data_processing.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "dd0e2fd8-63dc-49e0-b60f-ac0eb693db0c",
   "metadata": {},
   "source": [
    "Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd0e4db5-19a6-4718-ad4d-986dde8971b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aea1cb6-89ff-4d4b-90b7-f296b2fdd96b",
   "metadata": {},
   "source": [
    "## Combining datasets into a single dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e4d8a17-9093-4a67-8b66-93258912ad41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting Data Folder Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0bf01055-b908-4d14-adb9-4f638f05bb75",
   "metadata": {},
   "outputs": [],
   "source": [
    "male_data = 'E:/Coding/Suicide Rate Analysis/Mortality Data/Male'\n",
    "female_data = 'E:/Coding/Suicide Rate Analysis/Mortality Data/Female'\n",
    "unknown_data = 'E:/Coding/Suicide Rate Analysis/Mortality Data/Unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbe2e014-d350-4cca-9e80-dc7733b62682",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating empty dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83f1cf2a-5601-4c0c-a20e-da31af6b3576",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bab495d9-29b3-490f-a12b-10789d6e3af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Listing all csv files of respective folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2dd58531-a11c-40b6-8e06-8a41115c3f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "male_csv = [file for file in os.listdir(male_data) if file.endswith('.csv')]\n",
    "female_csv = [file for file in os.listdir(female_data) if file.endswith('.csv')]\n",
    "unknown_csv = [file for file in os.listdir(unknown_data) if file.endswith('.csv')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82063efb-000d-427d-80bb-6f0c4cc4f2d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looping through each file and concatenating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3736a77-1bbf-4b61-989a-925e7e08b81d",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region Code</th>\n",
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       "      <th>Death rate per 100 000 population</th>\n",
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       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1989</td>\n",
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       "      <th>94415</th>\n",
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       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2018</td>\n",
       "      <td>Male</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>94417</th>\n",
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       "      <td>Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2017</td>\n",
       "      <td>Male</td>\n",
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       "      <td>South Africa</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>Africa</td>\n",
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       "      <td>South Africa</td>\n",
       "      <td>2015</td>\n",
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       "<p>94420 rows × 12 columns</p>\n",
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      "text/plain": [
       "      Region Code                Region Name Country Code  \\\n",
       "0              EU                     Europe          ALB   \n",
       "1              EU                     Europe          ALB   \n",
       "2              EU                     Europe          ALB   \n",
       "3              EU                     Europe          ALB   \n",
       "4              EU                     Europe          ALB   \n",
       "...           ...                        ...          ...   \n",
       "94415         CSA  Central and South America          VEN   \n",
       "94416          AF                     Africa          ZAF   \n",
       "94417          AF                     Africa          ZAF   \n",
       "94418          AF                     Africa          ZAF   \n",
       "94419          AF                     Africa          ZAF   \n",
       "\n",
       "                             Country Name  Year   Sex Age group code  \\\n",
       "0                                 Albania  1987  Male          Age00   \n",
       "1                                 Albania  1988  Male          Age00   \n",
       "2                                 Albania  1989  Male          Age00   \n",
       "3                                 Albania  1992  Male          Age00   \n",
       "4                                 Albania  1993  Male          Age00   \n",
       "...                                   ...   ...   ...            ...   \n",
       "94415  Venezuela (Bolivarian Republic of)  2015  Male    Age_unknown   \n",
       "94416                        South Africa  2018  Male    Age_unknown   \n",
       "94417                        South Africa  2017  Male    Age_unknown   \n",
       "94418                        South Africa  2016  Male    Age_unknown   \n",
       "94419                        South Africa  2015  Male    Age_unknown   \n",
       "\n",
       "       Age Group  Number  \\\n",
       "0            [0]     0.0   \n",
       "1            [0]     0.0   \n",
       "2            [0]     0.0   \n",
       "3            [0]     0.0   \n",
       "4            [0]     0.0   \n",
       "...          ...     ...   \n",
       "94415  [Unknown]     0.0   \n",
       "94416  [Unknown]     0.0   \n",
       "94417  [Unknown]     0.0   \n",
       "94418  [Unknown]     2.0   \n",
       "94419  [Unknown]     0.0   \n",
       "\n",
       "       Percentage of cause-specific deaths out of total deaths  \\\n",
       "0                                               0.000000         \n",
       "1                                               0.000000         \n",
       "2                                               0.000000         \n",
       "3                                               0.000000         \n",
       "4                                               0.000000         \n",
       "...                                                  ...         \n",
       "94415                                           0.000000         \n",
       "94416                                           0.000000         \n",
       "94417                                           0.000000         \n",
       "94418                                           0.208768         \n",
       "94419                                           0.000000         \n",
       "\n",
       "       Age-standardized death rate per 100 000 standard population  \\\n",
       "0                                                    NaN             \n",
       "1                                                    NaN             \n",
       "2                                                    NaN             \n",
       "3                                                    NaN             \n",
       "4                                                    NaN             \n",
       "...                                                  ...             \n",
       "94415                                                NaN             \n",
       "94416                                                NaN             \n",
       "94417                                                NaN             \n",
       "94418                                                NaN             \n",
       "94419                                                NaN             \n",
       "\n",
       "       Death rate per 100 000 population  \n",
       "0                                    0.0  \n",
       "1                                    0.0  \n",
       "2                                    0.0  \n",
       "3                                    0.0  \n",
       "4                                    0.0  \n",
       "...                                  ...  \n",
       "94415                                NaN  \n",
       "94416                                NaN  \n",
       "94417                                NaN  \n",
       "94418                                NaN  \n",
       "94419                                NaN  \n",
       "\n",
       "[94420 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for csv_file in male_csv:\n",
    "    file_path = os.path.join(male_data, csv_file)\n",
    "    df = pd.read_csv(file_path)\n",
    "    combined_data = pd.concat([combined_data, df], ignore_index=True)\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9eef5792-11e0-46ab-a8f3-fccece2e1df0",
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   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1992</td>\n",
       "      <td>Male</td>\n",
       "      <td>Age00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>ALB</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>188835</th>\n",
       "      <td>CSA</td>\n",
       "      <td>Central and South America</td>\n",
       "      <td>VEN</td>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "      <td>2015</td>\n",
       "      <td>Female</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>188836</th>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2018</td>\n",
       "      <td>Female</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>188837</th>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2017</td>\n",
       "      <td>Female</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188838</th>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2016</td>\n",
       "      <td>Female</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188839</th>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2015</td>\n",
