Table_Visualization.ipynb

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    "\n",
    "TASKS\n",
    "1. Add one column as \\continent\" in the dataset and label each country/region\n",
    "in the dataset to an appropriate continent such as \\Europe\", \\Asia\",\n",
    "\\Africa\", \\North America\", \\South America\", \\Australia\", or \\Antarc-\n",
    "tica\". Explain how do you validate the correctness of your labelling. Out-\n",
    "put the updated dataset as a new CSV \f",
    "le. (1 point). (Note: You must\n",
    "write a Python program to complete the labelling, manully labelling won't\n",
    "get any credit).\n",
    "2. Write a Python program to \f",
    "nd the country/region in each continent that\n",
    "has the highest average HIV estimated prevalence of people ages from 15\n",
    "to 49 of from year 2000 to 2011. Find the country/region in each continent\n",
    "that has the lowest average HIV estimated prevalence of people ages from\n",
    "15 to 49 of from year 2000 to 2011. Create a bar chart to show the highest\n",
    "average HIV estimated prevalence of people ages from 15 to 49 of from year\n",
    "2000 to 2011 in each continent (1 point). Create a bar chart to show the\n",
    "lowest average HIV estimated prevalence of people ages from 15 to 49 of\n",
    "from year 2000 to 2011 in each continent (1 point). Create an overlaid bar\n",
    "chart to show the highest and lowest average HIV estimated prevalence of\n",
    "people ages from 15 to 49 of from year 2000 to 2011 in each continent (1\n",
    "point). Select a country/region that is di\u000b",
    "erent from the average highest\n",
    "or lowest HIV estimated prevalence of people ages from 15 to 49 from year\n",
    "2000 to 2011 from each continent, then create an overlaid line chart for\n",
    "the selected country/region, the average highest and lowest HIV estimated\n",
    "prevalence of people ages from 15 to 49 from year 2000 to 2011 for each\n",
    "continent (1 point).\n",
    "3. Write a Python progrma to calculate the average HIV estimated preva-\n",
    "lence of people ages from 15 to 49 for each year in the dataset for each\n",
    "continent (you only need simply add the estimate prevalence number of\n",
    "all countries/regions and divided by the number of the countries/regions\n",
    "in the continent). Based on the calculation, create a line chart for each\n",
    "continent to show the changes of the average HIV estimated prevalence\n",
    "from 1979 to 2011 (1 point). Create an overlaid line chart for all conti-\n",
    "nents to show their changes of the average HIV estimated prevalence from\n",
    "1\n",
    "1979 to 2011 (1 point).\n",
    "4. Create two scatter plots to show the data (i.e. each country/region) in\n",
    "year 1990 and year 2010, respectively. The vertical axis in the scatter\n",
    "plot is the HIV estimated prevalence, and the horizontal axis is the corre-\n",
    "sponding year average HIV estimated prevalence in each continent, which\n",
    "you calculated above. Using di\u000b",
    "erent color to show data from di\u000b",
    "erent\n",
    "continent (1 point). If you found any interesting result from the charts,\n",
    "explain it.\n",
    "5. Write a report to explain how each question is implemented and its output\n",
    "graphs (2 point).\n",
    "\n",
    "HOW TO RUN CODE\n",
    "UPDATE VARIABLE InputFilepath TO LOCATION OF INPUT FILE\n",
    "SAVE FILES HIVEstimatedPrevalence.csv AND CountryAndContinent.csv IN THE LOCATION\n",
    "EXECUTE EACH CELL IN THE ORDER\n",
    "OUTPUT FILE outputHIVTable.csv IS SAVED IN DEFAULT PATH.\n",
    "IF ANY PARTICULAR LOCATION IS PREFERRED THEN UPDATE VARIABLE outputfilepath"
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      "The Input Table:1st 5 rows \n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Country</th> <th>1979</th> <th>1980</th> <th>1981</th> <th>1982</th> <th>1983</th> <th>1984</th> <th>1985</th> <th>1986</th> <th>1987</th> <th>1988</th> <th>1989</th> <th>1990</th> <th>1991</th> <th>1992</th> <th>1993</th> <th>1994</th> <th>1995</th> <th>1996</th> <th>1997</th> <th>1998</th> <th>1999</th> <th>2000</th> <th>2001</th> <th>2002</th> <th>2003</th> <th>2004</th> <th>2005</th> <th>2006</th> <th>2007</th> <th>2008</th> <th>2009</th> <th>2010</th> <th>2011</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Abkhazia             </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Afghanistan          </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>0.06</td> <td>0.06</td> <td>0.06</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Akrotiri and Dhekelia</td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Albania              </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Algeria              </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.06</td> <td>0.1 </td> <td>0.1 </td> <td>0.1 </td> <td>0.1 </td> <td>0.1 </td> <td>nan </td> <td>nan </td> <td>nan </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (270 rows omitted)</p>"
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      "The Table with new colomn 'Continent':1st 5 rows \n",
      "/Users/jacobdenny/Desktop/Babloo/Masters/Semester3/Python/DataInPython/Tables_Visualization\n",
      "Reading from Output file The Cleaned and SORTED new HIVEstimator FILE 5 rows\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Country</th> <th>Continent</th> <th>Average2000_2011</th> <th>1979</th> <th>1980</th> <th>1981</th> <th>1982</th> <th>1983</th> <th>1984</th> <th>1985</th> <th>1986</th> <th>1987</th> <th>1990</th> <th>1991</th> <th>1992</th> <th>1993</th> <th>1994</th> <th>1995</th> <th>1996</th> <th>1997</th> <th>1998</th> <th>1999</th> <th>2000</th> <th>2001</th> <th>2002</th> <th>2003</th> <th>2004</th> <th>2005</th> <th>2006</th> <th>2007</th> <th>2008</th> <th>2009</th> <th>2010</th> <th>2011</th> <th>OverAllAverage</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Swaziland   </td> <td>AFRICA   </td> <td>25.58           </td> <td>nan     </td> <td>nan </td> <td>nan      </td> <td>0.01097</td> <td>nan </td> <td>nan      </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>2.3 </td> <td>3.2 </td> <td>4.4 </td> <td>6.1 </td> <td>8.1 </td> <td>10.6</td> <td>13.3</td> <td>16  </td> <td>18.5</td> <td>20.6</td> <td>22.3</td> <td>23.6</td> <td>24.5</td> <td>25.1</td> <td>25.5</td> <td>25.6</td> <td>25.7</td> <td>25.8</td> <td>25.9</td> <td>25.8</td> <td>25.9</td> <td>26  </td> <td>17.933        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Botswana    </td> <td>AFRICA   </td> <td>25.02           </td> <td>0.105976</td> <td>nan </td> <td>nan      </td> <td>nan    </td> <td>nan </td> <td>nan      </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>3.5 </td> <td>5.1 </td> <td>7.3 </td> <td>10.1</td> <td>13.3</td> <td>16.6</td> <td>19.7</td> <td>22.2</td> <td>24.1</td> <td>25.3</td> <td>26  </td> <td>26.3</td> <td>26.3</td> <td>26.1</td> <td>25.8</td> <td>25.5</td> <td>25.3</td> <td>25.1</td> <td>24.9</td> <td>24.1</td> <td>23.7</td> <td>23.4</td> <td>19.7844       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Lesotho     </td> <td>AFRICA   </td> <td>23.59           </td> <td>nan     </td> <td>nan </td> <td>nan      </td> <td>nan    </td> <td>nan </td> <td>0.0110793</td> <td>nan </td> <td>nan </td> <td>nan </td> <td>0.8 </td> <td>1.7 </td> <td>3.3 </td> <td>5.9 </td> <td>9.8 </td> <td>14.3</td> <td>18.5</td> <td>21.6</td> <td>23.4</td> <td>24.2</td> <td>24.5</td> <td>24.5</td> <td>24.3</td> <td>24.1</td> <td>23.8</td> <td>23.6</td> <td>23.5</td> <td>23.5</td> <td>23.6</td> <td>23  </td> <td>23.2</td> <td>23.3</td> <td>18            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Africa</td> <td>AFRICA   </td> <td>17.77           </td> <td>nan     </td> <td>nan </td> <td>0.0142086</td> <td>nan    </td> <td>nan </td> <td>nan      </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>0.7 </td> <td>1.2 </td> <td>1.8 </td> <td>2.9 </td> <td>4.3 </td> <td>6.1 </td> <td>8.4 </td> <td>10.7</td> <td>12.9</td> <td>14.8</td> <td>16.1</td> <td>17.1</td> <td>17.7</td> <td>18  </td> <td>18.1</td> <td>18.1</td> <td>18.1</td> <td>18  </td> <td>17.9</td> <td>17.2</td> <td>17.3</td> <td>17.3</td> <td>12.1868       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Zimbabwe    </td> <td>AFRICA   </td> <td>17.6            </td> <td>0.01    </td> <td>nan </td> <td>nan      </td> <td>nan    </td> <td>nan </td> <td>nan      </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>10.1</td> <td>13.6</td> <td>17.2</td> <td>20.6</td> <td>23.3</td> <td>25.1</td> <td>26.2</td> <td>26.5</td> <td>26.3</td> <td>25.7</td> <td>24.8</td> <td>23.7</td> <td>22.5</td> <td>21.1</td> <td>19.8</td> <td>18.4</td> <td>17.2</td> <td>16.1</td> <td>15.1</td> <td>15.7</td> <td>15.2</td> <td>14.9</td> <td>19.0296       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (149 rows omitted)</p>"
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    "from datascience import Table # Including Table functions from datascience package into the running instance\n",
    "import os# including os package into running instance\n",
    "from datascience.predicates import are\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "'''\n",
    "QUESTION 1\n",
    "Add one column as continent in the dataset and label each country/region in the dataset to an appropriate \n",
    "continent such as Europe\", Asia\",Africa\", North America, South America, Australia, or Antarctica.\n",
    "Explain how do you validate the correctness of your labelling. Out- put the updated dataset as a new CSV file. \n",
    "(1 point). (Note: You must write a Python program to complete the labelling, manully labelling won't get any credit).\n",
    "'''\n",
    "\n",
    "#LOAD DATA\n",
    "\n",
    "#Loading data from CSV file URL address \n",
    "InputFilepath='https://raw.githubusercontent.com/babloomarkose/DataInPython/master/Tables_Visualization'\n",
    "#InputFilepath='/Users/jacobdenny/Desktop/Babloo/Masters/Semester3/Python/DataInPython/Tables_Visualization'\n",
    "HIVEstimator=Table().read_table(InputFilepath+'/HIVEstimatedPrevalence.csv')\n",
    "\n",
    "#Print the HIVPrevelence Estimator Table\n",
    "print(\"The Input Table:1st 5 rows \")\n",
    "HIVEstimator.show(5) # Show only the first 20 records\n",
    "#---------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "#CREATE NEW COLOUMN CONTINENT\n",
    "\n",
    "#Load Table containing list of countries and their continent\n",
    "CountryToContinent=Table().read_table(InputFilepath+'/CountryAndContinent.csv')\n",
    "\n",
    "#Join two tables using common colomn Country To add Continent coloumn\n",
    "HIVEstimator=HIVEstimator.join('Country', CountryToContinent)\n",
    "\n",
    "#Move Coloumn Continent next to country coloumn\n",
    "HIVEstimator=HIVEstimator.move_column('Continent',1)\n",
    "\n",
    "#DATA CLEANING\n",
    "\n",
    "#1. DATA CLEANING :REMOVE DUPLICATES\n",
    "pandasDF=HIVEstimator.to_df()\n",
    "pandasDF.drop_duplicates(keep='first',inplace=True)#keep='first' retains the first among n duplicates and inplace=True modify original record\n",
    "#2. DATA CLEANING : REMOVE RECORDS THAT HAVE ALL COLOUMNS EMPTY (no values in any coloumns)\n",
    "pandasDF.dropna(axis=0, how='all',subset=['1979','1980','1981','1982','1983','1984','1985','1986','1987','1988',\n",
    "                                          '1989','1990','1991','1992','1993','1994','1995','1996','1997','1998',\n",
    "                                          '1999','2000','2001','2002','2003','2004','2005','2006','2007','2008',\n",
    "                                          '2009','2010','2011'], inplace=True)\n",
    "pandasDF.dropna(axis=1, how='all',inplace=True)\n",
    "\n",
    "#Calculate Average per year for Estimated HIV Prevelance for future use\n",
    "DF  = round(pandasDF.iloc[:,2:35].mean(axis=0), 4)\n",
    "\n",
    "#Create new colomn 'Average2000_2011', for 2000-2011 Average Estimated HIV Prevelance\n",
    "pandasDF['Average2000_2011']  = round(pandasDF.iloc[:,23:35].mean(axis=1), 4)\n",
    "pandasDF['OverAllAverage']  = round(pandasDF.iloc[:,2:35].mean(axis=1), 4)\n",
    "\n",
    "#Converting back to datascience table after data manipulation\n",
    "HIVEstimator1=Table.from_df(pandasDF)\n",
    "\n",
    "\n",
    "#OUTPUT TO FILE\n",
    "#Verifying Table has new colomn Continent\n",
    "print(\"The Table with new colomn 'Continent':1st 5 rows \")\n",
    "#SORT the table\n",
    "HIVEstimator1=(HIVEstimator1.sort('Average2000_2011','Desc')).sort('Continent')\n",
    "#Move colomn 'Average2000_2011', for 2000-2011 Average Estimated HIV Prevelance to index 2\n",
    "HIVEstimator1=HIVEstimator1.move_column('Average2000_2011',2)\n",
    "\n",
    "#Write output to file\n",
    "outputfilepath = os.getcwd()\n",
    "#outputfilepath='/Users/jacobdenny/Desktop/Babloo/Masters/Semester3/Python'\n",
