EDA_23rd August 2021_Monday .ipynb

{
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    "## Scenario 1\n",
    "You have given the datasets by the WHO to help the countries know better about their health status, as well as,help individuals to get prepared with the \n",
    "upcoming problems. There are 2 datasets available which are present in csv format\n",
    "and you have to explore it, align it if not in structured format."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec55b7b",
   "metadata": {},
   "source": [
    "**step1:**\n",
    "* Import the libraries numpy, pandas, matplotlib and seaborn that are going to use for data exploration.\n",
    "* Call the daasets using python and save them under the var name \"df1\" for \"dataset1\" and df2 for \"dataset2\".\n",
    "* View the top 5 rows of both the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0972e669",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d0e4169",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1= pd.read_csv(\"datasets\\countryData.csv\")\n",
    "df2= pd.read_csv(\"datasets\\immunitizationData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "487d25b3",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>8.16</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>8.18</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>8.13</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy   Adult Mortality  Alcohol  \\\n",
       "0  Afghanistan  2015  Developing              65.0            263.0     0.01   \n",
       "1  Afghanistan  2014  Developing              59.9            271.0     0.01   \n",
       "2  Afghanistan  2013  Developing              59.9            268.0     0.01   \n",
       "\n",
       "   percentage expenditure  Total expenditure         GDP  Population  \\\n",
       "0               71.279624               8.16  584.259210  33736494.0   \n",
       "1               73.523582               8.18  612.696514    327582.0   \n",
       "2               73.219243               8.13  631.744976  31731688.0   \n",
       "\n",
       "    thinness  1-19 years   thinness 5-9 years  \\\n",
       "0                   17.2                 17.3   \n",
       "1                   17.5                 17.5   \n",
       "2                   17.7                 17.7   \n",
       "\n",
       "   Income composition of resources  Schooling  \n",
       "0                            0.479       10.1  \n",
       "1                            0.476       10.0  \n",
       "2                            0.470        9.9  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2210f62b",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>62</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>19.1</td>\n",
       "      <td>83</td>\n",
       "      <td>6.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>64</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>18.6</td>\n",
       "      <td>86</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>66</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>18.1</td>\n",
       "      <td>89</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>69</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>17.6</td>\n",
       "      <td>93</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>71</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>17.2</td>\n",
       "      <td>97</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  infant deaths  Hepatitis B  Measles    BMI   \\\n",
       "0  Afghanistan  2015             62         65.0      1154   19.1   \n",
       "1  Afghanistan  2014             64         62.0       492   18.6   \n",
       "2  Afghanistan  2013             66         64.0       430   18.1   \n",
       "3  Afghanistan  2012             69         67.0      2787   17.6   \n",
       "4  Afghanistan  2011             71         68.0      3013   17.2   \n",
       "\n",
       "   under-five deaths   Polio  Diphtheria    HIV/AIDS  \n",
       "0                  83    6.0         65.0        0.1  \n",
       "1                  86   58.0         62.0        0.1  \n",
       "2                  89   62.0         64.0        0.1  \n",
       "3                  93   67.0         67.0        0.1  \n",
       "4                  97   68.0         68.0        0.1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eab2cb9",
   "metadata": {},
   "source": [
    "Make sure to merge the datsets 1:1 mapping not 1:N."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4f0aa43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    193\n",
       "2015    183\n",
       "2014    183\n",
       "2012    183\n",
       "2011    183\n",
       "2010    183\n",
       "2009    183\n",
       "2008    183\n",
       "2007    183\n",
       "2006    183\n",
       "2005    183\n",
       "2004    183\n",
       "2003    183\n",
       "2002    183\n",
       "2001    183\n",
       "2000    183\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.Year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1f7f524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan      1\n",
       "Lithuania        1\n",
       "Nigeria          1\n",
       "Niue             1\n",
       "Norway           1\n",
       "                ..\n",
       "Grenada          1\n",
       "Guatemala        1\n",
       "Guinea           1\n",
       "Guinea-Bissau    1\n",
       "Zimbabwe         1\n",
       "Name: Country, Length: 193, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[df1['Year']==2013,'Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09ca9f47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2938 entries, 0 to 2937\n",
      "Data columns (total 14 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Country                          2938 non-null   object \n",
      " 1   Year                             2938 non-null   int64  \n",
      " 2   Status                           2938 non-null   object \n",
      " 3   Life expectancy                  2928 non-null   float64\n",
      " 4   Adult Mortality                  2928 non-null   float64\n",
      " 5   Alcohol                          2744 non-null   float64\n",
      " 6   percentage expenditure           2938 non-null   float64\n",
      " 7   Total expenditure                2712 non-null   float64\n",
      " 8   GDP                              2490 non-null   float64\n",
      " 9   Population                       2286 non-null   float64\n",
      " 10   thinness  1-19 years            2904 non-null   float64\n",
      " 11   thinness 5-9 years              2904 non-null   float64\n",
      " 12  Income composition of resources  2771 non-null   float64\n",
      " 13  Schooling                        2775 non-null   float64\n",
      "dtypes: float64(11), int64(1), object(2)\n",
      "memory usage: 321.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09fb3730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan              True\n",
       "Peru                     True\n",
       "Nicaragua                True\n",
       "Niger                    True\n",
       "Nigeria                  True\n",
       "                         ... \n",
       "Niue                     True\n",
       "San Marino               True\n",
       "Nauru                    True\n",
       "Saint Kitts and Nevis    True\n",
       "Dominica                 True\n",
       "Name: Country, Length: 193, dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.Country.value_counts()== df1.Country.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43595b73",
   "metadata": {},
   "source": [
    "**Step:2**\n",
    "* When the dataset is loaded successfully, the first and foremost job of a Data Analyst is to get clarity on the structure of the dataset.\n",
    "**Once the dataset is called, understanding the feature names becomes important. Show the feature names of dataset.**\n",
    "* To get information we will do basic exploration, follow below steps.\n",
    "* What are the dimensions for the datasets?\n",
    "* Considering you have created the datasets. To apply any operation on a feature you need to understand its datatype.\n",
    "Show the datatypes of each columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "10a4a6ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',\n",
       "       'Alcohol', 'percentage expenditure', 'Total expenditure', 'GDP',\n",
       "       'Population', ' thinness  1-19 years', ' thinness 5-9 years',\n",
       "       'Income composition of resources', 'Schooling'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35863f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Life expectancy '>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxenplot(y=df1['Life expectancy '],x=df1['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1885a8bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'infant deaths', 'Hepatitis B', 'Measles ', ' BMI ',\n",
       "       'under-five deaths ', 'Polio', 'Diphtheria ', ' HIV/AIDS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "59e02265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2938, 14)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d284e78e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2938, 10)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36f7231f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                             object\n",
       "Year                                 int64\n",
       "Status                              object\n",
       "Life expectancy                    float64\n",
       "Adult Mortality                    float64\n",
       "Alcohol                            float64\n",
       "percentage expenditure             float64\n",
       "Total expenditure                  float64\n",
       "GDP                                float64\n",
       "Population                         float64\n",
       " thinness  1-19 years              float64\n",
       " thinness 5-9 years                float64\n",
       "Income composition of resources    float64\n",
       "Schooling                          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "184747c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                object\n",
       "Year                    int64\n",
       "infant deaths           int64\n",
       "Hepatitis B           float64\n",
       "Measles                 int64\n",
       " BMI                  float64\n",
       "under-five deaths       int64\n",
       "Polio                 float64\n",
       "Diphtheria            float64\n",
       " HIV/AIDS             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "16c3ea87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['infant deaths', 'Hepatitis B', 'Measles ', ' BMI ',\n",
       "       'under-five deaths ', 'Polio', 'Diphtheria ', ' HIV/AIDS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e679cc59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2938 entries, 0 to 2937\n",
      "Data columns (total 14 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Country                          2938 non-null   object \n",
      " 1   Year                             2938 non-null   int64  \n",
      " 2   Status                           2938 non-null   object \n",
      " 3   Life expectancy                  2928 non-null   float64\n",
      " 4   Adult Mortality                  2928 non-null   float64\n",
      " 5   Alcohol                          2744 non-null   float64\n",
      " 6   percentage expenditure           2938 non-null   float64\n",
      " 7   Total expenditure                2712 non-null   float64\n",
      " 8   GDP                              2490 non-null   float64\n",
      " 9   Population                       2286 non-null   float64\n",
      " 10   thinness  1-19 years            2904 non-null   float64\n",
      " 11   thinness 5-9 years              2904 non-null   float64\n",
      " 12  Income composition of resources  2771 non-null   float64\n",
      " 13  Schooling                        2775 non-null   float64\n",
      "dtypes: float64(11), int64(1), object(2)\n",
      "memory usage: 321.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc484938",
   "metadata": {},
   "source": [
    "**step3:**\n",
    "* You have just kick started your exploration and observed that there is not a single column with unique values that can be used to merge the datasets.\n",
    "    Also the column names have blank spaces, which you have to correct. You have to create a primary key column for both datafarames by usunng the apply method..\n",
    "    Perform below tasks:\n",
    "* Rename the column by removing blank spaces from both the dataset.Apply string strip function on each datasets clumn.\n",
    "* Create a unique column, named as \"UniqueKey\". We will explore unique columns from both the datasets, if not then will have to create it.\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2d698649",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Removing blank spaces from column name\n",
    "\n",
    "df1=df1.rename(columns=lambda x: x.strip())\n",
    "df2=df2.rename(columns=lambda y: y.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb979572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'Status', 'Life expectancy', 'Adult Mortality',\n",
       "       'Alcohol', 'percentage expenditure', 'Total expenditure', 'GDP',\n",
       "       'Population', 'thinness  1-19 years', 'thinness 5-9 years',\n",
       "       'Income composition of resources', 'Schooling'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da1040ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Year', 'infant deaths', 'Hepatitis B', 'Measles', 'BMI',\n",
       "       'under-five deaths', 'Polio', 'Diphtheria', 'HIV/AIDS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df1.columns=df1.columns.str.strip()\n",
    "df2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec6c0cc",
   "metadata": {},
   "source": [
    "If Want to merge columns from a dataset donot use direct **concatination** ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0cebbbad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2938, 15)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you use direct concatination rows not get well merge\n",
    "\n",
    "df1[\"UniqueKey\"] = df1[\"Country\"]+str(df1[\"Year\"])\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "91e3f616",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # just informative \n",
    "# df11=pd.merge(df1,df2,how='inner',on=['Country','Year'])\n",
    "# df11.shape\n",
    "\n",
    "# # df2=df2.drop(columns=[\"Uniquekey\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a6e726",
   "metadata": {},
   "source": [