       "      <td>Female</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188840 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Region Code                Region Name Country Code  \\\n",
       "0               EU                     Europe          ALB   \n",
       "1               EU                     Europe          ALB   \n",
       "2               EU                     Europe          ALB   \n",
       "3               EU                     Europe          ALB   \n",
       "4               EU                     Europe          ALB   \n",
       "...            ...                        ...          ...   \n",
       "188835         CSA  Central and South America          VEN   \n",
       "188836          AF                     Africa          ZAF   \n",
       "188837          AF                     Africa          ZAF   \n",
       "188838          AF                     Africa          ZAF   \n",
       "188839          AF                     Africa          ZAF   \n",
       "\n",
       "                              Country Name  Year     Sex Age group code  \\\n",
       "0                                  Albania  1987    Male          Age00   \n",
       "1                                  Albania  1988    Male          Age00   \n",
       "2                                  Albania  1989    Male          Age00   \n",
       "3                                  Albania  1992    Male          Age00   \n",
       "4                                  Albania  1993    Male          Age00   \n",
       "...                                    ...   ...     ...            ...   \n",
       "188835  Venezuela (Bolivarian Republic of)  2015  Female    Age_unknown   \n",
       "188836                        South Africa  2018  Female    Age_unknown   \n",
       "188837                        South Africa  2017  Female    Age_unknown   \n",
       "188838                        South Africa  2016  Female    Age_unknown   \n",
       "188839                        South Africa  2015  Female    Age_unknown   \n",
       "\n",
       "        Age Group  Number  \\\n",
       "0             [0]     0.0   \n",
       "1             [0]     0.0   \n",
       "2             [0]     0.0   \n",
       "3             [0]     0.0   \n",
       "4             [0]     0.0   \n",
       "...           ...     ...   \n",
       "188835  [Unknown]     0.0   \n",
       "188836  [Unknown]     0.0   \n",
       "188837  [Unknown]     0.0   \n",
       "188838  [Unknown]     0.0   \n",
       "188839  [Unknown]     0.0   \n",
       "\n",
       "        Percentage of cause-specific deaths out of total deaths  \\\n",
       "0                                                     0.0         \n",
       "1                                                     0.0         \n",
       "2                                                     0.0         \n",
       "3                                                     0.0         \n",
       "4                                                     0.0         \n",
       "...                                                   ...         \n",
       "188835                                                NaN         \n",
       "188836                                                0.0         \n",
       "188837                                                0.0         \n",
       "188838                                                0.0         \n",
       "188839                                                0.0         \n",
       "\n",
       "        Age-standardized death rate per 100 000 standard population  \\\n",
       "0                                                     NaN             \n",
       "1                                                     NaN             \n",
       "2                                                     NaN             \n",
       "3                                                     NaN             \n",
       "4                                                     NaN             \n",
       "...                                                   ...             \n",
       "188835                                                NaN             \n",
       "188836                                                NaN             \n",
       "188837                                                NaN             \n",
       "188838                                                NaN             \n",
       "188839                                                NaN             \n",
       "\n",
       "        Death rate per 100 000 population  \n",
       "0                                     0.0  \n",
       "1                                     0.0  \n",
       "2                                     0.0  \n",
       "3                                     0.0  \n",
       "4                                     0.0  \n",
       "...                                   ...  \n",
       "188835                                NaN  \n",
       "188836                                NaN  \n",
       "188837                                NaN  \n",
       "188838                                NaN  \n",
       "188839                                NaN  \n",
       "\n",
       "[188840 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for csv_file in female_csv:\n",
    "    file_path = os.path.join(female_data, csv_file)\n",
    "    df = pd.read_csv(file_path)\n",
    "    combined_data = pd.concat([combined_data, df], ignore_index=True)\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "43ff4cbd-e836-48bf-8e5e-756161f2b130",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region Code</th>\n",
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       "      <td>Europe</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>1989</td>\n",
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       "      <td>[0]</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EU</td>\n",
       "      <td>Europe</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1992</td>\n",
       "      <td>Male</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EU</td>\n",
       "      <td>Europe</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190755</th>\n",
       "      <td>AS</td>\n",
       "      <td>Asia</td>\n",
       "      <td>OMN</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2014</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190756</th>\n",
       "      <td>CSA</td>\n",
       "      <td>Central and South America</td>\n",
       "      <td>URY</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2016</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190757</th>\n",
       "      <td>CSA</td>\n",
       "      <td>Central and South America</td>\n",
       "      <td>URY</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2015</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190758</th>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2016</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Age_unknown</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>190759</th>\n",
       "      <td>AF</td>\n",
       "      <td>Africa</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2015</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Age_unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190760 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Region Code                Region Name Country Code  Country Name  \\\n",
       "0               EU                     Europe          ALB       Albania   \n",
       "1               EU                     Europe          ALB       Albania   \n",
       "2               EU                     Europe          ALB       Albania   \n",
       "3               EU                     Europe          ALB       Albania   \n",
       "4               EU                     Europe          ALB       Albania   \n",
       "...            ...                        ...          ...           ...   \n",
       "190755          AS                       Asia          OMN          Oman   \n",
       "190756         CSA  Central and South America          URY       Uruguay   \n",
       "190757         CSA  Central and South America          URY       Uruguay   \n",
       "190758          AF                     Africa          ZAF  South Africa   \n",
       "190759          AF                     Africa          ZAF  South Africa   \n",
       "\n",
       "        Year      Sex Age group code  Age Group  Number  \\\n",
       "0       1987     Male          Age00        [0]     0.0   \n",
       "1       1988     Male          Age00        [0]     0.0   \n",
       "2       1989     Male          Age00        [0]     0.0   \n",
       "3       1992     Male          Age00        [0]     0.0   \n",
       "4       1993     Male          Age00        [0]     0.0   \n",
       "...      ...      ...            ...        ...     ...   \n",
       "190755  2014  Unknown    Age_unknown  [Unknown]     0.0   \n",
       "190756  2016  Unknown    Age_unknown  [Unknown]     1.0   \n",
       "190757  2015  Unknown    Age_unknown  [Unknown]     1.0   \n",
       "190758  2016  Unknown    Age_unknown  [Unknown]     0.0   \n",
       "190759  2015  Unknown    Age_unknown  [Unknown]     0.0   \n",
       "\n",
       "        Percentage of cause-specific deaths out of total deaths  \\\n",
       "0                                                     0.0         \n",
       "1                                                     0.0         \n",
       "2                                                     0.0         \n",
