Team 11 - Data Analysis Team Project (Jazz).ipynb

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   "source": [
    "**Team 11 - Duc, Maxence, Idinma, Jieun**"
   ]
  },
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   "metadata": {},
   "source": [
    "# *Introduction - Southern and Eastern Africa*\n",
    "\n",
    "The cultures within Southern and Eastern Africa are multi-layered. Both regions are rich and diverse in culture, ethnicity, language, cuisine, geography, social and religious practices, and more. Social hierarchies are very significant in these regions; elders and people in positions of authority are well-respected. Thousands of languages spoken in Eastern/Southern Africa such as Swahili, Zulu, Shona, and Xhosa.\n",
    " \n",
    "Geographically, South Africa is situated in the middle of the Indian and South Atlantic oceans. East Africa is off the coast of the Gulf of Aden and the Arabian sea. The Great Escarpment, the Kalahari desert and the Zambezi river are three of the most prominent  geographic features in Southern Africa. Eastern Africa is home to Mount Kilimanjaro, Mount Kenya as well as famous national parks and game reserves like the Serengeti and Masai Mara .\n",
    " \n",
    "After the end of apartheid, which is a system of  institutionalised racial segregation that existed in South Africa from 1948 until the early 1990s, employment increased. However, due to restrictions and discrimination against indigenous Africans, more than half of people who wanted to work moved into self-employment, also categorized as informal employment. Indeed, recently revised time series of Statistics South Africa suggest that between 1994 and 2000, informal labour has spread widely.  \n",
    " \n",
    "Like all regions of the world, Eastern and Southern Africa have their fair share of socio-economic issues. Since 1994, life expectancy has fallen by almost 20 years – mainly because of the rise in HIV-related mortality. In 2006, life expectancy at birth in South Africa was 50.1 years, which is rather low, although still the highest in the sub-Saharan region. The expectancy was 51.0 years for females and 49.1 percent for male. The prevalence of these issues in both regions guided our research and approach to this analysis.\n"
   ]
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   "source": [
    "# *Analysis*\n",
    "\n",
    "We noticed that although Togo is listed in our region, it is actually a West African country, so we decided to remove it as an **obscure** value before moving forward. \n",
    "\n",
    "\n",
    "We selected the five features that best describe our region through a muli-step process. First, we classified the columns in our region into five categories of economy, technology, health, environment, and politics. Second, we filtered out the columns with more than one missing value, because too many missing values would impact the integrity of our analysis, in accordance with the rare event rule."
   ]
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     "text": [
      "<ipython-input-1-90db8a9b6455>:26: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  economy     = full[jazz][rem_togo].iloc[ : , [10, 11, 12, 13, 14, 15, 18, 19,\\\n",
      "<ipython-input-1-90db8a9b6455>:30: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  health      = full[jazz][rem_togo].iloc[ : , [4, 6, 7, 8, 17, 21, 24, 27, 29,\\\n",
      "<ipython-input-1-90db8a9b6455>:34: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  environment = full[jazz][rem_togo].iloc[ : , [9, 16, 22, 23]]\n",
      "<ipython-input-1-90db8a9b6455>:37: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  technology  = full[jazz][rem_togo].iloc[ : , [26, 30]]\n",
      "<ipython-input-1-90db8a9b6455>:40: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  politics    = full[jazz][rem_togo].iloc[ : , [39]]\n",
      "<ipython-input-1-90db8a9b6455>:100: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  top5 = full[jazz][rem_togo].iloc[ : , [1, 23, 26, 36, 39, 42]]\n"
     ]
    }
   ],
   "source": [
    "### importing packages\n",
    "import pandas            as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn           as sns\n",
    "import numpy             as np\n",
    "\n",
    "# the path to the dataset\n",
    "file = \"Final_Project_Dataset.xlsx\"\n",
    "\n",
    "# reading the file into Python through pandas\n",
    "full = pd.read_excel(file)\n",
    "\n",
    "# filtering for Jazz region\n",
    "jazz      = full.loc[ : , 'Cool Name' ] == 'Jazz'\n",
    "# filtering for the rest of the world\n",
    "rest      = full.loc[ : , 'Cool Name' ] != 'Jazz'\n",
    "# filtering for Sideswipe region\n",
    "sideswipe = full.loc[ : , 'Cool Name' ] == 'Sideswipe'\n",
    "# filtering for Bumblebee region\n",
    "bumblebee = full.loc[ : , 'Cool Name' ] == 'Bumblebee'\n",
    "# removing Togo from filter\n",
    "rem_togo  = full.loc[ : , 'Country Name' ] != 'Togo'\n",
    "\n",
    "# filtering the region into 5 categories\n",
    "# filter for the category economy\n",
    "economy     = full[jazz][rem_togo].iloc[ : , [10, 11, 12, 13, 14, 15, 18, 19,\\\n",
    "                                              20, 25, 42]]\n",
    "\n",
    "# filter for the category health\n",
    "health      = full[jazz][rem_togo].iloc[ : , [4, 6, 7, 8, 17, 21, 24, 27, 29,\\\n",
    "                                              31, 35, 36, 37, 40, 44]]\n",
    "\n",
    "# filter for the category environment\n",
    "environment = full[jazz][rem_togo].iloc[ : , [9, 16, 22, 23]]\n",
    "\n",
    "# filter for the category technology\n",
    "technology  = full[jazz][rem_togo].iloc[ : , [26, 30]]\n",
    "\n",
    "# filter for the category politics\n",
    "politics    = full[jazz][rem_togo].iloc[ : , [39]]\n",
    "\n",
    "\n",
    "# makes the new DataFrame independent for economy\n",
    "economy_filter = pd.DataFrame.copy(economy)\n",
    "    \n",
    "# for loops to find columns with only a maximum of 1 missing value\n",
    "for col in economy_filter:\n",
    "\n",
    "    # if statement to look for columns that has more than 1 missing value\n",
    "    if economy_filter[col].isnull().sum(axis = 0) > 1:\n",
    "        # deletes the column if there is more than 1 missing value\n",
    "        del economy_filter[col]\n",
    "        \n",
    "# makes the new DataFrame independent for health      \n",
    "health_filter = pd.DataFrame.copy(health)\n",
    "    \n",
    "# for loops to find columns with only a maximum of 1 missing value\n",
    "for col in health_filter:\n",
    "\n",
    "    # if statement to look for columns that has more than 1 missing value\n",
    "    if health_filter[col].isnull().sum(axis = 0) > 1:\n",
    "        # deletes the column if there is more than 1 missing value\n",
    "        del health_filter[col]\n",
    "\n",
    "# makes the new DataFrame independent for environment\n",
    "environment_filter = pd.DataFrame.copy(environment)\n",
    "    \n",
    "# for loops to find columns with only a maximum of 1 missing value\n",
    "for col in environment_filter:\n",
    "\n",
    "    # if statement to look for columns that has more than 1 missing value\n",
    "    if environment_filter[col].isnull().sum(axis = 0) > 1:\n",
    "        # deletes the column if there is more than 1 missing value\n",
    "        del environment_filter[col]\n",
