Waffle Charts.ipynb

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   "source": [
    "# Table of Contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "\n",
    "1. [Exploring Datasets with *p*andas](#0)<br>\n",
    "2. [Downloading and Prepping Data](#2)<br>\n",
    "3. [Visualizing Data using Matplotlib](#4) <br>\n",
    "4. [Waffle Charts](#6) <br>\n",
    "</div>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
    "\n",
    "Toolkits: The course heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in the course is [Matplotlib](http://matplotlib.org/).\n",
    "\n",
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this lab, we will focus on the Canadian Immigration data."
   ]
  },
  {
   "cell_type": "markdown",
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    }
   },
   "source": [
    "# Downloading and Prepping Data <a id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Import Primary Modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np  # useful for many scientific computing in Python\n",
    "import pandas as pd # primary data structure library\n",
    "from PIL import Image # converting images into arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
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    }
   },
   "source": [
    "Download the dataset and read it into a *pandas* dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://ibm.box.com/shared/static/lw190pt9zpy5bd1ptyg2aw15awomz9pu.xlsx',\n",
    "                       sheet_name='Canada by Citizenship',\n",
    "                       skiprows=range(20),\n",
    "                       skip_footer=2)\n",
    "\n",
    "print('Data downloaded and read into a dataframe!')"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Let's take a look at the first five items in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>OdName</th>\n",
       "      <th>AREA</th>\n",
       "      <th>AreaName</th>\n",
       "      <th>REG</th>\n",
       "      <th>RegName</th>\n",
       "      <th>DEV</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>935</td>\n",
       "      <td>Asia</td>\n",
       "      <td>5501</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>2978</td>\n",
       "      <td>3436</td>\n",
       "      <td>3009</td>\n",
       "      <td>2652</td>\n",
       "      <td>2111</td>\n",
       "      <td>1746</td>\n",
       "      <td>1758</td>\n",
       "      <td>2203</td>\n",
       "      <td>2635</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Albania</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1223</td>\n",
       "      <td>856</td>\n",
       "      <td>702</td>\n",
       "      <td>560</td>\n",
       "      <td>716</td>\n",
       "      <td>561</td>\n",
       "      <td>539</td>\n",
       "      <td>620</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>903</td>\n",
       "      <td>Africa</td>\n",
       "      <td>912</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>3616</td>\n",
       "      <td>3626</td>\n",
       "      <td>4807</td>\n",
       "      <td>3623</td>\n",
       "      <td>4005</td>\n",
       "      <td>5393</td>\n",
       "      <td>4752</td>\n",
       "      <td>4325</td>\n",
       "      <td>3774</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>909</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>957</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>902</td>\n",
       "      <td>Developing regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Immigrants</td>\n",
       "      <td>Foreigners</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>908</td>\n",
       "      <td>Europe</td>\n",