       "3                                                     0.0         \n",
       "4                                                     0.0         \n",
       "...                                                   ...         \n",
       "190755                                                NaN         \n",
       "190756                                               20.0         \n",
       "190757                                               25.0         \n",
       "190758                                                0.0         \n",
       "190759                                                0.0         \n",
       "\n",
       "        Age-standardized death rate per 100 000 standard population  \\\n",
       "0                                                     NaN             \n",
       "1                                                     NaN             \n",
       "2                                                     NaN             \n",
       "3                                                     NaN             \n",
       "4                                                     NaN             \n",
       "...                                                   ...             \n",
       "190755                                                NaN             \n",
       "190756                                                NaN             \n",
       "190757                                                NaN             \n",
       "190758                                                NaN             \n",
       "190759                                                NaN             \n",
       "\n",
       "        Death rate per 100 000 population  \n",
       "0                                     0.0  \n",
       "1                                     0.0  \n",
       "2                                     0.0  \n",
       "3                                     0.0  \n",
       "4                                     0.0  \n",
       "...                                   ...  \n",
       "190755                                NaN  \n",
       "190756                                NaN  \n",
       "190757                                NaN  \n",
       "190758                                NaN  \n",
       "190759                                NaN  \n",
       "\n",
       "[190760 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for csv_file in unknown_csv:\n",
    "    file_path = os.path.join(unknown_data, csv_file)\n",
    "    df = pd.read_csv(file_path)\n",
    "    combined_data = pd.concat([combined_data, df], ignore_index=True)\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b0edd5-495e-4159-a52a-fe4b247e1cfa",
   "metadata": {},
   "source": [
    "## Pre-processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2db7e61a-e305-48a8-b683-789337b6b792",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping unecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea36ab8c-f8e0-4efd-afe1-609d301e7a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
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       "      <td>1989</td>\n",
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       "      <td>[0]</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "      <td>[0]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>190755</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2014</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190756</th>\n",
       "      <td>Central and South America</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2016</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>190757</th>\n",
       "      <td>Central and South America</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2015</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190758</th>\n",
       "      <td>Africa</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2016</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190759</th>\n",
       "      <td>Africa</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2015</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190760 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Region Name  Country Name  Year      Sex  Age Group  \\\n",
       "0                          Europe       Albania  1987     Male        [0]   \n",
       "1                          Europe       Albania  1988     Male        [0]   \n",
       "2                          Europe       Albania  1989     Male        [0]   \n",
       "3                          Europe       Albania  1992     Male        [0]   \n",
       "4                          Europe       Albania  1993     Male        [0]   \n",
       "...                           ...           ...   ...      ...        ...   \n",
       "190755                       Asia          Oman  2014  Unknown  [Unknown]   \n",
       "190756  Central and South America       Uruguay  2016  Unknown  [Unknown]   \n",
       "190757  Central and South America       Uruguay  2015  Unknown  [Unknown]   \n",
       "190758                     Africa  South Africa  2016  Unknown  [Unknown]   \n",
       "190759                     Africa  South Africa  2015  Unknown  [Unknown]   \n",
       "\n",
       "        Number  Percentage of cause-specific deaths out of total deaths  \\\n",
       "0          0.0                                                0.0         \n",
       "1          0.0                                                0.0         \n",
       "2          0.0                                                0.0         \n",
       "3          0.0                                                0.0         \n",
       "4          0.0                                                0.0         \n",
       "...        ...                                                ...         \n",
       "190755     0.0                                                NaN         \n",
       "190756     1.0                                               20.0         \n",
       "190757     1.0                                               25.0         \n",
       "190758     0.0                                                0.0         \n",
       "190759     0.0                                                0.0         \n",
       "\n",
       "        Death rate per 100 000 population  \n",
       "0                                     0.0  \n",
       "1                                     0.0  \n",
       "2                                     0.0  \n",
       "3                                     0.0  \n",
       "4                                     0.0  \n",
       "...                                   ...  \n",
       "190755                                NaN  \n",
       "190756                                NaN  \n",
       "190757                                NaN  \n",
       "190758                                NaN  \n",
       "190759                                NaN  \n",
       "\n",
       "[190760 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data = combined_data.drop(['Region Code', 'Country Code', 'Age group code','Age-standardized death rate per 100 000 standard population'], axis=1)\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "67ea1eee-ef8b-4930-a9d8-892b106f2e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sorting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5619289f-8148-4d68-887e-37c51c91985e",
   "metadata": {},
   "outputs": [],
   "source": [
    "order = ['Region Name', 'Country Name', 'Year', 'Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4f09a5e4-cc95-4343-9239-d32fae077ac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = combined_data.sort_values(by=order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "116adafe-a4fa-4f60-982f-a7ce3341e9a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>190755</th>\n",
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       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
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       "      <td>23.0</td>\n",
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       "    <tr>\n",
       "      <th>190756</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[75-79]</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.487567</td>\n",
       "      <td>16.700067</td>\n",
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       "    <tr>\n",
       "      <th>190757</th>\n",
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       "      <td>[80-84]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.330852</td>\n",
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       "      <th>190758</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
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       "      <td>[85+]</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "       Region Name Country Name  Year     Sex  Age Group  Number  \\\n",
       "0           Africa   Cabo Verde  1980  Female        [0]     0.0   \n",
       "1           Africa   Cabo Verde  1980  Female      [1-4]     0.0   \n",
       "2           Africa   Cabo Verde  1980  Female    [15-19]     1.0   \n",
       "3           Africa   Cabo Verde  1980  Female    [20-24]     NaN   \n",
       "4           Africa   Cabo Verde  1980  Female    [30-34]     NaN   \n",
       "...            ...          ...   ...     ...        ...     ...   \n",
       "190755     Oceania  New Zealand  2016    Male    [60-64]    23.0   \n",
       "190756     Oceania  New Zealand  2016    Male    [75-79]    10.0   \n",
       "190757     Oceania  New Zealand  2016    Male    [80-84]     8.0   \n",
       "190758     Oceania  New Zealand  2016    Male      [85+]    10.0   \n",