    "\n",
    "# makes the new DataFrame independent for technology\n",
    "technology_filter = pd.DataFrame.copy(technology)\n",
    "    \n",
    "# for loops to find columns with only a maximum of 1 missing value\n",
    "for col in technology_filter:\n",
    "\n",
    "    # if statement to look for columns that has more than 1 missing value\n",
    "    if technology_filter[col].isnull().sum(axis = 0) > 1:\n",
    "        # deletes the column if there is more than 1 missing value\n",
    "        del technology_filter[col]\n",
    "\n",
    "# makes the new DataFrame independent for politics        \n",
    "politics_filter = pd.DataFrame.copy(politics)\n",
    "\n",
    "# for loops to find columns without missing values\n",
    "for col in politics_filter:\n",
    "\n",
    "    # if statement to look for columns that has more than 1 missing value\n",
    "    if politics_filter[col].isnull().sum(axis = 0) > 1:\n",
    "        # deletes the column if there is more than 1 missing value\n",
    "        del politics_filter[col]\n",
    "\n",
    "\n",
    "# filter for top 5 features for Jazz w/o Togo\n",
    "top5 = full[jazz][rem_togo].iloc[ : , [1, 23, 26, 36, 39, 42]]"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21 entries, 13 to 216\n",
      "Data columns (total 8 columns):\n",
      " #   Column                                                                  Non-Null Count  Dtype  \n",
      "---  ------                                                                  --------------  -----  \n",
      " 0   Contributing family workers, female (% of female employment)            21 non-null     float64\n",
      " 1   Contributing family workers, male (% of male employment)                21 non-null     float64\n",
      " 2   Contributing family workers, total (% of total employment)              21 non-null     float64\n",
      " 3   Employment to population ratio, 15+, female (%) (modeled ILO estimate)  21 non-null     float64\n",
      " 4   Employment to population ratio, 15+, male (%) (modeled ILO estimate)    21 non-null     float64\n",
      " 5   Employment to population ratio, 15+, total (%) (modeled ILO estimate)   21 non-null     float64\n",
      " 6   GDP per person employed (constant 2011 PPP $)                           21 non-null     float64\n",
      " 7   Self-employed, total (% of total employment)                            21 non-null     float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 1.5 KB\n"
     ]
    }
   ],
   "source": [
    "# shows the remaining columns that has no more than 1 missing value for Economy\n",
    "economy_filter.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21 entries, 13 to 216\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                                                Non-Null Count  Dtype  \n",
      "---  ------                                                                --------------  -----  \n",
      " 0   AIDS estimated deaths (UNAIDS estimates)                              21 non-null     float64\n",
      " 1   Adolescent fertility rate (births per 1,000 women ages 15-19)         21 non-null     float64\n",
      " 2   Antiretroviral therapy coverage (% of people living with HIV)         20 non-null     float64\n",
      " 3   Fertility rate, total (births per woman)                              21 non-null     float64\n",
      " 4   Immunization, measles (% of children ages 12-23 months)               20 non-null     float64\n",
      " 5   Incidence of tuberculosis (per 100,000 people)                        20 non-null     float64\n",
      " 6   Life expectancy at birth, total (years)                               21 non-null     float64\n",
      " 7   Maternal mortality ratio (modeled estimate, per 100,000 live births)  21 non-null     float64\n",
      " 8   Mortality rate, infant (per 1,000 live births)                        21 non-null     float64\n",
      " 9   Prevalence of HIV, total (% of population ages 15-49)                 21 non-null     float64\n",
      " 10  Tuberculosis death rate (per 100,000 people), including HIV           20 non-null     float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 2.0 KB\n"
     ]
    }
   ],
   "source": [
    "# shows the remaining columns that has no more than 1 missing value for Health\n",
    "health_filter.info()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21 entries, 13 to 216\n",
      "Data columns (total 3 columns):\n",
      " #   Column                                                        Non-Null Count  Dtype  \n",
      "---  ------                                                        --------------  -----  \n",
      " 0   CO2 emissions (metric tons per capita)                        20 non-null     float64\n",
      " 1   Improved sanitation facilities (% of population with access)  20 non-null     float64\n",
      " 2   Improved water source (% of population with access)           20 non-null     float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 672.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# shows the remaining columns that has no more than 1 missing value for Environment\n",
    "environment_filter.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21 entries, 13 to 216\n",
      "Data columns (total 2 columns):\n",
      " #   Column                                          Non-Null Count  Dtype  \n",
      "---  ------                                          --------------  -----  \n",
      " 0   Internet users (per 100 people)                 20 non-null     float64\n",
      " 1   Mobile cellular subscriptions (per 100 people)  20 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 504.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# shows the remaining columns that has no more than 1 missing value for Technology\n",
    "technology_filter.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21 entries, 13 to 216\n",
      "Data columns (total 1 columns):\n",
      " #   Column                                                         Non-Null Count  Dtype  \n",
      "---  ------                                                         --------------  -----  \n",
      " 0   Proportion of seats held by women in national parliaments (%)  20 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 336.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# shows the remaining columns that has no more than 1 missing value for Politics\n",
    "politics_filter.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Top 5 Features*\n",
    "\n",
    "1. **Economy**: We decided to take data showing totals as opposed to one for male or female, which left us with three columns. Of the remaining three, we picked the total number of self-employed since this is a category that is important for this region, based on external research (van Klaveren et al., 2020). \n",
    "\n",
    "\n",
    "2. **Health**: We picked prevalence of HIV for this category because HIV/AIDS is one of the leading causes of mortality in our region.\n",
    "\n",
    "\n",
    "3. **Environment**: Of the three choices, we found that CO2 emissions has some outliers, so we decided to drop this column. After plotting boxplots for the remaining two, we picked improved water sources because the data in this category is more homogenous than the data in improved sanitation facilities."