       "      <td>925</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>901</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "<p>5 rows × 43 columns</p>\n",
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       "         Type    Coverage          OdName  AREA AreaName   REG  \\\n",
       "0  Immigrants  Foreigners     Afghanistan   935     Asia  5501   \n",
       "1  Immigrants  Foreigners         Albania   908   Europe   925   \n",
       "2  Immigrants  Foreigners         Algeria   903   Africa   912   \n",
       "3  Immigrants  Foreigners  American Samoa   909  Oceania   957   \n",
       "4  Immigrants  Foreigners         Andorra   908   Europe   925   \n",
       "\n",
       "           RegName  DEV             DevName  1980  ...   2004  2005  2006  \\\n",
       "0    Southern Asia  902  Developing regions    16  ...   2978  3436  3009   \n",
       "1  Southern Europe  901   Developed regions     1  ...   1450  1223   856   \n",
       "2  Northern Africa  902  Developing regions    80  ...   3616  3626  4807   \n",
       "3        Polynesia  902  Developing regions     0  ...      0     0     1   \n",
       "4  Southern Europe  901   Developed regions     0  ...      0     0     1   \n",
       "\n",
       "   2007  2008  2009  2010  2011  2012  2013  \n",
       "0  2652  2111  1746  1758  2203  2635  2004  \n",
       "1   702   560   716   561   539   620   603  \n",
       "2  3623  4005  5393  4752  4325  3774  4331  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     1     0     0     0     0     1     1  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_can.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's find out how many entries there are in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 43)\n"
     ]
    }
   ],
   "source": [
    "# print the dimensions of the dataframe\n",
    "print(df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* for a detailed description of this preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data dimensions: (195, 38)\n"
     ]
    }
   ],
   "source": [
    "# clean up the dataset to remove unnecessary columns (eg. REG) \n",
    "df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis = 1, inplace = True)\n",
    "\n",
    "# let's rename the columns so that they make sense\n",
    "df_can.rename (columns = {'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace = True)\n",
    "\n",
    "# for sake of consistency, let's also make all column labels of type string\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "\n",
    "# set the country name as index - useful for quickly looking up countries using .loc method\n",
    "df_can.set_index('Country', inplace = True)\n",
    "\n",
    "# add total column\n",
    "df_can['Total'] =  df_can.sum (axis = 1)\n",
    "\n",
    "# years that we will be using in this lesson - useful for plotting later on\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "print ('data dimensions:', df_can.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Visualizing Data using Matplotlib<a id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import `matplotlib`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matplotlib version:  2.1.2\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches # needed for waffle Charts\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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    }
   },
   "source": [
    "# Waffle Charts <a id=\"6\"></a>\n",
    "\n",
    "\n",
    "A `waffle chart` is an interesting visualization that is normally created to display progress toward goals. It is commonly an effective option when you are trying to add interesting visualization features to a visual that consists mainly of cells, such as an Excel dashboard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's revisit the previous case study about Denmark, Norway, and Sweden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