       "190759     Oceania  New Zealand  2016    Male  [Unknown]     0.0   \n",
       "\n",
       "        Percentage of cause-specific deaths out of total deaths  \\\n",
       "0                                                0.000000         \n",
       "1                                                0.000000         \n",
       "2                                                2.272727         \n",
       "3                                                     NaN         \n",
       "4                                                     NaN         \n",
       "...                                                   ...         \n",
       "190755                                           2.300000         \n",
       "190756                                           0.487567         \n",
       "190757                                           0.330852         \n",
       "190758                                           0.216263         \n",
       "190759                                                NaN         \n",
       "\n",
       "        Death rate per 100 000 population  \n",
       "0                                0.000000  \n",
       "1                                0.000000  \n",
       "2                                     NaN  \n",
       "3                                     NaN  \n",
       "4                                     NaN  \n",
       "...                                   ...  \n",
       "190755                          18.456107  \n",
       "190756                          16.700067  \n",
       "190757                          21.228606  \n",
       "190758                          31.938678  \n",
       "190759                                NaN  \n",
       "\n",
       "[190760 rows x 8 columns]"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(drop=True, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "564c9ec8-7775-4a55-8e32-5ce49685990b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking different age groups present in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5d830a7d-9846-4ef8-9160-02e1062b91a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[0]', '[1-4]', '[15-19]', '[20-24]', '[30-34]', '[25-29]',\n",
       "       '[40-44]', '[45-49]', '[50-54]', '[35-39]', '[5-9]', '[10-14]',\n",
       "       '[65-69]', '[55-59]', '[60-64]', '[70-74]', '[80-84]', '[85+]',\n",
       "       '[75-79]', '[Unknown]'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age Group'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9594f786-a0fb-4518-a20b-ad203c4f1e90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating different age group columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "55912430-f0c7-4339-adf1-ade958a63e2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['0 Year', '1-4 Years', '5-9 Years', '10-14 Years', '15-19 Years', '20-24 Years', '25-29 Years', '30-34 Years', '35-39 Years',\n",
    "           '40-44 Years', '45-49 Years', '50-54 Years', '55-59 Years', '60-64 Years', '65-69 Years', '70-74 Years', '75-79 Years',\n",
    "           '80-84 Years', '85+ Years', 'Unknown Age']"
   ]
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  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aafd4a28-c09b-4f56-897a-02aa6578c238",
   "metadata": {},
   "outputs": [
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       "      <td>16.700067</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190757</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[80-84]</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.330852</td>\n",
       "      <td>21.228606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190758</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[85+]</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.216263</td>\n",
       "      <td>31.938678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190759</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190760 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Region Name Country Name  Year     Sex  Age Group  Number  \\\n",
       "0           Africa   Cabo Verde  1980  Female        [0]     0.0   \n",
       "1           Africa   Cabo Verde  1980  Female      [1-4]     0.0   \n",
       "2           Africa   Cabo Verde  1980  Female    [15-19]     1.0   \n",
       "3           Africa   Cabo Verde  1980  Female    [20-24]     NaN   \n",
       "4           Africa   Cabo Verde  1980  Female    [30-34]     NaN   \n",
       "...            ...          ...   ...     ...        ...     ...   \n",
       "190755     Oceania  New Zealand  2016    Male    [60-64]    23.0   \n",
       "190756     Oceania  New Zealand  2016    Male    [75-79]    10.0   \n",
       "190757     Oceania  New Zealand  2016    Male    [80-84]     8.0   \n",
       "190758     Oceania  New Zealand  2016    Male      [85+]    10.0   \n",
       "190759     Oceania  New Zealand  2016    Male  [Unknown]     0.0   \n",
       "\n",
       "        Percentage of cause-specific deaths out of total deaths  \\\n",
       "0                                                0.000000         \n",
       "1                                                0.000000         \n",
       "2                                                2.272727         \n",
       "3                                                     NaN         \n",
       "4                                                     NaN         \n",
       "...                                                   ...         \n",
       "190755                                           2.300000         \n",
       "190756                                           0.487567         \n",
       "190757                                           0.330852         \n",
       "190758                                           0.216263         \n",
       "190759                                                NaN         \n",
       "\n",
       "        Death rate per 100 000 population  0 Year  1-4 Years  ...  \\\n",
       "0                                0.000000     NaN        NaN  ...   \n",
       "1                                0.000000     NaN        NaN  ...   \n",
       "2                                     NaN     NaN        NaN  ...   \n",
       "3                                     NaN     NaN        NaN  ...   \n",
       "4                                     NaN     NaN        NaN  ...   \n",
       "...                                   ...     ...        ...  ...   \n",
       "190755                          18.456107     NaN        NaN  ...   \n",
       "190756                          16.700067     NaN        NaN  ...   \n",
       "190757                          21.228606     NaN        NaN  ...   \n",
       "190758                          31.938678     NaN        NaN  ...   \n",
       "190759                                NaN     NaN        NaN  ...   \n",
       "\n",
       "        45-49 Years  50-54 Years  55-59 Years  60-64 Years  65-69 Years  \\\n",
       "0               NaN          NaN          NaN          NaN          NaN   \n",
       "1               NaN          NaN          NaN          NaN          NaN   \n",
       "2               NaN          NaN          NaN          NaN          NaN   \n",
       "3               NaN          NaN          NaN          NaN          NaN   \n",
       "4               NaN          NaN          NaN          NaN          NaN   \n",
       "...             ...          ...          ...          ...          ...   \n",
       "190755          NaN          NaN          NaN          NaN          NaN   \n",
       "190756          NaN          NaN          NaN          NaN          NaN   \n",
       "190757          NaN          NaN          NaN          NaN          NaN   \n",
       "190758          NaN          NaN          NaN          NaN          NaN   \n",
       "190759          NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "        70-74 Years  75-79 Years  80-84 Years  85+ Years  Unknown Age  \n",
       "0               NaN          NaN          NaN        NaN          NaN  \n",
       "1               NaN          NaN          NaN        NaN          NaN  \n",
       "2               NaN          NaN          NaN        NaN          NaN  \n",
       "3               NaN          NaN          NaN        NaN          NaN  \n",
       "4               NaN          NaN          NaN        NaN          NaN  \n",
       "...             ...          ...          ...        ...          ...  \n",
       "190755          NaN          NaN          NaN        NaN          NaN  \n",
       "190756          NaN          NaN          NaN        NaN          NaN  \n",
       "190757          NaN          NaN          NaN        NaN          NaN  \n",
       "190758          NaN          NaN          NaN        NaN          NaN  \n",
       "190759          NaN          NaN          NaN        NaN          NaN  \n",
       "\n",
       "[190760 rows x 28 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.assign(**{column: pd.Series(dtype='int') for column in columns})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0dd1451-3064-47f8-a880-5b3de6953fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rearranging columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b3b3dee9-01c3-4738-ba44-773392edb39b",
   "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",
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       "        text-align: right;\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>Region Name</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>0 Year</th>\n",
       "      <th>1-4 Years</th>\n",
       "      <th>5-9 Years</th>\n",
       "      <th>10-14 Years</th>\n",