   ]
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   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "<ipython-input-7-560b58caa321>:17: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data   = environment_filter[rem_togo]) # DataFrame where features exist\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-560b58caa321>:37: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data   = environment_filter[rem_togo]) # DataFrame where features exist\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-560b58caa321>:56: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data   = environment_filter[rem_togo]) # DataFrame where features exist\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################################\n",
    "###    Boxplot for Environment   ###\n",
    "####################################\n",
    "\n",
    "### Boxplot for CO2 emissions\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = (15, 5))\n",
    "\n",
    "plt.subplot(3, 1, 1) # 3 rows, 1 column, space 1\n",
    "\n",
    "# developing a boxplot for CO2\n",
    "sns.boxplot(x      = 'CO2 emissions (metric tons per capita)',  # x-variable\n",
    "            y      = None,     # optional y-variable\n",
    "            hue    = None,     # optional categorical feature\n",
    "            orient = 'h',      # horizontal or vertical\n",
    "            data   = environment_filter[rem_togo]) # DataFrame where features exist\n",
    "\n",
    "# formatting and displaying the plot\n",
    "plt.title(label = 'Boxplots for Environment')\n",
    "plt.xlabel(xlabel = 'CO2 emissions (metric tons per capita)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "### Boxplot for Improved sanitation facilities\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = (15, 5))\n",
    "\n",
    "plt.subplot(3, 1, 2) # 3 rows, 1 column, space 2\n",
    "\n",
    "# developing a boxplot for Improved sanitation facilities\n",
    "sns.boxplot(x      = 'Improved sanitation facilities (% of population with access)',  # x-variable\n",
    "            y      = None,     # optional y-variable\n",
    "            hue    = None,     # optional categorical feature\n",
    "            orient = 'h',      # horizontal or vertical\n",
    "            data   = environment_filter[rem_togo]) # DataFrame where features exist\n",
    "\n",
    "# formatting and displaying the plot\n",
    "plt.xlabel(xlabel = 'Improved sanitation facilities (% of population with access)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "### Boxplot for Improved water source\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = (15, 5))\n",
    "\n",
    "plt.subplot(3, 1, 3) # 3 rows, 1 column, space 3\n",
    "\n",
    "# developing a boxplot for Improved water source\n",
    "sns.boxplot(x      = 'Improved water source (% of population with access)',  # x-variable\n",
    "            y      = None,     # optional y-variable\n",
    "            hue    = None,     # optional categorical feature\n",
    "            orient = 'h',      # horizontal or vertical\n",
    "            data   = environment_filter[rem_togo]) # DataFrame where features exist\n",
    "\n",
    "# formatting and displaying the plot\n",
    "plt.xlabel(xlabel = 'Improved water source (% of population with access)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. **Technology**: We plotted boxplots for both columns within this category, and selected internet users since mobile phone users contain an outlier.  Thanks to the boxplot, we can see that the countries in our region are rather  homogenous when it comes to internet usage. \n",
    "\n",
    "\n",
    "5. **Politics**: After the steps outlined above, we are left with only one relevant column in this category, which is the proportion of seats held by women in parliament.\n",
    "\n",
    "**After these steps, we were left with one column in each category for a total of five.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-f8ef9a7bf337>:17: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data   = technology_filter[rem_togo]) # DataFrame where features exist\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-f8ef9a7bf337>:37: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data   = technology_filter[rem_togo]) # DataFrame where features exist\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "####################################\n",
    "###    Boxplot for Technology   ###\n",
    "####################################\n",
    "\n",
    "### Boxplot for Internet users\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = (15, 5))\n",
    "\n",
    "plt.subplot(3, 1, 1) # 3 rows, 1 column, space 1\n",
    "\n",
    "# developing a boxplot for Internet users\n",
    "sns.boxplot(x      = 'Internet users (per 100 people)',  # x-variable\n",
    "            y      = None,     # optional y-variable\n",
    "            hue    = None,     # optional categorical feature\n",
    "            orient = 'h',      # horizontal or vertical\n",
    "            data   = technology_filter[rem_togo]) # DataFrame where features exist\n",
    "\n",
    "# formatting and displaying the plot\n",
    "plt.title(label = 'Boxplots for Technology')\n",
    "plt.xlabel(xlabel = 'Internet users (per 100 people)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "### Boxplot for Mobile cellular subscriptions\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = (15, 5))\n",
    "\n",
    "plt.subplot(3, 1, 2) # 3 rows, 1 column, space 2\n",
    "\n",
    "# developing a boxplot for Mobile cellular subscriptions\n",
    "sns.boxplot(x      = 'Mobile cellular subscriptions (per 100 people)',  # x-variable\n",
    "            y      = None,     # optional y-variable\n",
    "            hue    = None,     # optional categorical feature\n",
    "            orient = 'h',      # horizontal or vertical\n",