    "**apply method** to Concatinate 2 columns of same dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "823b5004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2938, 11)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"UniqueKey\"]=df2.apply(lambda z: z[\"Country\"]+str(z[\"Year\"]), axis=1)\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "48e695d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df3= pd.concat([df2[\"UniqueKey\"], df2.drop([\"UniqueKey\"],axis=1)],axis=1)\n",
    "# df3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "002da740",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df31=pd.concat([df2.UniqueKey, df2.drop([\"UniqueKey\"], axis=1)],axis=1)\n",
    "# df31"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd19a986",
   "metadata": {},
   "source": [
    "**Step4:**\n",
    "* Both  the datasets have columns \"Country\" and \"Year\" as common, so we can drop columns \"Country\" and \"Year\" from any dataframe, or else  after merging\n",
    "you willfind two columns each related to \"Country\" and \"Year\"\n",
    "* Remove The Columns \"country\" and \"Year\" from df2.\n",
    "* Apply merge operations on the datasets df1 and df2, using the common \"UniqueKey\", to form a single dataset and name it as df.\n",
    "*Check dim of df using other joins also(left,right,inner,outer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "96621cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2938, 15), (2938, 9))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop([\"Country\",\"Year\"],inplace=True,axis=1)\n",
    "df1.shape, df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3fe13c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>...</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>UniqueKey</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Country, Year, Status, Life expectancy, Adult Mortality, Alcohol, percentage expenditure, Total expenditure, GDP, Population, thinness  1-19 years, thinness 5-9 years, Income composition of resources, Schooling, UniqueKey, infant deaths, Hepatitis B, Measles, BMI, under-five deaths, Polio, Diphtheria, HIV/AIDS]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 23 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.merge(df1,df2,how='inner',on='UniqueKey')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "38644650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 23) (2938, 23) (2938, 23) (5876, 23)\n"
     ]
    }
   ],
   "source": [
    "df_inner= pd.merge(df1,df2,on=\"UniqueKey\",how=\"inner\")\n",
    "i=df_inner.shape\n",
    "df_right= pd.merge(df1,df2,on=\"UniqueKey\",how=\"right\")\n",
    "r=df_right.shape\n",
    "df_left= pd.merge(df1,df2,on=\"UniqueKey\",how=\"left\")\n",
    "l=df_left.shape\n",
    "df_outer= pd.merge(df1,df2,on=\"UniqueKey\",how=\"outer\")\n",
    "o=df_outer.shape\n",
    "print(i,r,l,o)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa12d90",
   "metadata": {},
   "source": [
    "## Scenario2:\n",
    "You Should give yorself a pat. You have successfully merged the dataset. Before youcan start exploring the relationship and draw insights, cleaning the dataset\n",
    "is the most important job. Complete below tasks to prepare your dataset for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26585e2d",
   "metadata": {},
   "source": [
    "**Step5**\n",
    "* There can be chanc you have recieved a dataset with duplicate values. Remove them and analyze the dimension of the dataframe before and after removing.\n",
    "* For each feature, identity the total nimber of unique columns.\n",
    "* Information for the feature \"UniqueKey\" is already reflected in \"Country\" and \"Year\".\n",
    "You can remove it and verify your results by viewing the names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0cf78c6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2938, 23)\n",
      "(2938, 23)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.drop_duplicates(inplace=True)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bd8dc3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                             193\n",
       "Year                                 16\n",
       "Status                                2\n",
       "Life expectancy                     362\n",
       "Adult Mortality                     425\n",
       "Alcohol                            1076\n",
       "percentage expenditure             2328\n",
       "Total expenditure                   818\n",
       "GDP                                2490\n",
       "Population                         2278\n",
       "thinness  1-19 years                200\n",
       "thinness 5-9 years                  207\n",
       "Income composition of resources     625\n",
       "Schooling                           173\n",
       "UniqueKey                          2938\n",
       "infant deaths                       209\n",
       "Hepatitis B                          87\n",
       "Measles                             958\n",
       "BMI                                 608\n",
       "under-five deaths                   252\n",
       "Polio                                73\n",
       "Diphtheria                           81\n",
       "HIV/AIDS                            200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique values in datasets\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "63529e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unwanted features\n",
    "df.drop(\"UniqueKey\", inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "75104e80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2938 entries, 0 to 2937\n",
      "Data columns (total 22 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Country                          2938 non-null   object \n",
      " 1   Year                             2938 non-null   int64  \n",
      " 2   Status                           2938 non-null   object \n",
      " 3   Life expectancy                  2928 non-null   float64\n",
      " 4   Adult Mortality                  2928 non-null   float64\n",
      " 5   Alcohol                          2744 non-null   float64\n",
      " 6   percentage expenditure           2938 non-null   float64\n",
      " 7   Total expenditure                2712 non-null   float64\n",
      " 8   GDP                              2490 non-null   float64\n",
      " 9   Population                       2286 non-null   float64\n",
      " 10  thinness  1-19 years             2904 non-null   float64\n",
      " 11  thinness 5-9 years               2904 non-null   float64\n",
      " 12  Income composition of resources  2771 non-null   float64\n",
      " 13  Schooling                        2775 non-null   float64\n",
      " 14  infant deaths                    2938 non-null   int64  \n",
      " 15  Hepatitis B                      2385 non-null   float64\n",
      " 16  Measles                          2938 non-null   int64  \n",
      " 17  BMI                              2904 non-null   float64\n",
      " 18  under-five deaths                2938 non-null   int64  \n",
      " 19  Polio                            2919 non-null   float64\n",
      " 20  Diphtheria                       2919 non-null   float64\n",
      " 21  HIV/AIDS                         2938 non-null   float64\n",
      "dtypes: float64(16), int64(4), object(2)\n",
      "memory usage: 527.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0cbb03a",
   "metadata": {},
   "source": [
    "**Step6:** \n",
    "* Show the Total NA values in each feature of the dataset.\n",
    "* Replace the NA values with suitable function(CEntral Tendancy).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "923d4551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                              0\n",
       "Year                                 0\n",
       "Status                               0\n",
       "Life expectancy                     10\n",
       "Adult Mortality                     10\n",
       "Alcohol                            194\n",
       "percentage expenditure               0\n",
       "Total expenditure                  226\n",
       "GDP                                448\n",
       "Population                         652\n",
       "thinness  1-19 years                34\n",
       "thinness 5-9 years                  34\n",
       "Income composition of resources    167\n",
       "Schooling                          163\n",
       "infant deaths                        0\n",
       "Hepatitis B                        553\n",
       "Measles                              0\n",
       "BMI                                 34\n",
       "under-five deaths                    0\n",
       "Polio                               19\n",
       "Diphtheria                          19\n",
       "HIV/AIDS                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "86ed03e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.2\n",
      "4.5\n"
     ]
    }
   ],
   "source": [
    "# for example\n",
    "l=[1,1,0,4,5,6,7,18,90,0]\n",
    "print(np.mean(l))\n",
    "print(np.median(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c07540c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69.22493169398912\n",
      "nan\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(df[\"Life expectancy\"]))\n",
    "print(np.median(df[\"Life expectancy\"]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef282459",
   "metadata": {},
   "source": [
    "Choose wisely !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d6ff18ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.303948264125257\n",
      "3.0\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(df[\"infant deaths\"]))\n",
    "print(np.median(df[\"infant deaths\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "16761197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.iloc[rowIndex,columnIndex] :: no stepSize\n",
    "df.iloc[:,3].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a85832b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                              0\n",
       "Year                                 0\n",
       "Status                               0\n",
       "Life expectancy                     10\n",
       "Adult Mortality                     10\n",
       "Alcohol                            194\n",
       "percentage expenditure               0\n",
       "Total expenditure                  226\n",
       "GDP                                448\n",
       "Population                         652\n",
       "thinness  1-19 years                34\n",
       "thinness 5-9 years                  34\n",
       "Income composition of resources    167\n",
       "Schooling                          163\n",
       "infant deaths                        0\n",
       "Hepatitis B                        553\n",
       "Measles                              0\n",
       "BMI                                 34\n",
       "under-five deaths                    0\n",
       "Polio                               19\n",
       "Diphtheria                          19\n",
       "HIV/AIDS                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "845d815b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.fillna(df[\"Status\"].median())\n",
    "# # \"Status\" is object datatype cannot fill median\n",
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c93939b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                            0\n",
       "Year                               0\n",
       "Status                             0\n",
       "Life expectancy                    0\n",
       "Adult Mortality                    0\n",
       "Alcohol                            0\n",
       "percentage expenditure             0\n",
       "Total expenditure                  0\n",
       "GDP                                0\n",
       "Population                         0\n",
       "thinness  1-19 years               0\n",
       "thinness 5-9 years                 0\n",
       "Income composition of resources    0\n",
       "Schooling                          0\n",
       "infant deaths                      0\n",
       "Hepatitis B                        0\n",
       "Measles                            0\n",
       "BMI                                0\n",
       "under-five deaths                  0\n",
       "Polio                              0\n",
       "Diphtheria                         0\n",
       "HIV/AIDS                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(3,len(df.columns)):\n",
    "    df.iloc[:,i].fillna(df.iloc[:,i].median(), inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f45e59d6",
   "metadata": {},
   "source": [
    "##  Scenario3\n",
    "Your managers want an urgent report for upcoming plan for effectively allocatng expenses for the healthcare sector. Making queries and analysing data using dataframe operations is time consuming. You decide tomove ahead with visualiztion metod to draw more insights and find relationship between different columns.\n",
    "\n",
    "* Univariate analysis- exploring one column at a time.\n",
    "* Bivariate analysis- exploring 2 columns at a time. NUm|NUm, Cat|Num, Cat|Cat."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b69b1fc",
   "metadata": {},
   "source": [
    "**Step7:**\n",
    "\n",
    "You can start exploring a single columnat a timeand draw key insights. There are few categorical types and others numerical type columns. your job to pick up the key important columns. Start by following tasks.\n",
    "\n",
    "* Show the distribution chart for life expectancy. Also make sure to label axises and title.\n",
    "* Show the distribution of status features using seaborn, showing count of each record. Here also axises and title shall be visible clearly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9ea195a2",
   "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"
    }
   ],
   "source": [
    "plt.hist(df['Life expectancy'],bins=50)\n",
    "plt.xlabel('Life expectancy')\n",
    "plt.ylabel('counts')\n",
    "plt.title('Life Expectancy Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e9dd7e21",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Status', ylabel='count'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(df['Status'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "102aa65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GDP', ylabel='Count'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.histplot(df['GDP'],bins=19)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1657443",
   "metadata": {},
   "source": [
    "Need to write a query to find perticuler thing..  \n",
    "\n",