    "print(outputfilepath)\n",
    "HIVEstimator1.to_csv(outputfilepath+'/outputHIVTable.csv')\n",
    "\n",
    "#Displaying Data from output file\n",
    "print(\"Reading from Output file The Cleaned and SORTED new HIVEstimator FILE 5 rows\")\n",
    "HIVEstimatorTable=Table.read_table(outputfilepath+'/outputHIVTable.csv')\n",
    "HIVEstimatorTable.show(5)\n",
    "#---------------------------------------------------------------------------------------------------------------------\n"
   ]
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      "The List of Countries with Maximum Average HIV Estimator in each Continent\n"
     ]
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Continent</th> <th>Country</th> <th>Average2000_2011</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>AFRICA       </td> <td>Swaziland       </td> <td>25.58           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Thailand        </td> <td>1.39            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AUSTRALIA    </td> <td>Papua New Guinea</td> <td>0.75            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>EUROPE       </td> <td>Estonia         </td> <td>1.1             </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NORTH AMERICA</td> <td>Bahamas         </td> <td>2.97            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SOUTH AMERICA</td> <td>Guyana          </td> <td>1.16            </td>\n",
       "        </tr>\n",
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      "The List of Countries with Minimum Average HIV Estimator in each Continent\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Continent</th> <th>Country</th> <th>Average2000_2011</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>AFRICA       </td> <td>Tunisia        </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AFRICA       </td> <td>Egypt          </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Turkey         </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Sri Lanka      </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>South Korea    </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Qatar          </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Philippines    </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Mongolia       </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Maldives       </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Japan          </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>China          </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Bangladesh     </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>ASIA         </td> <td>Afghanistan    </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AUSTRALIA    </td> <td>Fiji           </td> <td>0.088           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>EUROPE       </td> <td>Slovak Republic</td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>EUROPE       </td> <td>Czech Republic </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>EUROPE       </td> <td>Croatia        </td> <td>0.06            </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>NORTH AMERICA</td> <td>Cuba           </td> <td>0.112           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>SOUTH AMERICA</td> <td>Bolivia        </td> <td>0.23            </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lDz/88MrS0tLWO+64Y83ll19e+sYbb3wUCvXsmFtDoIpI/7G7J0X/1HzTmriBbQr3DIqUOQ6iwwN5al5DoA5cGgJVRAaGzj0ptrdC0+o94b7zA6jze6MOZ+657h4d1rcV82mFfnfOIomhMBeR/iuU5p1qzxrvPXcOWmv3PjW/5Q/e/L409DSFuSSUwlxEBg4z7yg8Oswbcx2gbRe07nNXUu+KZHe/jshBUJiLyMAWzvImkQDTrWkiIiIBpzAXEREJOJ1mFxGRlBAOh6dPmDChsa2tzcrKyhp/85vfrMzJyWnv/pWiI3MREUkJ6enp7UuWLFn08ccff5CWlubuueeewmS3KSgU5iIiknKOO+64HUuXLk0HOPXUU8cfccQRFWVlZUfcfffdu7tPzcrKmvYv//IvxZMmTaqcOnVq+Zo1ayIAv/rVr3KPPPLI8oqKisrPfvazEzvmX3/99SO/9KUvlR577LETiouLp8yePTvvqquuGjVx4sTK448/fkJzc7MB3HjjjSMmT55cMWHChCMuvvjiMe3tqX9yQKfZRURkL/e8s6lgeX1LQodAHTc42nzDp4p61MNca2srzz///ODTTz+9HuCXv/zlyqKiorYdO3bYtGnTKr/yla9sGz58eFtjY2PomGOO2XHfffetu+qqq0bdd999hT/5yU82nHbaaTtmzJixJBQKce+99xbcdtttw3/xi1+sBVi1alX63/72t48WLFiQcfLJJ5fPnj172QMPPLD2tNNOG/+b3/wm96tf/er273znO5/cfffdGwDOPffcsU8++WTuJZdcUnegNiebwlxERFJCc3NzqLy8vBLg6KOPbrj22ms3A9x1111Ff/jDH/IANm7cmPbBBx9kDB8+fGdaWpqbMWNGHcD06dN3/ulPfxoMsGLFiui55547qra2Nq2lpSU0evTo5o73OPXUU+vS09NddXV1Y1tbm11wwQX1AEcccUTjihUrouD1BX/vvfcOb2pqCm3fvj1SWVnZCCjMRUQkOHp6BJ1oHdfM4+fNnTs359VXX82pqalZkpOT015dXT2psbExBBCJRFzHQCSRSIRYLGYAV199dcm111678dJLL62bO3duzm233TYy7j0ceGOYx7++Y6jUXbt22Q033DBm3rx5i8rKylqvv/76kU1NTSl/STrlGygiIgPX9u3bw7m5uW05OTntb7/9dsbChQsHdfeahoaGcElJSSvAo48+OvRg3m/Xrl0hgOHDh8fq6upCzzzzTP6htbxv6chcRERS1vnnn1/34IMPFk6cOLFy/PjxTVOnTu1yaNF4t9xyy/qLL754fFFRUUtVVdXO1atX9/j6f0FBQdull15aW1lZecSoUaNaevJ+qUBDoIqIBIiGQB24DjQEqk6zi4iIBJzCXEREJOC6DXMzG21mL5vZYjP7wMyu7WKdk8yszsze8adbe6e5IiLSS9rb29st2Y2Qrvk/m/32XtOTArgYcINzboGZ5QDzzexF59yiTuv9xTl35mG0VUREkuf92traysLCwrpQKJScYirpUnt7u9XW1uYC7+9vnW7D3Dm3AdjgP24ws8VAMdA5zEVEJKBisdg3N27c+NDGjRsno0uwqaYdeD8Wi31zfysc1K1pZlYKTAPmdbH4GDNbCKwHbnTOfdDF668ErgQoKSk5mLcWEZFeNH369E+As5PdDjk0Pf72ZWbZwP8C1znn6jstXgCMcc5NBe4DnupqG865B51zVc65qsJCDYYjIiKSCD0KczNLwwvyXzrnftd5uXOu3jm3w3/8LJBmZgWd1xMREZHE60k1uwEPA4udc/fuZ53h/nqYWbW/3S2JbKiIiIh0rSfXzI8Fvgq8Z2bv+PNuBkoAnHMPABcA3zKzGNAIzHDJ6lpORERkgOlJNfvrwAHvPXTOzQJmJapRIiIi0nO6/UBERCTgFOYiIiIBpzAXEREJOIW5iIhIwCnMRUREAk5hLiIiEnAKcxERkYBTmIuIiAScwlxERCTgFOYiIiIBpzAXEREJOIW5iIhIwCnMRUREAk5hLiIiEnAKcxERkYBTmIuIiAScwlxERCTgFOYiIiIBpzAXEREJOIW5iIhIwCnMRUREAk5hLiIiEnAKcxERkYDrNszNbLSZvWxmi83sAzO7tot1zMxmmtlSM3vXzI7qneaKiIhIZ5EerBMDbnDOLTCzHGC+mb3onFsUt84XgAn+dDTwX/6/IiIi0su6PTJ3zm1wzi3wHzcAi4HiTqudAzzmPG8CeWY2IuGtFRERkX0c1DVzMysFpgHzOi0qBtbEPV/LvoGPmV1pZjVmVlNbW3twLRUREZEu9TjMzSwb+F/gOudcfefFXbzE7TPDuQedc1XOuarCwsKDa6mIiIh0qUdhbmZpeEH+S+fc77pYZS0wOu75KGD94TdPREREutOTanYDHgYWO+fu3c9qTwOX+VXtnwHqnHMbEthOERER2Y+eVLMfC3wVeM/M3vHn3QyUADjnHgCeBb4ILAV2AV9LfFNFRESkK92GuXPudbq+Jh6/jgP+OVGNEhERkZ5TD3AiIiIBpzAXEREJOIW5iIhIwCnMRUREAk5hLiIiEnAKcxERkYBTmIuIiAScwlxERCTgFOYiIiIBpzAXEREJOIW5iIhIwCnMRUREAk5hLiIiEnAKcxERkYBTmIuIiAScwlxERCTgFOYiIiIBpzAXEREJOIW5iIhIwCnMRUREAk5hLiIiEnAKcxERkYDrNszN7BEz+8TM3t/P8pPMrM7M3vGnWxPfTBEREdmfSA/WeRSYBTx2gHX+4pw7MyEtEhERkYPS7ZG5c+41YGsftEVEREQOQaKumR9jZgvN7DkzO2J/K5nZlWZWY2Y1tbW1CXprERGRgS0RYb4AGOOcmwrcBzy1vxWdcw8656qcc1WFhYUJeGsRERE57DB3ztU753b4j58F0sys4LBbJiIiIj1y2GFuZsPNzPzH1f42txzudkVERKRnuq1mN7MngJOAAjNbC/wQSANwzj0AXAB8y8xiQCMwwznneq3FIiIispduw9w5d3E3y2fh3brWJ3a0tvHJrlhfvR0Ag6NhhmaE8U9AiIiIpJSe3GeeUt7Z3MhdCzb1+fsOzYhQkZ/hT+mMHZxOJKRwFxGR5AtcmFfkZ/D9quF9+p61jTEWb2tiybYmXt+wA4Bo2JiY6wV7RX4G5fkZ5ETDfdouERERCGCYD82IMDSj75t9ZmkuAFua9gT74m1N/G5FHW3LtgNQPCiNyiHe0Xt5XgajstN0al5ERHpd4MI82YZmRDhuRDbHjcgGoLmtnY+3N7NkexOLtzXz5qZdvLimAYCcaJjyvHTK/dPzE/PSSQ9rbBsREUkshflhSg+HmDw0k8lDMwFwzrF+ZyuLt3nhvnhbE2994vWGGw4Z4wZHKc/L2H39vSBTPwIRETk8SpIEMzOKs6MUZ0c5dbQ3r6GljQ+3N/sB38QLaxp4ZmUdAAWZ8YV1GZTmRFVYJyIiB0Vh3gdyomGqhmVRNSwLgFi7Y2VDy+5wX7ytib+s9wrr0sMhJual7w73SXnpKqwTEZEDUpgnQSRklOWmU5abzll+Yd3mxhhLtjexaGsTS7Y3MWfZdtr9vndGZ0cpz0+n0g/4kYNUWCciInsozFNEQWaE4zL3FNY1xdpZWrfn1PwbG3fuU1jXcfQ+QYV1IiIDmsI8RWVE9i2sW7e7sM4rrutcWBd/7T0Zt++JiEhy6BM/IMyMUdlRRmVHOW30YMArrFsSF+5/XF3P0yu8wrpCv7CuPD+DSr+wLqzCOhGRfklhHmA50TCfLhrEp4sGAV5h3Yr65t23xH2wtYnX4grrJvn3vFfmZzApP53sNBXWiYj0BwrzfiQSMibkZTAhL4Ozx+4prFsU12Nd58K6jr7mVVgnIhJcCvN+riAzwgmZ2Zwwck9h3Ue7e6xr4q8bd/DCmnrAGx2uPC7cJ+SmE1VhnYhIylOYDzAZkRBHFmRyZMGewrq1O1r3uuf975t2Al5h3fhOhXVDVFgnIpJy9Mk8wJkZo3OijM6JcnqJV1hXH1dYt2RbM8+truf3fmHdsMzI7uvuFfkZjFFhnYhI0inMZR+Do2GqiwZRHVdYt7y+eXe4vx9XWJcRCTExN53KId5IceX56QxSYZ2ISJ9SmEu3IiFjYl4GE/MyOGesd2q+tjHGkrj+5n+9dBvOgZlXWFfp3xZXkZ/BiKyICutERHqRwlwOmpkxLCuNYVlp+xTW7e5rfsMO/rjaK6zL3V1Y543x3pdGZqVpXHkR6fcU5pIQXRXWrYkrrFuyrYl5fmFdX9O48iLS3ynMpVeYGSU5UUpyonzeL6yra25jc1Osz9rQ7hyrGlr8++z37f5W48qLSH+hTzDpM7npYXLT+7Y4bkJeBqfGdX8bP67882vquxxXvjwvnbGD0zWuvIgEhsJcBoyuxpVfUd+8VyFfV+PKd5yi17jyIpKqug1zM3sEOBP4xDk3uYvlBvwH8EVgF3CFc25Bohsqkmjx3d/Gjyu/+zr/fsaV7ziCL1b3tyKSInpyZP4oMAt4bD/LvwBM8Kejgf/y/xUJnILMCMdnZnN8XJX+x3XNu4v4NK68iKSibsPcOfeamZUeYJVzgMeccw5408zyzGyEc25DgtookjQZkRBThmYyZeje3d929G2vceVFJBUk4pOmGFgT93ytP2+fMDezK4ErAUpKShLw1iJ9K777245x5etb2vhwW9PuqnmNKy8ifS0RYd7VJ5PrakXn3IPAgwBVVVVdriMSNIM1rryIJFkiwnwtMDru+ShgfQK2KxJIXY0rX9vYyuJtzQccV97r3z5d48qLyEFLRJg/DVxtZk/iFb7V6Xq5yN4KM9MozNy3+9uuxpXPiYb9a+4aV15EeqYnt6Y9AZwEFJjZWuCHQBqAc+4B4Fm829KW4t2a9rXeaqxIf6Fx5UUkkcy55Fy6rqqqcjU1NUl5b5Eg6Dyu/Ed1TbS0eX+vwzIjFGdH6cvj9ZGD0vz+7dMpzOzbAXNkDzOb75yrSnY7JLXo671IijrQuPKLtzVR29iX/dzD+1ub9ur+dk/f9ur+ViTZFOYiAdF5XPm+1tbuWLl74BrvC8XrG7wq/WjYmJi75zq/ur8V6VsKcxHpkXDIGJ+bzvjc9L26v93TgU4Tv1tRR9uy7QCMyo7uDnd1fyvSuxTmInLICjIjHJeZzXEjvCr95rZ2Po4buObNTbvU/a1IH1CYi0jCpIdDTB6ayeS47m/X72xlUVwh3/66vy3P07jyIodKfzki0mvMjOLsKMXZe7q/bfCr9DuGno3v/rYgM0JlfgZluelE+3FB3fhcrxdAkURRmItIn8rZT/e3HeG+aNue7m/7qwvG5ynMJaEU5iKSVF2NK7+jtY229iQ3rBelh/vvWQdJDoW5iKQcDT4jcnBUSioiIhJwCnMREZGAU5iLiIgEnMJcREQk4BTmIiIiAacwFxERCTiFuYiISMApzEVERAJOYS4iIhJwCnMREZGAU5iLiIgEnMJcREQk4BTmIiIiAdejMDezM8zsQzNbamY3dbH8CjOrNbN3/OmbiW+qiIiIdKXbIVDNLAzcD5wGrAXeMrOnnXOLOq36a+fc1b3QRhERETmAnhyZVwNLnXPLnXMtwJPAOb3bLBEREempnoR5MbAm7vlaf15n55vZu2Y2x8xGd7UhM7vSzGrMrKa2tvYQmisiIiKd9STMrYt5rtPzZ4BS59yRwJ+A2V1tyDn3oHOuyjlXVVhYeHAtFRERkS71JMzXAvFH2qOA9fErOOe2OOea/ae/AKYnpnkiIiLSnZ6E+VvABDMba2ZRYAbwdPwKZjYi7unZwOLENVFEREQOpNtqdudczMyuBp4HwsAjzrkPzOw2oMY59zRwjZmdDcSArcAVvdhmERERiWPOdb783TeqqqpcTU1NUt5bRCSozGy+c64q2e2Q1KIe4ERERAJOYS4iIhJwCnMREZGAU5iLiIgEnMJcREQk4BTmIiIiAacwFxERCTiFuYiISMApzEVERAJOYS4iIhJwCnMREZGAU5iLiIgEXLejpomI9Ef1DTtZsmwNi5eu4pPN2/v0vY+eVsEJRx/Zp+8p/ZvCXET6PeccazfUsujjVSxeupoly1azbuNmACKRMIVD8vyeZo0AAAk9SURBVDDru/aUlRb33ZvJgKAwF5F+p6mphY9WrGHx0tW7w3vnriYABmcPoqKshFOPPYrKCWMoKy0mGk1LcotFDo/CXEQCzTnH5q11u0N70cerWLFmI+3t7QCUjBzGcVVTqCgroWJCCSOGDcX68jBcpA8ozEUkUGKxNlas2eAfdXunzbdsqwcgIz3KxLGjuPCLJ1AxYQyTxo0me1Bmklss0vsU5iKS0hp27GLJstW7T5l/tHwtLa2tAAwbmscRE0qpmFBCRdkYSkcVEQ6Hk9xikb6nMJde1dLSyvb6HWyv38G2uh00Nbcku0kSAI1NzXy0Yi2Ll65m7YZaAMLhEONLRvKFkz5NeVkJ5eNLKBiSm+SWiqQGhbkctPiA3l6/k211DXHPvdDueNxRdCRysHIGZVFRVsIpn51GeVkJE0qLSU+PJrtZIilJYS4AtLbG/CBuYHv9zrjH+4b2/gJ6UFYGeYOzyc/NYezo4eTn5vjPs8kb7E1Zmel9vGcSRGmRCIVD81SoJtJDgQvzBe99xENPPpfsZvQbbe1t1DXsPKyA7pjS0gL36yQi0i8E7tM3KzODkuJhyW5GvxEK2T6h3BHUuTmDdP+tiEgA9CjMzewM4D+AMPCQc+7OTsvTgceA6cAW4CLn3MrENtVTXlbCTWUlvbFpERGRQOp2oBUzCwP3A18AKoGLzayy02rfALY558qAnwF3JbqhIiIi0rWejJpWDSx1zi13zrUATwLndFrnHGC2/3gOcIqpckVERKRP9CTMi4E1cc/X+vO6XMc5FwPqgKGdN2RmV5pZjZnV1NbWHlqLRUREZC89CfOujrDdIayDc+5B51yVc66qsLCwJ+0TERGRbvQkzNcCo+OejwLW728dM4sAucDWRDRQREREDqwnYf4WMMHMxppZFJgBPN1pnaeBy/3HFwB/ds7tc2QuIiIiidftrWnOuZiZXQ08j3dr2iPOuQ/M7Dagxjn3NPAw8LiZLcU7Ip/Rm40WERGRPXp0n7lz7lng2U7zbo173ARcmNimiYiISE9Yss6Gm1ktsOoQX14AbE5gc1JNf94/7Vtw9ef9C9K+jXHOqYJY9pK0MD8cZlbjnKtKdjt6S3/eP+1bcPXn/evP+yYDQ08K4ERERCSFKcxFREQCLqhh/mCyG9DL+vP+ad+Cqz/vX3/eNxkAAnnNXERERPYI6pG5iIiI+BTmIiIiAZcyYW5mo83sZTNbbGYfmNm1/vwhZvaimX3s/5vvzzczm2lmS83sXTM7Km5bl/vrf2xml+/vPftKovbNzD5lZm/423jXzC5K5n75bUrYz81fPtjM1pnZrGTsT2cJ/r0sMbMX/G0tMrPS5OzV7vYkct9+4m9jsb9OUodAPoR9K/f/tprN7MZO2zrDzD709/umZOyPSLeccykxASOAo/zHOcBHQCXwE+Amf/5NwF3+4y8Cz+GN2PYZYJ4/fwiw3P8333+c30/2bSIwwX88EtgA5PWHfYvb3n8AvwJmJft3MtH7B7wCnOY/zgay+sO+AZ8F/orX3XMYeAM4KWD7Ngz4NPBvwI1x2wkDy4BxQBRYCFQm+/dSk6bOU8ocmTvnNjjnFviPG4DFeOOknwPM9lebDZzrPz4HeMx53gTyzGwE8HngRefcVufcNuBF4Iw+3JV9JGrfnHMfOec+9rezHvgESGpPUAn8uWFm04Ei4IU+3IUDStT+mVklEHHOvehva4dzbldf7ktnCfzZOSADL+zSgTRgU5/tSBcOdt+cc584594CWjttqhpY6pxb7pxrAZ70tyGSUlImzOP5px+nAfOAIufcBvD+QPG+QYP3h7km7mVr/Xn7m58SDnPf4rdTjffhuax3W9xzh7NvZhYC7gG+01ftPViH+bObCGw3s9+Z2dtm9lMzC/dV27tzOPvmnHsDeBnvTNEG4Hnn3OK+aXn3erhv+5PSnyciHVIuzM0sG/hf4DrnXP2BVu1injvA/KRLwL51bGcE8DjwNedce2JbeWgSsG//BDzrnFvTxfKkS8D+RYDjgRvxTueOA65IcDMPyeHum5mVARXAKLygO9nMTkh8Sw/eQezbfjfRxbyU+DwRiZdSYW5maXh/eL90zv3On70p7jTsCLxTy+B9Qx4d9/JRwPoDzE+qBO0bZjYY+APwff9UZ9IlaN+OAa42s5XA3cBlZnZnHzS/Wwn8vXzbP10bA54C9ir+S4YE7dt5wJv+pYMdeNfVP9MX7T+Qg9y3/UnJzxORzlImzP3q14eBxc65e+MWPQ10VKRfDvw+bv5lfoXtZ4A6/7TZ88DpZpbvV6qe7s9LmkTtm5lFgf/Du2752z5q/gElat+cc5c650qcc6V4R6+POeeSXjmcwN/Lt4B8M+uocTgZWNTrO3AACdy31cCJZhbxA/REvGvUSXMI+7Y/bwETzGys//c3w9+GSGrp7Qq7nk7AcXinr94F3vGnLwJDgZeAj/1/h/jrG3A/3jXj94CquG19HVjqT1/rL/sGfAWvQOeduOlT/WHfOm3zClKnmj2Rv5en+dt5D3gUiPaHfcOr+P45XoAvAu4N4M9tON5ReD2w3X882F/2Rbxq+GXALcneN02auprUnauIiEjApcxpdhERETk0CnMREZGAU5iLiIgEnMJcREQk4BTmIiIiAacwFxERCTiFuchhSKX+1UVk4FKYy4BhZrd3jGvtP/83M7vGzL5jZm/5Y3T/OG75U2Y23x8P+8q4+TvM7DYzm4fXDa2ISFIpzGUgeRi/K09/lLYZeEN1TsAb6vJTwPS4QUK+7pybDlQB15jZUH/+IOB959zRzrnX+3IHRES6Ekl2A0T6inNupZltMbNpeOOmv403gtnp/mOAbLxwfw0vwM/z54/2528B2vAG8BARSQkKcxloHsLr+3048AhwCvD/nHM/j1/JzE4CTgWOcc7tMrNXgAx/cZNzrq2vGiwi0h2dZpeB5v+AM/COyJ/3p6/7415jZsVmNgzIBbb5QV5OCgzpKSKyPzoylwHFOddiZi8D2/2j6xfMrAJ4wxs1kx14o9P9EbjKzN4FPgRSYux4EZGuaNQ0GVD8wrcFwIXOuY+T3R4RkUTQaXYZMMysEm+M+5cU5CLSn+jIXEREJOB0ZC4iIhJwCnMREZGAU5iLiIgEnMJcREQk4BTmIiIiAff/Ae7BxjvaTrk7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "QUESTION 2\n",
    "Write a Python program to \f",
    "nd the country/region in each continent that has the highest average HIV estimated \n",
    "prevalence of people ages from 15 to 49 of from year 2000 to 2011. Find the country/region in each continent \n",