    "            data   = technology_filter[rem_togo]) # DataFrame where features exist\n",
    "\n",
    "# formatting and displaying the plot\n",
    "plt.xlabel(xlabel = 'Mobile cellular subscriptions (per 100 people)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Zambia*\n",
    "\n",
    "To select the country that best represents East/South Africa, we plotted bar charts to compare each country to the region mean and median, and found that **Zambia** is the most representative country on average. \n",
    "\n",
    "\n",
    "Zambia is in Southern-Central Africa. It is experiencing a generalised HIV/AIDS epidemic, with a national HIV prevalence rate of 12% among adults. About 61% of Zambians live below the recognised national poverty line. Unemployment  in urban areas are serious problems and self-employment  in Zambia was reported at 78 % in 2020.\n",
    "\n",
    "\n",
    "According to our data, in Zambia, 15% of seats in the national parliament are held by women. 2017 data shows that this number is up to 18% (\"Zambia | ZM”, 2017). This growth in the number of seats held by women is consistent with what we know about our region from the “comparison with the rest of the world” markdown section above (Bathily, 2020). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-f352068adfca>:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  country   = full[jazz][rem_togo].iloc[ : , 1]\n",
      "<ipython-input-9-f352068adfca>:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  water     = full[jazz][rem_togo].iloc[ : , 23]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-f352068adfca>:48: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  country  = full[jazz][rem_togo].iloc[ : , 1]\n",
      "<ipython-input-9-f352068adfca>:49: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  internet = full[jazz][rem_togo].iloc[ : , 26]\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2016x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-f352068adfca>:87: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  country  = full[jazz][rem_togo].iloc[ : , 1]\n",
      "<ipython-input-9-f352068adfca>:88: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  hiv      = full[jazz][rem_togo].iloc[ : , 36]\n"
     ]
    },
    {
     "data": {
      "image/png": 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9jH9Hkplec3sbzdeGD87k9CW3JPlaTXrrH5nk25u8/qNJfr+q9kvyntba5VV1UZIjqmqPTE6J+IlMNugPySSUTpKn1OSXq6sy+YLzoEwOwJPkPcP9Rbnt15Z3SXJqTXpftUx2BGO2UPtd2Fq7Mkmq6pJMdrLXJflya+3fhte/PZNQZ8O8npQkrbV/rsmvz++yYd2cx32HeR+Q5F2ttUuH/+vBSc4Zfti6MpMd7wZvH5bxoZr02Lxra+3abWiDWenpifPO1tr3klxeVV/OJAB6cCZf3Ke19rmq+vds3+/tpbDQafd6t6O3JHn3MPyITMKPjw/r4K5JrhqeW2ibsL3rPX3h/2TyA45ksv171Nwnq+oumXxJdP4w6tRMPiBusNl7eJP575fkHTU5c8Udkvxbdjw3ZfIh+BcyCcqShde7+dr8jUl+J8l7k/x8kucN4w+uSc+3u2by4X8qp0gbm+HXyz+S5LS6rbPjHYf7Dyd5c1W9M7ftv7dme3pWm/RQ+G5VXZXJD6QenOT01tr1w/Lfk8kxxMVL+octkWEfuyaT3r2bnv5+vuOXh2byZfeG18/d3m1pe7gi8x8nzLeMLR2jPTyT44KvD8v+xjDtQtuHM9vk9ODLYpH2nPeYqCY9V96SyRdd3xq2mX9ekx4nt2TjdfDjrbWvJklVfSnJPw3jP5XkYXOm29LxwlwPzXCs2lo7q6q++f+3d+axdlVVHP4WZba1sQgGE6CAGCIkEkEGQWhFCIgBGiaRYFEEMRgGBQIGzQPCJIgMMSASqUgZTDQIIshMH6VltO2jFYLYVhCoQhDKUC10+cfah3vefeecO/S179zX35c0ve/cc885d92911577b3WqvpuLfpSLemyfbdzTj8xL1gI3AnsYxGVNtHdn0v3HDW4+1tmdgPxnfP9qarv3eXuy81sgLDb707HB4j5QzMraNhnN9LQyXkmm9kZhPNyArGB6Y6uvlQ92CA3n3qK2OAM0b5OsaiFugD4WJLxbjTmoyeZ2ZT0ejNi3vQ65bbB7iT9Q+iai9NrAy6wiIhdQUTRfwJ4ddi+5WrE3d82sx2JcXcy0aYuJBYiszJoWTr+y9PfWcrsAWCsuy8FlprZsmSPvkNnMlqHcv094hTJyKIu91+ollO73JbGngVWEDVedn93n1ZxzarxqtO+UBeGzLHMbG1gGXCdmd1J4/n7CRlsSbTn44CHicV26N7fVPlbjQKq5j6F393CwJlOBKM8lQ6vTBsbTfMvrzg+zt2zVOY3EZtBoFoflvkWF7r78+n4jbSeM9SGkdaviSNz170l/f10wXl5X1eeXYEZ2bwtN9+qvfwLOA+Y7+63pL/LbB6I32cAwMzmE2sqnuzYiblr/iHNLd+zyOa4M7GpIqPWNkAHNMuumQdz9tKbNOzxAWKDc0aRn28c5W3pXo+SH5kfZQ/g51Sv0+xK+BxmpjnquoQvQdQYLbCLYcfdZyXH2sbErql8KYL1m05/J/c6f27zef/Nvf6ARtstM4p6miYZtpUu0N1vskiHcwDwZzP7dnJ4LiIWJR4lFskmA1sDfzWzLYkdmp939zfMbBqDZZ/JPS/z84jBZ0pyuD3U9RcdPuZTXiOmSn5F7arq/KL3WrXBrAb7psBDFikSFxKD+24ln2m+Zs+1czPbipDpv4io1TKKvmvZb9BKn6ypvNP6FACWeaPuugG/dvez8ie0oRPWFJa7e9Y28/qvXVr14auAy9z9djObRKOW2GhiBXA4cJ+Z/dDdL6Ck3SUKZe7uMy1Sk+0FjHH3LM3jNCJqcK5F6ZlJq+6r1Iq1gP8UbRRx9xPMbBfCDpiTJsKdpBzudEysK7cDlxJtYqPc8Sr7Zcg4W6EPq2RSeI8iGy1dp2h8r9IP7er74aRMnoU2kUXZh1uAc3P99VRgCRH9uxbhcM/It7sVub9XMFj3tmMbFR0rsx1K+1LN6aZ9tzrnCcKx83diUfTjxILHUwXXGC1cTji0rs8dq+p7WXrUFWaWH6+a22kZg9qmma1PRIPt5O4vWpQt6HV767005xlPLNKcCFzp7v+0KKOzHxGRM4GwD95296VJ1l8GdnP3dy3SaGeyqLLHivr7UcT8ece0IWIRPS7XZLs/RMwjB4CpLT6S16HN+nVtOpdRlf6uBSUymlPxkSq/UzN5GRaO/yX3n9biPkV2xyS66wu1xN3ft0ihuzeRyeJ7RMabfiJd9CeJDB+nE2PajPTRbv1NLX+rHmca5XOfsu/eB7zk7tfDsLSxqmfoNV4nMtfkmUD1xvd27dl2fNZ19KsOYST1q5ltROiM7c3MiQ2OnjYnNpP3dQ26DMW/QU/IPyP13UOAz+UOV43nwzW/qr0N0IoS2TWzMvLqxMfgud+qcJ0m/X+vux9Z8byiZqgGuxh2LOoXjiEMlsVETc/10mR774qPLiIi26B8sTTPY8Aki4iZdYDDun/qetEkwxlErakxFvX79iRSiCwldkpln9mKiKi6knC+ZbusZhAO4hk0JjNzkvH8UcJZ+2baMbh/G483nkjhCZHypA48AKxnqWYMfFg3aS/K5VfGs8BW1ojWOSL33gzCiMkG6ddSFMzOFpEwpaTorDOBs4hUaRtbZCrAop7wdrnTszrxewBvenWEfO1Icr6GSNnUanPAYWa2lkWd1a0I2eTl/Glg83R8EbBDOn8zYndlxvKkB0SDQTqiifuBQ81sEwCLWrpb0J1OGA1UyWoIqU++YY36YUcTkRcZrfpwXo+2cpT2LO7+LrHj/ygzO5bydteKG4jdwvmFkHHAK6nfHzW8T15f3P0tYKGZHQYRmWJmn02vt3b3x9z9x8BrRFRKmT5tt83PAA42sw3N7CPAFMKWqDO/IhZ3B5qOl9kveRltT8N+KtOHVXZC4T1KbLT7iQj5jdI5EwquUQf9UCbPQpuISKU5rylCYDzwSopSOZqwcTulyF4oe579aThNlwCbpPnCeqQopKq+VHM6bd8tz3H3/xFpIQ8HZhN9/DTq39e7JkUw/ZbIspIxnH1vLRrz2a8TKebzZA7Q1yyyKbQz9+0Jks1zEnBazjafBZxCYz6ab1/jgTfSYs+2ROROK2aSarEy2AYYT6RCX25mk4msdz2LRe3pbXKHdiB02kQz+1Q61myDtqJTGQ2H/l5llMhoMTFWl8lpEQ2/0yG5z3Y0H2hx/6r7lI1X3fSF2pJ023h3/xPR/3dIbz1GZJBZ4e7LiMW67zBYJxSNZx3/PqOMjuY+ZvZVIgr9pNzhlW1jo2b+5e5vE99lb/jQDt+PGK+Xmlkmm6/lPtapPnwW2DLZrhDR1/lr1c2vOoiR1q+EbXSDu2/h7hPdfTNiA8QeHVxjFrCXxcbpsvnWMR0+12rFYpPi9URpnKW5t4bD5jnIzNZP89FJNDKJ5O9RWxugFRWy65YiP19VW9on+bw2AA4m7FeoXqeZDeye9a/kh+nVzAFrDFpgF8PFBmY2xyIVzq3AVHf/wN1fJJwX84jURFUpRS8FvmtmjxKRE5WkBcs+YsC8j+I0Mb1EoQyJ2n7ziNooDxD1ll9Nx943s7lmdiqh6J9Jn9+WWJCAUNabArPcfQmx46wfwN3nEr/JfMJhlyn7Kn4CXGhmM6nJ4JoGoSnE4PWCRQqcPuBlyuVXdq33iJrzd5vZI4QTI1sc6wN2skgfexEN59vmDE4xWcZtRBrIXQhj8WIzm0tMKr+QO++N1A+uITn+zGwnM7uujXuMFFn7nU/0x3uAc3Lvly20P0cY43cRdW6WERE9Yyx2yN4KHOORtngmYVAPEPoi3+evBeaZ2fRh/E514kP9kP5d1MZnrgXuskj1NAh3XwCcDdyT2vO9wKZd6oReokyOpbKqYCpwSZLfDkQd9owhfbiJPiItcT+xEDpqSYsY+xHtbRsK2l0bl5lOOB9vzh37EeGgu5eY5I9WNjSzl3L/vk84tI5N48d8opYaRHscMLNniMnaXMr16YPEBsg5ZnbEkLsm3P1pIlrlcULe17l7LdPDZ7j7S+5+RcFbZfbL1cDY1CbPIG3CK9OHLeyEsnsMsdHcfT5wPvBw+i0vS+f2USP9UCHPPoptotOAfXN69kCiHU41s9lEasFuIvGL7IU85wB7mtnTwL7AP9LzLyf082NEVG1eX5T1pdrSRftu95x+YEnaGNVPpEsftQvsiZ8yeM7Zx/D1vXeA7SzKdX2JwTYCHqWffknYtLcx1KHZ06RxYi6NhYl+oj7l3wj7fQKN9nU3sHbSJecRjsVWnAycaGZPEI7NjOmEXnqS6N+9bh+MJdKOLkjy+QyxYfubRFsdIKKrrungmp3KaDj096qkSEZ9aYwok9M5wBWpr+cjHu8ApqSx64u0R+H9W9yncLyiu75QF4rmWOOAP6bv8zARCUmyQ1+k8f3607nZxrGysaot23UU0+nc5wdEloDHk8zOZeXb2Gibf30DODvZ5w8A57j7C8T8/Vozm0VEQGd2fkf6MOmh44E705xhce7t2vlVCxhp/Xok4c/N8zti42JbeJSUPR74fbL3s/I9vSD/jBOATYCr83qWKO2ysjbP40R5qNnAee7+ctP7dbcBWlEmu27HkCI/X1VbeoQoZTSHqM3+ZDpetU7zb2Kh/ubU72YztDSaqBnWOrhQCCHWLMxsrEe9ISPqozzv7j+rOP8S4DfuvtJ1qi3SdJ2WG3h7HjM7BDjQ3esQiSfEKmU09uGRxswOBQ5y96NH+lmEgM7tBLH6sUi9t5OnGvdCCCGEEEK0IrPz0+sziUCEk0f4sYQQQtSU2tftEUKIEeA4M5sKrEtEr/2i6mR3P321PFUPkiLXzge+NdLPIoToPczsKiI191dG+lmEyNGRnSCEEEIIIYToCQ4ws7OINZPF1DyFuBBCiJFFEexCCCGEEEIIIYQQQgghhBBCCCFEG6gGuxBCCCGEEEIIIYQQQgghhBBCCNEGWmAXQgghhBBCCCGEEEIIIYQQQggh2kAL7EIIIYQQQgghhBBCCCGEEEIIIUQbaIFdCCGEEEIIIYQQQgghhBBCCCGEaAMtsAshhBBCCCGEEEIIIYQQQgghhBBt8H+v0sBk+EvEswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2016x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-f352068adfca>:126: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  country = full[jazz][rem_togo].iloc[ : , 1]\n",
      "<ipython-input-9-f352068adfca>:127: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  seats    = full[jazz][rem_togo].iloc[ : , 39]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-f352068adfca>:165: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  country   = full[jazz][rem_togo].iloc[ : , 1]\n",
      "<ipython-input-9-f352068adfca>:166: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  self_empl = full[jazz][rem_togo].iloc[ : , 42]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "######################################\n",
    "###   Bar chart for water column   ###\n",
    "######################################\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = [28, 10])\n",
    "\n",
    "# setting variable to filter country and water columns\n",
    "country   = full[jazz][rem_togo].iloc[ : , 1]\n",
    "water     = full[jazz][rem_togo].iloc[ : , 23]\n",
    "\n",
    "# setting a color for all countries\n",
    "colors    = ['#1f77b4'] * len(country)\n",
    "\n",
    "# highlighting Zambia as a different color\n",
    "colors[19] = 'g'\n",
    "\n",
    "# creating a bar chart with labels\n",
    "plt.bar(country, water, align = 'center', color = colors)\n",
    "plt.ylabel('Improved water source (% of population with access)')\n",
    "\n",
    "# setting a custom y axis range to increment at 10\n",
    "plt.yticks(np.arange(0, max(water) + 10, 10))\n",
    "\n",
    "# adds a horizontal line to the chart to show the mean\n",
    "plt.axhline(y = water.mean(),\n",
    "            color = 'orange')\n",
    "\n",
    "# adds a horizontal line to the chart to show the median\n",
    "plt.axhline(y = water.median(),\n",
    "            color = 'red')\n",
    "\n",
    "# adds a legend for mean and median\n",
    "plt.legend(labels =  ['mean', 'median'])\n",
    "\n",
    "# displays the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#########################################\n",
    "###   Bar chart for internet column   ###\n",
    "#########################################\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = [28, 10])\n",
    "\n",
    "# setting variable to filter country and internet columns\n",
    "country  = full[jazz][rem_togo].iloc[ : , 1]\n",
    "internet = full[jazz][rem_togo].iloc[ : , 26]\n",
    "\n",
    "# setting a color for all countries\n",
    "colors     = ['#1f77b4'] * len(country)\n",
    "\n",
    "# highlighting Zambia as a different color\n",
    "colors[19] = 'g'\n",
    "\n",
    "# creating a bar chart with labels\n",
    "plt.bar(country, internet, align = 'center', color = colors)\n",
    "plt.ylabel('Internet users (per 100 people)')\n",
    "\n",
    "# setting a custom y axis range to increment at 1\n",
    "plt.yticks(np.arange(0, max(internet) + 1, 1))\n",
    "\n",
    "# adds a horizontal line to the chart to show the mean\n",
    "plt.axhline(y = internet.mean(),\n",
    "            color = 'orange')\n",
    "\n",
    "# adds a horizontal line to the chart to show the median\n",
    "plt.axhline(y = internet.median(),\n",
    "            color = 'red')\n",
    "\n",
    "# adds a legend for mean and median\n",
    "plt.legend(labels =  ['mean', 'median'])\n",
    "\n",
    "# displays the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "####################################\n",
    "###   Bar chart for HIV column   ###\n",
    "####################################\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = [28, 10])\n",
    "\n",
    "# setting variable to filter country and hiv columns\n",
    "country  = full[jazz][rem_togo].iloc[ : , 1]\n",
    "hiv      = full[jazz][rem_togo].iloc[ : , 36]\n",
    "\n",
    "# setting a color for all countries\n",
    "colors     = ['#1f77b4'] * len(country)\n",
    "\n",
    "# highlighting Zambia as a different color\n",
    "colors[19] = 'g'\n",
    "\n",
    "# creating a bar chart with labels\n",
    "plt.bar(country, hiv, align = 'center', color = colors)\n",
    "plt.ylabel('Prevalence of HIV, total (% of population ages 15-49)')\n",
    "\n",
    "# setting a custom y axis range to increment at 5\n",
    "plt.yticks(np.arange(0, max(hiv) + 5, 5))\n",
    "\n",
    "# adds a horizontal line to the chart to show the mean\n",