    "eg. find the minimum GDP of india?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "be65b2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 22)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_india=df[df[\"Country\"]==\"India\"]\n",
    "df_india.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a9cec0bc",
   "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"
    }
   ],
   "source": [
    "# sns.histplot(df_india,bins=19)   #// wrong choice\n",
    "\n",
    "plt.bar(x=df_india[\"Year\"], height=df_india[\"GDP\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9eb2db3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='GDP'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n9oIFMHo0vP712SeLhgYYNy67GKQmKFmI1JL2dpg8GQ4/PDzO5bJNFppjIRElC5FaEndux9u25HLwxBOhLyML2vRIIkoWIrXi1VdDZ/aCBX1lcSf3gw9WP57e3jDHQjULYZDJwswmpx2ISN3buDGsLlsoWWTRFPXUUyEe1SyEEsnCzFrMbAew3sy6zOwtVYpLpP7kd27H5syBAw/MJllo2KzkKVWzuAR4m7tPA94H/N/0QxKpU+3tYWXX172ur2zUKDj66GyShSbkSZ5SyWK3uz8M4O5rgNekH5JInWpvDyvMjum3vmdWI6KULCRPqVVnG8zss8Ueu/tl6YQlUmfcQ7J473v3fS6Xg5/8BP76V5gwoXoxdXbCoYfCa/QdUUrXLH5AqE3EP/0fi0gSnnwS/vKXvfsrYvEaUdUeEaVhs5JnwJqFu/9rtQIRqWuFOrdj+WtEvaWKY0w6O0N/iQiDGDprZm83s+vMbEP08yszO3kob2pmF0bXesDMfmFmB5jZJDO73cw2RceJeedfZGabzWyjmZ0xlPcWqUlxsjj22H2fmzULxo+vbr+Fu2oWspdSQ2ffDVwF3AR8GPgIcDNwlZm9q5I3NLPpwD8ATe4+HxgNLAW+CNzh7nOBO6LHmNm86PkcsBi4wsxGV/LeIkCYbLZ9e/jmvH17eJy19nY48sjC/QOjRsG8edVNFk8/DS+9pGGz8r9K1Sw+D5zl7j9093Xu3u7uVwFnAV8YwvuOAQ40szHAOGArsAS4Onr+6ug9iMqvcfced38c2AwsHMJ7Sz3r7YX168PGQrNnh+P69dknjHXrCjdBxao9IkojoaSfUsliqruv61/o7vcDh1Xyhu7+JPANYAuwDXjW3X8HHObu26JztgEN0UumA0/kXaIrKtuHmZ1vZm1m1rZjx45KwpORrrsblizpm3DW2Rked3dnF9Pzz8PmzaWTxfbtsHNndWKKk4VqFhIplSx2VfhcUVFfxBJgDnA4MN7MPjrQSwqUeaET3X25uze5e9OUKVMqCU9Gup6evkQR6+wM5VnJ38OimGov+xH/jVSzkEipeRZHmtkNBcoNOKLC93wH8Li77wAws+uBtwBPmdk0d99mZtOA+KteFzAz7/UzCM1WIuUbOzbcAPMTRmNjKM/KQCOhYvnJ4m1vSzuiULM45JDqzuuQmlYqWSwpUBZ/q/9Ghe+5BVhkZuOAl4BTgTZCTWUZcGl0XBGdfwPwczO7jFATmQusrfC9pd41NIQJbuecExJGYyOsWBHKs9LeHia/TS/YuhrMnBk6v6tZs1ATlOQplSwmADPc/bsAZrYWmEJIGBV1cLv7GjP7FXAvsBu4D1gOHARca2Z/Q0goH4jO32Bm1wIPRudf4O57KnlvEczg0kvhu9+Fgw6CqVNh7tww4igr/fewKMSsuiOiOjrCtq4ikVKfkH8kfLOP7Q80AScDn6j0Td39Ynd/vbvPd/dzopFOf3H3U919bnTcmXf+Je5+pLu/zt1vqfR9RejshJtvDhPcTj0VfvrTbBPF7t1hNNZATVCxao2IclfNQvZR6lOyv7vnj0T6U3RT3wKMTzEukXS0tobj4sVw3HGwcmW28RTaw6KYXC6M2kp7pN8zz4QRWurcljylksXE/Afu/qm8hxpuJMNPa2to+58/H5qbYc0aePnl7OIZTOd2LF4jKu3ahYbNSgGlksUaM/t4/0Iz+zvUySzD0apVYSLe6NEhWfT0wD33ZBdPoT0siqnW8FlteiQFlOrgvhD4jZl9mNAhDXA8MJa+GdYiw8Nzz4X+gX/+5/D4rW8Nx5UrqzMctZD29lBj2G+/0ucefngYzlqtmoWaoSTPgDULd+9297cAXwU6op+vuPsJ7v5U+uGJJGjNmrCsR7xy66RJYbOhrPot4j0sBtMEBWFEVDU6uTs7w0ixSZPSfR8ZVkrVLABw998Dv085FpF0tbaGkU9vfnNfWXMz/OhH8Oqrg/t2n6StW8OCfYNNFhCSxfXXh0Qz0FDboYhXm03r+jIsZThmUKTKWltDTeLgg/vKmpth1y64777qx1NO53YslwubJKW5lpWGzUoBShZSH/bsgdWr4cQT9y5vbg7HLJqiBtrDophqdHJ3dChZyD6ULKQ+rF8PL7ywb7KYOhVe+9rsksWRR+5d0ykl7WTx7LNhr291bks/ShZSH+LJeIW2JW1uhrvuqv6eFuV0bsemToWJE9NLFho2K0UoWUh9WLUqDD0t9I25uTl8m37ggerFM5g9LApJe0SUhs1KEUoWUh9aW0MTVKERPln0WwxmD4ti4mThBbd1GRrVLKQIJQsZ+Z58MtwECzVBQfgWPWtWdZNFJSOhYrlcWL9p+/YkIwo6OuDAA0Gbh0k/ShaSrN7ecBPr7AzHrPe2htAEBft2budrboY//jGdb+uFDGYPi2LiNaLSaDaL9/jQHAvpR8lCktPbG0YdLVoUmjEWLQqPs04Yra3h2/JA3+JPOinMXXjkkerENJg9LIpJc0SUhs1KEUoWkpzubliypK/du7MzPE5zAtlgrFoFCxcOPEO7mv0W5exhUUhDA0yenF6yUOe2FJBJsjCzCWb2KzN72MweMrMTzGySmd1uZpui48S88y8ys81mttHMzsgiZimhtze0o+fvbQ3hcU9PNjEBvPhimJ09UBMUhN3yDjusOsminD0sikljRNQLL4TZ4apZSAFZ1Sz+A7jV3V8PvAF4CPgicIe7zwXuiB5jZvOApUAOWAxcYWajM4la9rVnD/zP/8Ab3gAPP7zvt9LGxrAEd1bWrg3f5It1bsfMQu2iGsliKJ3bsTRGRMWJXjULKaDqycLMDgaagSsB3P0Vd/8rsAS4OjrtavqWQF8CXBNtvfo4sBlYWM2YpYDdu8OWpPPnw9KlIWkceCCsWNF3s2lsDI8bGrKLM+7cPuGE0uc2N8OWLfvWjpK2bh3sv//g9rAoJpcLS64/+WRycWnYrAwgi5rFEcAO4Idmdp+Z/beZjQcOc/dtANExvsNMB/K3du2KyvZhZuebWZuZte1Ie+vJevXqq/DDH8LRR8M554R+gGuvDSNzFi8OC/WtXh2WA//+98PjLPe4bm0NsQ5mue1q9VuUs4dFMWl0cmtCngwgi0/xGOA44Hvu/kZgF1GTUxGFhosUrHu7+3J3b3L3pikaJ56sV16B5cvDOkrnnRe2Jv31r8ON7wMf6EsIo0aFJSlWr4Z3vhMefTS7mHt7Q82iVH9FbP78sJTGH/+YXkzl7mFRTBrJorMz1HimTk3umjJiZJEsuoAud18TPf4VIXk8ZWbTAKJjd975M/NePwPYWqVY5eWX4bvfhaOOgr/7u9CkdNNN8Oc/w1lnFa81tLSE4403Vi3UfTz8cFjGY7DJYtSosGNemjWLbdtgx46hJ4vJk8P/i6RrFo2N2dYEpWZV/V+Fu28HnjCzuMH2VOBB4AZgWVS2DFgR/X4DsNTMxprZHGAu2v87fS++CN/6FhxxBHzqU2GG8223hRrDu99den7AnDnhm3qWyWKgxQOLaW6GTZvCTT0NSXRux5IeEaVhszKArL5C/D3wMzO7H1gAfA24FDjNzDYBp0WPcfcNwLWEhHIrcIG778ki6BGp/4zrXbvg3/893OwvvDB0wv7+92FV1tNPL28SWUtLeN0zz6QX/0BWrQrLVsydO/jXxP0Wd92VTkyV7GFRTC4HDz6Y3IgobXokA8gkWbh7e9S3cKy7n+Xuz7j7X9z9VHefGx135p1/ibsf6e6vc/dbsoh5RCo043rNGrjuujAUduVK+MMf4O1vr2ymcUtLGCV1662Jhz4ora2hVlFO7G98I4wfn15TVHt7qK0dcsjQr5XLhdVrn3ii9LmlvPQSPPWUahZSlBon61mhGdfnnReGxP7ud6H9figWLgzf7LNoiuruDs1J5TRBAYwZE/o40kwWSTRBQV8ndxJrRG3ZEo6qWUgRShb16tFHYevWwjOuhzKkM9/o0aF/45ZbwpDbarr77nAcbOd2vubmUOPaubP0ueWodA+LYpIcEaVhs1KCkkW9uece+OAHwxDYLVvSn3Hd0hJGJMWdzdXS2hqGgR5/fPmvTavfYv360L+QVLKYNAmmTUs2WahmIUUoWdSD3l747W/h5JND09Dvfgef/3xookl7xvXpp4ebdrWbolpbQ6I44IDyX7twYUiYSTdFJTkSKpbUiKjOztAEd/jhQ7+WjEhKFiNZT0+YbX3MMXDmmaHp6ZvfDB2il14akkI847qjIxyTnnF90EGhg7yayaKnB9raKmuCgpAoFi1KJ1lMmgQzZiR3zXhE1FCXge/ogJkzQ9OhSAFKFiPRs8/C178eRt2cd174xviTn8Bjj8FnPxtmX8fiGdeNjeGYxoSslpbQ2bxxY/LXLuTPfw4zzsvt3M7X3Az33hv6GZIylD0sisnlwpyYoa5npWGzUoKSxUjS1QWf+1z4hviFL8C8eWEiXXs7fPSjyXVcl+vMM8OxWrWLePHAoSaLeLmQJAx1D4tikurk1oQ8KUHJYjgptmXp+vVw7rlhIt23vhW+yd97L9x+e/kT6dLQ2BgmoVUrWbS2wpFHhv0pKnXCCaFGllRT1COPhKVTkk4W8+aF41CSRU9PmLGumoUMQMliuCg0ge7ee+Eznwk34uuvhwsuCP0SP/tZmFxWS1pawk38L39J933cw/tU2l8RGz8+dJAnlSzS6NwGmDAh7OM9lGTxxBPh76aahQxAyWK4KDSB7v3vh3e8Ay65JAyD/da3avcDH8/mviXlCfiPPhoW6htqsoDQFLV2bZjdPFTt7WFU2OtfP/Rr9TfUEVEaNiuDoGSRpmLNRsXO3bo1jEi69lr4xjfgH/4hrOx63HGhg7jQBLr58+FLXxrcfg1ZetObQrNQ2k1RlSweWMxJJ4WO8jVrSp9bShJ7WBSTy8FDD1U+IkqbHskgjMk6gJrS2xu+wff0hOGTDQ2Vjw6Km43i2kBjY2gqeumlsDzDli17/zz55L6znA85JKz2OmtWGNLY2Lh3wmhsrGweQRZGjQod3b/8ZbgB779/Ou/T2hqaZuK2/KE48cTQ37NyZZijUql4D4t42fak5XLh39Xjj4e+mnJ1dIT/P9ML7ikmAihZ9Cl0c1+xIgw/ff758n8++1n427/du9no7LPh8svhE58IN/8ZM0IiOPHEvqQQ/8ycufdic729IZ7+8WW5ZWm5WlrgyivDzOhTT03nPVatCp3TSQwBnjChb0HFoUhqD4ti8teIqiRZdHaGf4tZjZaTYUHJIlaoT2DJknBzP/vs0q8/6KAwfyH/p1CzUS4XOhSnTStvAtSoUX0T6JKo+WThHe8Icd94YzrJ4plnQtv90qXJXbO5GX7wg6HVhtLq3I7lj4hasqT812vYrAyCkkWsp6fwzf2oo8Je0vlJoH9iGD9+35v29u2Fm40OPrjybSvjCXTD1fjxIUnceGNIwkkP6V29OhyT6NyONTfDt78dRp4tWlTZNZLcw6KQgw8OtdFKO7k7O0P/jMgAlCxiY8cWvrlPmRK2Ey1XQ8PwbzZKQ0sL3Hxz6JBNol8hX2trqK0tXJjcNeNl2leuHFqySGoPi2IqHRH16qthMqdqFlJCZm0YZjbazO4zs5uix5PM7HYz2xQdJ+ade5GZbTazjWZ2RioBxTf3pBbVy282SmvdpeEozdncra2hqWf8+OSu2dAQhrsOpd8iyT0sisnlwp7je8rcRLKrK/SHaSSUlJDlnevTwEN5j78I3OHuc4E7oseY2TxgKZADFgNXmFnyq52lcXOvxrpLw82MGWHCYNLJ4tVXw5yIJJugYiedFDrly70RA7zwQrJ7WBSTy4Wm1EcfLe91GjYrg5TJ3cvMZgDvBv47r3gJcHX0+9XAWXnl17h7j7s/DmwGEmxnyKObe3W0tITNiZ5+OrlrrlsXFtRLYn5Ff83N8NxzcP/95b826T0siql0jShteiSDlNXd8FvAPwL5s4gOc/dtANExbv+ZDuRvMtwVle3DzM43szYza9uxY0fiQUtCWlpC08fNNyd3zXgyXho1i/x+i3KlPRIqdvTR4VhusujsDAMNZs5MPiYZUaqeLMzsTKDb3f882JcUKPNCJ7r7cndvcvemKVOmVByjpOy448LQ4SSbolatCiOCktwrIjZzZliksdJkMXFiOnHlO+ig0JRUSc1i2rRkd0eUESmLmsWJwHvMrAO4BjjFzH4KPGVm0wCiY3d0fheQ/7VnBrC1euFK4uLZ3LfdFuYvDFW8eGAaTVCx5uaQLLzg95Ti0tjDophKRkRpHwsZpKonC3e/yN1nuPtsQsf17939o8ANwLLotGXAiuj3G4ClZjbWzOYAc4G1VQ5bktbSEma6//GPQ79WvFxKGk1Qsebm0Mfy8MODf83u3aGfI+0mqFguFzaY2r178K/p6FCykEGppR7cS4HTzGwTcFr0GHffAFwLPAjcClzg7hUMS5GacuqpYV2rJJqiktjsqJTm5nAspylq06Z09rAoJpcLNbXNmwd3/p49YTUBdW7LIGSaLNz9Tnc/M/r9L+5+qrvPjY478867xN2PdPfXuXvKa1xLVYwbF5b/uPHG8pt2+mttDXMr0pohDWHNpWnTyqsJVatzO5a/RtRgbN0aaiGqWcgg1FLNQurNe94TmkGGuiXoqlVhdvWYFBckMAvzLcrpt0hzD4tCjj46xDnYv6eGzUoZlCwkO/Fs7htuqPwazz8f5lik2QQVa24OfSOPPz6489vbw7f9tJZj72/cuLCsyGCThSbkSRmULCQ706ZBU9PQ+i3WrAlzNtLs3I6V02/hDvfdV70mqFg5I6LimsWsWamFIyOHkoVkq6Ul3PC7u0ufW8iqVaHppdJF/spx9NFw6KGDSxbbt6e7h0UxuRw88sjghiR3dobdCw88MP24ZNhTspBstbSEb+G//W1lr29tDduVprmia2zUqDCbezDJotqd27FcLnRab9pU+lwNm5UyKFlIthYsCLObK2mK2rMnrDFVjSaoWHNzWKzvyScHPi9OFm94Q+oh7aWcNaK06ZGUQclCsmUWOrp/97swJ6EcGzaEDu5qdG7H4n6Lu+4a+Lz29rBESDVqPPle//pQAyqVLHp7w2RG1SxkkJQsJHstLbBrF9x5Z3mvS3PxwGIWLAi7I5aab1GNPSwKOeCAMCekVLLYvj30a6hmIYOkZCHZO+WUMOyz3KaoVavCUvJz5qQTVyGjR8Nb3zpwv8ULL4Q+gyySBQxuRJSGzUqZlCwkewccAKedVv5s7njxwGos0pevuRkefDCMdiqkWntYFJPLhWTV01P8HE3IkzIpWUhtaGkJ6xQNdoOhbdvC5LhqNkHF4n6LP/2p8PNZjYSK5XKh8/+RR4qfE9cslCxkkJQspDa8+93hONimqGosHlhMU1OoDRVrior3sMhqQ6HBrBHV0QGTJ4d9MEQGQclCasPUqbBw4eCTRWtruGEfd1y6cRWy//5wwgkDJ4tq7WFRyOteF/pWBuq30LBZKZOShdSOlhZYuzaM1CmltRXe9KbqrbvUX3NzSArPPrt3ebX3sChk7FiYO3fgZKFNj6RMShZSO1pawrHUbO6XXoJ7782mCSp20klhrkI8fDdW7T0sihloRJR7SBaqWUgZstiDe6aZ/cHMHjKzDWb26ah8kpndbmabouPEvNdcZGabzWyjmZ1R7ZilSo49NixqV6op6p57wjf4LDq3Y29+M+y3375NUVl3bsdyuTDTvNBExx07QsJVzULKkEXNYjfwf9z9aGARcIGZzQO+CNzh7nOBO6LHRM8tBXLAYuAKMxudQdySNrNQu7j99oFnc8ed2yecUJ24Chk3LjSDFUoW1dzDophcLtR8Cm0Dq2GzUoEs9uDe5u73Rr8/DzwETAeWAFdHp10NnBX9vgS4xt173P1xYDOwsKpBS/W0tMCLL8Lvf1/8nNbW0Ik7eXL14iqkuTnUcl58sa+s2ntYFDPQGlGakCcVyLTPwsxmA28E1gCHufs2CAkFaIhOmw48kfeyrqhMRqKTTw7DOYttiNTbG2oWWTZBxZqbQ3PY6tV9ZevWZd8EBaGDe8yYwslCNQupQGbJwswOAq4DPuPuzw10aoGygtN8zex8M2szs7YdxWbXSm0bOxZOPx1uuqnwbO5HHoGdO7Pt3I695S1h0b64KWr7dnjqqdpIFvvvD699bfFkMWFC9Rc5lGEtk2RhZvsREsXP3P36qPgpM5sWPT8NiHfD6QLyZzfNALYWuq67L3f3JndvmjJlSjrBS/paWsIS4Pfdt+9zWSweWMwhh4TEECeLWuncjhUbEaVhs1KBLEZDGXAl8JC7X5b31A3Asuj3ZcCKvPKlZjbWzOYAc4G11YpXMvCud4XO7kKjolpbYdKk0GdRC046Keyp0dOT3R4WxeRy8Nhje/epgCbkSUWyqFmcCJwDnGJm7dHPu4BLgdPMbBNwWvQYd98AXAs8CNwKXODuezKIW6qloSFsk1ooWaxalc3igcU0N4eRW21tIVnMnl07zTu5XGjKyx8RFc+xUM1CyjSm2m/o7n+icD8EwKlFXnMJcElqQUntaWmBL30Jtm6Fww8PZU8/DRs3wsc+lmloe3nrW8Nx5crs9rAoJn+NqHhZlJ07wxLqqllImTSDW2pTPJv7ppv6yrJcPLCYyZPDTfmWW0Lney0li6OOCh3d+f0WGjYrFVKykNqUy4UbWn5T1KpVYdb0m96UWVgFNTeHbVaz3MOikP32C307+ckiHjarZCFlqnozlMigxLO5f/CD0EE7blzo3D7uODjwwKyj29uSJWHzpkmTwk24tzcMqa0Fudze80A0x0IqVCP/okUKaGkJncd33BH2i77nntpqgoKQGBoa4MILw4TCk04KO+X19mYdWZDLhQTxwgvhcWdn2EN84sQBXybSn5KF1K6TTgo3thtvDKvM9vTUxvyKfN3d8N739vUFdHaGmkZ398Cvq5a4k/uhh8IxHjZbK6PJZNhQspDatf/+sHhx6OSOtzCttZpFT09fooh1dg68/3U19V8jSsNmpUJKFlLbWlrCftvf/z7MmQPTpmUd0d7Gjt23/b+xMZTXgiOPDLHEyaKjQ8lCKqIObqltZ54J118fOo9Hj66tzmMI/RUrVoSmp3hDoRUrQnktGD06LJe+YQP89a9hZz91bksFlCykdvX2wpYtofM4/0Z8zDG1kzBGjQrxrF4dmp7Gjg2Jolbig9AU9ac/aY6FDEkN/YsW6ae7u+8bO9Re53Fs1CiYOjUks6lTaytRQEgWW7aEUVqgmoVUpMb+VYvkqfXO4+Ei7uS+5ZZwVM1CKqBkIbWr1juPh4s4Wdx6a5jQmPUOgzIsKVlI7Yo7j+OEUWudx8PFnDlwwAFhEcHZszXHQiqiDm6pXcOh83g4GD0a5s0LExvVBCUVUrKQ2hZ3HsvQtLTAP/1TqJ1t366kK2XTvxaRka63N8yEv/BCOP74sLFULa1fJcPCsEkWZrbYzDaa2WYz+2LW8YgMG93dsHRp7Q9Blpo2LJKFmY0Gvgu8E5gHfMjM5mUblcgwoSHIkoBhkSyAhcBmd3/M3V8BrgGWZByTyPCgIciSgOGSLKYDT+Q97orK9mJm55tZm5m17dixo2rBidQ0DUGWBAyX0VCFBob7PgXuy4HlAE1NTfs8L1KXNARZEjBckkUXMDPv8Qxga0axiAw/GoIsQzRcvlrcA8w1szlmtj+wFLgh45hEROrGsKhZuPtuM/sUcBswGrjK3TdkHJaISN0YFskCwN1vBm7OOg4RkXo0XJqhREQkQ0oWIiJSkrmPzBGmZrYD6Cx5YmGTgacTDCdptR4fKMYk1Hp8UPsx1np8UFsxPg3g7ov7PzFik8VQmFmbuzdlHUcxtR4fKMYk1Hp8UPsx1np8MDxiBDVDiYjIIChZiIhISUoWhS3POoASaj0+UIxJqPX4oPZjrPX4YHjEqD4LEREpTTULEREpSclCRERKqotkYWYzzewPZvaQmW0ws09H5ZPM7HYz2xQdJ+a95qJoC9eNZnZGXvnxZrY+eu7bZlZo+fRM4jOzcWb2WzN7OLrOpUONLekY+13zBjN7oBZjNLP9zWy5mT0S/T3fV2PxfSj6d3i/md1qZpOHGl8lMZrZodH5L5jZd/pdK/PPSrH4aumzMtDfMO+aiX5WKuLuI/4HmAYcF/3+GuARwvasXwe+GJV/Efh/0e/zgHXAWGAO8CgwOnpuLXACYY+NW4B31kp8wDjg7dE5+wN3JRFf0n/D6PmzgZ8DD9To/+d/Bf4t+n0UMLlW4iOs6dYdxxS9/l8y+huOB94KfAL4Tr9r1cJnpWB8NfZZKfo3TOuzUtF/V5Zvntl/NKwATgM2AtPy/gdvjH6/CLgo7/zbon/004CH88o/BPxXrcRX4Dr/AXy8lv6G0e8HAX+KPkCpfQCGGOMTwPga/Xe4H7ADaCTciL8PnJ9FjHnnfYy9b8Y18VkpFl+B62T2WRkoxmp9VgbzUxfNUPnMbDbwRmANcJi7bwOIjvE+k8W2cZ0e/d6/vFbiy7/OBKAFuCPJ+BKK8avAN4EXk44tiRijvx3AV83sXjP7pZkdVivxufurwCeB9YRNwOYBVyYZXxkxFlMrn5XBXGcC2X5WBpL6Z2Ww6ipZmNlBwHXAZ9z9uYFOLVDmA5QnIoH44uuMAX4BfNvdH0sqviRiNLMFwFHu/usk49rrjYf+dxxD2I2x1d2PA+4GvlEr8ZnZfoRk8UbgcOB+Qi0kMWXEWPQSBcqy+KyUuk4tfFaKvX4BKX9WylE3ySL6gF0H/Mzdr4+KnzKzadHz0wjtwFB8G9eu6Pf+5bUSX2w5sMndv5VEbAnHeAJwvJl1EKrXrzWzO2ssxr8QvsnFH9JfAsfVUHwLANz9UQ9tFdcCb0kivgpiLKZWPiul1MJnpZhUPyvlqotkEY3CuBJ4yN0vy3vqBmBZ9PsyQttiXL7UzMaa2RxgLrA2qjo+b2aLomuem/eazOOLrvVvwCHAZ4YaVxoxuvv33P1wd59N6NR7xN1PrrEYHbgRiOM6FXiwVuIDngTmmdmU6LzTgIeGGl+FMRZUQ5+Vga5VK5+VgtL8rFQkyw6Tav1Ef2gnVNfbo593AYcS2ik3RcdJea/5MmH0yUbyRkkATcAD0XPfIZoFXwvxEb69OeHGEV/nb2vtb5j3/GySHQ2V5P/nRmBldK07gFk1Ft8nov/P9xMS26EZ/g07gJ3AC4Qaxbwa+6zsEx+191kp+DdM67NSyY+W+xARkZLqohlKRESGRslCRERKUrIQEZGSlCxERKQkJQsRESlJyUIkIRb8yczemVf2QTO7Ncu4RJKgobMiCTKz+YQZ328krBDbDix290cruNZod9+TbIQilVGyEEmYmX0d2EVYenoXYYLfMYQ1p/7F3VdEC8z9JDoH4FPuvsrMTgYuBrYBC9x9XnWjFylMyUIkYWY2HrgXeAW4Cdjg7j+NVjddS6h1ONDr7i+b2VzgF+7eFCWL3wLz3f3xLOIXKWRM1gGIjDTuvsvM/oewdMMHgRYz+1z09AHALMKiet+JVhbdA7w27xJrlSik1ihZiKSjN/ox4H3uvjH/STP7F+Ap4A2EgSYv5z29q0oxigyaRkOJpOs24O+jlUgxszdG5YcA29y9FziH0BkuUrOULETS9VXCNqj3m9kD0WOAK4BlZraa0ASl2oTUNHVwi4hISapZiIhISUoWIiJSkpKFiIiUpGQhIiIlKVmIiEhJShYiIlKSkoWIiJT0/wHFv2wYVJxApQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(df_india[\"Year\"],df_india['GDP'],marker='o',color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858a76a1",
   "metadata": {},
   "source": [
    "**Step8:**\n",
    "\n",
    "There is a lot of information present and you can successfully draw more insights using two columns at a time. This is known as biavariate analysis. Since the report is urgent, you shall focus in comparing columns that are wrapped around more on expences \"Life expectancy\" aand \"Year\". Each of the tasks shall be articuted, highlighting title, x-axis and y-axis name.\n",
    "\n",
    "Note: Line plot plays a  very critical role to analyze the growth of a coutry in respect to years.\n",
    "you can have basic understanding from the reference link shared here, beforeyou proceed to complete below tasks.\n",
    "\n",
    "\n",
    "* Show the total expenditure|Year, using the line plot.\n",
    "* Show how \"Life expectancy\" changed over the years, using line plots.\n",
    "* Compare the features of \"life Expectancy and Schooling\".\n",
    "* What is the trend followed for life expectancy against the status of countries? Draw the INSIGHTS using seaborn bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fcc3c036",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2938 entries, 0 to 2937\n",
      "Data columns (total 22 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Country                          2938 non-null   object \n",
      " 1   Year                             2938 non-null   int64  \n",
      " 2   Status                           2938 non-null   object \n",
      " 3   Life expectancy                  2938 non-null   float64\n",
      " 4   Adult Mortality                  2938 non-null   float64\n",
      " 5   Alcohol                          2938 non-null   float64\n",
      " 6   percentage expenditure           2938 non-null   float64\n",
      " 7   Total expenditure                2938 non-null   float64\n",
      " 8   GDP                              2938 non-null   float64\n",
      " 9   Population                       2938 non-null   float64\n",
      " 10  thinness  1-19 years             2938 non-null   float64\n",
      " 11  thinness 5-9 years               2938 non-null   float64\n",
      " 12  Income composition of resources  2938 non-null   float64\n",
      " 13  Schooling                        2938 non-null   float64\n",
      " 14  infant deaths                    2938 non-null   int64  \n",