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    "run_control": {
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    }
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Region</th>\n",
       "      <th>DevName</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
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       "      <th>1986</th>\n",
       "      <th>...</th>\n",
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       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
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       "      <th>Denmark</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>272</td>\n",
       "      <td>293</td>\n",
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       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "      <td>73</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>62</td>\n",
       "      <td>101</td>\n",
       "      <td>97</td>\n",
       "      <td>108</td>\n",
       "      <td>81</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>3901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>116</td>\n",
       "      <td>77</td>\n",
       "      <td>106</td>\n",
       "      <td>51</td>\n",
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       "      <td>75</td>\n",
       "      <td>46</td>\n",
       "      <td>49</td>\n",
       "      <td>53</td>\n",
       "      <td>59</td>\n",
       "      <td>2327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>Developed regions</td>\n",
       "      <td>281</td>\n",
       "      <td>308</td>\n",
       "      <td>222</td>\n",
       "      <td>176</td>\n",
       "      <td>128</td>\n",
       "      <td>158</td>\n",
       "      <td>187</td>\n",
       "      <td>...</td>\n",
       "      <td>205</td>\n",
       "      <td>139</td>\n",
       "      <td>193</td>\n",
       "      <td>165</td>\n",
       "      <td>167</td>\n",
       "      <td>159</td>\n",
       "      <td>134</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>5866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Continent           Region            DevName  1980  1981  1982  1983  \\\n",
       "Country                                                                         \n",
       "Denmark    Europe  Northern Europe  Developed regions   272   293   299   106   \n",
       "Norway     Europe  Northern Europe  Developed regions   116    77   106    51   \n",
       "Sweden     Europe  Northern Europe  Developed regions   281   308   222   176   \n",
       "\n",
       "         1984  1985  1986  ...    2005  2006  2007  2008  2009  2010  2011  \\\n",
       "Country                    ...                                               \n",
       "Denmark    93    73    93  ...      62   101    97   108    81    92    93   \n",
       "Norway     31    54    56  ...      57    53    73    66    75    46    49   \n",
       "Sweden    128   158   187  ...     205   139   193   165   167   159   134   \n",
       "\n",
       "         2012  2013  Total  \n",
       "Country                     \n",
       "Denmark    94    81   3901  \n",
       "Norway     53    59   2327  \n",
       "Sweden    140   140   5866  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's create a new dataframe for these three countries \n",
    "df_dsn = df_can.loc[['Denmark', 'Norway', 'Sweden'], :]\n",
    "\n",
    "# let's take a look at our dataframe\n",