    "plt.axhline(y = hiv.mean(),\n",
    "            color = 'orange')\n",
    "\n",
    "# adds a horizontal line to the chart to show the median\n",
    "plt.axhline(y = hiv.median(),\n",
    "            color = 'red')\n",
    "\n",
    "# adds a legend for mean and median\n",
    "plt.legend(labels =  ['mean', 'median'])\n",
    "\n",
    "# displays the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "######################################\n",
    "###   Bar chart for seats column   ###\n",
    "######################################\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = [28, 10])\n",
    "\n",
    "# setting variable to filter country and seats columns\n",
    "country = full[jazz][rem_togo].iloc[ : , 1]\n",
    "seats    = full[jazz][rem_togo].iloc[ : , 39]\n",
    "\n",
    "# setting a color for all countries\n",
    "colors     = ['#1f77b4'] * len(country)\n",
    "\n",
    "# highlighting Zambia as a different color\n",
    "colors[19] = 'g'\n",
    "\n",
    "# creating a bar chart with labels\n",
    "plt.bar(country, seats, align = 'center', color = colors)\n",
    "plt.ylabel('Proportion of seats held by women in national parliaments (%)')\n",
    "\n",
    "# setting a custom y axis range to increment at 5\n",
    "plt.yticks(np.arange(0, max(seats) + 5, 5))\n",
    "\n",
    "# adds a horizontal line to the chart to show the mean\n",
    "plt.axhline(y = seats.mean(),\n",
    "            color = 'orange')\n",
    "\n",
    "# adds a horizontal line to the chart to show the median\n",
    "plt.axhline(y = seats.median(),\n",
    "            color = 'red')\n",
    "\n",
    "# adds a legend for mean and median\n",
    "plt.legend(labels =  ['mean', 'median'])\n",
    "\n",
    "# displays the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "##############################################\n",
    "###   Bar chart for self employed column   ###\n",
    "##############################################\n",
    "\n",
    "# setting figure size\n",
    "fig, ax = plt.subplots(figsize = [28, 10])\n",
    "\n",
    "# setting variable to filter country and self employed columns\n",
    "country   = full[jazz][rem_togo].iloc[ : , 1]\n",
    "self_empl = full[jazz][rem_togo].iloc[ : , 42]\n",
    "\n",
    "# setting a color for all countries\n",
    "colors     = ['#1f77b4'] * len(country)\n",
    "\n",
    "# highlighting Zambia as a different color\n",
    "colors[19] = 'g'\n",
    "\n",
    "# creating a bar chart with labels\n",
    "plt.bar(country, self_empl, align = 'center', color = colors)\n",
    "plt.ylabel('Self-employed, total (% of total employment)')\n",
    "\n",
    "# setting a custom y axis range to increment at 10\n",
    "plt.yticks(np.arange(0, max(self_empl) + 10, 10))\n",
    "\n",
    "# adds a horizontal line to the chart to show the mean\n",
    "plt.axhline(y = self_empl.mean(),\n",
    "            color = 'orange')\n",
    "\n",
    "# adds a horizontal line to the chart to show the median\n",
    "plt.axhline(y = self_empl.median(),\n",
    "            color = 'red')\n",
    "\n",
    "# adds a legend for mean and median\n",
    "plt.legend(labels =  ['mean', 'median'])\n",
    "\n",
    "# displays the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Correlation Between Top 5 Features*\n",
    "\n",
    "We checked for the correlation between the five selected variables.  The strongest correlations were between HIV prevalence and water source to the total number of self-employed (-0.62 and -0.65 respectively). However, these factors are not particularly related and we know that correlation does not equal causality. The next highest correlation is between internet users and the number of self-employed people. The majority of self-employed people in the region are engaged in informal, and often unskilled labour, which does not require internet access (van Klaveren, et al. 2020). This could explain why there is a relatively high correlation of -0.56 between both factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#######################################\n",
    "###     Heatmap for Top 5 Columns   ###\n",
    "#######################################\n",
    "\n",
    "# converting correlation matrix into a DataFrame\n",
    "top5_corr = top5.corr(method = 'pearson').round(decimals = 2)\n",
    "\n",
    "# instantiating a heatmap\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "\n",
    "# developing a spicy heatmap\n",
    "sns.heatmap(data       = top5_corr,     # the correlation matrix\n",
    "            cmap       = 'inferno',     # changing to SPICY colors\n",
    "            square     = True,          # tightening the layout\n",
    "            annot      = True,          # should there be numbers in the heatmap\n",
    "            linecolor  = 'black',       # lines between boxes\n",
    "            linewidths = 0.5)           # how thick should the lines be?\n",
    "\n",
    "\n",
    "# title and displaying the plot\n",
    "plt.title(\"\"\"\n",
    "Linear Correlation Heatmap for Top 5 Features\n",
    "\"\"\")\n",
    "\n",
    "# displaying the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "############################################\n",
    "###     Scatter plot for Top 5 Columns   ###\n",
    "############################################\n",
    "\n",
    "# instantiating a scatter plot for internet users and self-eployment\n",
    "sns.lmplot(x          = 'Internet users (per 100 people)',  # x-axis feature\n",
    "           y          = 'Self-employed, total (% of total employment)',  # y-axis feature\n",
    "           hue        = None,     # categorical data for subsets\n",
    "           legend_out = False,    # formats legend if hue != None\n",
    "           scatter    = True,     # renders a scatter plot\n",
    "           fit_reg    = True,     # renders a regression line\n",