    "that has the lowest average HIV estimated prevalence of people ages from 15 to 49 of from year 2000 to 2011. \n",
    "Create a bar chart to show the highest average HIV estimated prevalence of people ages from 15 to 49 of \n",
    "from year 2000 to 2011 in each continent (1 point). Create a bar chart to show the lowest average HIV \n",
    "estimated prevalence of people ages from 15 to 49 of from year 2000 to 2011 in each continent (1 point). Create \n",
    "an overlaid bar chart to show the highest and lowest average HIV estimated prevalence of people ages from 15 to 49 \n",
    "of from year 2000 to 2011 in each continent (1 point). Select a country/region that is di\u000b",
    "erent from the average\n",
    "highest or lowest HIV estimated prevalence of people ages from 15 to 49 from year 2000 to 2011 from each continent,\n",
    "then create an overlaid line chart for the selected country/region, the average highest and lowest HIV estimated\n",
    "prevalence of people ages from 15 to 49 from year 2000 to 2011 for each continent (1 point).'''\n",
    "\n",
    "#Create new table with Highest and Lowest Average value per continent \n",
    "Highest_average_Per_Continent=HIVEstimator1.select(\"Continent\",'Country','Average2000_2011').group(\"Continent\", max)\n",
    "Lowest_average_Per_Continent=HIVEstimator1.select(\"Continent\",'Country','Average2000_2011').group(\"Continent\", min)\n",
    "\n",
    "#Append new tables with average value to HIVEstimator Table and \n",
    "#drop string columns that doesnot make sense in a max/min operation\n",
    "HIVEstimator1=HIVEstimator1.join('Continent', Highest_average_Per_Continent).drop('Country max')\n",
    "HIVEstimator1=HIVEstimator1.join('Continent', Lowest_average_Per_Continent).drop('Country min')\n",
    "\n",
    "#HIGHEST AVERAGE PER CONTINENT\n",
    "#Display countries from each continent having highest HIV Estimator value\n",
    "print(\"The List of Countries with Maximum Average HIV Estimator in each Continent\")\n",
    "Highest_average_Per_Continent=HIVEstimator1.select(\"Continent\",'Country','Average2000_2011','Average2000_2011 max').where('Average2000_2011',are.equal_to,'Average2000_2011 max').drop('Average2000_2011 max')\n",
    "Highest_average_Per_Continent.show(10)\n",
    "\n",
    "#LOWEST AVERAGE PER CONTINENT\n",
    "#Display countries from each continent having lowest HIV Estimator value\n",
    "print(\"The List of Countries with Minimum Average HIV Estimator in each Continent\")\n",
    "Lowest_average_Per_Continent=HIVEstimator1.select(\"Continent\",'Country','Average2000_2011','Average2000_2011 min').where('Average2000_2011',are.equal_to,'Average2000_2011 min').drop('Average2000_2011 min')\n",
    "Lowest_average_Per_Continent.show(19)\n",
    "\n",
    "#BAR CHART: HIGHEST AVERAGE PER CONTINENT\n",
    "# Bar Chart for countries with highest Average HIV Estimator in each continent\n",
    "Highest_average_Per_Continent.bar(\"Continent\",'Average2000_2011')\n",
    "plt.show()\n",
    "#BAR CHART: LOWEST AVERAGE PER CONTINENT\n",
    "# Bar Chart for countries with lowest Average HIV Estimator in each continent\n",
    "#Since there are multiple entries for each continent having the same low value group the table according to continent\n",
    "Lowest_average_Per_Continent=Lowest_average_Per_Continent.group(\"Continent\", min).relabel('Average2000_2011 min','Average2000_2011').relabel('Country min','Country')\n",
    "Lowest_average_Per_Continent.bar(\"Continent\",'Average2000_2011',label=\"The Bar chart Countries with Minimum Average HIV Estimator in each Continent\")\n",
    "plt.show()\n",
    "#OVERLAID BAR CHART: HIGHEST AND LOWEST AVERAGE PER CONTINENT\n",
    "Highest_average_Per_Continent=Highest_average_Per_Continent.join(\"Continent\",Lowest_average_Per_Continent).relabel('Average2000_2011','MaxAverage').relabel('Average2000_2011_2','MinAverage')\n",
    "Highest_average_Per_Continent.select(\"Continent\",'MaxAverage','MinAverage').barh('Continent')\n",
    "\n",
    "#OVERLAID Line CHART: HIGHEST AND LOWEST  for selected country from each Continent\n",
    "\n",
    "#For each selected Country save the yearly estiamtes(200-2011), minimum per yer and max per year per continent to new tables\n",
    "Uganda=HIVEstimator1.where('Country',are.equal_to('Uganda')).drop(\"Continent\",'Average2000_2011','1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999','Average2000_2011 max', 'Average2000_2011 min')\n",
    "India=HIVEstimator1.where('Country',are.equal_to('India')).drop(\"Continent\",'Average2000_2011','1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999','Average2000_2011 max', 'Average2000_2011 min')\n",
    "Australia=HIVEstimator1.where('Country',are.equal_to('Australia')).drop(\"Continent\",'Average2000_2011','1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999','Average2000_2011 max', 'Average2000_2011 min')\n",
    "Iceland=HIVEstimator1.where('Country',are.equal_to('Iceland')).drop(\"Continent\",'Average2000_2011','1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999','Average2000_2011 max', 'Average2000_2011 min')\n",
    "Uruguay=HIVEstimator1.where('Country',are.equal_to('Uruguay')).drop(\"Continent\",'Average2000_2011','1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999','Average2000_2011 max', 'Average2000_2011 min')\n",
    "Panama=HIVEstimator1.where('Country',are.equal_to('Panama')).drop(\"Continent\",'Average2000_2011','1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999','Average2000_2011 max', 'Average2000_2011 min')\n",
    "Min=HIVEstimator1.select(\"Continent\",'2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011').group(\"Continent\", min)\n",
    "Max=HIVEstimator1.select(\"Continent\",'2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011').group(\"Continent\", max)\n",
    "\n",
    "# Append the yearly estimate and minimum and maximum per continent for each year into a single table\n",
    "HIVEstimatorYearwise=Table().with_columns('year',(2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011),\n",
    "                          'India',(India.column(1).item(0), India.column(2).item(0), India.column(3).item(0), India.column(4).item(0), India.column(5).item(0), India.column(6).item(0), India.column(7).item(0), India.column(8).item(0), India.column(9).item(0), India.column(10).item(0), 0, 0),\n",