       "      <th>15-19 Years</th>\n",
       "      <th>...</th>\n",
       "      <th>60-64 Years</th>\n",
       "      <th>65-69 Years</th>\n",
       "      <th>70-74 Years</th>\n",
       "      <th>75-79 Years</th>\n",
       "      <th>80-84 Years</th>\n",
       "      <th>85+ Years</th>\n",
       "      <th>Unknown Age</th>\n",
       "      <th>Number</th>\n",
       "      <th>Percentage of cause-specific deaths out of total deaths</th>\n",
       "      <th>Death rate per 100 000 population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>[0]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>[1-4]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>[15-19]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-24]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>[30-34]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190755</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[60-64]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>18.456107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190756</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[75-79]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.487567</td>\n",
       "      <td>16.700067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190757</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[80-84]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.330852</td>\n",
       "      <td>21.228606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190758</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[85+]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.216263</td>\n",
       "      <td>31.938678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190759</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190760 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Region Name Country Name  Year     Sex  Age Group  0 Year  1-4 Years  \\\n",
       "0           Africa   Cabo Verde  1980  Female        [0]     NaN        NaN   \n",
       "1           Africa   Cabo Verde  1980  Female      [1-4]     NaN        NaN   \n",
       "2           Africa   Cabo Verde  1980  Female    [15-19]     NaN        NaN   \n",
       "3           Africa   Cabo Verde  1980  Female    [20-24]     NaN        NaN   \n",
       "4           Africa   Cabo Verde  1980  Female    [30-34]     NaN        NaN   \n",
       "...            ...          ...   ...     ...        ...     ...        ...   \n",
       "190755     Oceania  New Zealand  2016    Male    [60-64]     NaN        NaN   \n",
       "190756     Oceania  New Zealand  2016    Male    [75-79]     NaN        NaN   \n",
       "190757     Oceania  New Zealand  2016    Male    [80-84]     NaN        NaN   \n",
       "190758     Oceania  New Zealand  2016    Male      [85+]     NaN        NaN   \n",
       "190759     Oceania  New Zealand  2016    Male  [Unknown]     NaN        NaN   \n",
       "\n",
       "        5-9 Years  10-14 Years  15-19 Years  ...  60-64 Years  65-69 Years  \\\n",
       "0             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "1             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "2             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "3             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "4             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "...           ...          ...          ...  ...          ...          ...   \n",
       "190755        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190756        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190757        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190758        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190759        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "\n",
       "        70-74 Years  75-79 Years  80-84 Years  85+ Years  Unknown Age  Number  \\\n",
       "0               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "1               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "2               NaN          NaN          NaN        NaN          NaN     1.0   \n",
       "3               NaN          NaN          NaN        NaN          NaN     NaN   \n",
       "4               NaN          NaN          NaN        NaN          NaN     NaN   \n",
       "...             ...          ...          ...        ...          ...     ...   \n",
       "190755          NaN          NaN          NaN        NaN          NaN    23.0   \n",
       "190756          NaN          NaN          NaN        NaN          NaN    10.0   \n",
       "190757          NaN          NaN          NaN        NaN          NaN     8.0   \n",
       "190758          NaN          NaN          NaN        NaN          NaN    10.0   \n",
       "190759          NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "\n",
       "        Percentage of cause-specific deaths out of total deaths  \\\n",
       "0                                                0.000000         \n",
       "1                                                0.000000         \n",
       "2                                                2.272727         \n",
       "3                                                     NaN         \n",
       "4                                                     NaN         \n",
       "...                                                   ...         \n",
       "190755                                           2.300000         \n",
       "190756                                           0.487567         \n",
       "190757                                           0.330852         \n",
       "190758                                           0.216263         \n",
       "190759                                                NaN         \n",
       "\n",
       "        Death rate per 100 000 population  \n",
       "0                                0.000000  \n",
       "1                                0.000000  \n",
       "2                                     NaN  \n",
       "3                                     NaN  \n",
       "4                                     NaN  \n",
       "...                                   ...  \n",
       "190755                          18.456107  \n",
       "190756                          16.700067  \n",
       "190757                          21.228606  \n",
       "190758                          31.938678  \n",
       "190759                                NaN  \n",
       "\n",
       "[190760 rows x 28 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df.iloc[:, :5], df.iloc[:, 8:], df.iloc[:, 5:8]], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "979a463c-a636-4146-a649-ce85c7bf8b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling NaN values of number column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2066b703-b62a-404c-9aed-2b6cfe0d1aea",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190760 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Region Name Country Name  Year     Sex  Age Group  0 Year  1-4 Years  \\\n",
       "0           Africa   Cabo Verde  1980  Female        [0]     NaN        NaN   \n",
       "1           Africa   Cabo Verde  1980  Female      [1-4]     NaN        NaN   \n",
       "2           Africa   Cabo Verde  1980  Female    [15-19]     NaN        NaN   \n",
       "3           Africa   Cabo Verde  1980  Female    [20-24]     NaN        NaN   \n",
       "4           Africa   Cabo Verde  1980  Female    [30-34]     NaN        NaN   \n",
       "...            ...          ...   ...     ...        ...     ...        ...   \n",
       "190755     Oceania  New Zealand  2016    Male    [60-64]     NaN        NaN   \n",
       "190756     Oceania  New Zealand  2016    Male    [75-79]     NaN        NaN   \n",
       "190757     Oceania  New Zealand  2016    Male    [80-84]     NaN        NaN   \n",
       "190758     Oceania  New Zealand  2016    Male      [85+]     NaN        NaN   \n",
       "190759     Oceania  New Zealand  2016    Male  [Unknown]     NaN        NaN   \n",
       "\n",
       "        5-9 Years  10-14 Years  15-19 Years  ...  60-64 Years  65-69 Years  \\\n",
       "0             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "1             NaN          NaN          NaN  ...          NaN          NaN   \n",
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       "3             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "4             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "...           ...          ...          ...  ...          ...          ...   \n",
       "190755        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190756        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190757        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190758        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190759        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "\n",
       "        70-74 Years  75-79 Years  80-84 Years  85+ Years  Unknown Age  Number  \\\n",
       "0               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "1               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "2               NaN          NaN          NaN        NaN          NaN     1.0   \n",
       "3               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "4               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "...             ...          ...          ...        ...          ...     ...   \n",
       "190755          NaN          NaN          NaN        NaN          NaN    23.0   \n",
       "190756          NaN          NaN          NaN        NaN          NaN    10.0   \n",