    "df_dsn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Unfortunately, unlike R, `waffle` charts are not built into any of the Python visualization libraries. Therefore, we will learn how to create them from scratch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Step 1.** The first step into creating a waffle chart is determing the proportion of each category with respect to the total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Denmark: 0.32255663965602777\n",
      "Norway: 0.1924094592359848\n",
      "Sweden: 0.48503390110798744\n"
     ]
    }
   ],
   "source": [
    "# compute the proportion of each category with respect to the total\n",
    "total_values = sum(df_dsn['Total'])\n",
    "category_proportions = [(float(value) / total_values) for value in df_dsn['Total']]\n",
    "\n",
    "# print out proportions\n",
    "for i, proportion in enumerate(category_proportions):\n",
    "    print (df_dsn.index.values[i] + ': ' + str(proportion))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Step 2.** The second step is defining the overall size of the `waffle` chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tiles is  400\n"
     ]
    }
   ],
   "source": [
    "width = 40 # width of chart\n",
    "height = 10 # height of chart\n",
    "\n",
    "total_num_tiles = width * height # total number of tiles\n",
    "\n",
    "print ('Total number of tiles is ', total_num_tiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Step 3.** The third step is using the proportion of each category to determe it respective number of tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Denmark: 129\n",
      "Norway: 77\n",
      "Sweden: 194\n"
     ]
    }
   ],
   "source": [
    "# compute the number of tiles for each catagory\n",
    "tiles_per_category = [round(proportion * total_num_tiles) for proportion in category_proportions]\n",
    "\n",
    "# print out number of tiles per category\n",
    "for i, tiles in enumerate(tiles_per_category):\n",
    "    print (df_dsn.index.values[i] + ': ' + str(tiles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Based on the calculated proportions, Denmark will occupy 129 tiles of the `waffle` chart, Norway will occupy 77 tiles, and Sweden will occupy 194 tiles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Step 4.** The fourth step is creating a matrix that resembles the `waffle` chart and populating it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waffle chart populated!\n"
     ]
    }
   ],
   "source": [
    "# initialize the waffle chart as an empty matrix\n",
    "waffle_chart = np.zeros((height, width))\n",
    "\n",
    "# define indices to loop through waffle chart\n",
    "category_index = 0\n",
    "tile_index = 0\n",
    "\n",
    "# populate the waffle chart\n",
    "for col in range(width):\n",
    "    for row in range(height):\n",
    "        tile_index += 1\n",
    "\n",
    "        # if the number of tiles populated for the current category is equal to its corresponding allocated tiles...\n",
    "        if tile_index > sum(tiles_per_category[0:category_index]):\n",
    "            # ...proceed to the next category\n",
    "            category_index += 1       \n",
    "            \n",
    "        # set the class value to an integer, which increases with class\n",
    "        waffle_chart[row, col] = category_index\n",
    "        \n",
    "print ('Waffle chart populated!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's take a peek at how the matrix looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2.,\n",
       "        2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,\n",