      " 15  Hepatitis B                      2938 non-null   float64\n",
      " 16  Measles                          2938 non-null   int64  \n",
      " 17  BMI                              2938 non-null   float64\n",
      " 18  under-five deaths                2938 non-null   int64  \n",
      " 19  Polio                            2938 non-null   float64\n",
      " 20  Diphtheria                       2938 non-null   float64\n",
      " 21  HIV/AIDS                         2938 non-null   float64\n",
      "dtypes: float64(16), int64(4), object(2)\n",
      "memory usage: 592.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b0301318",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Total expenditure'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.lineplot(df[\"Year\"],df[\"Total expenditure\"],marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "96d1d5b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": [
    "sns.lineplot(df[\"Year\"],df[\"Life expectancy\"],marker=\"d\",color='orange')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6ac2bbff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot(df)\n",
    "# heatmap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "88754e90",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x282a4c95430>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(df[\"Life expectancy\"], df[\"Schooling\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "98bbb86c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Life expectancy', ylabel='Schooling'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(df[\"Life expectancy\"],df[\"Schooling\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "dff03074",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Status', ylabel='Life expectancy'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.barplot(df[\"Status\"],df[\"Life expectancy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ad11bcd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)\n",
    "sns.lineplot(df[\"Year\"],df[\"Hepatitis B\"])\n",
    "plt.subplot(222)\n",
    "sns.lineplot(df[\"Year\"],df[\"Polio\"])\n",
    "plt.subplot(223)\n",
    "sns.lineplot(df[\"Year\"],df[\"Diphtheria\"])\n",
    "plt.subplot(224)\n",
    "sns.lineplot(df[\"Year\"],df[\"HIV/AIDS\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a303b0",
   "metadata": {},
   "source": [
    "**Step8:**\n",
    "    \n",
    "As data Analyst, you have to compare all the columns with each other and find patters, Comparing 2 or 3 columns at a time can betime consuming. IF your dataset has maximum number of numerical columns, you can use seaborn heatmap to find correlation between all of them. Complete these tasks to have in-depth knowledge of dataset;\n",
    "\n",
    "* Create a heatmap for dataframes correlation and show annotations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0a9e4039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,12))\n",
    "sns.heatmap(df.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "abd89471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2.938000e+03</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.518720</td>\n",
       "      <td>69.234717</td>\n",
       "      <td>164.725664</td>\n",
       "      <td>4.546875</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>5.924098</td>\n",
       "      <td>6611.523863</td>\n",
       "      <td>1.023085e+07</td>\n",
       "      <td>4.821886</td>\n",
       "      <td>4.852144</td>\n",
       "      <td>0.630362</td>\n",
       "      <td>12.009837</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>83.022124</td>\n",
       "      <td>2419.592240</td>\n",
       "      <td>38.381178</td>\n",
       "      <td>42.035739</td>\n",
       "      <td>82.617767</td>\n",
       "      <td>82.393125</td>\n",
       "      <td>1.742103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.613841</td>\n",
       "      <td>9.509115</td>\n",
       "      <td>124.086215</td>\n",
       "      <td>3.921946</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>2.400770</td>\n",
       "      <td>13296.603449</td>\n",
       "      <td>5.402242e+07</td>\n",
       "      <td>4.397621</td>\n",
       "      <td>4.485854</td>\n",
       "      <td>0.205140</td>\n",
       "      <td>3.265139</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>22.996984</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>19.935375</td>\n",
       "      <td>160.445548</td>\n",
       "      <td>23.367166</td>\n",
       "      <td>23.655562</td>\n",
       "      <td>5.077785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>63.200000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>1.092500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>580.486996</td>\n",
       "      <td>4.189172e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.504250</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>5.755000</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1.386542e+06</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>75.600000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>7.390000</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>7.330000</td>\n",
       "      <td>4779.405190</td>\n",
       "      <td>4.584371e+06</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.772000</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>56.100000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>119172.741800</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>20.700000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>212183.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Life expectancy  Adult Mortality      Alcohol  \\\n",
       "count  2938.000000      2938.000000      2938.000000  2938.000000   \n",
       "mean   2007.518720        69.234717       164.725664     4.546875   \n",
       "std       4.613841         9.509115       124.086215     3.921946   \n",
       "min    2000.000000        36.300000         1.000000     0.010000   \n",
       "25%    2004.000000        63.200000        74.000000     1.092500   \n",
       "50%    2008.000000        72.100000       144.000000     3.755000   \n",
       "75%    2012.000000        75.600000       227.000000     7.390000   \n",
       "max    2015.000000        89.000000       723.000000    17.870000   \n",
       "\n",
       "       percentage expenditure  Total expenditure            GDP    Population  \\\n",
       "count             2938.000000        2938.000000    2938.000000  2.938000e+03   \n",
       "mean               738.251295           5.924098    6611.523863  1.023085e+07   \n",
       "std               1987.914858           2.400770   13296.603449  5.402242e+07   \n",
       "min                  0.000000           0.370000       1.681350  3.400000e+01   \n",
       "25%                  4.685343           4.370000     580.486996  4.189172e+05   \n",
       "50%                 64.912906           5.755000    1766.947595  1.386542e+06   \n",
       "75%                441.534144           7.330000    4779.405190  4.584371e+06   \n",
       "max              19479.911610          17.600000  119172.741800  1.293859e+09   \n",
       "\n",
       "       thinness  1-19 years  thinness 5-9 years  \\\n",
       "count           2938.000000         2938.000000   \n",
       "mean               4.821886            4.852144   \n",
       "std                4.397621            4.485854   \n",
       "min                0.100000            0.100000   \n",
       "25%                1.600000            1.600000   \n",
       "50%                3.300000            3.300000   \n",
       "75%                7.100000            7.200000   \n",
       "max               27.700000           28.600000   \n",
       "\n",
       "       Income composition of resources    Schooling  infant deaths  \\\n",
       "count                      2938.000000  2938.000000    2938.000000   \n",
       "mean                          0.630362    12.009837      30.303948   \n",
       "std                           0.205140     3.265139     117.926501   \n",
       "min                           0.000000     0.000000       0.000000   \n",
       "25%                           0.504250    10.300000       0.000000   \n",
       "50%                           0.677000    12.300000       3.000000   \n",
       "75%                           0.772000    14.100000      22.000000   \n",
       "max                           0.948000    20.700000    1800.000000   \n",
       "\n",
       "       Hepatitis B        Measles          BMI  under-five deaths  \\\n",
       "count  2938.000000    2938.000000  2938.000000        2938.000000   \n",
       "mean     83.022124    2419.592240    38.381178          42.035739   \n",
       "std      22.996984   11467.272489    19.935375         160.445548   \n",
       "min       1.000000       0.000000     1.000000           0.000000   \n",
       "25%      82.000000       0.000000    19.400000           0.000000   \n",
       "50%      92.000000      17.000000    43.500000           4.000000   \n",
       "75%      96.000000     360.250000    56.100000          28.000000   \n",
       "max      99.000000  212183.000000    87.300000        2500.000000   \n",
       "\n",
       "             Polio   Diphtheria     HIV/AIDS  \n",
       "count  2938.000000  2938.000000  2938.000000  \n",
       "mean     82.617767    82.393125     1.742103  \n",
       "std      23.367166    23.655562     5.077785  \n",
       "min       3.000000     2.000000     0.100000  \n",
       "25%      78.000000    78.000000     0.100000  \n",
       "50%      93.000000    93.000000     0.100000  \n",
       "75%      97.000000    97.000000     0.800000  \n",
       "max      99.000000    99.000000    50.600000  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61fb3aa",
   "metadata": {},
   "source": [
    "**Scenario4**\n",
    "\n",
    "Congratulations, you have successfully sent a report based on visualiztion to your managers. They will get back to you and explit problem statements in order to draw outcomes. You have already sent insights using visualization and now you can move ahead with more hidden patterns0r find relationships among features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecfd979e",
   "metadata": {},
   "source": [
    "**Step9:**\n",
    "\n",
    "Life expectancy in countries, whether developed or developing, depends on various health facors. You will start with finding the countries which are impacted maximum or minimum against them.\n",
    "* Study the stastical parameters of all features\n",
    "* Countries having minimum BMI\n",
    "* Countries having max of alcohol consumption cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "19496032",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>thinness  1-19 years</th>\n",
       "      <th>thinness 5-9 years</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2.938000e+03</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.518720</td>\n",
       "      <td>69.234717</td>\n",
       "      <td>164.725664</td>\n",
       "      <td>4.546875</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>5.924098</td>\n",
       "      <td>6611.523863</td>\n",
       "      <td>1.023085e+07</td>\n",
       "      <td>4.821886</td>\n",
       "      <td>4.852144</td>\n",
       "      <td>0.630362</td>\n",
       "      <td>12.009837</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>83.022124</td>\n",
       "      <td>2419.592240</td>\n",
       "      <td>38.381178</td>\n",
       "      <td>42.035739</td>\n",
       "      <td>82.617767</td>\n",
       "      <td>82.393125</td>\n",
       "      <td>1.742103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.613841</td>\n",
       "      <td>9.509115</td>\n",
       "      <td>124.086215</td>\n",
       "      <td>3.921946</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>2.400770</td>\n",
       "      <td>13296.603449</td>\n",
       "      <td>5.402242e+07</td>\n",
       "      <td>4.397621</td>\n",
       "      <td>4.485854</td>\n",
       "      <td>0.205140</td>\n",
       "      <td>3.265139</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>22.996984</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>19.935375</td>\n",
       "      <td>160.445548</td>\n",
       "      <td>23.367166</td>\n",
       "      <td>23.655562</td>\n",
       "      <td>5.077785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>63.200000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>1.092500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>4.370000</td>\n",
       "      <td>580.486996</td>\n",
       "      <td>4.189172e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.504250</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>5.755000</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1.386542e+06</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>75.600000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>7.390000</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>7.330000</td>\n",
       "      <td>4779.405190</td>\n",
       "      <td>4.584371e+06</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.772000</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>56.100000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>17.600000</td>\n",
       "      <td>119172.741800</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>20.700000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>212183.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Life expectancy  Adult Mortality      Alcohol  \\\n",
       "count  2938.000000      2938.000000      2938.000000  2938.000000   \n",
       "mean   2007.518720        69.234717       164.725664     4.546875   \n",
       "std       4.613841         9.509115       124.086215     3.921946   \n",
       "min    2000.000000        36.300000         1.000000     0.010000   \n",
       "25%    2004.000000        63.200000        74.000000     1.092500   \n",
       "50%    2008.000000        72.100000       144.000000     3.755000   \n",
       "75%    2012.000000        75.600000       227.000000     7.390000   \n",
       "max    2015.000000        89.000000       723.000000    17.870000   \n",
       "\n",
       "       percentage expenditure  Total expenditure            GDP    Population  \\\n",
       "count             2938.000000        2938.000000    2938.000000  2.938000e+03   \n",
       "mean               738.251295           5.924098    6611.523863  1.023085e+07   \n",
       "std               1987.914858           2.400770   13296.603449  5.402242e+07   \n",
       "min                  0.000000           0.370000       1.681350  3.400000e+01   \n",
       "25%                  4.685343           4.370000     580.486996  4.189172e+05   \n",
