quickdata-exploration.ipynb

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    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
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    "$( document ).ready(code_toggle);\n",
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    "The raw code for this IPython notebook is by default hidden for easier reading.\n",
    "To toggle on/off the raw code, click <a href=\"javascript:code_toggle()\">here</a>.''')\n"
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  {
   "cell_type": "markdown",
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   "source": [
    "![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banner_Top_06.06.18.jpg?raw=true)  \n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "<h1 align='center'>Stats Can Notebook Template: Quick Dataset Exploration</h1>\n",
    "\n",
    "<h4 align='center'>Laura Gutierrez Funderburk $\\mid$ Stats Can Notebook</h4>\n",
    "\n",
    "<h2 align='center'>Abstract</h2>\n",
    "\n",
    "This notebook may be used to quickly explore most data sets from Stats Can. To explore the contents of a dataset, simply visit https://www150.statcan.gc.ca/n1/en/type/data?MM=1 and select a \"Table\".\n",
    "\n",
    "To select a table, copy the string next to Table, under the data set name. Here is an example. \n",
    "\n",
    "![](./CAN_G.gif)\n",
    "\n",
    "In this case, the data set's table is 10-10-0122-01.\n",
    "\n",
    "Simply copy and paste that table in the box below, and press the Download Dataset button. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "%run -i ./StatsCan/helpers.py\n",
    "%run -i ./StatsCan/scwds.py\n",
    "%run -i ./StatsCan/sc.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "from ipywidgets import widgets, VBox, HBox, Button\n",
    "from ipywidgets import Button, Layout, widgets\n",
    "from IPython.display import display, Javascript, Markdown, HTML\n",
    "import datetime as dt\n",
    "import qgrid as q\n",
    "import pandas as pd\n",
    "import json\n",
    "import datetime\n",
    "import qgrid \n",
    "from tqdm import tnrange, tqdm_notebook\n",
    "from time import sleep\n",
    "import sys\n",
    "\n",
    "grid_features = { 'fullWidthRows': True,\n",
    "                  'syncColumnCellResize': True,\n",
    "                  'forceFitColumns': True,\n",
    "                  'enableColumnReorder': True,\n",
    "                  'enableTextSelectionOnCells': True,\n",
    "                  'editable': False,\n",
    "                  'filterable': True,\n",
    "                  'sortable': False,\n",
    "                  'highlightSelectedRow': True}\n",
    "\n",
    "def rerun_cell( b ):\n",
    "    \n",
    "    display(Javascript('IPython.notebook.execute_cell_range(IPython.notebook.get_selected_index()+1,IPython.notebook.get_selected_index()+3)'))    \n",
    "\n",
    "    \n",
    "def run_4cell( b ):\n",
    "    \n",
    "    display(Javascript('IPython.notebook.execute_cell_range(IPython.notebook.get_selected_index()+1,IPython.notebook.get_selected_index()+5)'))    \n",
    "\n",
    "style = {'description_width': 'initial'}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": false
   },
   "source": [
    "<h2 align='center'>Downloading Stats Can Data</h2>\n",
    "\n",
    "To download a full dataset, enter a product ID and press the Download Dataset button. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
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       "model_id": "ce13f11714d442918cc3957779adbd8c",
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       "version_minor": 0
      },
      "text/plain": [
       "Text(value='10-10-0122-01', description='productID value', placeholder='ProductID value', style=DescriptionSty…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "Button(button_style='success', description='Download Dataset', layout=Layout(height='30px', width='15%'), styl…"
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     },
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     "output_type": "display_data"
    },
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      "application/javascript": [
       "IPython.notebook.execute_cell_range(IPython.notebook.get_selected_index()+1,IPython.notebook.get_selected_index()+5)"
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      "text/plain": [
       "<IPython.core.display.Javascript object>"
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    "prod_ID = widgets.Text(\n",
    "    value=\"10-10-0122-01\",\n",
    "    placeholder='ProductID value',\n",
    "    description='productID value',\n",
    "    disabled=False,\n",
    "    style=style\n",
    ")\n",
    "\n",
    "\n",
    "DS_button = widgets.Button(\n",
    "    button_style='success',\n",
    "    description=\"Download Dataset\", \n",
    "    layout=Layout(width='15%', height='30px'),\n",
    "    style=style\n",
    ")    \n",
    "DS_button.on_click( run_4cell )\n",
    "display(prod_ID)\n",
    "display(DS_button)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
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    "scrolled": true
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       "HBox(children=(IntProgress(value=0, description='Downloading Dataset', max=1, style=ProgressStyle(description_…"
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     "output_type": "stream",
     "text": [
      "\n",
      "PARSING DATA AS PANDAS DATAFRAME\n"
     ]
    },
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       "<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>REF_DATE</th>\n",
       "      <th>GEO</th>\n",
