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    "<img src=\"http://sydney.edu.au/images/common/standard_logo.jpg\">\n",
    "\n",
    "\n",
    "<center><h1>Python for Business Analytics</h1></center>\n",
    "<center><h2>Working with data in Python (exercises) </h2></center>\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Task 1</h2>\n",
    "\n",
    "The marketing team at the financial services company that offers the credit card would like to better understand their clients in order to identify high value existing and potential customers to send offers to. Using descriptive statistics, investigate \n",
    "\n",
    "**(a)** Do gender or ethinicity seem related to the number of cards that a customer owns?  \n",
    "\n",
    "**(b)** Which variables have the highest correlation with monthly credit card balance? Are there any other interesting correlations in the data? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Obs</th>\n",
       "      <th>Income</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Cards</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Student</th>\n",
       "      <th>Married</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.891</td>\n",
       "      <td>3606</td>\n",
       "      <td>283</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>106.025</td>\n",
       "      <td>6645</td>\n",
       "      <td>483</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Asian</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>104.593</td>\n",
       "      <td>7075</td>\n",
       "      <td>514</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>148.924</td>\n",
       "      <td>9504</td>\n",
       "      <td>681</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>55.882</td>\n",
       "      <td>4897</td>\n",
       "      <td>357</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Obs   Income  Limit  Rating  Cards  Age  Education  Gender Student Married  \\\n",
       "0    1   14.891   3606     283      2   34         11    Male      No     Yes   \n",
       "1    2  106.025   6645     483      3   82         15  Female     Yes     Yes   \n",
       "2    3  104.593   7075     514      4   71         11    Male      No      No   \n",
       "3    4  148.924   9504     681      3   36         11  Female      No      No   \n",
       "4    5   55.882   4897     357      2   68         16    Male      No     Yes   \n",
       "\n",
       "   Ethnicity  Balance  \n",
       "0  Caucasian      333  \n",
       "1      Asian      903  \n",
       "2      Asian      580  \n",
       "3      Asian      964  \n",
       "4  Caucasian      331  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_csv('credit.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Task 2</h2>\n",
    "\n",
    "The <a href=\"http://www.marcelscharth.com/python/population.xlsx\"><TT>population.xlsx</TT></a> file contains <a href=\"http://data.worldbank.org/\" target=\"_blank\">World Bank</a> data on the total population of countries and regions in 1960 and 2015.\n",
    "\n",
    "**(a)** Use the appropriate pandas function to import the data and specify the country name as the index label (the column name is 'Country' in the original file).\n",
    "\n",
    "**(b)** Display the first five rows.\n",
    "\n",
    "**(c)** Display the data for Australia, China, and New Zealand only.\n",
    "\n",
    "**(d)** Display the population size for all countries with population higher than 100 million in 2015.\n",
    "\n",
    "**(e)** Create a new dataframe, *large_population*, which contains a copy of the data selected in (d). Save it as an Excel file. Open it in Excel, do some basic formatting, and transfer the final table to Word as you may do when writing a report. \n",
    "\n",
    "The cell below starts the exercise. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_excel('population.xlsx', index_col='Country')\n",
    "data.head(5)"
   ]
  }
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