       "50%                 64.912906           5.755000    1766.947595  1.386542e+06   \n",
       "75%                441.534144           7.330000    4779.405190  4.584371e+06   \n",
       "max              19479.911610          17.600000  119172.741800  1.293859e+09   \n",
       "\n",
       "       thinness  1-19 years  thinness 5-9 years  \\\n",
       "count           2938.000000         2938.000000   \n",
       "mean               4.821886            4.852144   \n",
       "std                4.397621            4.485854   \n",
       "min                0.100000            0.100000   \n",
       "25%                1.600000            1.600000   \n",
       "50%                3.300000            3.300000   \n",
       "75%                7.100000            7.200000   \n",
       "max               27.700000           28.600000   \n",
       "\n",
       "       Income composition of resources    Schooling  infant deaths  \\\n",
       "count                      2938.000000  2938.000000    2938.000000   \n",
       "mean                          0.630362    12.009837      30.303948   \n",
       "std                           0.205140     3.265139     117.926501   \n",
       "min                           0.000000     0.000000       0.000000   \n",
       "25%                           0.504250    10.300000       0.000000   \n",
       "50%                           0.677000    12.300000       3.000000   \n",
       "75%                           0.772000    14.100000      22.000000   \n",
       "max                           0.948000    20.700000    1800.000000   \n",
       "\n",
       "       Hepatitis B        Measles          BMI  under-five deaths  \\\n",
       "count  2938.000000    2938.000000  2938.000000        2938.000000   \n",
       "mean     83.022124    2419.592240    38.381178          42.035739   \n",
       "std      22.996984   11467.272489    19.935375         160.445548   \n",
       "min       1.000000       0.000000     1.000000           0.000000   \n",
       "25%      82.000000       0.000000    19.400000           0.000000   \n",
       "50%      92.000000      17.000000    43.500000           4.000000   \n",
       "75%      96.000000     360.250000    56.100000          28.000000   \n",
       "max      99.000000  212183.000000    87.300000        2500.000000   \n",
       "\n",
       "             Polio   Diphtheria     HIV/AIDS  \n",
       "count  2938.000000  2938.000000  2938.000000  \n",
       "mean     82.617767    82.393125     1.742103  \n",
       "std      23.367166    23.655562     5.077785  \n",
       "min       3.000000     2.000000     0.100000  \n",
       "25%      78.000000    78.000000     0.100000  \n",
       "50%      93.000000    93.000000     0.100000  \n",
       "75%      97.000000    97.000000     0.800000  \n",
       "max      99.000000    99.000000    50.600000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fa58416a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Population</th>\n",
       "      <th>...</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2887</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</td>\n",
       "      <td>73.8</td>\n",
       "      <td>137.0</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1386542.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.584</td>\n",
       "      <td>10.7</td>\n",
       "      <td>30</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6755</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39</td>\n",
       "      <td>92.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy  Adult Mortality  Alcohol  \\\n",
       "2887  Viet Nam  2002  Developing             73.8            137.0     2.03   \n",
       "\n",
       "      percentage expenditure  Total expenditure          GDP  Population  ...  \\\n",
       "2887                     0.0                4.7  1766.947595   1386542.0  ...   \n",
       "\n",
       "      Income composition of resources  Schooling  infant deaths  Hepatitis B  \\\n",
       "2887                            0.584       10.7             30         92.0   \n",
       "\n",
       "      Measles  BMI  under-five deaths  Polio  Diphtheria  HIV/AIDS  \n",
       "2887     6755  1.0                 39   92.0        75.0       0.2  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Countires having min BMI\n",
    "df[\"BMI\"]\n",
    "df[df[\"BMI\"]== df[\"BMI\"].min()]\n",
    "# df[df[\"BMI\"]== df[\"BMI\"].min()][\"Country\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e3deac50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "874    Estonia\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Countries having max of alcohol consuption cases\n",
    "df[df[\"Alcohol\"]==df[\"Alcohol\"].max()][\"Country\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "38463ed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1200    2001\n",
       "1201    2000\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# maximum infants deaths in country.\n",
    "df[df[\"infant deaths\"]==df[\"infant deaths\"].max()][\"Year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "88db6358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69.23471749489448"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(df[\"Life expectancy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ec819199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>...</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>2009</td>\n",
       "      <td>Developing</td>\n",
       "      <td>76.3</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>100.503021</td>\n",
       "      <td>9.22</td>\n",
       "      <td>597.165900</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.651</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</td>\n",
       "      <td>72.7</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>491.497891</td>\n",
       "      <td>5.90</td>\n",
       "      <td>3432.247841</td>\n",
       "      <td>34.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.601</td>\n",
       "      <td>11.8</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>75</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year      Status  Life expectancy  Adult Mortality  Alcohol  \\\n",
       "1608  Maldives  2009  Developing             76.3             75.0     1.59   \n",
       "1614  Maldives  2003  Developing             72.7            112.0     1.75   \n",
       "\n",
       "      percentage expenditure  Total expenditure          GDP  Population  ...  \\\n",
       "1608              100.503021               9.22   597.165900        36.0  ...   \n",
       "1614              491.497891               5.90  3432.247841        34.0  ...   \n",
       "\n",
       "      Income composition of resources  Schooling  infant deaths  Hepatitis B  \\\n",
       "1608                            0.651       11.8              0         98.0   \n",
       "1614                            0.601       11.8              0         98.0   \n",
       "\n",
       "      Measles   BMI  under-five deaths  Polio  Diphtheria  HIV/AIDS  \n",
       "1608        6  21.2                  0   98.0        98.0       0.1  \n",
       "1614       75  16.7                  0   98.0        98.0       0.1  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# country with poplution <40\n",
    "df[df[\"Population\"]<40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "54f87a10",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Population'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# draw the plot of maldives populationabs\n",
    "\n",
    "# m=df[df[\"Country\"]==\"Maldives\"]\n",
    "sns.lineplot(df[\"Year\"],df[df[\"Country\"]==\"Maldives\"][\"Population\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da9f996",
   "metadata": {},
   "source": [
    "**Step10:**\n",
    "    \n",
    "You have to study the total expenses done by countries towards the health sector. There is a hidden pattern you can highlight to your manager that can be relatedunrelated to healthe. Compplete below tasks to show your result.\n",
    "* Maldives have the smallest population. YOu can study the percentage of expenses contributed by a country despite such a population. Colllect the data for \"Maldives\" and take it's features, Year, Population, Total  expenditur. sort the data with respect to \"Year\" values.\n",
    "* You have shown a keen interest to find out the Country having maximum Life Expectancy. Studying about their health facilities and expenses done given by the goverment. can help other countries too execute new planes.\n",
    "* Your job role requires you to study about countries spending max of Total Expenditure towards Health, their life expectancy pattern can play a critical role in allocating resources and expenses.\n",
    "* Your job requires you to study about countries spending less than 1% of total expenditure on heealth, identify those countries as they mightbe on the vergee of an emergency.\n",
    "* FInd out the developing countries, which are having max and min populations. This can help you as data analyst in drawing this insights for Total Expences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7ed59311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total expenditure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>2000</td>\n",
       "      <td>286.0</td>\n",
       "      <td>8.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1616</th>\n",
       "      <td>2001</td>\n",
       "      <td>292.0</td>\n",
       "      <td>5.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1615</th>\n",
       "      <td>2002</td>\n",
       "      <td>297.0</td>\n",
       "      <td>5.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>2003</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1613</th>\n",
       "      <td>2004</td>\n",
       "      <td>312.0</td>\n",
       "      <td>5.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612</th>\n",
       "      <td>2005</td>\n",
       "      <td>321.0</td>\n",
       "      <td>9.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>2006</td>\n",
       "      <td>333.0</td>\n",
       "      <td>7.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1610</th>\n",
       "      <td>2007</td>\n",
       "      <td>349.0</td>\n",
       "      <td>6.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>2008</td>\n",
       "      <td>362.0</td>\n",
       "      <td>9.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>2009</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1607</th>\n",
       "      <td>2010</td>\n",
       "      <td>367.0</td>\n",
       "      <td>7.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>2011</td>\n",
       "      <td>377.0</td>\n",
       "      <td>8.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>2012</td>\n",
       "      <td>385.0</td>\n",
       "      <td>9.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1604</th>\n",
       "      <td>2013</td>\n",
       "      <td>393.0</td>\n",
       "      <td>11.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>2014</td>\n",
       "      <td>41.0</td>\n",
       "      <td>13.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>2015</td>\n",
       "      <td>49163.0</td>\n",
       "      <td>5.755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Population  Total expenditure\n",
       "1617  2000       286.0              8.000\n",
       "1616  2001       292.0              5.840\n",
       "1615  2002       297.0              5.780\n",
       "1614  2003        34.0              5.900\n",
       "1613  2004       312.0              5.890\n",
       "1612  2005       321.0              9.520\n",
       "1611  2006       333.0              7.370\n",
       "1610  2007       349.0              6.640\n",
       "1609  2008       362.0              9.300\n",
       "1608  2009        36.0              9.220\n",
       "1607  2010       367.0              7.930\n",
       "1606  2011       377.0              8.110\n",
       "1605  2012       385.0              9.160\n",
       "1604  2013       393.0             11.160\n",
       "1603  2014        41.0             13.730\n",
       "1602  2015     49163.0              5.755"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Country\"]==\"Maldives\"][[\"Year\",\"Population\",\"Total expenditure\"]].sort_values(by=\"Year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "908de483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>...</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>12.60</td>\n",
       "      <td>7163.348923</td>\n",
       "      <td>1.59</td>\n",
       "      <td>47439.39684</td>\n",
       "      <td>112957.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.890</td>\n",
       "      <td>16.3</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>70</td>\n",
       "      <td>63.4</td>\n",
       "      <td>1</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>Finland</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>89.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>8.80</td>\n",
       "      <td>6164.455402</td>\n",
       "      <td>9.68</td>\n",
       "      <td>49914.61864</td>\n",
       "      <td>5461512.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.890</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61.7</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>France</td>\n",
       "      <td>2008</td>\n",
       "      <td>Developing</td>\n",
       "      <td>89.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>11.90</td>\n",
       "      <td>7002.785925</td>\n",
       "      <td>1.57</td>\n",
       "      <td>45413.65710</td>\n",
       "      <td>6437499.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.877</td>\n",
       "      <td>16.1</td>\n",
       "      <td>3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>604</td>\n",
       "      <td>59.1</td>\n",
       "      <td>3</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>France</td>\n",
       "      <td>2007</td>\n",
       "      <td>Developing</td>\n",
       "      <td>89.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>12.20</td>\n",
       "      <td>64.737149</td>\n",
       "      <td>1.45</td>\n",
       "      <td>416.58397</td>\n",
       "      <td>6416229.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.873</td>\n",
       "      <td>16.1</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>39</td>\n",
       "      <td>58.6</td>\n",
       "      <td>3</td>\n",
       "      <td>99.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>11.03</td>\n",
       "      <td>941.756291</td>\n",
       "      <td>11.30</td>\n",
       "      <td>4792.65288</td>\n",