    "           aspect     = 2,        # aspect ratio for plot\n",
    "           data       = top5)     # DataFrame where features exist\n",
    "\n",
    "\n",
    "# formatting and displaying the plot\n",
    "plt.xlabel(xlabel  = 'Internet users (per 100 people)')\n",
    "plt.ylabel(ylabel  = 'Self-employed, total (% of total employment)')\n",
    "plt.xlim(0, 10)  # changing the x-range for better viewability\n",
    "plt.ylim(0, 100) # changing the y-range for better viewability\n",
    "\n",
    "# displaying the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Comparison*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting variable to water column for the rest of the world\n",
    "rest_water          = full[rest].iloc[ : , 23]\n",
    "\n",
    "# setting variable to internet column for the rest of the world\n",
    "rest_internet       = full[rest].iloc[ : , 26]\n",
    "\n",
    "# setting variable to hiv column for the rest of the world\n",
    "rest_hiv            = full[rest].iloc[ : , 36]\n",
    "\n",
    "# setting variable to seats column for the rest of the world\n",
    "rest_seats          = full[rest].iloc[ : , 39]\n",
    "\n",
    "# setting variable to self employed column for the rest of the world\n",
    "rest_self_empl      = full[rest].iloc[ : , 42]\n",
    "\n",
    "# setting variable to water column for Sideswipe\n",
    "sideswipe_water     = full[sideswipe].iloc[ : , 23]\n",
    "\n",
    "# setting variable to internet column for Sideswipe\n",
    "sideswipe_internet  = full[sideswipe].iloc[ : , 26]\n",
    "\n",
    "# setting variable to hiv column for Sideswipe\n",
    "sideswipe_hiv       = full[sideswipe].iloc[ : , 36]\n",
    "\n",
    "# setting variable to seats column for Sideswipe\n",
    "sideswipe_seats     = full[sideswipe].iloc[ : , 39]\n",
    "\n",
    "# setting variable to self employed column for Sideswipe\n",
    "sideswipe_self_empl = full[sideswipe].iloc[ : , 42]\n",
    "\n",
    "# setting variable to water column for Bumblebee\n",
    "bumblebee_water     = full[bumblebee].iloc[ : , 23]\n",
    "\n",
    "# setting variable to internet column for Bumblebee\n",
    "bumblebee_internet  = full[bumblebee].iloc[ : , 26]\n",
    "\n",
    "# setting variable to hiv column for Bumblebee\n",
    "bumblebee_hiv       = full[bumblebee].iloc[ : , 36]\n",
    "\n",
    "# setting variable to seats column for Bumblebee\n",
    "bumblebee_seats     = full[bumblebee].iloc[ : , 39]\n",
    "\n",
    "# setting variable to self employed column for Bumblebee\n",
    "bumblebee_self_empl = full[bumblebee].iloc[ : , 42]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Comparison With The Rest of The World*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Water**: On average, about 70% of people in our region have access to improved water sources, compared to over 90% for the rest of the world. This finding is consistent with the status of our region as underdeveloped/developing.\n",
    "\n",
    "\n",
    "2. **Internet**: Internet usage in our region is significantly lower than in the rest of the world, which confirms that this region is not as technologically advanced as some others.\n",
    "\n",
    "\n",
    "3. **HIV**: When comparing the mean from East and Southern Africa with the rest of the world, we can see results that are consistent with our research. For example, since we know that HIV/AIDS is one of the top causes of mortality in our region, it follows that HIV prevalence is higher in both our regions than in the rest of the world. \n",
    "\n",
    "\n",
    "4. **Seats**: On average, East/Southern Africa is doing better than the rest of the world in terms of number of seats held by women in parliament.  Recent data shows that Africa (21%) is only second to Europe (24%) in the number of female parliamentary speakers across all continents (Bathily, 2020), so this information is consistent.\n",
    "\n",
    "\n",
    "5. **Self-Employment**: The number of self-employed people is higher in our region than the rest of the world for reasons previously explained in the “socio-economic issues” markdown section above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting a variable that has the values of mean of top 5 columns for Jazz\n",
    "jazz_col = [water.mean(), internet.mean(), hiv.mean(), seats.mean(), self_empl.mean()]\n",
    "\n",
    "# setting a variable that has the values of mean of top 5 columns for the rest of the world\n",
    "rest_col = [rest_water.mean(), rest_internet.mean(), rest_hiv.mean(), rest_seats.mean(), rest_self_empl.mean()]\n",
    "\n",
    "# naming the labels for the corresponding index\n",
    "labels = ['Water', 'Internet', 'HIV',\n",
    "         'Seats', 'Self-employment']\n",
    "\n",
    "# a DataFrame to compare Jazz and the rest of the world\n",
    "comparison = pd.DataFrame({'East-South Africa': jazz_col,\n",
    "                 'Rest of the world': rest_col}, index = labels)\n",
    "\n",
    "# make the DataFrame as type float\n",
    "comparison = comparison.astype(float)\n",
    "\n",
    "# show the bar and labeling y axis as mean\n",
    "show = comparison.plot.bar(figsize=(8, 5), rot = 0).set_ylabel('mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Comparison Within Africa*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to comparing the averages from our region with the rest of the world, we thought it would be interesting to compare with the other countries in sub-Saharan Africa as well as the countries north of the Sahara desert to see if there would be any interesting findings.\n",
    "\n",
    "\n",
    "Across all categories, the numbers for East/Central Africa are relatively similar to those for the rest of sub-Saharan Africa. This tells us that in terms of development, East/Central Africa are faring about the same as other countries south of the Sahara. \n",
    "\n",
    "\n",