       "        3., 3., 3., 3., 3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "waffle_chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "As expected, the matrix consists of three categories and the total number of each category's instances matches the total number of tiles allocated to each category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Step 5.** Map the `waffle` chart matrix into a visual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1a1844f8898>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1844f88d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SQcaRHUmSJEm1VOc1O5IkSZIOYjWvxiZJkiTpYOXIjiRJkqRacs2OJEmSpDpqOrIjSZIkqZYmxjdlGN/IJUmSJA1cv0Z2ImIlcANwBDALbMrMjXPavAX4YPH2GeCdmfmV4trjwF5gHzCTmSd3e+b4TsCTJEmSNHiNiepHZzPA+zJzNbAWuCQijpvT5pvAqZn5KuC3gE1zrp+emSdUSXTAkR1JkiRJnfRpZCczdwO7i9d7I2IHMAVsb2vz120f2QqsWMgzTXYkSZIkleuxGltEPND2dlNmzh2dISJWAScC93W41cXAn7W9bwJ3RUQT+KP57juXyY4kSZKkUr2u2ek2xSwiDgG2AO/OzKdL2pxOK9k5pe30uszcFREvBe6OiEcy855Oz3LNjiRJkqRy/VuzQ0RM0kp0bszMW0vavAq4Fjg3M5/cfz4zdxU/9wC3AWu6Pc9kR5IkSVKp5sSSykcnEdEANgM7MvOqkjZHArcCF2bm37Wdf35EvGD/a+AM4OFusTuNTZIkSVKpZoURm4rWARcCD0XEg8W5y4EjATLzGuA3gRcDn4gI+JcS0y8DbivOLQU+nZl3dHugyY4kSZKkcv2rxnYv0PFmmfkO4B3znH8MeHWvzzTZkSRJklSufyM7i85kR5IkSVKpXquxjRKTHUmSJEnlHNmRJEmSVEfNzstsRprJjiRJkqRSzYnxTRnGN3JJkiRJA+eaHUmSJEm11Md9dhadyY4kSZKkco7sSJIkSaojR3YkSZIk1dJBUY0tIpYADwA7M3P94EKSJEmSNCqaE0uGHcIB62Vk513ADuCnBhSLJEmSpBHTZHynsVWKPCJWAG8Arh1sOJIkSZJGSbPRqHyMmkaz2ezaKCJuAX4PeAHw/vmmsUXEBmADQGa+5pGv7+14z6NWLudbTzx7IDGrIvt48Maxj49eOTnsEHoyuWwZP3ruuWGHUXtV+vmHDz+6SNHU0yGrj+GZHd8Ydhi1Zh8Pnn28cIeefDwwVotgmv/wyN9UbnzET58II/T7dZ3GFhHrgT2Z+eWIOK2sXWZuAjYVb5sXv3dbx/tuvuokurXRwtjHgzeOffypj00NO4SeTE2tYOfO6WGHUXtV+nn72vMWKZp6OmXrFu61DwfKPh48+3jh1s98bdgh9GycCxRUmca2DviliHgcuBn42Yj4k4FGJUmSJGkkNBsTlY9R03VkJzMvAy4DKEZ23p+Zbx1wXJIkSZJGwCiuxanKfXYkSZIklZptHBylp8nMzwOfH0gkkiRJkkbOOK/ZcWRHkiRJUql+rcWJiJXADcARwCywKTM3zmnTADYCZwPPAhdl5rbi2tuA3yia/nZmfrLbM0dvFZEkSZKkkdGkUfnoYgZ4X2auBtYCl0TEcXPanAUcWxwbgD8EiIjDgCuA1wJrgCsi4kXdHmiyI0mSJKlUv6qxZebu/aM0mbkX2AHM3RPjXOCGzGxm5lbg0Ih4OfCLwN2Z+VRmfg+4GzizW+xOY5MkSZJUqtc1OxHxQNvbTcV+nHPbrAJOBO6bc2kKeKLt/XRxrux8RyY7kiRJkkr1Wno6M0/udD0iDgG2AO/OzKfnXJ7vYc0O5ztyGpskSZKkUrPNJZWPbiJiklaic2Nm3jpPk2lgZdv7FcCuDuc7cmRHkiRJUql+lZ4uKq1tBnZk5lUlzT4HXBoRN9MqRvCDzNwdEXcCv9tWlOAM4LJuzzTZkSRJklSqj/vsrAMuBB6KiAeLc5cDRwJk5jXA7bTKTj9Kq/T024trT0XEbwH3F5+7MjOf6vZAkx1JkiRJpfqV7GTmvcy/9qa9TRO4pOTadcB1vTzTZEeSJElSqT6O7Cw6kx1JkiRJpZpNkx1JkiRJNeTIjiRJkqRamh3j3WpMdiRJkiSVchqbJEmSpFqadRqbJEmSpDpyzY4kSZKkWnIamyRJkqRacmRHkiRJUi05siNJkiSplmablp6WJI2o7a98Q9c2h23dwva15y1CNJKkcTM77AAWwGRHkiRJUimnsUmSJEmqJQsUSJIkSaolR3YkSZIk1ZIjO5IkSZJqaZ8jO5IkSZLqqPbT2CLiUOBa4HigCfynzPzSIAOTJEmSNHzN5rAjOHBVR3Y2Andk5psiYhmwfIAxSZIkSRoRs31csxMR1wHrgT2Zefw81z8AvKV4uxRYDbwkM5+KiMeBvcA+YCYzT+72vK7JTkT8FPB64CKAzHwOeK7KLyNJkiRpvPV5Gtv1wNXADfNdzMyPAB8BiIhzgPdk5lNtTU7PzO9WfViVkZ1XAN8B/jgiXg18GXhXZv5je6OI2ABsKIJk81UndbzpUSuXd22jhbGPB28c+3hqanLYIfRkctkypqZWDDuMsXbY1i1d2xyy+hhOqdBOB84+Hjz7ePDs44NTP6exZeY9EbGqYvMLgJsW8rwqyc5S4CTgVzPzvojYCPw68N/aG2XmJmBT8bZ58Xu3dbzp5qtOolsbLYx9PHjj2Mef+tjUsEPoydTUCnbunB52GGNt+9rzurY5ZesW7q3QTgfOPh48+3jw7OOFWz/ztWGH0LNeS09HxANtbzcVeUKv91gOnAlc+q9Cgbsiogn8UZX7Vkl2poHpzLyveH8LrWRHkiRJUs3tm+0t2amylqaCc4C/mjOFbV1m7oqIlwJ3R8QjmXlPp5tMdHtKZv4D8EREvLI49XPA9gONWpIkSdL4aDarH310PnOmsGXmruLnHuA2YE23m1StxvarwI1FJbbHgLf3FKokSZKksdTPamxVRMQLgVOBt7adez4wkZl7i9dnAFd2u1elZCczHwT6MRwlSZIkaYz0c8QmIm4CTgMOj4hp4ApgEiAzrymavRG4a05BtJcBt0UEtHKYT2fmHd2eV3VkR5IkSdJBqJ+lpzPzggptrqdVorr93GPAq3t9nsmOJEmSpFKz/V2Ls6hMdiRJkiSV6nPhgUVlsiNJkiSp1L4+TmNbbCY7kiRJkko5siNJkiSplkx2JEmSJNXSrNPYJEmSJNWRIzuSJEmSaslkR5IkSVItuc+OJEmSpFqanXXNjiRJkqQacmRHkiRJUi25ZkeSJElSLZnsSJIkSaolp7FJkiRJqiVHdiRJkiTV0uzssCM4cCY7kiRJkkqZ7EiSJEmqJdfsSJIkSaqlZk+LdkZrA1KTHUmSJEml+lmgICKuA9YDezLz+HmunwZ8FvhmcerWzLyyuHYmsBFYAlybmR/u9jyTHUmSJEml+rxm53rgauCGDm2+mJnr209ExBLg48AvANPA/RHxuczc3ulhEwuLVZIkSVKdNZvVj24y8x7gqQMIYw3waGY+lpnPATcD53b7kCM7kiRJkkrt63FkJyIeaHu7KTM39fjI10XEV4BdwPsz86vAFPBEW5tp4LXdbmSyI0mSJKlUs6dybA0y8+QFPG4bcFRmPhMRZwOfAY5l/soHXQNzGpskSZKkUrPN6