02_Read_Create_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in data\n",
    "To load data into python use pandas's ```read_..``` methods. There are plenty of read methods for different file formats.\n",
    "\n",
    "For example, loading data from excel..\n",
    "```python\n",
    "excel_file = pd.ExcelFile('path_to_excel.xlsx')\n",
    "df = pd.read_excel(excel_file, 'Sheet1')\n",
    "```\n",
    "for other files try typing ```pd.read_``` and then hit the \\<tab\\> key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: be careful when using the `\\` (backslash) as path separator (default in Windows), as it is used to \"escape\" characters to give them new meaning, e.g. `\\n` means new line.\n",
    "If you need to use backslash, put a 'r' in front of the string to have python read it \"raw\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>country</th>\n",
       "      <th>description</th>\n",
       "      <th>designation</th>\n",
       "      <th>points</th>\n",
       "      <th>price</th>\n",
       "      <th>province</th>\n",
       "      <th>region_1</th>\n",
       "      <th>region_2</th>\n",
       "      <th>taster_name</th>\n",
       "      <th>taster_twitter_handle</th>\n",
       "      <th>title</th>\n",
       "      <th>variety</th>\n",
       "      <th>winery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Aromas include tropical fruit, broom, brimston...</td>\n",
       "      <td>Vulkà Bianco</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sicily &amp; Sardinia</td>\n",
       "      <td>Etna</td>\n",
       "      <td>None</td>\n",
       "      <td>Kerin O’Keefe</td>\n",
       "      <td>@kerinokeefe</td>\n",
       "      <td>Nicosia 2013 Vulkà Bianco  (Etna)</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Nicosia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>This is ripe and fruity, a wine that is smooth...</td>\n",
       "      <td>Avidagos</td>\n",
       "      <td>87</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Douro</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Quinta dos Avidagos 2011 Avidagos Red (Douro)</td>\n",
       "      <td>Portuguese Red</td>\n",
       "      <td>Quinta dos Avidagos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>US</td>\n",
       "      <td>Soft, supple plum envelopes an oaky structure ...</td>\n",
       "      <td>Mountain Cuvée</td>\n",
       "      <td>87</td>\n",
       "      <td>19.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Napa Valley</td>\n",
       "      <td>Napa</td>\n",
       "      <td>Virginie Boone</td>\n",
       "      <td>@vboone</td>\n",
       "      <td>Kirkland Signature 2011 Mountain Cuvée Caberne...</td>\n",
       "      <td>Cabernet Sauvignon</td>\n",
       "      <td>Kirkland Signature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>US</td>\n",
       "      <td>Fresh apple, lemon and pear flavors are accent...</td>\n",
       "      <td>None</td>\n",
       "      <td>88</td>\n",
       "      <td>18.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>Finger Lakes</td>\n",
       "      <td>Finger Lakes</td>\n",
       "      <td>Anna Lee C. Iijima</td>\n",
       "      <td>None</td>\n",
       "      <td>Ventosa 2015 Pinot Gris (Finger Lakes)</td>\n",
       "      <td>Pinot Gris</td>\n",
       "      <td>Ventosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>US</td>\n",
       "      <td>Dusty mineral, smoke and struck flint lend a s...</td>\n",
       "      <td>Red Oak Vineyard</td>\n",
       "      <td>87</td>\n",
       "      <td>20.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>Finger Lakes</td>\n",
       "      <td>Finger Lakes</td>\n",
       "      <td>Anna Lee C. Iijima</td>\n",
       "      <td>None</td>\n",
       "      <td>Lamoreaux Landing 2014 Red Oak Vineyard Riesli...</td>\n",
       "      <td>Riesling</td>\n",
       "      <td>Lamoreaux Landing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>US</td>\n",
       "      <td>Intensely smoky tones of struck flint and ash ...</td>\n",
       "      <td>Yellow Dog Vineyard</td>\n",
       "      <td>87</td>\n",
       "      <td>20.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>Finger Lakes</td>\n",
       "      <td>Finger Lakes</td>\n",
       "      <td>Anna Lee C. Iijima</td>\n",
       "      <td>None</td>\n",
       "      <td>Lamoreaux Landing 2014 Yellow Dog Vineyard Rie...</td>\n",
       "      <td>Riesling</td>\n",
       "      <td>Lamoreaux Landing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Chile</td>\n",
       "      <td>A bright nose with green apple and citric arom...</td>\n",
       "      <td>Single Vineyard Falaris Hill</td>\n",
       "      <td>87</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Leyda Valley</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Michael Schachner</td>\n",
       "      <td>@wineschach</td>\n",
       "      <td>Leyda 2015 Single Vineyard Falaris Hill Chardo...</td>\n",
       "      <td>Chardonnay</td>\n",
       "      <td>Leyda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Made with 65% Sangiovese, 20% Merlot and 15% C...</td>\n",
       "      <td>Nativo</td>\n",