       "      <td>89825.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.920</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2</td>\n",
       "      <td>88.0</td>\n",
       "      <td>443</td>\n",
       "      <td>61.9</td>\n",
       "      <td>3</td>\n",
       "      <td>94.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8.98</td>\n",
       "      <td>4270.914882</td>\n",
       "      <td>8.49</td>\n",
       "      <td>31174.56118</td>\n",
       "      <td>57685327.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.846</td>\n",
       "      <td>15.6</td>\n",
       "      <td>2</td>\n",
       "      <td>96.0</td>\n",
       "      <td>599</td>\n",
       "      <td>57.6</td>\n",
       "      <td>3</td>\n",
       "      <td>97.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2010</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>9.62</td>\n",
       "      <td>6664.148708</td>\n",
       "      <td>11.20</td>\n",
       "      <td>33691.34837</td>\n",
       "      <td>1386542.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.899</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>43</td>\n",
       "      <td>64.8</td>\n",
       "      <td>0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>Norway</td>\n",
       "      <td>2009</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>6.68</td>\n",
       "      <td>142.374943</td>\n",
       "      <td>9.49</td>\n",
       "      <td>817.77681</td>\n",
       "      <td>4828726.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.936</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2</td>\n",
       "      <td>58.5</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.88</td>\n",
       "      <td>271.254553</td>\n",
       "      <td>9.50</td>\n",
       "      <td>2277.53613</td>\n",
       "      <td>14162.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.837</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2433</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2007</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>11.05</td>\n",
       "      <td>510.932701</td>\n",
       "      <td>8.36</td>\n",
       "      <td>3279.41400</td>\n",
       "      <td>4522683.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.849</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2</td>\n",
       "      <td>96.0</td>\n",
       "      <td>267</td>\n",
       "      <td>61.7</td>\n",
       "      <td>2</td>\n",
       "      <td>96.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>2007</td>\n",
       "      <td>Developed</td>\n",
       "      <td>89.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.90</td>\n",
       "      <td>7593.391622</td>\n",
       "      <td>8.92</td>\n",
       "      <td>53324.37937</td>\n",
       "      <td>914892.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.895</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1</td>\n",
       "      <td>56.1</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year      Status  Life expectancy  Adult Mortality  \\\n",
       "241       Belgium  2014   Developed             89.0             76.0   \n",
       "915       Finland  2014  Developing             89.0             78.0   \n",
       "937        France  2008  Developing             89.0             88.0   \n",
       "938        France  2007  Developing             89.0             89.0   \n",
       "995       Germany  2014   Developed             89.0             69.0   \n",
       "1293        Italy  2004   Developed             89.0             66.0   \n",
       "1850  New Zealand  2010   Developed             89.0             71.0   \n",
       "1916       Norway  2009   Developed             89.0             67.0   \n",
       "2056     Portugal  2014   Developed             89.0             78.0   \n",
       "2433        Spain  2007   Developed             89.0             72.0   \n",
       "2513       Sweden  2007   Developed             89.0             63.0   \n",
       "\n",
       "      Alcohol  percentage expenditure  Total expenditure          GDP  \\\n",
       "241     12.60             7163.348923               1.59  47439.39684   \n",
       "915      8.80             6164.455402               9.68  49914.61864   \n",
       "937     11.90             7002.785925               1.57  45413.65710   \n",
       "938     12.20               64.737149               1.45    416.58397   \n",
       "995     11.03              941.756291              11.30   4792.65288   \n",
       "1293     8.98             4270.914882               8.49  31174.56118   \n",
       "1850     9.62             6664.148708              11.20  33691.34837   \n",
       "1916     6.68              142.374943               9.49    817.77681   \n",
       "2056     9.88              271.254553               9.50   2277.53613   \n",
       "2433    11.05              510.932701               8.36   3279.41400   \n",
       "2513     6.90             7593.391622               8.92  53324.37937   \n",
       "\n",
       "      Population  ...  Income composition of resources  Schooling  \\\n",
       "241     112957.0  ...                            0.890       16.3   \n",
       "915    5461512.0  ...                            0.890       17.0   \n",
       "937    6437499.0  ...                            0.877       16.1   \n",
       "938    6416229.0  ...                            0.873       16.1   \n",
       "995      89825.0  ...                            0.920       17.0   \n",
       "1293  57685327.0  ...                            0.846       15.6   \n",
       "1850   1386542.0  ...                            0.899       20.3   \n",
       "1916   4828726.0  ...                            0.936       17.4   \n",
       "2056     14162.0  ...                            0.837       16.8   \n",
       "2433   4522683.0  ...                            0.849       16.0   \n",
       "2513    914892.0  ...                            0.895       15.8   \n",
       "\n",
       "      infant deaths  Hepatitis B  Measles   BMI  under-five deaths  Polio  \\\n",
       "241               0         98.0       70  63.4                  1   99.0   \n",
       "915               0         92.0        0  61.7                  0   98.0   \n",
       "937               3         47.0      604  59.1                  3   98.0   \n",
       "938               3         42.0       39  58.6                  3   99.0   \n",
       "995               2         88.0      443  61.9                  3   94.0   \n",
       "1293              2         96.0      599  57.6                  3   97.0   \n",
       "1850              0          9.0       43  64.8                  0   93.0   \n",
       "1916              0         92.0        2  58.5                  0   94.0   \n",
       "2056              0         98.0        0   6.9                  0   98.0   \n",
       "2433              2         96.0      267  61.7                  2   96.0   \n",
       "2513              0         92.0        1  56.1                  0   98.0   \n",
       "\n",
       "      Diphtheria  HIV/AIDS  \n",
       "241         99.0       0.1  \n",
       "915         98.0       0.1  \n",
       "937         98.0       0.1  \n",
       "938         98.0       0.1  \n",
       "995         95.0       0.1  \n",
       "1293        94.0       0.1  \n",
       "1850        93.0       0.1  \n",
       "1916        94.0       0.1  \n",
       "2056        98.0       0.1  \n",
       "2433        96.0       0.1  \n",
       "2513        98.0       0.1  \n",
       "\n",
       "[11 rows x 22 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Life expectancy\"]==df[\"Life expectancy\"].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5d09ffde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Life expectancy\"]==df[\"Life expectancy\"].max()][\"Country\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "403070dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Timor-Leste'], dtype=object)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# country spending less than 1%\n",
    "df[df[\"Total expenditure\"]<1][\"Country\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c51409f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>...</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2616</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</td>\n",
       "      <td>58.7</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>49.069672</td>\n",
       "      <td>3.260</td>\n",
       "      <td>422.286330</td>\n",
       "      <td>87167.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2615</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.4</td>\n",
       "      <td>269.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>6.556583</td>\n",
       "      <td>3.750</td>\n",
       "      <td>56.424987</td>\n",
       "      <td>892531.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.8</td>\n",
       "      <td>3</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>4</td>\n",
       "      <td>93.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2613</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</td>\n",
       "      <td>61.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.440</td>\n",
       "      <td>46.391328</td>\n",
       "      <td>2.480</td>\n",
       "      <td>471.456582</td>\n",
       "      <td>96852.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.485</td>\n",
       "      <td>9.8</td>\n",
       "      <td>3</td>\n",
       "      <td>92.0</td>\n",
       "      <td>94</td>\n",
       "      <td>12.9</td>\n",
       "      <td>4</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</td>\n",
       "      <td>62.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>0.480</td>\n",
       "      <td>5.649123</td>\n",
       "      <td>3.890</td>\n",
       "      <td>48.615520</td>\n",
       "      <td>923825.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.475</td>\n",
       "      <td>9.8</td>\n",
       "      <td>3</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>4</td>\n",
       "      <td>38.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2612</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developing</td>\n",
       "      <td>62.3</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.380</td>\n",
       "      <td>8.415789</td>\n",
       "      <td>1.310</td>\n",
       "      <td>467.543830</td>\n",
       "      <td>996698.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.484</td>\n",
       "      <td>10.2</td>\n",
       "      <td>3</td>\n",
       "      <td>92.0</td>\n",
       "      <td>41</td>\n",
       "      <td>13.2</td>\n",
       "      <td>3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2611</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2005</td>\n",
       "      <td>Developing</td>\n",
       "      <td>63.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.360</td>\n",
       "      <td>7.127145</td>\n",
       "      <td>1.500</td>\n",
       "      <td>478.331859</td>\n",
       "      <td>126484.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.492</td>\n",
       "      <td>10.6</td>\n",
       "      <td>3</td>\n",
       "      <td>92.0</td>\n",
       "      <td>203</td>\n",
       "      <td>13.5</td>\n",
       "      <td>3</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2610</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2006</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.9</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.040</td>\n",
       "      <td>28.125605</td>\n",
       "      <td>0.650</td>\n",
       "      <td>441.532260</td>\n",
       "      <td>148621.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>90</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2609</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2007</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.8</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>11.757669</td>\n",
       "      <td>0.370</td>\n",
       "      <td>524.895936</td>\n",
       "      <td>164973.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.541</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2608</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2008</td>\n",
       "      <td>Developing</td>\n",
       "      <td>66.2</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.060</td>\n",
       "      <td>36.691989</td>\n",
       "      <td>0.740</td>\n",
       "      <td>643.719110</td>\n",
       "      <td>17811.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.566</td>\n",
       "      <td>11.7</td>\n",
       "      <td>2</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>3</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2607</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2009</td>\n",
       "      <td>Developing</td>\n",
       "      <td>66.6</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0.090</td>\n",
       "      <td>36.199487</td>\n",
       "      <td>1.200</td>\n",
       "      <td>757.311444</td>\n",
       "      <td>19221.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.599</td>\n",
       "      <td>12.1</td>\n",
       "      <td>2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10</td>\n",
       "      <td>15.1</td>\n",
       "      <td>3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2606</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2010</td>\n",
       "      <td>Developing</td>\n",
       "      <td>66.9</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>38.583766</td>\n",
       "      <td>0.920</td>\n",
       "      <td>849.862697</td>\n",
       "      <td>119591.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.599</td>\n",
       "      <td>12.4</td>\n",
       "      <td>2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>50</td>\n",
       "      <td>15.5</td>\n",
       "      <td>3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2605</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>67.2</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>2.726571</td>\n",
       "      <td>0.760</td>\n",
       "      <td>114.561790</td>\n",
       "      <td>1131523.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.607</td>\n",
       "      <td>12.5</td>\n",
       "      <td>2</td>\n",
       "      <td>67.0</td>\n",
       "      <td>763</td>\n",
       "      <td>15.8</td>\n",
       "      <td>3</td>\n",
       "      <td>66.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2604</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>67.4</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>30.179985</td>\n",
       "      <td>1.100</td>\n",
       "      <td>1117.777240</td>\n",