sdCZebTmflM8fp2YDIiDqc1krOyrekKWiM/HVUa2YmI9wDvoJU9PQS8PTP/qcfYJUmSJI2ZflZj6yYijgC+nZnNiFhDa3DmSeD7wLERcTSwEzgfeHO3+3VNdiJiCvg14LjM/GFEZHHz6w/4t5AkSZI0Fmb7uKtoRNwEnAYcHhHTwBXAJEBmXgO8CXhnRMwAPwTOz8wmMBMRlwJ30io9fV2xlqejqmt2lgI/GRE/ApZTYchIkiRJ0vjr58hOZl7Q5frVtEpTz3ftduD2Xp7Xdc1OZu4E/gD4e2A38IPMvKuXh0iSJEkaT/0sPb3YGs0uUUXEi4AtwK/Qmiv3v4FbMvNP5rTbAGwAyMzXPPL1vR3ve9TK5XzriWcPPHJ1ZR8P3jj28dErJ4cdQk8mly3jR889N+wwxtoPH360a5tDVh/DMzu+sQjRHLzs48GzjwfPPl64Q08+HuavLDaqmlfeOFO58W++ZSmM0O9XZRrbzwPfzMzvAETErcC/B/5VslPUz95fQ7t58Xu3dbzp5qtOolsbLYx9PHjj2Mef+tjUsEPoydTUCnbunB52GGNt+9rzurY5ZesW7q3QTgfOPh48+3jw7OOFWz/ztWGH0LNugyOjrEqy8/fA2ohYTmuR0M8BD3T+iCRJkqQ6mO1xU9FRUmXNzn3ALbQ2+Hmo+Eyvu6BKkiRJGkPNZrPyMWoqVWPLzCtolYWTJEmSdBDpY+XpRVe19LQkSZKkg1BzjLMdkx1JkiRJpUZwdlplJjuSJEmSSu3bN74VCkx2pDExbiWjJUlSPTTHN9cx2ZEkSZJUbnaM57GZ7EiSJEkqNYolpasy2ZEkSZJUatZqbJIkSZLqaIwHdkx2JEmSJJVznx1JkiRJtWTpaUmSJEm15MiOJEmSpFoa41zHZEeSJElSuX6O7ETEdcB6YE9mHj/P9bcAHyzePgO8MzO/Ulx7HNgL7ANmMvPkbs8z2ZEkSZJUqs/77FwPXA3cUHL9m8Cpmfm9iDgL2AS8tu366Zn53aoPM9mRJEmSVKqf++xk5j0RsarD9b9ue7sVWLGQ500s5MOSJEmS6q3ZbFY++uxi4M/aQwHuiogvR8SGKjdwZEeSJElSqdmZ3kpPR8QDbW83ZeamXp8ZEafTSnZOaTu9LjN3RcRLgbsj4pHMvKfTfUx2JEmSJJWa7XHEpkrhgE4i4lXAtcBZmflk2313FT/3RMRtwBqgY7LjNDZJkiRJpZqzzcrHQkXEkcCtwIWZ+Xdt558fES/Y/xo4A3i42/0c2ZEkSZJUqp9rcSLiJuA04PCImAauACYBMvMa4DeBFwOfiAj4lxLTLwNuK84tBT6dmXd0e57JjiRJkqRSfa7GdkGX6+8A3jHP+ceAV/f6vMYAqiZAq1KCJEmSpB/XGHYAPWi+5bKdlRvf+HtTMEK/36DW7DS6HRHx5SrtPA78sI/t4zoc9rH9XJfDPraP63DYx307xsoQS08vmNPYJEmSJJXaN7Nv2CEcMJMdSZIkSaVGccSmqmEmOz1vLqSe2ceDZx8Pnn28OOznwbOPB88+Hjz7+CDUj5LSwzKoAgWSJEmSxl/zTe96rHLjWza+AkZoXZLT2CRJkiSVmm3ODjuEA2ayI0mSJKnUOE9jM9mRJEmSVGp2nyM7kiRJkmpodtZkR5IkSVINOY1NkiRJUi01LVAgSZIkqY4c2ZEkSZJUSyY7kiRJkmrJfXYkSZIk1dLszL5hh3DATHYkSZIklXIamyRJkqRashqbJEmSpFqadWRHkiRJUh01Z/s3shMR1wHrgT2Zefw81xvARuBs4FngoszcVlx7G/AbRdPfzsxPdnveRL8ClyRJklQ/zdlm5aOC64EzO1w/Czi2ODYAfwgQEYcBVwCvBdYAV0TEi7o9zGRHkiRJUqlmc7by0U1m3gM81aHJucANmdnMzK3AoRHxcuAXgbsz86nM/B5wN52TJsBpbJIkSZI6WOTS01PAE23vp4tzZec7MtmRJEmSVOZb9/6fU4+q2vjZZ5998qKLLnqg7dSmzNzUw/Ma85xrdjjfkcmOJEmSpDKremm8fPlyMnMhz5sGVra9XwHsKs6fNuf857vdzGRHkiRJ0qj4HHBpRNxMqxjBDzJzd0TcCfxuW1GCM4DLut3MZEeSJEnSooiIm2iN0BweEdO0KqxNAmTmNcDttMpOP0qr9PTbi2tPRcRvAfcXt7oyMzsVOgCg0WyO7yZBkiRJklTG0tOSJEmSaslkR5IkSVItmexIkiRJqiWTHUmSJEm1ZLIjSZIkqZZMdiRJkiTVksmOJEmSpFoy2ZEkSZJUS/8f2+4jhANBM1YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ffe97198>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# instantiate a new figure object\n",
    "fig = plt.figure()\n",
    "\n",
    "# use matshow to display the waffle chart\n",
    "colormap = plt.cm.coolwarm\n",
    "plt.matshow(waffle_chart, cmap=colormap)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Step 6.** Prettify the chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], <a list of 0 Text yticklabel objects>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1859bbf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18488b630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# instantiate a new figure object\n",
    "fig = plt.figure()\n",
    "\n",
    "# use matshow to display the waffle chart\n",
    "colormap = plt.cm.coolwarm\n",
    "plt.matshow(waffle_chart, cmap=colormap)\n",
    "plt.colorbar()\n",
    "\n",
    "# get the axis\n",
    "ax = plt.gca()\n",
    "\n",
    "# set minor ticks\n",
    "ax.set_xticks(np.arange(-.5, (width), 1), minor=True)\n",
    "ax.set_yticks(np.arange(-.5, (height), 1), minor=True)\n",
    "    \n",
    "# add gridlines based on minor ticks\n",
    "ax.grid(which='minor', color='w', linestyle='-', linewidth=2)\n",
    "\n",
    "plt.xticks([])\n",