       "      <td>87</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Tuscany</td>\n",
       "      <td>Toscana</td>\n",
       "      <td>None</td>\n",
       "      <td>Kerin O’Keefe</td>\n",
       "      <td>@kerinokeefe</td>\n",
       "      <td>Madonna Alta 2014 Nativo Red (Toscana)</td>\n",
       "      <td>Red Blend</td>\n",
       "      <td>Madonna Alta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Made predominantly with Trebbiano and Malvasia...</td>\n",
       "      <td>Villa Antinori</td>\n",
       "      <td>87</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Tuscany</td>\n",
       "      <td>Toscana</td>\n",
       "      <td>None</td>\n",
       "      <td>Kerin O’Keefe</td>\n",
       "      <td>@kerinokeefe</td>\n",
       "      <td>Marchesi Antinori 2015 Villa Antinori White (T...</td>\n",
       "      <td>White Blend</td>\n",
       "      <td>Marchesi Antinori</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Italy</td>\n",
       "      <td>A blend of Cabernet Sauvignon, Merlot, Caberne...</td>\n",
       "      <td>Castiglioni</td>\n",
       "      <td>87</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Tuscany</td>\n",
       "      <td>Toscana</td>\n",
       "      <td>None</td>\n",
       "      <td>Kerin O’Keefe</td>\n",
       "      <td>@kerinokeefe</td>\n",
       "      <td>Marchesi de' Frescobaldi 2014 Castiglioni Red ...</td>\n",
       "      <td>Red Blend</td>\n",
       "      <td>Marchesi de' Frescobaldi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Aromas of yellow stone fruit, white spring flo...</td>\n",
       "      <td>Ammiraglia Massovivo</td>\n",
       "      <td>87</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Tuscany</td>\n",
       "      <td>Toscana</td>\n",
       "      <td>None</td>\n",
       "      <td>Kerin O’Keefe</td>\n",
       "      <td>@kerinokeefe</td>\n",
       "      <td>Marchesi de' Frescobaldi 2015 Ammiraglia Masso...</td>\n",
       "      <td>Vermentino</td>\n",
       "      <td>Marchesi de' Frescobaldi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>US</td>\n",
       "      <td>Lots of spearmint, coyote mint, hot licorice, ...</td>\n",
       "      <td>J.D. Hurley</td>\n",
       "      <td>87</td>\n",
       "      <td>26.0</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Clara Valley</td>\n",
       "      <td>Central Coast</td>\n",
       "      <td>Matt Kettmann</td>\n",
       "      <td>@mattkettmann</td>\n",
       "      <td>Martin Ranch 2014 J.D. Hurley Zinfandel (Santa...</td>\n",
       "      <td>Zinfandel</td>\n",
       "      <td>Martin Ranch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Italy</td>\n",
       "      <td>An easy-drinking blend of Merlot, Sangiovese a...</td>\n",
       "      <td>Le Volte</td>\n",
       "      <td>87</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Tuscany</td>\n",
       "      <td>Toscana</td>\n",
       "      <td>None</td>\n",
       "      <td>Kerin O’Keefe</td>\n",
       "      <td>@kerinokeefe</td>\n",
       "      <td>Ornellaia 2014 Le Volte Red (Toscana)</td>\n",
       "      <td>Red Blend</td>\n",
       "      <td>Ornellaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>France</td>\n",
       "      <td>This is a dry wine, very spicy, with a tight, ...</td>\n",
       "      <td>None</td>\n",
       "      <td>87</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>Alsace</td>\n",
       "      <td>None</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Leon Beyer 2012 Gewurztraminer (Alsace)</td>\n",
       "      <td>Gewürztraminer</td>\n",
       "      <td>Leon Beyer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>France</td>\n",
       "      <td>Produced from cru vines at the base of Mount B...</td>\n",
       "      <td>Les Quartelets</td>\n",
       "      <td>87</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Beaujolais</td>\n",
       "      <td>Brouilly</td>\n",
       "      <td>None</td>\n",
       "      <td>Roger Voss</td>\n",
       "      <td>@vossroger</td>\n",
       "      <td>Pardon et Fils 2015 Les Quartelets  (Brouilly)</td>\n",
       "      <td>Gamay</td>\n",
       "      <td>Pardon et Fils</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country                                        description  \\\n",
       "0       Italy  Aromas include tropical fruit, broom, brimston...   \n",
       "1    Portugal  This is ripe and fruity, a wine that is smooth...   \n",
       "10         US  Soft, supple plum envelopes an oaky structure ...   \n",
       "100        US  Fresh apple, lemon and pear flavors are accent...   \n",
       "101        US  Dusty mineral, smoke and struck flint lend a s...   \n",
       "102        US  Intensely smoky tones of struck flint and ash ...   \n",
       "103     Chile  A bright nose with green apple and citric arom...   \n",
       "104     Italy  Made with 65% Sangiovese, 20% Merlot and 15% C...   \n",
       "105     Italy  Made predominantly with Trebbiano and Malvasia...   \n",
       "106     Italy  A blend of Cabernet Sauvignon, Merlot, Caberne...   \n",
       "107     Italy  Aromas of yellow stone fruit, white spring flo...   \n",