       "      <th>Rates</th>\n",
       "      <th>UOM</th>\n",
       "      <th>SCALAR_FACTOR</th>\n",
       "      <th>VALUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1934-01-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>-99.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1934-02-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>-99.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1934-03-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>2.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1934-04-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>2.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1934-05-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>-99.999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    REF_DATE     GEO                                            Rates  \\\n",
       "0 1934-01-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "1 1934-02-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "2 1934-03-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "3 1934-04-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "4 1934-05-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "\n",
       "       UOM SCALAR_FACTOR   VALUE  \n",
       "0  Percent        units  -99.999  \n",
       "1  Percent        units  -99.999  \n",
       "2  Percent        units    2.980  \n",
       "3  Percent        units    2.560  \n",
       "4  Percent        units  -99.999  "
      ]
     },
     "execution_count": 21,
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   ],
   "source": [
    "# # Download data \n",
    "productId = prod_ID.value\n",
    "\n",
    "if \"-\" not in productId:\n",
    "    if len(productId)!=10:\n",
    "        print(\"WARNING: THIS IS LIKELY A NUMBER NOT ASSOCIATED WITH A DATA TABLE. VERIFY AND TRY AGAIN\")\n",
    "        sys.exit(1)\n",
    "\n",
    "        \n",
    "\n",
    "else: \n",
    "    if len(productId.split(\"-\")) !=4:\n",
    "        print(\"WARNING: THIS IS LIKELY A NUMBER NOT ASSOCIATED WITH A DATA TABLE. VERIFY AND TRY AGAIN\")\n",
    "        sys.exit(1)\n",
    "\n",
    "        \n",
    "download_tables(str(productId))\n",
    "\n",
    "def download_and_store_json(productId):\n",
    "    \n",
    "    with open(str(productId) +'.json') as f:\n",
    "        data = json.load(f)\n",
    "    f.close()\n",
    "    \n",
    "    return data\n",
    "\n",
    "import zipfile\n",
    "\n",
    "def read_data_compute_df(productID):\n",
    "\n",
    "    zf = zipfile.ZipFile('./' + str(productID) + '-eng.zip') \n",
    "    df = pd.read_csv(zf.open(str(productID)+'.csv'))\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "# Example\n",
    "#data = download_and_store_json(productId)\n",
    "\n",
    "# Example, we will select the study we downloaded previously \n",
    "\n",
    "df_fullDATA = zip_table_to_dataframe(productId)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "cols = list(df_fullDATA.loc[:,'REF_DATE':'UOM'])+ ['SCALAR_FACTOR'] +  ['VALUE']\n",
    "df_less = df_fullDATA[cols]\n",
    "df_less2 = df_less.drop([\"DGUID\"], axis=1)\n",
    "df_less2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration_nr = df_less2.shape[1]\n",
    "categories = []\n",
    "for i in range(iteration_nr-1):\n",
    "    categories.append(df_less2.iloc[:,i].unique())\n",
    "    \n",
    "    \n",
    "all_the_widgets = []\n",
    "for i in range(len(categories)):\n",
    "    if i==0:\n",
    "        a_category = widgets.Dropdown(\n",
    "                value = categories[i][0],\n",
    "                options = categories[i], \n",
    "                description ='Start Date:', \n",
    "                style = style, \n",
    "                disabled=False\n",
    "            )\n",
    "        b_category = widgets.Dropdown(\n",
    "                value = categories[i][-1],\n",
    "                options = categories[i], \n",
    "                description ='End Date:', \n",
    "                style = style, \n",
    "                disabled=False\n",
    "            )\n",
    "        all_the_widgets.append(a_category)\n",
    "        all_the_widgets.append(b_category)\n",
    "    elif i==1:\n",
    "        a_category = widgets.Dropdown(\n",
    "                value = categories[i][0],\n",
    "                options = categories[i], \n",
    "                description ='Location:', \n",
    "                style = style, \n",
    "                disabled=False\n",
    "            )\n",
    "        all_the_widgets.append(a_category)\n",
    "    elif i==len(categories)-1:\n",
    "        a_category = widgets.Dropdown(\n",
    "                value = categories[i][0],\n",
    "                options = categories[i], \n",
    "                description ='Scalar factor:', \n",
    "                style = style, \n",
    "                disabled=False\n",
    "            )\n",
    "        all_the_widgets.append(a_category)\n",
    "        \n",
    "    elif i==len(categories)-2:\n",
    "        a_category = widgets.Dropdown(\n",
    "                value = categories[i][0],\n",
    "                options = categories[i], \n",
    "                description ='Units of Measure :', \n",
    "                style = style, \n",
    "                disabled=False\n",
    "            )\n",
    "        all_the_widgets.append(a_category)\n",
    "    else:\n",
    "        a_category = widgets.Dropdown(\n",
    "                value = categories[i][0],\n",
    "                options = categories[i], \n",
    "                description ='Subcategory ' + str(i), \n",
    "                style = style, \n",
