\",\"#FBB51A\",\"#FABB21\",\"#FAC128\",\"#F9C72F\",\"#F8CB34\",\"#F7D13C\",\"#F5D745\",\"#F4DC4F\",\"#F3E056\",\"#F2E660\",\"#F1EB6C\",\"#F1F079\",\"#F2F381\",\"#F4F78D\",\"#F7FB99\",\"#FCFEA4\"],\"index\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93],\"value\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[94]}},\"selected\":{\"id\":\"11834\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"11835\",\"type\":\"UnionRenderers\"}},\"id\":\"11515\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"field\":\"angle\"},\"id\":\"11514\",\"type\":\"CumSum\"},{\"attributes\":{\"end_angle\":{\"expr\":{\"id\":\"11514\",\"type\":\"CumSum\"},\"units\":\"rad\"},\"fill_color\":{\"field\":\"color\"},\"line_color\":{\"value\":\"white\"},\"radius\":{\"units\":\"data\",\"value\":0.4},\"start_angle\":{\"expr\":{\"id\":\"11513\",\"type\":\"CumSum\"},\"units\":\"rad\"},\"x\":{\"value\":0},\"y\":{\"value\":1}},\"id\":\"11517\",\"type\":\"Wedge\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"11510\",\"type\":\"HoverTool\"}]},\"id\":\"11511\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null,\"renderers\":\"auto\",\"tooltips\":\"@{Bakery Items}: @value\"},\"id\":\"11510\",\"type\":\"HoverTool\"},{\"attributes\":{\"field\":\"angle\",\"include_zero\":true},\"id\":\"11513\",\"type\":\"CumSum\"}],\"root_ids\":[\"11490\"]},\"title\":\"Bokeh Application\",\"version\":\"1.0.0\"}};\n",
       "  var render_items = [{\"docid\":\"b215a253-f365-4857-8d5a-05ebda9d6924\",\"roots\":{\"11490\":\"905d51a8-ea64-4505-8953-cbd489b0d2e9\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "11490"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def create_circle_graph(title, slices_dict, colour_palette_fuc):\n",
    "    number_of_slices = len(slices_dict)\n",
    "    data = pd.Series(slices_dict).reset_index(name='value').rename(columns={'index': title})\n",
    "    data['angle'] = data['value']/data['value'].sum() * 2*pi\n",
    "    data['color'] = colour_palette_fuc(number_of_slices)\n",
    "\n",
    "    p = figure(plot_height=350, title=title, toolbar_location=None,\n",
    "               tools=\"hover\", tooltips=\"@{\"+ title + \"}: @value\", x_range=(-0.5, 1.0))\n",
    "\n",
    "    p.wedge(x=0, y=1, radius=0.4,\n",
    "            start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),\n",
    "            line_color=\"white\", fill_color='color', source=data)\n",
    "\n",
    "    p.axis.axis_label=None\n",
    "    p.axis.visible=False\n",
    "    p.grid.grid_line_color = None\n",
    "\n",
    "    return p\n",
    "\n",
    "slices = transaction_percentage.to_dict()['Transaction']\n",
    "circle_graph = create_circle_graph('Bakery Items', slices, inferno)\n",
    "show(circle_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
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