       "190757          NaN          NaN          NaN        NaN          NaN     8.0   \n",
       "190758          NaN          NaN          NaN        NaN          NaN    10.0   \n",
       "190759          NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "\n",
       "        Percentage of cause-specific deaths out of total deaths  \\\n",
       "0                                                0.000000         \n",
       "1                                                0.000000         \n",
       "2                                                2.272727         \n",
       "3                                                     NaN         \n",
       "4                                                     NaN         \n",
       "...                                                   ...         \n",
       "190755                                           2.300000         \n",
       "190756                                           0.487567         \n",
       "190757                                           0.330852         \n",
       "190758                                           0.216263         \n",
       "190759                                                NaN         \n",
       "\n",
       "        Death rate per 100 000 population  \n",
       "0                                0.000000  \n",
       "1                                0.000000  \n",
       "2                                     NaN  \n",
       "3                                     NaN  \n",
       "4                                     NaN  \n",
       "...                                   ...  \n",
       "190755                          18.456107  \n",
       "190756                          16.700067  \n",
       "190757                          21.228606  \n",
       "190758                          31.938678  \n",
       "190759                                NaN  \n",
       "\n",
       "[190760 rows x 28 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Number'].fillna(0, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "48492624-d762-4968-be30-77b2eacfe4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Restructuring data values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0cb1059a-c8a1-4b64-a1f6-ec8d90e6cbea",
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = ['0', '1-4', '5-9', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69',\n",
    "        '70-74', '75-79', '80-84', '85+', 'Unknown']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "81557ba9-31fb-49c1-b9e2-6b9f9d05d458",
   "metadata": {},
   "outputs": [
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       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>[15-19]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>[20-24]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>[30-34]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190755</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[60-64]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>18.456107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190756</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[75-79]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.487567</td>\n",
       "      <td>16.700067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190757</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[80-84]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.330852</td>\n",
       "      <td>21.228606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190758</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[85+]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.216263</td>\n",
       "      <td>31.938678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190759</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190760 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Region Name Country Name  Year     Sex  Age Group  0 Year  1-4 Years  \\\n",
       "0           Africa   Cabo Verde  1980  Female        [0]     0.0        NaN   \n",
       "1           Africa   Cabo Verde  1980  Female      [1-4]     NaN        0.0   \n",
       "2           Africa   Cabo Verde  1980  Female    [15-19]     NaN        NaN   \n",
       "3           Africa   Cabo Verde  1980  Female    [20-24]     NaN        NaN   \n",
       "4           Africa   Cabo Verde  1980  Female    [30-34]     NaN        NaN   \n",
       "...            ...          ...   ...     ...        ...     ...        ...   \n",
       "190755     Oceania  New Zealand  2016    Male    [60-64]     NaN        NaN   \n",
       "190756     Oceania  New Zealand  2016    Male    [75-79]     NaN        NaN   \n",
       "190757     Oceania  New Zealand  2016    Male    [80-84]     NaN        NaN   \n",
       "190758     Oceania  New Zealand  2016    Male      [85+]     NaN        NaN   \n",
       "190759     Oceania  New Zealand  2016    Male  [Unknown]     NaN        NaN   \n",
       "\n",
       "        5-9 Years  10-14 Years  15-19 Years  ...  60-64 Years  65-69 Years  \\\n",
       "0             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "1             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "2             NaN          NaN          1.0  ...          NaN          NaN   \n",
       "3             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "4             NaN          NaN          NaN  ...          NaN          NaN   \n",
       "...           ...          ...          ...  ...          ...          ...   \n",
       "190755        NaN          NaN          NaN  ...         23.0          NaN   \n",
       "190756        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190757        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190758        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190759        NaN          NaN          NaN  ...          NaN          NaN   \n",
       "\n",
       "        70-74 Years  75-79 Years  80-84 Years  85+ Years  Unknown Age  Number  \\\n",
       "0               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "1               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "2               NaN          NaN          NaN        NaN          NaN     1.0   \n",
       "3               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "4               NaN          NaN          NaN        NaN          NaN     0.0   \n",
       "...             ...          ...          ...        ...          ...     ...   \n",
       "190755          NaN          NaN          NaN        NaN          NaN    23.0   \n",
       "190756          NaN         10.0          NaN        NaN          NaN    10.0   \n",
       "190757          NaN          NaN          8.0        NaN          NaN     8.0   \n",
       "190758          NaN          NaN          NaN       10.0          NaN    10.0   \n",
       "190759          NaN          NaN          NaN        NaN          0.0     0.0   \n",
       "\n",
       "        Percentage of cause-specific deaths out of total deaths  \\\n",
       "0                                                0.000000         \n",
       "1                                                0.000000         \n",
       "2                                                2.272727         \n",
       "3                                                     NaN         \n",
       "4                                                     NaN         \n",
       "...                                                   ...         \n",
       "190755                                           2.300000         \n",
       "190756                                           0.487567         \n",
       "190757                                           0.330852         \n",
       "190758                                           0.216263         \n",
       "190759                                                NaN         \n",
       "\n",
       "        Death rate per 100 000 population  \n",
       "0                                0.000000  \n",
       "1                                0.000000  \n",
       "2                                     NaN  \n",
       "3                                     NaN  \n",
       "4                                     NaN  \n",
       "...                                   ...  \n",
       "190755                          18.456107  \n",
       "190756                          16.700067  \n",
       "190757                          21.228606  \n",
       "190758                          31.938678  \n",
       "190759                                NaN  \n",
       "\n",
       "[190760 rows x 28 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for age in ages:\n",
    "    if age == '0':\n",
    "        df.loc[df[\"Age Group\"]== f\"[{age}]\", f\"{age} Year\"] = df[\"Number\"]\n",
    "    elif age == 'Unknown':\n",
    "        df.loc[df[\"Age Group\"]== f\"[{age}]\", f\"{age} Age\"] = df[\"Number\"]\n",
    "    else:\n",
    "        df.loc[df[\"Age Group\"]== f\"[{age}]\", f\"{age} Years\"] = df[\"Number\"]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a7b7174-241a-4831-9d2c-8009306d7aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping age group column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1aeda496-f10a-40d9-990b-8159a8694f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region Name</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th>0 Year</th>\n",
       "      <th>1-4 Years</th>\n",
       "      <th>5-9 Years</th>\n",
       "      <th>10-14 Years</th>\n",
       "      <th>15-19 Years</th>\n",
       "      <th>20-24 Years</th>\n",
       "      <th>...</th>\n",
       "      <th>55-59 Years</th>\n",
       "      <th>60-64 Years</th>\n",