       "108        US  Lots of spearmint, coyote mint, hot licorice, ...   \n",
       "109     Italy  An easy-drinking blend of Merlot, Sangiovese a...   \n",
       "11     France  This is a dry wine, very spicy, with a tight, ...   \n",
       "110    France  Produced from cru vines at the base of Mount B...   \n",
       "\n",
       "                      designation  points  price           province  \\\n",
       "0                    Vulkà Bianco      87    NaN  Sicily & Sardinia   \n",
       "1                        Avidagos      87   15.0              Douro   \n",
       "10                 Mountain Cuvée      87   19.0         California   \n",
       "100                          None      88   18.0           New York   \n",
       "101              Red Oak Vineyard      87   20.0           New York   \n",
       "102           Yellow Dog Vineyard      87   20.0           New York   \n",
       "103  Single Vineyard Falaris Hill      87   18.0       Leyda Valley   \n",
       "104                        Nativo      87   16.0            Tuscany   \n",
       "105                Villa Antinori      87   14.0            Tuscany   \n",
       "106                   Castiglioni      87   30.0            Tuscany   \n",
       "107          Ammiraglia Massovivo      87   18.0            Tuscany   \n",
       "108                   J.D. Hurley      87   26.0         California   \n",
       "109                      Le Volte      87   30.0            Tuscany   \n",
       "11                           None      87   30.0             Alsace   \n",
       "110                Les Quartelets      87   23.0         Beaujolais   \n",
       "\n",
       "               region_1       region_2         taster_name  \\\n",
       "0                  Etna           None       Kerin O’Keefe   \n",
       "1                  None           None          Roger Voss   \n",
       "10          Napa Valley           Napa      Virginie Boone   \n",
       "100        Finger Lakes   Finger Lakes  Anna Lee C. Iijima   \n",
       "101        Finger Lakes   Finger Lakes  Anna Lee C. Iijima   \n",
       "102        Finger Lakes   Finger Lakes  Anna Lee C. Iijima   \n",
       "103                None           None   Michael Schachner   \n",
       "104             Toscana           None       Kerin O’Keefe   \n",
       "105             Toscana           None       Kerin O’Keefe   \n",
       "106             Toscana           None       Kerin O’Keefe   \n",
       "107             Toscana           None       Kerin O’Keefe   \n",
       "108  Santa Clara Valley  Central Coast       Matt Kettmann   \n",
       "109             Toscana           None       Kerin O’Keefe   \n",
       "11               Alsace           None          Roger Voss   \n",
       "110            Brouilly           None          Roger Voss   \n",
       "\n",
       "    taster_twitter_handle                                              title  \\\n",
       "0            @kerinokeefe                  Nicosia 2013 Vulkà Bianco  (Etna)   \n",
       "1              @vossroger      Quinta dos Avidagos 2011 Avidagos Red (Douro)   \n",
       "10                @vboone  Kirkland Signature 2011 Mountain Cuvée Caberne...   \n",
       "100                  None             Ventosa 2015 Pinot Gris (Finger Lakes)   \n",
       "101                  None  Lamoreaux Landing 2014 Red Oak Vineyard Riesli...   \n",
       "102                  None  Lamoreaux Landing 2014 Yellow Dog Vineyard Rie...   \n",
       "103           @wineschach  Leyda 2015 Single Vineyard Falaris Hill Chardo...   \n",
       "104          @kerinokeefe             Madonna Alta 2014 Nativo Red (Toscana)   \n",
       "105          @kerinokeefe  Marchesi Antinori 2015 Villa Antinori White (T...   \n",
       "106          @kerinokeefe  Marchesi de' Frescobaldi 2014 Castiglioni Red ...   \n",
       "107          @kerinokeefe  Marchesi de' Frescobaldi 2015 Ammiraglia Masso...   \n",
       "108         @mattkettmann  Martin Ranch 2014 J.D. Hurley Zinfandel (Santa...   \n",
       "109          @kerinokeefe              Ornellaia 2014 Le Volte Red (Toscana)   \n",
       "11             @vossroger            Leon Beyer 2012 Gewurztraminer (Alsace)   \n",
       "110            @vossroger     Pardon et Fils 2015 Les Quartelets  (Brouilly)   \n",
       "\n",
       "                variety                    winery  \n",
       "0           White Blend                   Nicosia  \n",
       "1        Portuguese Red       Quinta dos Avidagos  \n",
       "10   Cabernet Sauvignon        Kirkland Signature  \n",
       "100          Pinot Gris                   Ventosa  \n",
       "101            Riesling         Lamoreaux Landing  \n",
       "102            Riesling         Lamoreaux Landing  \n",
       "103          Chardonnay                     Leyda  \n",
       "104           Red Blend              Madonna Alta  \n",
       "105         White Blend         Marchesi Antinori  \n",
       "106           Red Blend  Marchesi de' Frescobaldi  \n",
       "107          Vermentino  Marchesi de' Frescobaldi  \n",
       "108           Zinfandel              Martin Ranch  \n",