    "When we compare our region with North Africa however, we see clear differences. In North Africa, internet usage is significantly higher, HIV is not as prevalent and there are fewer people engaged in (informal) self employment. This data and accompanying chart suggest that North Africa is more developed than sub-Saharan Africa with respect to these categories.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting a variable that has the values of mean of top 5 columns for Jazz\n",
    "jazz_col = [water.mean(), internet.mean(), hiv.mean(), seats.mean(), self_empl.mean()]\n",
    "\n",
    "# setting a variable that has the values of mean of top 5 columns for Sideswipe\n",
    "sideswipe_col = [sideswipe_water.mean(), sideswipe_internet.mean(), sideswipe_hiv.mean(),\\\n",
    "                 sideswipe_seats.mean(), sideswipe_self_empl.mean()]\n",
    "\n",
    "# setting a variable that has the values of mean of top 5 columns for Bumblebee\n",
    "bumblebee_col = [bumblebee_water.mean(), bumblebee_internet.mean(), bumblebee_hiv.mean(),\\\n",
    "                 bumblebee_seats.mean(), bumblebee_self_empl.mean()]\n",
    "\n",
    "# naming the labels for the corresponding index\n",
    "labels = ['Water', 'Internet', 'HIV',\n",
    "         'Seats', 'Self-employment']\n",
    "\n",
    "# a DataFrame to compare Jazz and Africa\n",
    "comparison = pd.DataFrame({'East-South Africa': jazz_col,\n",
    "                 'Sub-Saharan Africa': sideswipe_col,\n",
    "                 'North Sub-Saharan Africa': bumblebee_col}, index = labels)\n",
    "\n",
    "# make the DataFrame as type float\n",
    "comparison = comparison.astype(float)\n",
    "\n",
    "# show the bar and labeling y axis as mean\n",
    "show = comparison.plot.bar(figsize=(8, 5), rot = 0).set_ylabel('mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Conclusion*\n",
    "\n",
    "We looked at a multi-category approach to analyzing the East/South Africa region.\n",
    " \n",
    " \n",
    "The choice was made from an economic, health, technological, environmental and political perspective. Through these five themes, we analyzed issues that affect our region such as HIV, water and internet access, but we also looked at the role of women in politics and self-employment to cover all the themes we decided to analyze.\n",
    " \n",
    "\n",
    "It is interesting to see that the different topics we selected make our region quite unique. Indeed, it is known that Africa is quite strongly affected by the problem of HIV, particularly the East/South Africa region. It is also obvious that access to water and internet is still quite low compared to the rest of the world but also compared to the rest of Africa.\n",
    " \n",
    "\n",
    "However, it is interesting to see that the East/South Africa region is ahead of its time in terms of gender parity in politics which implies an important role for women in this region or at least some recognition.\n",
    " \n",
    " \n",
    "As far as self-employment is concerned, it is a rather peculiar case because it is quite high in comparison with the rest of the world because of the unskilled level of work, but it is not the highest in Africa.\n",
    " \n",
    " \n",
    "This analysis therefore highlights some of the problems that our region faces in terms of health and ecology, but also highlights a hidden side of our region which is the growing empowerment of women."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Sources*\n",
    "\n",
    "1. Bathily, N. (2020). Africa takes historic lead in female parliamentary speakers. Retrieved 8 November 2020, from https://blogs.worldbank.org/nasikiliza/africa-takes-historic-lead-female-parliamentary-speakers\n",
    "\n",
    " \n",
    "2. Bendor-Samuel, J. (2017). Bantu languages | Definition, Characteristics, & Facts. Retrieved 5 November 2020, from https://www.britannica.com/art/Bantu-languages\n",
    " \n",
    " \n",
    "3. East Africa Living Encyclopedia. (2020). Retrieved 5 November 2020, from https://www.africa.upenn.edu/NEH/overview.html\n",
    " \n",
    " \n",
    "4. Languages | East African Language Solutions. (2020). Retrieved 5 November 2020, from http://www.ealanguagesolutions.com/languages.html#:~:text=Millions%20of%20people%20speak%20Swahili,native%20language%20or%20second%20language\n",
    " \n",
    " \n",
    "5. Marks, S. (2020). Southern Africa | History, Countries, Map, Population, & Facts. Retrieved 5 November 2020, from https://www.britannica.com/place/Southern-Africa\n",
    " \n",
    " \n",
    "6. Life in East Africa. (2020). Retrieved 5 November 2020, from https://www.lonelyplanet.com/east-africa/background/other-features/e8c9baa1-cfc0-438e-84ee-450fec4d5346/a/nar/e8c9baa1-cfc0-438e-84ee-450fec4d5346/1338594\n",
    " \n",
    "\n",
    "7. The World Bank. (2020). Zambia - Self-employed; Total (% Of Total Employed).\n",
    "https://tradingeconomics.com/zambia/self-employed-total-percent-of-total-employed-wb-data.html\n",
    "\n",
    "\n",
    "8. van Klaveren, M., Tijdens, K., Hughie-Williams, M., & Martin, N. (2020). An Overview of Women’s Work and Employment in South Africa: Decisions for Life MDG3 Project Country report No. 3. International Trade Union Confederation. Retrieved from https://www.ituc-csi.org/IMG/pdf/Country_Report_No3-South_Africa_EN.pdf\n",
    "\n",
    "\n",
    "9. Zambia. (2020). Retrieved 8 November 2020, from https://en.wikipedia.org/wiki/Zambia\n",
    " \n",
    " \n",
    "10. Zambia | ZM: Proportion of Seats Held by Women in National Parliaments | Economic Indicators. (2017). Retrieved 8 November 2020, from https://www.ceicdata.com/en/zambia/policy-and-institutions/zm-proportion-of-seats-held-by-women-in-national-parliaments#:~:text=Zambia's%20ZM%3A%20Proportion%20of%20Seats,number%20of%2018.000%20%25%20for%202016.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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