    "                          'Uganda',(Uganda.column(1).item(0), Uganda.column(2).item(0), Uganda.column(3).item(0), Uganda.column(4).item(0), Uganda.column(5).item(0), Uganda.column(6).item(0), Uganda.column(7).item(0), Uganda.column(8).item(0), Uganda.column(9).item(0), Uganda.column(10).item(0), Uganda.column(11).item(0), Uganda.column(12).item(0)),                         \n",
    "                          'Australia',(Australia.column(1).item(0), Australia.column(2).item(0), Australia.column(3).item(0), Australia.column(4).item(0), Australia.column(5).item(0), Australia.column(6).item(0), Australia.column(7).item(0), Australia.column(8).item(0), Australia.column(9).item(0), Australia.column(10).item(0), Australia.column(11).item(0), Australia.column(12).item(0)),\n",
    "                          'Iceland',(Iceland.column(1).item(0), Iceland.column(2).item(0), Iceland.column(3).item(0), Iceland.column(4).item(0), Iceland.column(5).item(0), Iceland.column(6).item(0), Iceland.column(7).item(0), Iceland.column(8).item(0), Iceland.column(9).item(0), Iceland.column(10).item(0), Iceland.column(11).item(0), Iceland.column(12).item(0)),                          \n",
    "                          'Uruguay',(Uruguay.column(1).item(0), Uruguay.column(2).item(0), Uruguay.column(3).item(0), Uruguay.column(4).item(0), Uruguay.column(5).item(0), Uruguay.column(6).item(0), Uruguay.column(7).item(0), Uruguay.column(8).item(0), Uruguay.column(9).item(0), Uruguay.column(10).item(0), Uruguay.column(11).item(0), Uruguay.column(12).item(0)),\n",
    "                          'Panama',(Panama.column(1).item(0), Panama.column(2).item(0), Panama.column(3).item(0), Panama.column(4).item(0), Panama.column(5).item(0), Panama.column(6).item(0), Panama.column(7).item(0), Panama.column(8).item(0), Panama.column(9).item(0), Panama.column(10).item(0), Panama.column(11).item(0), Panama.column(12).item(0)),\n",
    "                         'AfricaMin',(Min.column(1).item(0), Min.column(2).item(0), Min.column(3).item(0), Min.column(4).item(0), Min.column(5).item(0), Min.column(6).item(0), Min.column(7).item(0), Min.column(8).item(0), Min.column(9).item(0), Min.column(10).item(0), Min.column(11).item(0), Min.column(12).item(0)),\n",
    "                         'AfricaMax',(Max.column(1).item(0), Max.column(2).item(0), Max.column(3).item(0), Max.column(4).item(0), Max.column(5).item(0), Max.column(6).item(0), Max.column(7).item(0), Max.column(8).item(0), Max.column(9).item(0), Max.column(10).item(0), Max.column(11).item(0), Max.column(12).item(0)),\n",
    "                          'AsiaMin',(Min.column(1).item(1), Min.column(2).item(1), Min.column(3).item(1), Min.column(4).item(1), Min.column(5).item(1), Min.column(6).item(1), Min.column(7).item(1), Min.column(8).item(1), Min.column(9).item(1), Min.column(10).item(1), Min.column(11).item(1), Min.column(12).item(1)),\n",
    "                         'AsiaMax',(Max.column(1).item(1), Max.column(2).item(1), Max.column(3).item(1), Max.column(4).item(1), Max.column(5).item(1), Max.column(6).item(1), Max.column(7).item(1), Max.column(8).item(1), Max.column(9).item(1), Max.column(10).item(1), Max.column(11).item(1), Max.column(12).item(1)),\n",
    "                         'AustraliaMin',(Min.column(1).item(2), Min.column(2).item(2), Min.column(3).item(2), Min.column(4).item(2), Min.column(5).item(2), Min.column(6).item(2), Min.column(7).item(2), Min.column(8).item(2), Min.column(9).item(2), Min.column(10).item(2), Min.column(11).item(2), Min.column(12).item(2)),\n",
    "                         'AustraliaMax',(Max.column(1).item(2), Max.column(2).item(2), Max.column(3).item(2), Max.column(4).item(2), Max.column(5).item(2), Max.column(6).item(2), Max.column(7).item(2), Max.column(8).item(2), Max.column(9).item(2), Max.column(10).item(2), Max.column(11).item(2), Max.column(12).item(2)),\n",
    "                          'EuropeMin',(Min.column(1).item(3), Min.column(2).item(3), Min.column(3).item(3), Min.column(4).item(3), Min.column(5).item(3), Min.column(6).item(3), Min.column(7).item(3), Min.column(8).item(3), Min.column(9).item(3), Min.column(10).item(3), Min.column(11).item(3), Min.column(12).item(3)),\n",
    "                         'EuropeMax',(Max.column(1).item(3), Max.column(2).item(3), Max.column(3).item(3), Max.column(4).item(3), Max.column(5).item(3), Max.column(6).item(3), Max.column(7).item(3), Max.column(8).item(3), Max.column(9).item(3), Max.column(10).item(3), Max.column(11).item(3), Max.column(12).item(3)),\n",
    "                          'NAmericaMin',(Min.column(1).item(4), Min.column(2).item(4), Min.column(3).item(4), Min.column(4).item(4), Min.column(5).item(4), Min.column(6).item(4), Min.column(7).item(4), Min.column(8).item(4), Min.column(9).item(4), Min.column(10).item(4), Min.column(11).item(4), Min.column(12).item(4)),\n",
    "                         'NAmericaMax',(Max.column(1).item(4), Max.column(2).item(4), Max.column(3).item(4), Max.column(4).item(4), Max.column(5).item(4), Max.column(6).item(4), Max.column(7).item(4), Max.column(8).item(4), Max.column(9).item(4), Max.column(10).item(4), Max.column(11).item(4), Max.column(12).item(4)),\n",
    "                          'SAmericaMin',(Min.column(1).item(5), Min.column(2).item(5), Min.column(3).item(5), Min.column(4).item(5), Min.column(5).item(5), Min.column(6).item(5), Min.column(7).item(5), Min.column(8).item(5), Min.column(9).item(5), Min.column(10).item(5), Min.column(11).item(5), Min.column(12).item(5)),\n",
    "                         'SAmericaMax',(Max.column(1).item(5), Max.column(2).item(5), Max.column(3).item(5), Max.column(4).item(5), Max.column(5).item(5), Max.column(6).item(5), Max.column(7).item(5), Max.column(8).item(5), Max.column(9).item(5), Max.column(10).item(5), Max.column(11).item(5), Max.column(12).item(5)))\n",
    "\n",
    "#LINE CHART FOR SELECTED COUNTRY INDIA FROM CONTINENT ASIA OVERLAID WITH HIGHEST AND LOWEST FOR THAT CONTINENT\n",
    "HIVEstimatorYearwise.select('year','AsiaMin','AsiaMax','India').plot('year')\n",
    "\n",
    "#LINE CHART FOR SELECTED COUNTRY UGANDA FROM CONTINENT AFRICA OVERLAID WITH HIGHEST AND LOWEST FOR THAT CONTINENT\n",
    "HIVEstimatorYearwise.select('year','AfricaMin','AfricaMax','Uganda').plot('year')\n",
    "\n",
    "#LINE CHART FOR SELECTED COUNTRY AUSTRALIA FROM CONTINENT AUSTRALIA OVERLAID WITH HIGHEST AND LOWEST FOR THAT CONTINENT\n",
    "HIVEstimatorYearwise.select('year','AustraliaMin','AustraliaMax','Australia').plot('year')\n",
    "\n",