       "      <th>65-69 Years</th>\n",
       "      <th>70-74 Years</th>\n",
       "      <th>75-79 Years</th>\n",
       "      <th>80-84 Years</th>\n",
       "      <th>85+ Years</th>\n",
       "      <th>Unknown Age</th>\n",
       "      <th>Percentage of cause-specific deaths out of total deaths</th>\n",
       "      <th>Death rate per 100 000 population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190755</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>18.456107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190756</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.487567</td>\n",
       "      <td>16.700067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190757</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.330852</td>\n",
       "      <td>21.228606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190758</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.216263</td>\n",
       "      <td>31.938678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190759</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190760 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Region Name Country Name  Year     Sex  0 Year  1-4 Years  5-9 Years  \\\n",
       "0           Africa   Cabo Verde  1980  Female     0.0        NaN        NaN   \n",
       "1           Africa   Cabo Verde  1980  Female     NaN        0.0        NaN   \n",
       "2           Africa   Cabo Verde  1980  Female     NaN        NaN        NaN   \n",
       "3           Africa   Cabo Verde  1980  Female     NaN        NaN        NaN   \n",
       "4           Africa   Cabo Verde  1980  Female     NaN        NaN        NaN   \n",
       "...            ...          ...   ...     ...     ...        ...        ...   \n",
       "190755     Oceania  New Zealand  2016    Male     NaN        NaN        NaN   \n",
       "190756     Oceania  New Zealand  2016    Male     NaN        NaN        NaN   \n",
       "190757     Oceania  New Zealand  2016    Male     NaN        NaN        NaN   \n",
       "190758     Oceania  New Zealand  2016    Male     NaN        NaN        NaN   \n",
       "190759     Oceania  New Zealand  2016    Male     NaN        NaN        NaN   \n",
       "\n",
       "        10-14 Years  15-19 Years  20-24 Years  ...  55-59 Years  60-64 Years  \\\n",
       "0               NaN          NaN          NaN  ...          NaN          NaN   \n",
       "1               NaN          NaN          NaN  ...          NaN          NaN   \n",
       "2               NaN          1.0          NaN  ...          NaN          NaN   \n",
       "3               NaN          NaN          0.0  ...          NaN          NaN   \n",
       "4               NaN          NaN          NaN  ...          NaN          NaN   \n",
       "...             ...          ...          ...  ...          ...          ...   \n",
       "190755          NaN          NaN          NaN  ...          NaN         23.0   \n",
       "190756          NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190757          NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190758          NaN          NaN          NaN  ...          NaN          NaN   \n",
       "190759          NaN          NaN          NaN  ...          NaN          NaN   \n",
       "\n",
       "        65-69 Years  70-74 Years  75-79 Years  80-84 Years  85+ Years  \\\n",
       "0               NaN          NaN          NaN          NaN        NaN   \n",
       "1               NaN          NaN          NaN          NaN        NaN   \n",
       "2               NaN          NaN          NaN          NaN        NaN   \n",
       "3               NaN          NaN          NaN          NaN        NaN   \n",
       "4               NaN          NaN          NaN          NaN        NaN   \n",
       "...             ...          ...          ...          ...        ...   \n",
       "190755          NaN          NaN          NaN          NaN        NaN   \n",
       "190756          NaN          NaN         10.0          NaN        NaN   \n",
       "190757          NaN          NaN          NaN          8.0        NaN   \n",
       "190758          NaN          NaN          NaN          NaN       10.0   \n",
       "190759          NaN          NaN          NaN          NaN        NaN   \n",
       "\n",
       "        Unknown Age  Percentage of cause-specific deaths out of total deaths  \\\n",
       "0               NaN                                           0.000000         \n",
       "1               NaN                                           0.000000         \n",
       "2               NaN                                           2.272727         \n",
       "3               NaN                                                NaN         \n",
       "4               NaN                                                NaN         \n",
       "...             ...                                                ...         \n",
       "190755          NaN                                           2.300000         \n",
       "190756          NaN                                           0.487567         \n",
       "190757          NaN                                           0.330852         \n",
       "190758          NaN                                           0.216263         \n",
       "190759          0.0                                                NaN         \n",
       "\n",
       "        Death rate per 100 000 population  \n",
       "0                                0.000000  \n",
       "1                                0.000000  \n",
       "2                                     NaN  \n",
       "3                                     NaN  \n",
       "4                                     NaN  \n",
       "...                                   ...  \n",
       "190755                          18.456107  \n",
       "190756                          16.700067  \n",
       "190757                          21.228606  \n",
       "190758                          31.938678  \n",
       "190759                                NaN  \n",
       "\n",
       "[190760 rows x 26 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Age Group','Number'], axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b2f967ef-081b-4cee-b2a7-be626ecab3ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouping data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e5e38c17-3e5e-421f-ba72-3a8e0d0c3e91",
   "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>Region Name</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th>0 Year</th>\n",
       "      <th>1-4 Years</th>\n",
       "      <th>5-9 Years</th>\n",
       "      <th>10-14 Years</th>\n",
       "      <th>15-19 Years</th>\n",
       "      <th>20-24 Years</th>\n",
       "      <th>...</th>\n",
       "      <th>55-59 Years</th>\n",
       "      <th>60-64 Years</th>\n",
       "      <th>65-69 Years</th>\n",
       "      <th>70-74 Years</th>\n",
       "      <th>75-79 Years</th>\n",
       "      <th>80-84 Years</th>\n",
       "      <th>85+ Years</th>\n",
       "      <th>Unknown Age</th>\n",
       "      <th>Percentage of cause-specific deaths out of total deaths</th>\n",
       "      <th>Death rate per 100 000 population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.983156</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>2011</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.161273</td>\n",
       "      <td>2.395998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>2011</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.804557</td>\n",
       "      <td>28.024735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>1965</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026743</td>\n",
       "      <td>0.074515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9533</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2014</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.703106</td>\n",
       "      <td>16.744376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9534</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2015</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.828921</td>\n",
       "      <td>5.458483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9535</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2015</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.143815</td>\n",
       "      <td>16.703400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9536</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.362872</td>\n",
       "      <td>5.342887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9537</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.974208</td>\n",
       "      <td>17.383315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9538 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region Name Country Name  Year     Sex  0 Year  1-4 Years  5-9 Years  \\\n",
       "0         Africa   Cabo Verde  1980  Female     0.0        0.0        0.0   \n",
       "1         Africa   Cabo Verde  1980    Male     0.0        0.0        0.0   \n",
       "2         Africa   Cabo Verde  2011  Female     0.0        0.0        0.0   \n",
       "3         Africa   Cabo Verde  2011    Male     0.0        0.0        0.0   \n",
       "4         Africa        Egypt  1965  Female     0.0        0.0        0.0   \n",
       "...          ...          ...   ...     ...     ...        ...        ...   \n",
       "9533     Oceania  New Zealand  2014    Male     0.0        0.0        0.0   \n",
       "9534     Oceania  New Zealand  2015  Female     0.0        0.0        0.0   \n",