    "plt.yticks([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "**Step 7.** Create a legend and add it to chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a185b4c390>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a185b4a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a185b54080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# instantiate a new figure object\n",
    "fig = plt.figure()\n",
    "\n",
    "# use matshow to display the waffle chart\n",
    "colormap = plt.cm.coolwarm\n",
    "plt.matshow(waffle_chart, cmap=colormap)\n",
    "plt.colorbar()\n",
    "\n",
    "# get the axis\n",
    "ax = plt.gca()\n",
    "\n",
    "# set minor ticks\n",
    "ax.set_xticks(np.arange(-.5, (width), 1), minor=True)\n",
    "ax.set_yticks(np.arange(-.5, (height), 1), minor=True)\n",
    "    \n",
    "# add gridlines based on minor ticks\n",
    "ax.grid(which='minor', color='w', linestyle='-', linewidth=2)\n",
    "\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "\n",
    "# compute cumulative sum of individual categories to match color schemes between chart and legend\n",
    "values_cumsum = np.cumsum(df_dsn['Total'])\n",
    "total_values = values_cumsum[len(values_cumsum) - 1]\n",
    "\n",
    "# create legend\n",
    "legend_handles = []\n",
    "for i, category in enumerate(df_dsn.index.values):\n",
    "    label_str = category + ' (' + str(df_dsn['Total'][i]) + ')'\n",
    "    color_val = colormap(float(values_cumsum[i])/total_values)\n",
    "    legend_handles.append(mpatches.Patch(color=color_val, label=label_str))\n",
    "\n",
    "# add legend to chart\n",
    "plt.legend(handles=legend_handles,\n",
    "           loc='lower center', \n",
    "           ncol=len(df_dsn.index.values),\n",
    "           bbox_to_anchor=(0., -0.2, 0.95, .1)\n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "And there you go! What a good looking *delicious* `waffle` chart, don't you think?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now it would very inefficient to repeat these seven steps every time we wish to create a `waffle` chart. So let's combine all seven steps into one function called *create_waffle_chart*. This function would take the following parameters as input:\n",
    "\n",
    "> 1. **categories**: Unique categories or classes in dataframe.\n",
    "> 2. **values**: Values corresponding to categories or classes.\n",
    "> 3. **height**: Defined height of waffle chart.\n",
    "> 4. **width**: Defined width of waffle chart.\n",
    "> 5. **colormap**: Colormap class\n",
    "> 6. **value_sign**: In order to make our function more generalizable, we will add this parameter to address signs that could be associated with a value such as %, $, and so on. **value_sign** has a default value of empty string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def create_waffle_chart(categories, values, height, width, colormap, value_sign=''):\n",
    "\n",
    "    # compute the proportion of each category with respect to the total\n",
    "    total_values = sum(values)\n",
    "    category_proportions = [(float(value) / total_values) for value in values]\n",
    "\n",
    "    # compute the total number of tiles\n",
    "    total_num_tiles = width * height # total number of tiles\n",
    "    print ('Total number of tiles is', total_num_tiles)\n",
    "    \n",
    "    # compute the number of tiles for each catagory\n",
    "    tiles_per_category = [round(proportion * total_num_tiles) for proportion in category_proportions]\n",
    "\n",
    "    # print out number of tiles per category\n",
    "    for i, tiles in enumerate(tiles_per_category):\n",
    "        print (df_dsn.index.values[i] + ': ' + str(tiles))\n",
    "    \n",
    "    # initialize the waffle chart as an empty matrix\n",
    "    waffle_chart = np.zeros((height, width))\n",
    "\n",
    "    # define indices to loop through waffle chart\n",