       "      <td>115676.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.618</td>\n",
       "      <td>12.5</td>\n",
       "      <td>2</td>\n",
       "      <td>83.0</td>\n",
       "      <td>16</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2</td>\n",
       "      <td>83.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2603</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>67.7</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>3.379875</td>\n",
       "      <td>1.290</td>\n",
       "      <td>117.765674</td>\n",
       "      <td>1184366.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.620</td>\n",
       "      <td>12.5</td>\n",
       "      <td>2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4</td>\n",
       "      <td>16.6</td>\n",
       "      <td>2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2602</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>68.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>28.145785</td>\n",
       "      <td>1.480</td>\n",
       "      <td>1153.515780</td>\n",
       "      <td>1212814.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.612</td>\n",
       "      <td>12.5</td>\n",
       "      <td>2</td>\n",
       "      <td>77.0</td>\n",
       "      <td>47</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2601</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>68.3</td>\n",
       "      <td>152.0</td>\n",
       "      <td>3.755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.755</td>\n",
       "      <td>1161.769350</td>\n",
       "      <td>124977.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.603</td>\n",
       "      <td>12.5</td>\n",
       "      <td>2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>43</td>\n",
       "      <td>17.4</td>\n",
       "      <td>2</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Year      Status  Life expectancy  Adult Mortality  \\\n",
       "2616  Timor-Leste  2000  Developing             58.7            276.0   \n",
       "2615  Timor-Leste  2001  Developing             59.4            269.0   \n",
       "2613  Timor-Leste  2003  Developing             61.0             25.0   \n",
       "2614  Timor-Leste  2002  Developing             62.0            261.0   \n",
       "2612  Timor-Leste  2004  Developing             62.3            229.0   \n",
       "2611  Timor-Leste  2005  Developing             63.7             26.0   \n",
       "2610  Timor-Leste  2006  Developing             64.9            186.0   \n",
       "2609  Timor-Leste  2007  Developing             65.8            173.0   \n",
       "2608  Timor-Leste  2008  Developing             66.2            168.0   \n",
       "2607  Timor-Leste  2009  Developing             66.6            165.0   \n",
       "2606  Timor-Leste  2010  Developing             66.9            163.0   \n",
       "2605  Timor-Leste  2011  Developing             67.2            161.0   \n",
       "2604  Timor-Leste  2012  Developing             67.4            159.0   \n",
       "2603  Timor-Leste  2013  Developing             67.7            158.0   \n",
       "2602  Timor-Leste  2014  Developing             68.0            155.0   \n",
       "2601  Timor-Leste  2015  Developing             68.3            152.0   \n",
       "\n",
       "      Alcohol  percentage expenditure  Total expenditure          GDP  \\\n",
       "2616    0.500               49.069672              3.260   422.286330   \n",
       "2615    0.500                6.556583              3.750    56.424987   \n",
       "2613    0.440               46.391328              2.480   471.456582   \n",
       "2614    0.480                5.649123              3.890    48.615520   \n",
       "2612    0.380                8.415789              1.310   467.543830   \n",
       "2611    0.360                7.127145              1.500   478.331859   \n",
       "2610    0.040               28.125605              0.650   441.532260   \n",
       "2609    0.050               11.757669              0.370   524.895936   \n",
       "2608    0.060               36.691989              0.740   643.719110   \n",
       "2607    0.090               36.199487              1.200   757.311444   \n",
       "2606    0.100               38.583766              0.920   849.862697   \n",
       "2605    0.500                2.726571              0.760   114.561790   \n",
       "2604    0.010               30.179985              1.100  1117.777240   \n",
       "2603    0.010                3.379875              1.290   117.765674   \n",
       "2602    0.010               28.145785              1.480  1153.515780   \n",
       "2601    3.755                0.000000              5.755  1161.769350   \n",
       "\n",
       "      Population  ...  Income composition of resources  Schooling  \\\n",
       "2616     87167.0  ...                            0.000        0.0   \n",
       "2615    892531.0  ...                            0.470        9.8   \n",
       "2613     96852.0  ...                            0.485        9.8   \n",
       "2614    923825.0  ...                            0.475        9.8   \n",
       "2612    996698.0  ...                            0.484       10.2   \n",
       "2611    126484.0  ...                            0.492       10.6   \n",
       "2610    148621.0  ...                            0.511       11.0   \n",
       "2609    164973.0  ...                            0.541       11.3   \n",
       "2608     17811.0  ...                            0.566       11.7   \n",
       "2607     19221.0  ...                            0.599       12.1   \n",
       "2606    119591.0  ...                            0.599       12.4   \n",
       "2605   1131523.0  ...                            0.607       12.5   \n",
       "2604    115676.0  ...                            0.618       12.5   \n",
       "2603   1184366.0  ...                            0.620       12.5   \n",
       "2602   1212814.0  ...                            0.612       12.5   \n",
       "2601    124977.0  ...                            0.603       12.5   \n",
       "\n",
       "      infant deaths  Hepatitis B  Measles   BMI  under-five deaths  Polio  \\\n",
       "2616              3         92.0        0  11.9                  4   93.0   \n",
       "2615              3         92.0        0  12.3                  4   93.0   \n",
       "2613              3         92.0       94  12.9                  4   55.0   \n",
       "2614              3         92.0        0  12.6                  4   38.0   \n",
       "2612              3         92.0       41  13.2                  3   57.0   \n",
       "2611              3         92.0      203  13.5                  3   55.0   \n",
       "2610              2         92.0       90  13.9                  3   62.0   \n",
       "2609              2         92.0        0  14.2                  3    7.0   \n",
       "2608              2         79.0        0  14.7                  3   79.0   \n",
       "2607              2         72.0       10  15.1                  3   78.0   \n",
       "2606              2         72.0       50  15.5                  3   72.0   \n",
       "2605              2         67.0      763  15.8                  3   66.0   \n",
       "2604              2         83.0       16  16.2                  2   83.0   \n",
       "2603              2         82.0        4  16.6                  2   82.0   \n",
       "2602              2         77.0       47  17.0                  2   76.0   \n",
       "2601              2         76.0       43  17.4                  2   75.0   \n",
       "\n",
       "      Diphtheria  HIV/AIDS  \n",
       "2616        93.0       0.1  \n",
       "2615        93.0       0.1  \n",
       "2613        55.0       0.1  \n",
       "2614        54.0       0.1  \n",
       "2612        57.0       0.1  \n",
       "2611        55.0       0.1  \n",
       "2610        63.0       0.1  \n",
       "2609         7.0       0.1  \n",
       "2608        79.0       0.1  \n",
       "2607        72.0       0.1  \n",
       "2606        72.0       0.1  \n",
       "2605        67.0       0.1  \n",
       "2604        83.0       0.1  \n",
       "2603        82.0       0.1  \n",
       "2602        77.0       0.1  \n",
       "2601        76.0       0.1  \n",
       "\n",
       "[16 rows x 22 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Country\"]==\"Timor-Leste\"].sort_values(\"Life expectancy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "5f738400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>...</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2798</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developed</td>\n",
       "      <td>78.7</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1386542.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.677</td>\n",
       "      <td>12.3</td>\n",
       "      <td>25</td>\n",
       "      <td>91.0</td>\n",
       "      <td>220</td>\n",
       "      <td>67.5</td>\n",
       "      <td>29</td>\n",
       "      <td>94.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Country  Year     Status  Life expectancy  \\\n",
       "2798  United States of America  2011  Developed             78.7   \n",
       "\n",
       "      Adult Mortality  Alcohol  percentage expenditure  Total expenditure  \\\n",
       "2798             16.0     8.67                     0.0               17.6   \n",
       "\n",
       "              GDP  Population  ...  Income composition of resources  \\\n",
       "2798  1766.947595   1386542.0  ...                            0.677   \n",
       "\n",
       "      Schooling  infant deaths  Hepatitis B  Measles   BMI  under-five deaths  \\\n",
       "2798       12.3             25         91.0      220  67.5                 29   \n",
       "\n",
       "      Polio  Diphtheria  HIV/AIDS  \n",
       "2798   94.0        96.0       0.1  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Countries spending max of total expenditure towards health\n",
    "df[df[\"Total expenditure\"]==df[\"Total expenditure\"].max()]\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "6ee800f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Status</th>\n",
       "      <th>Life expectancy</th>\n",
       "      <th>Adult Mortality</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>percentage expenditure</th>\n",
       "      <th>Total expenditure</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Population</th>\n",
       "      <th>...</th>\n",
       "      <th>Income composition of resources</th>\n",
       "      <th>Schooling</th>\n",
       "      <th>infant deaths</th>\n",
       "      <th>Hepatitis B</th>\n",
       "      <th>Measles</th>\n",
       "      <th>BMI</th>\n",
       "      <th>under-five deaths</th>\n",
       "      <th>Polio</th>\n",
       "      <th>Diphtheria</th>\n",
       "      <th>HIV/AIDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>India</td>\n",
       "      <td>2000</td>\n",
       "      <td>Developing</td>\n",
       "      <td>62.5</td>\n",
       "      <td>224.0</td>\n",
       "      <td>0.930</td>\n",
       "      <td>19.266157</td>\n",
       "      <td>4.260</td>\n",
       "      <td>438.864634</td>\n",
       "      <td>1.535912e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1800</td>\n",
       "      <td>92.0</td>\n",
       "      <td>38835</td>\n",
       "      <td>11.4</td>\n",
       "      <td>2500</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>India</td>\n",
       "      <td>2001</td>\n",
       "      <td>Developing</td>\n",
       "      <td>62.9</td>\n",
       "      <td>222.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>19.003406</td>\n",
       "      <td>4.500</td>\n",
       "      <td>447.138966</td>\n",
       "      <td>1.714779e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.494</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1800</td>\n",
       "      <td>92.0</td>\n",
       "      <td>51780</td>\n",
       "      <td>11.8</td>\n",
       "      <td>2400</td>\n",
       "      <td>58.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>India</td>\n",
       "      <td>2002</td>\n",
       "      <td>Developing</td>\n",
       "      <td>63.3</td>\n",
       "      <td>219.0</td>\n",
       "      <td>1.100</td>\n",
       "      <td>17.812056</td>\n",
       "      <td>4.400</td>\n",
       "      <td>466.284200</td>\n",
       "      <td>1.898711e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.499</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1700</td>\n",
       "      <td>92.0</td>\n",
       "      <td>40044</td>\n",
       "      <td>12.2</td>\n",
       "      <td>2300</td>\n",
       "      <td>58.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>India</td>\n",
       "      <td>2003</td>\n",
       "      <td>Developing</td>\n",
       "      <td>63.7</td>\n",
       "      <td>216.0</td>\n",
       "      <td>1.190</td>\n",
       "      <td>19.480868</td>\n",
       "      <td>4.300</td>\n",
       "      <td>541.135228</td>\n",
       "      <td>1.182785e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.505</td>\n",
       "      <td>8.6</td>\n",
       "      <td>1700</td>\n",
       "      <td>92.0</td>\n",
       "      <td>47147</td>\n",
       "      <td>12.6</td>\n",
       "      <td>2200</td>\n",
       "      <td>57.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>India</td>\n",
       "      <td>2004</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>1.200</td>\n",
       "      <td>27.338009</td>\n",
       "      <td>4.220</td>\n",
       "      <td>621.318377</td>\n",
       "      <td>1.126136e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.518</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1600</td>\n",
       "      <td>6.0</td>\n",
       "      <td>55443</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2100</td>\n",
       "      <td>58.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>India</td>\n",
       "      <td>2005</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.4</td>\n",
       "      <td>211.0</td>\n",
       "      <td>1.270</td>\n",
       "      <td>3.509637</td>\n",
       "      <td>4.280</td>\n",
       "      <td>77.819000</td>\n",
       "      <td>1.144119e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.526</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1500</td>\n",
       "      <td>8.0</td>\n",
       "      <td>36711</td>\n",
       "      <td>13.5</td>\n",
       "      <td>2000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>India</td>\n",
       "      <td>2006</td>\n",
       "      <td>Developing</td>\n",
       "      <td>64.8</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.370</td>\n",