    "                disabled=False\n",
    "            )\n",
    "        all_the_widgets.append(a_category)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": false
   },
   "source": [
    "## <h2 align='center'>Select Data Subsets: One-Dimensional Plotting</h2>\n",
    "\n",
    "\n",
    "Use the user menu below to select a cateory within the full subset you are interested in exploring. \n",
    "\n",
    "Choose a starting and end date to plot results. \n",
    "\n",
    "If there is data available, it will appear under the headers. \n",
    "\n",
    "Be careful to select dataframes with actual data in them!. \n",
    "\n",
    "Use the Select Dataset button to help you preview the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": false,
    "scrolled": true
   },
   "outputs": [
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      "text/plain": [
       "Tab(children=(VBox(children=(HBox(children=(Dropdown(description='Start Date:', options=(numpy.datetime64('193…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.execute_cell_range(IPython.notebook.get_selected_index()+1,IPython.notebook.get_selected_index()+5)"
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      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     "data": {
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   ],
   "source": [
    "CD_button = widgets.Button(\n",
    "    button_style='success',\n",
    "    description=\"Preview Dataset\", \n",
    "    layout=Layout(width='15%', height='30px'),\n",
    "    style=style\n",
    ")    \n",
    "CD_button.on_click( run_4cell )\n",
    "\n",
    "tab3 = VBox(children=[HBox(children=all_the_widgets[0:3]),\n",
    "                      HBox(children=all_the_widgets[3:5]),\n",
    "                      HBox(children=all_the_widgets[5:len(all_the_widgets)]),\n",
    "                      CD_button])\n",
    "tab = widgets.Tab(children=[tab3])\n",
    "tab.set_title(0, 'Load Data Subset')\n",
    "display(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REF_DATE</th>\n",
       "      <th>GEO</th>\n",
       "      <th>Rates</th>\n",
       "      <th>UOM</th>\n",
       "      <th>SCALAR_FACTOR</th>\n",
       "      <th>VALUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1934-01-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>-99.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1934-02-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>-99.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1934-03-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>2.980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1934-04-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>2.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1934-05-01</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Treasury bill auction - average yields: 3 month</td>\n",
       "      <td>Percent</td>\n",
       "      <td>units</td>\n",
       "      <td>-99.999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    REF_DATE     GEO                                            Rates  \\\n",
       "0 1934-01-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "1 1934-02-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "2 1934-03-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "3 1934-04-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "4 1934-05-01  Canada  Treasury bill auction - average yields: 3 month   \n",
       "\n",
       "       UOM SCALAR_FACTOR   VALUE  \n",
       "0  Percent        units  -99.999  \n",
       "1  Percent        units  -99.999  \n",
       "2  Percent        units    2.980  \n",
       "3  Percent        units    2.560  \n",
       "4  Percent        units  -99.999  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub = df_less2[(df_less2[\"REF_DATE\"]>=all_the_widgets[0].value) & \n",
    "                  (df_less2[\"REF_DATE\"]<=all_the_widgets[1].value) &\n",
    "                  (df_less2[\"GEO\"]==all_the_widgets[2].value) &\n",
    "                  (df_less2[\"UOM\"]==all_the_widgets[-2].value) & \n",
    "                  (df_less2[\"SCALAR_FACTOR\"]==all_the_widgets[-1].value) ]\n",
    "\n",
    "\n",
    "\n",
    "df_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TO HANDLE THE REST OF THE COLUMNS, SIMPLY SUBSTITUTE VALUES \n",
    "col_name = df_sub.columns[2]\n",
    "\n",
    "# weather_data = pd.read_csv(\"DATA.csv\",sep=',')\n",
    "col_name\n",
    "\n",
    "df_sub_final = df_sub[(df_sub[col_name]==all_the_widgets[3].value)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig1 = plt.figure(facecolor='w',figsize=(18,18))\n",
    "plt.subplot(3, 3, 1)\n",
    "plt.axis('off');\n",
    "\n",
    "plt.subplot(3, 3, 2)\n",
    "plt.plot(df_sub_final[\"REF_DATE\"],df_sub_final[\"VALUE\"],'b--',label='Value')\n",
    "#plt.plot(df_20_USA[\"REF_DATE\"],df_20_USA[\"VALUE\"],'r--',label='U.S. dollar, daily average')\n",
    "plt.xlabel('Year-Month', fontsize=20)\n",
    "plt.ylabel('Value',fontsize=20)\n",
    "plt.title(str(all_the_widgets[3].value) + \", \"+  str(all_the_widgets[2].value),fontsize=20)\n",
    "plt.xticks(rotation=90)\n",
    "plt.grid(True)\n",
    "\n",
    "\n",
    "plt.subplot(3, 3, 3);\n",
    "plt.axis('off');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": false
   },
   "source": [
    "<h2 align='center'>References</h2>\n",
    "\n",
    "Statistics Canada.  \n",
    "\n",
    "https://www150.statcan.gc.ca/n1/en/type/data?MM=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": false
   },
   "source": [
    "# ![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banners_Bottom_06.06.18.jpg?raw=true)"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}