    "#LINE CHART FOR SELECTED COUNTRY ICELAND FROM CONTINENT EUROPE OVERLAID WITH HIGHEST AND LOWEST FOR THAT CONTINENT\n",
    "HIVEstimatorYearwise.select('year','EuropeMin','EuropeMax','Iceland').plot('year')\n",
    "\n",
    "#LINE CHART FOR SELECTED COUNTRY URUGUAY FROM CONTINENT SOUTH AMERICA OVERLAID WITH HIGHEST AND LOWEST FOR THAT CONTINENT\n",
    "HIVEstimatorYearwise.select('year','SAmericaMin','SAmericaMax','Uruguay').plot('year')\n",
    "\n",
    "#LINE CHART FOR SELECTED COUNTRY PANAMA FROM CONTINENT NORTH AMERICA OVERLAID WITH HIGHEST AND LOWEST FOR THAT CONTINENT\n",
    "HIVEstimatorYearwise.select('year','NAmericaMin','NAmericaMax','Panama').plot('year')\n",
    "#---------------------------------------------------------------------------------------------------------------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "QUESTION 3\n",
    "Write a Python progrma to calculate the average HIV estimated prevalence of people ages from 15 to 49 for each year\n",
    "in the dataset for each continent (you only need simply add the estimate prevalence number of all countries/regions \n",
    "and divided by the number of the countries/regions in the continent). Based on the calculation, create a line chart\n",
    "for each continent to show the changes of the average HIV estimated prevalence from 1979 to 2011 (1 point). \n",
    "Create an overlaid line chart for all conti- nents to show their changes of the average HIV estimated prevalence from \n",
    "1979 to 2011 (1 point).'''\n",
    "\n",
    "#Calculate Average per year FOR EACH CONTINENT\n",
    "\n",
    "\n",
    "pandasDFAfrica=pandasDF.loc[pandasDF['Continent'] == 'AFRICA']\n",
    "AverageAfrica  = round(pandasDFAfrica.iloc[:,4:35].mean(axis=0), 4)\n",
    "AverageAfrica.plot(legend='TRUE',label='Africa')\n",
    "plt.show()\n",
    "\n",
    "pandasDFAsia=pandasDF.loc[pandasDF['Continent'] == 'ASIA']\n",
    "AverageAsia = round(pandasDFAsia.iloc[:,4:35].mean(axis=1), 4)\n",
    "AverageAsia.plot(legend='TRUE',label='HIV Prevalance in ASIA  from 1979-2011')\n",
    "plt.show()\n",
    "\n",
    "pandasDFAustralia=pandasDF.loc[pandasDF['Continent'] == 'AUSTRALIA']\n",
    "AverageAustralia = round(pandasDFAustralia.iloc[:,4:35].mean(axis=1), 4)\n",
    "AverageAustralia.plot(legend='TRUE',label='HIV Prevalance in AUSTRALIA from 1979-2011')\n",
    "plt.show()\n",
    "\n",
    "pandasDFEurope=pandasDF.loc[pandasDF['Continent'] == 'EUROPE']\n",
    "AverageEurope = round(pandasDFEurope.iloc[:,4:35].mean(axis=1), 4)\n",
    "AverageEurope.plot(legend='TRUE',label='HIV Prevalance in EUROPE from 1979-2011')\n",
    "plt.show()\n",
    "\n",
    "pandasDFNAmerica=pandasDF.loc[pandasDF['Continent'] == 'NORTH AMERICA']\n",
    "AverageNAmerica  = round(pandasDFNAmerica.iloc[:,4:35].mean(axis=1), 4)\n",
    "AverageNAmerica.plot(legend='TRUE',label='HIV Prevalance in NORTH AMERICA from 1979-2011')\n",
    "plt.show()\n",
    "\n",
    "pandasDFSAmerica=pandasDF.loc[pandasDF['Continent'] == 'SOUTH AMERICA']\n",
    "AverageSAmerica  = round(pandasDFSAmerica.iloc[:,4:35].mean(axis=1), 4)\n",
    "AverageSAmerica.plot(legend='TRUE',label='HIV Prevalance in SOUTH AMERICA from 1979-2011')\n",
    "plt.show()\n",
    "\n",
    "AverageAfrica.plot(legend='TRUE',label='Africa')\n",
    "AverageAsia.plot(legend='TRUE',label='ASIA')\n",
    "AverageEurope.plot(legend='TRUE',label='EUROPE')\n",
    "AverageAustralia.plot(legend='TRUE',label='AUSTRALIA')\n",
    "AverageNAmerica.plot(legend='TRUE',label='NORTH AMERICA')\n",
    "AverageSAmerica.plot(legend='TRUE',label='SOUTH AMERICA')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jacobdenny/Desktop/Babloo/Masters/Semester3/Python/DataInPython/Tables_Visualization\n",
      "Reading from Output file The Cleaned and SORTED new HIVEstimator FILE 5 rows\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Continent</th> <th>Country</th> <th>Average2000_2011</th> <th>1979</th> <th>1980</th> <th>1981</th> <th>1982</th> <th>1983</th> <th>1984</th> <th>1985</th> <th>1986</th> <th>1987</th> <th>1990</th> <th>1991</th> <th>1992</th> <th>1993</th> <th>1994</th> <th>1995</th> <th>1996</th> <th>1997</th> <th>1998</th> <th>1999</th> <th>2000</th> <th>2001</th> <th>2002</th> <th>2003</th> <th>2004</th> <th>2005</th> <th>2006</th> <th>2007</th> <th>2008</th> <th>2009</th> <th>2010</th> <th>2011</th> <th>OverAllAverage</th> <th>Average2000_2011 max</th> <th>Average2000_2011 min</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>AFRICA   </td> <td>Swaziland   </td> <td>25.58           </td> <td>nan     </td> <td>nan </td> <td>nan      </td> <td>0.01097</td> <td>nan </td> <td>nan      </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>2.3 </td> <td>3.2 </td> <td>4.4 </td> <td>6.1 </td> <td>8.1 </td> <td>10.6</td> <td>13.3</td> <td>16  </td> <td>18.5</td> <td>20.6</td> <td>22.3</td> <td>23.6</td> <td>24.5</td> <td>25.1</td> <td>25.5</td> <td>25.6</td> <td>25.7</td> <td>25.8</td> <td>25.9</td> <td>25.8</td> <td>25.9</td> <td>26  </td> <td>17.933        </td> <td>25.58               </td> <td>0.06                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AFRICA   </td> <td>Botswana    </td> <td>25.02           </td> <td>0.105976</td> <td>nan </td> <td>nan      </td> <td>nan    </td> <td>nan </td> <td>nan      </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>3.5 </td> <td>5.1 </td> <td>7.3 </td> <td>10.1</td> <td>13.3</td> <td>16.6</td> <td>19.7</td> <td>22.2</td> <td>24.1</td> <td>25.3</td> <td>26  </td> <td>26.3</td> <td>26.3</td> <td>26.1</td> <td>25.8</td> <td>25.5</td> <td>25.3</td> <td>25.1</td> <td>24.9</td> <td>24.1</td> <td>23.7</td> <td>23.4</td> <td>19.7844       </td> <td>25.58               </td> <td>0.06                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AFRICA   </td> <td>Lesotho     </td> <td>23.59           </td> <td>nan     </td> <td>nan </td> <td>nan      </td> <td>nan    </td> <td>nan </td> <td>0.0110793</td> <td>nan </td> <td>nan </td> <td>nan </td> <td>0.8 </td> <td>1.7 </td> <td>3.3 </td> <td>5.9 </td> <td>9.8 </td> <td>14.3</td> <td>18.5</td> <td>21.6</td> <td>23.4</td> <td>24.2</td> <td>24.5</td> <td>24.5</td> <td>24.3</td> <td>24.1</td> <td>23.8</td> <td>23.6</td> <td>23.5</td> <td>23.5</td> <td>23.6</td> <td>23  </td> <td>23.2</td> <td>23.3</td> <td>18            </td> <td>25.58               </td> <td>0.06                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AFRICA   </td> <td>South Africa</td> <td>17.77           </td> <td>nan     </td> <td>nan </td> <td>0.0142086</td> <td>nan    </td> <td>nan </td> <td>nan      </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>0.7 </td> <td>1.2 </td> <td>1.8 </td> <td>2.9 </td> <td>4.3 </td> <td>6.1 </td> <td>8.4 </td> <td>10.7</td> <td>12.9</td> <td>14.8</td> <td>16.1</td> <td>17.1</td> <td>17.7</td> <td>18  </td> <td>18.1</td> <td>18.1</td> <td>18.1</td> <td>18  </td> <td>17.9</td> <td>17.2</td> <td>17.3</td> <td>17.3</td> <td>12.1868       </td> <td>25.58               </td> <td>0.06                </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>AFRICA   </td> <td>Zimbabwe    </td> <td>17.6            </td> <td>0.01    </td> <td>nan </td> <td>nan      </td> <td>nan    </td> <td>nan </td> <td>nan      </td> <td>nan </td> <td>nan </td> <td>nan </td> <td>10.1</td> <td>13.6</td> <td>17.2</td> <td>20.6</td> <td>23.3</td> <td>25.1</td> <td>26.2</td> <td>26.5</td> <td>26.3</td> <td>25.7</td> <td>24.8</td> <td>23.7</td> <td>22.5</td> <td>21.1</td> <td>19.8</td> <td>18.4</td> <td>17.2</td> <td>16.1</td> <td>15.1</td> <td>15.7</td> <td>15.2</td> <td>14.9</td> <td>19.0296       </td> <td>25.58               </td> <td>0.06                </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (149 rows omitted)</p>"
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     "metadata": {},
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "QUESTION 4\n",
    "Create two scatter plots to show the data (i.e. each country/region) in year 1990 and year 2010, respectively. \n",
    "The vertical axis in the scatter plot is the HIV estimated prevalence, and the horizontal axis is the corresponding \n",
    "year average HIV estimated prevalence in each continent, which you calculated above. Using different color to show data\n",
    "from different continent (1 point). If you found any interesting result from the charts, explain it.'''\n",
    "\n",
    "#Get the necessary data into new Table\n",
    "ScatterData1= HIVEstimator1.select('Continent','Country','1990','OverAllAverage')\n",
    "ScatterData2 = HIVEstimator1.select('Continent','Country','2010','OverAllAverage')\n",
    "#The overall average of each country is plotted against the HIV estimated prevalence data for year  1990\n",
    "ScatterData1.scatter('OverAllAverage','1990',group='Continent')\n",
    "#The overall average of each country is plotted against the HIV estimated prevalence data for year  2010\n",
    "ScatterData2.scatter('OverAllAverage','2010',group='Continent')\n",
    "\n",
    "#Write final output to file\n",
    "outputfilepath = os.getcwd()\n",
    "#outputfilepath='/Users/jacobdenny/Desktop/Babloo/Masters/Semester3/Python'\n",
    "print(outputfilepath)\n",
    "HIVEstimator1.to_csv(outputfilepath+'/outputHIVTable.csv')\n",
    "\n",
    "#Displaying Data from output file\n",
    "print(\"Reading from Output file The Cleaned and SORTED new HIVEstimator FILE 5 rows\")\n",
    "HIVEstimatorTable=Table.read_table(outputfilepath+'/outputHIVTable.csv')\n",
    "HIVEstimatorTable.show(5)"
   ]
  },
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   "metadata": {},
   "source": [
    "\n",
    "QUESTION 5\n",
    "Write a report to explain how each question is implemented and its output graphs (2 point).\n",
    "\n",
    "Report\n",
    "\n",
    "Part 1\n",
    "A CSV file with all countries mapped to respective continent is acquired online. This file is loaded into a datascience table and joined to the Input table. The joining is based on the common column Country. And the countries are prepared in the same format as the input table. So Continents are mapped to each country in the input table\n",
    "Here a new column Average2000_2011 is calculated by taking mean of all vaues from columns 2000 to 2011  across all rows. This is used in the next task\n",
    "\n",
    "Part 2\n",
    " \n",
    "Create new tables for Highest and Lowest Average value, by selecting colomns Continent Country and Average2000_2011, and group it per continent to find maximum and minimum values per continent from Average2000_2011 calulated in the previous step.This table is appeneded to input table as  Average2000_2011 min and Average2000_2011 max columns and year fields from 1979 to 1999 are dropped. \n",
    "Country with the highest is found by comparing values of average and highest average and only those records are retained in the new table.\n",
    "\n",
    "Same is done for Lowest average value. As there are multiple countries with same low values output table has multiple entries one for each country-continent pair\n",
    "\n",
    "The new table created here is used to plot highest average HIV prevalance per continent . For Highest Average value, it is straight forward. For lowest we need to select only one among the multiple lowest value. This is done by grouping again only based on Continent and selecting min.\n",
    "\n",
    "The new tables created are joined on column Continent and used to plot minimum, maximum overlaid barchart across each continent\n",
    "\n",
    "One country is selected from each continent and that country's row is selected with just values from year 2000 to 2011 country,continent,and min/max of each continent columns\n",
    "A new Table is created with each of these country as columns. This table is ploted to get minimum,maximum per continent and the selected ountry in that continent\n",
    "\n",
    "Part 3\n",
    "\n",
    "New/Seperate data frame is created for each continent and mean is calculated on axis =1 for 1979 to 2011 to get average of each year. For each continent this table is plotted seperately and for the overlaid chart, these are plotted in a single plot.\n",
    "\n",
    "Part 4\n",
    "\n",
    "The overall average of each country is plotted against the HIV estimated prevalence data for year 1990 and in second scatter the overall average of each country is plotted against the HIV estimated prevalence data for year 2010.\n",
    "It is to be noted that in year 1990 the number of countries with HIV estimated prevalence significantly different fron the overall average was more. But in 2010 both these values are almost same.\n",
    "Secondly, overall average didnot increase significantly in 2010,but African countries are showing an drastic increase compared to 1990.The overall average remained almost same as the HIV estimated prevalencein other continents decreased."
   ]
  }
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