       "109           Red Blend                 Ornellaia  \n",
       "11       Gewürztraminer                Leon Beyer  \n",
       "110               Gamay            Pardon et Fils  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_json(r'data\\wine-reviews\\winemag-data-130k-v2_tiny.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional options\n",
    "All the read methods have additional useful options.\n",
    "\n",
    "The main ones for `read_csv` (some of which are in common with other `read_..` methods): \n",
    "- `sep`: defines the type of column separator\n",
    "- `index_col`: defines which column should be read as the index (row label)\n",
    "- `usecols`: allows reading only a subset of the columns\n",
    "- `skiprows` and `skipfooter`: allows ignoring of initial/final rows\n",
    "- `nrows`: allows reading of a limited amount of rows\n",
    "- `na_values`: defines the values to be treated as null\n",
    "- `parse_dates`: defines the columns that contain dates to be parsed\n",
    "- `dayfirst`: defines that the day comes before the month in the date to be parsed\n",
    "- `thousands`: defines the thousands numeric separator\n",
    "- `decimal`: defines the decimal numeric separator\n",
    "- `encoding`: defines the type of encoding to be used, default utf-8.\n",
    "\n",
    "for more information consult the corresponding [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html).\n",
    "\n",
    "**TIP**: if you get a `UnicodeDecodeError: 'utf-8' codec` error, try adding `encoding='latin'` to the read options, that usually solves the issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>designation</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wine_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Vulkà Bianco</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>Avidagos</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>Reserve Late Harvest</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>Vintner's Reserve Wild Child Block</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country                         designation  price\n",
       "wine_id                                                     \n",
       "0           Italy                        Vulkà Bianco    NaN\n",
       "1        Portugal                            Avidagos   15.0\n",
       "2              US                                 NaN   14.0\n",
       "3              US                Reserve Late Harvest   13.0\n",
       "4              US  Vintner's Reserve Wild Child Block   65.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\n",
    "    'data/wine-reviews/winemag-data-130k-v2.csv',\n",
    "    usecols=['wine_id', 'country', 'designation', 'price'],\n",
    "    index_col='wine_id',\n",
    "    nrows=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***EXERCISE 2.1***\n",
    "Using the pandas documentation, find a way to read the file `data/wine-reviews/exercise_2.1.txt` in the following way:\n",
    "- read the file as tab-separated\n",
    "- parse the date in the `date of rating` column, in day/month format\n",
    "- set `country` as the index\n",
    "- skip the 3rd and 4th rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# insert solution here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Data\n",
    "In order to creating a pd.DataFrame from data we have to follow the contructor specifications.\n",
    "\n",
    "In general they can be created using:\n",
    "- dictionary\n",
    "- iterable (np.array, list, pd.Series..)"
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>Cats</th>\n",
       "      <th>Dogs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mary</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Cats  Dogs\n",
       "0   Bob     2     1\n",
       "1  Mary     0     2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using dictionary\n",
    "data = {'Name': ['Bob', 'Mary'], 'Cats':[2,0], 'Dogs':[1,2]}\n",
    "\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "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>Name</th>\n",
       "      <th>Cats</th>\n",
       "      <th>Dogs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bob</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mary</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Cats  Dogs\n",
       "0   Bob     2     1\n",
       "1  Mary     0     2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using 2D iterable\n",
    "data = [['Bob', 2, 1], ['Mary', 0, 2]]\n",
    "labels = ['Name','Cats','Dogs'] \n",
    "\n",
    "pd.DataFrame(data, columns=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ***EXERCISE 2.2***\n",
    "Create a DataFrame like the following:\n",
    "\n",
    "|    \t| r   \t| g   \t| b  \t| hex     \t|\n",
    "|--------\t|-----\t|-----\t|----\t|---------\t|\n",
    "| blue   \t| 0   \t| 0   \t| 1  \t| #0000ff \t|\n",
    "| olive  \t| 85  \t| 107 \t| 47 \t| #556B2F \t|\n",
    "| sienna \t| 160 \t| 82  \t| 45 \t| #A0522D \t|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# insert solution here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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