    "    category_index = 0\n",
    "    tile_index = 0\n",
    "\n",
    "    # populate the waffle chart\n",
    "    for col in range(width):\n",
    "        for row in range(height):\n",
    "            tile_index += 1\n",
    "\n",
    "            # if the number of tiles populated for the current category \n",
    "            # is equal to its corresponding allocated tiles...\n",
    "            if tile_index > sum(tiles_per_category[0:category_index]):\n",
    "                # ...proceed to the next category\n",
    "                category_index += 1       \n",
    "            \n",
    "            # set the class value to an integer, which increases with class\n",
    "            waffle_chart[row, col] = category_index\n",
    "    \n",
    "    # instantiate a new figure object\n",
    "    fig = plt.figure()\n",
    "\n",
    "    # use matshow to display the waffle chart\n",
    "    colormap = plt.cm.coolwarm\n",
    "    plt.matshow(waffle_chart, cmap=colormap)\n",
    "    plt.colorbar()\n",
    "\n",
    "    # get the axis\n",
    "    ax = plt.gca()\n",
    "\n",
    "    # set minor ticks\n",
    "    ax.set_xticks(np.arange(-.5, (width), 1), minor=True)\n",
    "    ax.set_yticks(np.arange(-.5, (height), 1), minor=True)\n",
    "    \n",
    "    # add dridlines based on minor ticks\n",
    "    ax.grid(which='minor', color='w', linestyle='-', linewidth=2)\n",
    "\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    # compute cumulative sum of individual categories to match color schemes between chart and legend\n",
    "    values_cumsum = np.cumsum(values)\n",
    "    total_values = values_cumsum[len(values_cumsum) - 1]\n",
    "\n",
    "    # create legend\n",
    "    legend_handles = []\n",
    "    for i, category in enumerate(categories):\n",
    "        if value_sign == '%':\n",
    "            label_str = category + ' (' + str(values[i]) + value_sign + ')'\n",
    "        else:\n",
    "            label_str = category + ' (' + value_sign + str(values[i]) + ')'\n",
    "            \n",
    "        color_val = colormap(float(values_cumsum[i])/total_values)\n",
    "        legend_handles.append(mpatches.Patch(color=color_val, label=label_str))\n",
    "\n",
    "    # add legend to chart\n",
    "    plt.legend(\n",
    "        handles=legend_handles,\n",
    "        loc='lower center', \n",
    "        ncol=len(categories),\n",
    "        bbox_to_anchor=(0., -0.2, 0.95, .1)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now to create a `waffle` chart, all we have to do is call the function `create_waffle_chart`. Let's define the input parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "width = 40 # width of chart\n",
    "height = 10 # height of chart\n",
    "\n",
    "categories = df_dsn.index.values # categories\n",
    "values = df_dsn['Total'] # correponding values of categories\n",
    "\n",
    "colormap = plt.cm.coolwarm # color map class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "And now let's call our function to create a `waffle` chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tiles is 400\n",
      "Denmark: 129\n",
      "Norway: 77\n",
      "Sweden: 194\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a184649f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1848fea20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_waffle_chart(categories, values, height, width, colormap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is part of the free course on **Cognitive Class** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this free self-paced course online by clicking [here](https://cocl.us/DV0101EN_Lab4)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}