       "      <td>34.859427</td>\n",
       "      <td>4.250</td>\n",
       "      <td>792.259698</td>\n",
       "      <td>1.161978e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.536</td>\n",
       "      <td>9.7</td>\n",
       "      <td>1500</td>\n",
       "      <td>6.0</td>\n",
       "      <td>64185</td>\n",
       "      <td>13.9</td>\n",
       "      <td>2000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>India</td>\n",
       "      <td>2007</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.590</td>\n",
       "      <td>5.234770</td>\n",
       "      <td>4.230</td>\n",
       "      <td>118.166373</td>\n",
       "      <td>1.179681e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.546</td>\n",
       "      <td>9.9</td>\n",
       "      <td>1400</td>\n",
       "      <td>6.0</td>\n",
       "      <td>41144</td>\n",
       "      <td>14.4</td>\n",
       "      <td>1900</td>\n",
       "      <td>67.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>India</td>\n",
       "      <td>2008</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.930</td>\n",
       "      <td>43.030433</td>\n",
       "      <td>4.340</td>\n",
       "      <td>991.484634</td>\n",
       "      <td>1.197147e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.556</td>\n",
       "      <td>10.2</td>\n",
       "      <td>1300</td>\n",
       "      <td>29.0</td>\n",
       "      <td>44258</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1800</td>\n",
       "      <td>69.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>India</td>\n",
       "      <td>2009</td>\n",
       "      <td>Developing</td>\n",
       "      <td>66.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.844186</td>\n",
       "      <td>4.380</td>\n",
       "      <td>19.317765</td>\n",
       "      <td>1.214271e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.563</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1300</td>\n",
       "      <td>37.0</td>\n",
       "      <td>56188</td>\n",
       "      <td>15.4</td>\n",
       "      <td>1700</td>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>India</td>\n",
       "      <td>2010</td>\n",
       "      <td>Developing</td>\n",
       "      <td>66.4</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2.770</td>\n",
       "      <td>57.733599</td>\n",
       "      <td>4.280</td>\n",
       "      <td>1345.771530</td>\n",
       "      <td>1.239869e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.569</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1200</td>\n",
       "      <td>38.0</td>\n",
       "      <td>31458</td>\n",
       "      <td>15.9</td>\n",
       "      <td>1600</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>India</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>66.8</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>64.605901</td>\n",
       "      <td>4.330</td>\n",
       "      <td>1461.671957</td>\n",
       "      <td>1.247236e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.580</td>\n",
       "      <td>10.8</td>\n",
       "      <td>1100</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33634</td>\n",
       "      <td>16.4</td>\n",
       "      <td>1500</td>\n",
       "      <td>79.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>India</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>67.3</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.100</td>\n",
       "      <td>64.969645</td>\n",
       "      <td>4.390</td>\n",
       "      <td>1446.985410</td>\n",
       "      <td>1.263659e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.590</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1100</td>\n",
       "      <td>73.0</td>\n",
       "      <td>18668</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>79.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>India</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>67.6</td>\n",
       "      <td>187.0</td>\n",
       "      <td>3.110</td>\n",
       "      <td>67.672304</td>\n",
       "      <td>4.530</td>\n",
       "      <td>1452.195373</td>\n",
       "      <td>1.278562e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.599</td>\n",
       "      <td>11.5</td>\n",
       "      <td>1000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13822</td>\n",
       "      <td>17.5</td>\n",
       "      <td>1300</td>\n",
       "      <td>82.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>India</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>68.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>3.070</td>\n",
       "      <td>86.521539</td>\n",
       "      <td>4.690</td>\n",
       "      <td>1573.118890</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.607</td>\n",
       "      <td>11.6</td>\n",
       "      <td>957</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79563</td>\n",
       "      <td>18.1</td>\n",
       "      <td>1200</td>\n",
       "      <td>84.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>India</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>68.3</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3.755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.755</td>\n",
       "      <td>1613.188780</td>\n",
       "      <td>1.395398e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.615</td>\n",
       "      <td>11.6</td>\n",
       "      <td>910</td>\n",
       "      <td>87.0</td>\n",
       "      <td>90387</td>\n",
       "      <td>18.7</td>\n",
       "      <td>1100</td>\n",
       "      <td>86.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Year      Status  Life expectancy  Adult Mortality  Alcohol  \\\n",
       "1201   India  2000  Developing             62.5            224.0    0.930   \n",
       "1200   India  2001  Developing             62.9            222.0    1.000   \n",
       "1199   India  2002  Developing             63.3            219.0    1.100   \n",
       "1198   India  2003  Developing             63.7            216.0    1.190   \n",
       "1197   India  2004  Developing             64.0            214.0    1.200   \n",
       "1196   India  2005  Developing             64.4            211.0    1.270   \n",
       "1195   India  2006  Developing             64.8             28.0    1.370   \n",
       "1194   India  2007  Developing             65.2             26.0    1.590   \n",
       "1193   India  2008  Developing             65.5             23.0    1.930   \n",
       "1192   India  2009  Developing             66.0              2.0    2.500   \n",
       "1191   India  2010  Developing             66.4            196.0    2.770   \n",
       "1190   India  2011  Developing             66.8            193.0    3.000   \n",
       "1189   India  2012  Developing             67.3             19.0    3.100   \n",
       "1188   India  2013  Developing             67.6            187.0    3.110   \n",
       "1187   India  2014  Developing             68.0            184.0    3.070   \n",
       "1186   India  2015  Developing             68.3            181.0    3.755   \n",
       "\n",
       "      percentage expenditure  Total expenditure          GDP    Population  \\\n",
       "1201               19.266157              4.260   438.864634  1.535912e+06   \n",
       "1200               19.003406              4.500   447.138966  1.714779e+08   \n",
       "1199               17.812056              4.400   466.284200  1.898711e+07   \n",
       "1198               19.480868              4.300   541.135228  1.182785e+07   \n",
       "1197               27.338009              4.220   621.318377  1.126136e+09   \n",
       "1196                3.509637              4.280    77.819000  1.144119e+09   \n",
       "1195               34.859427              4.250   792.259698  1.161978e+09   \n",
       "1194                5.234770              4.230   118.166373  1.179681e+09   \n",
       "1193               43.030433              4.340   991.484634  1.197147e+08   \n",
       "1192                0.844186              4.380    19.317765  1.214271e+08   \n",
       "1191               57.733599              4.280  1345.771530  1.239869e+07   \n",
       "1190               64.605901              4.330  1461.671957  1.247236e+08   \n",
       "1189               64.969645              4.390  1446.985410  1.263659e+08   \n",
       "1188               67.672304              4.530  1452.195373  1.278562e+08   \n",
       "1187               86.521539              4.690  1573.118890  1.293859e+09   \n",
       "1186                0.000000              5.755  1613.188780  1.395398e+06   \n",
       "\n",
       "      ...  Income composition of resources  Schooling  infant deaths  \\\n",
       "1201  ...                            0.489        8.3           1800   \n",
       "1200  ...                            0.494        8.3           1800   \n",
       "1199  ...                            0.499        8.4           1700   \n",
       "1198  ...                            0.505        8.6           1700   \n",
       "1197  ...                            0.518        9.2           1600   \n",
       "1196  ...                            0.526        9.4           1500   \n",
       "1195  ...                            0.536        9.7           1500   \n",
       "1194  ...                            0.546        9.9           1400   \n",
       "1193  ...                            0.556       10.2           1300   \n",
       "1192  ...                            0.563       10.5           1300   \n",
       "1191  ...                            0.569       10.4           1200   \n",
       "1190  ...                            0.580       10.8           1100   \n",
       "1189  ...                            0.590       11.3           1100   \n",
       "1188  ...                            0.599       11.5           1000   \n",
       "1187  ...                            0.607       11.6            957   \n",
       "1186  ...                            0.615       11.6            910   \n",
       "\n",
       "      Hepatitis B  Measles   BMI  under-five deaths  Polio  Diphtheria  \\\n",
       "1201         92.0    38835  11.4               2500   57.0        58.0   \n",
       "1200         92.0    51780  11.8               2400   58.0        59.0   \n",
       "1199         92.0    40044  12.2               2300   58.0        59.0   \n",
       "1198         92.0    47147  12.6               2200   57.0        61.0   \n",
       "1197          6.0    55443  13.0               2100   58.0        63.0   \n",
       "1196          8.0    36711  13.5               2000   65.0        65.0   \n",
       "1195          6.0    64185  13.9               2000   66.0        65.0   \n",
       "1194          6.0    41144  14.4               1900   67.0        64.0   \n",
       "1193         29.0    44258  14.9               1800   69.0         7.0   \n",
       "1192         37.0    56188  15.4               1700   73.0        74.0   \n",
       "1191         38.0    31458  15.9               1600   76.0        79.0   \n",
       "1190         44.0    33634  16.4               1500   79.0        82.0   \n",
       "1189         73.0    18668  17.0               1400   79.0        82.0   \n",
       "1188          7.0    13822  17.5               1300   82.0        83.0   \n",
       "1187         79.0    79563  18.1               1200   84.0        85.0   \n",
       "1186         87.0    90387  18.7               1100   86.0        87.0   \n",
       "\n",
       "      HIV/AIDS  \n",
       "1201       0.3  \n",
       "1200       0.3  \n",
       "1199       0.3  \n",
       "1198       0.3  \n",
       "1197       0.3  \n",
       "1196       0.3  \n",
       "1195       0.3  \n",
       "1194       0.3  \n",
       "1193       0.3  \n",
       "1192       0.2  \n",
       "1191       0.2  \n",
       "1190       0.2  \n",
       "1189       0.2  \n",
       "1188       0.2  \n",
       "1187       0.2  \n",
       "1186       0.2  \n",
       "\n",
       "[16 rows x 22 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[df[\"Country\"]==\"India\"].sort_values(\"Year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "401ffe21",
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       "     Country  Year      Status  Life expectancy  Adult Mortality  Alcohol  \\\n",
       "1187   India  2014  Developing             68.0            184.0     3.07   \n",
       "\n",
       "      percentage expenditure  Total expenditure         GDP    Population  \\\n",
       "1187               86.521539               4.69  1573.11889  1.293859e+09   \n",
       "\n",
       "      ...  Income composition of resources  Schooling  infant deaths  \\\n",
       "1187  ...                            0.607       11.6            957   \n",
       "\n",
       "      Hepatitis B  Measles   BMI  under-five deaths  Polio  Diphtheria  \\\n",
       "1187         79.0    79563  18.1               1200   84.0        85.0   \n",
       "\n",
       "      HIV/AIDS  \n",
       "1187       0.2  \n",
       "\n",
       "[1 rows x 22 columns]"
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     "execution_count": 140,
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   "execution_count": 142,
   "id": "1023dbd1",
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   "outputs": [
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       "      <td>72.7</td>\n",
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       "       Country  Year      Status  Life expectancy  Adult Mortality  Alcohol  \\\n",
       "1614  Maldives  2003  Developing             72.7            112.0     1.75   \n",
       "\n",
       "      percentage expenditure  Total expenditure          GDP  Population  ...  \\\n",
       "1614              491.497891                5.9  3432.247841        34.0  ...   \n",
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       "      Income composition of resources  Schooling  infant deaths  Hepatitis B  \\\n",
       "1614                            0.601       11.8              0         98.0   \n",
       "\n",
       "      Measles   BMI  under-five deaths  Polio  Diphtheria  HIV/AIDS  \n",
       "1614       75  16.7                  0   98.0        98.0       0.1  \n",
       "\n",
       "[1 rows x 22 columns]"
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     "execution_count": 142,
     "metadata": {},
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    "df[(df[\"Status\"]=='Developing')&(df[\"Population\"]==df['Population'].min())]"
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   "execution_count": null,
   "id": "3d1b613a",
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