       "9535     Oceania  New Zealand  2015    Male     0.0        0.0        0.0   \n",
       "9536     Oceania  New Zealand  2016  Female     0.0        0.0        0.0   \n",
       "9537     Oceania  New Zealand  2016    Male     0.0        0.0        0.0   \n",
       "\n",
       "      10-14 Years  15-19 Years  20-24 Years  ...  55-59 Years  60-64 Years  \\\n",
       "0             0.0          1.0          0.0  ...          0.0          0.0   \n",
       "1             0.0          2.0          0.0  ...          1.0          0.0   \n",
       "2             0.0          0.0          1.0  ...          2.0          0.0   \n",
       "3             3.0          1.0          4.0  ...          0.0          0.0   \n",
       "4             0.0          6.0          1.0  ...          0.0          2.0   \n",
       "...           ...          ...          ...  ...          ...          ...   \n",
       "9533          2.0         27.0         41.0  ...         32.0         26.0   \n",
       "9534          4.0         25.0         16.0  ...          7.0         12.0   \n",
       "9535          5.0         32.0         38.0  ...         27.0         24.0   \n",
       "9536          3.0         10.0         16.0  ...         13.0          4.0   \n",
       "9537          5.0         34.0         52.0  ...         36.0         23.0   \n",
       "\n",
       "      65-69 Years  70-74 Years  75-79 Years  80-84 Years  85+ Years  \\\n",
       "0             0.0          0.0          0.0          0.0        0.0   \n",
       "1             1.0          0.0          1.0          0.0        0.0   \n",
       "2             0.0          0.0          0.0          0.0        0.0   \n",
       "3             0.0          1.0          0.0          1.0        1.0   \n",
       "4             0.0          0.0          0.0          0.0        0.0   \n",
       "...           ...          ...          ...          ...        ...   \n",
       "9533         13.0         14.0          4.0          8.0       10.0   \n",
       "9534          7.0          3.0          4.0          1.0        1.0   \n",
       "9535         14.0          7.0         13.0          9.0        6.0   \n",
       "9536          5.0          3.0          2.0          2.0        2.0   \n",
       "9537         18.0          5.0         10.0          8.0       10.0   \n",
       "\n",
       "      Unknown Age  Percentage of cause-specific deaths out of total deaths  \\\n",
       "0             0.0                                           0.227273         \n",
       "1             0.0                                           0.983156         \n",
       "2             0.0                                           1.161273         \n",
       "3             0.0                                           3.804557         \n",
       "4             0.0                                           0.026743         \n",
       "...           ...                                                ...         \n",
       "9533          0.0                                          10.703106         \n",
       "9534          0.0                                           8.828921         \n",
       "9535          0.0                                          11.143815         \n",
       "9536          0.0                                           8.362872         \n",
       "9537          0.0                                          11.974208         \n",
       "\n",
       "      Death rate per 100 000 population  \n",
       "0                              0.000000  \n",
       "1                              0.000000  \n",
       "2                              2.395998  \n",
       "3                             28.024735  \n",
       "4                              0.074515  \n",
       "...                                 ...  \n",
       "9533                          16.744376  \n",
       "9534                           5.458483  \n",
       "9535                          16.703400  \n",
       "9536                           5.342887  \n",
       "9537                          17.383315  \n",
       "\n",
       "[9538 rows x 26 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.groupby(['Region Name','Country Name','Year', 'Sex'])\n",
    "df = df.aggregate(np.mean)\n",
    "df.reset_index(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "07a97ac8-bd34-4c9e-9195-5d1555b3a9ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating number of suicide for each row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eea71dc8-4ba5-4633-bea3-e032355f45e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Region Name</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th>0 Year</th>\n",
       "      <th>1-4 Years</th>\n",
       "      <th>5-9 Years</th>\n",
       "      <th>10-14 Years</th>\n",
       "      <th>15-19 Years</th>\n",
       "      <th>20-24 Years</th>\n",
       "      <th>...</th>\n",
       "      <th>60-64 Years</th>\n",
       "      <th>65-69 Years</th>\n",
       "      <th>70-74 Years</th>\n",
       "      <th>75-79 Years</th>\n",
       "      <th>80-84 Years</th>\n",
       "      <th>85+ Years</th>\n",
       "      <th>Unknown Age</th>\n",
       "      <th>No of Suicides</th>\n",
       "      <th>Percentage of cause-specific deaths out of total deaths</th>\n",
       "      <th>Death rate per 100 000 population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.983156</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>2011</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>1.161273</td>\n",
       "      <td>2.395998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>2011</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>3.804557</td>\n",
       "      <td>28.024735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>1965</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.026743</td>\n",
       "      <td>0.074515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9533</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2014</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>10.703106</td>\n",
       "      <td>16.744376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9534</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2015</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>8.828921</td>\n",
       "      <td>5.458483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9535</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2015</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>11.143815</td>\n",
       "      <td>16.703400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9536</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>8.362872</td>\n",
       "      <td>5.342887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9537</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>11.974208</td>\n",
       "      <td>17.383315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9538 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region Name Country Name  Year     Sex  0 Year  1-4 Years  5-9 Years  \\\n",
       "0         Africa   Cabo Verde  1980  Female     0.0        0.0        0.0   \n",
       "1         Africa   Cabo Verde  1980    Male     0.0        0.0        0.0   \n",
       "2         Africa   Cabo Verde  2011  Female     0.0        0.0        0.0   \n",
       "3         Africa   Cabo Verde  2011    Male     0.0        0.0        0.0   \n",
       "4         Africa        Egypt  1965  Female     0.0        0.0        0.0   \n",
       "...          ...          ...   ...     ...     ...        ...        ...   \n",
       "9533     Oceania  New Zealand  2014    Male     0.0        0.0        0.0   \n",
       "9534     Oceania  New Zealand  2015  Female     0.0        0.0        0.0   \n",
       "9535     Oceania  New Zealand  2015    Male     0.0        0.0        0.0   \n",
       "9536     Oceania  New Zealand  2016  Female     0.0        0.0        0.0   \n",
       "9537     Oceania  New Zealand  2016    Male     0.0        0.0        0.0   \n",
       "\n",
       "      10-14 Years  15-19 Years  20-24 Years  ...  60-64 Years  65-69 Years  \\\n",
       "0             0.0          1.0          0.0  ...          0.0          0.0   \n",
       "1             0.0          2.0          0.0  ...          0.0          1.0   \n",
       "2             0.0          0.0          1.0  ...          0.0          0.0   \n",
       "3             3.0          1.0          4.0  ...          0.0          0.0   \n",
       "4             0.0          6.0          1.0  ...          2.0          0.0   \n",
       "...           ...          ...          ...  ...          ...          ...   \n",
       "9533          2.0         27.0         41.0  ...         26.0         13.0   \n",
       "9534          4.0         25.0         16.0  ...         12.0          7.0   \n",
       "9535          5.0         32.0         38.0  ...         24.0         14.0   \n",
       "9536          3.0         10.0         16.0  ...          4.0          5.0   \n",
       "9537          5.0         34.0         52.0  ...         23.0         18.0   \n",
       "\n",
       "      70-74 Years  75-79 Years  80-84 Years  85+ Years  Unknown Age  \\\n",
       "0             0.0          0.0          0.0        0.0          0.0   \n",
       "1             0.0          1.0          0.0        0.0          0.0   \n",
       "2             0.0          0.0          0.0        0.0          0.0   \n",
       "3             1.0          0.0          1.0        1.0          0.0   \n",
       "4             0.0          0.0          0.0        0.0          0.0   \n",
       "...           ...          ...          ...        ...          ...   \n",
       "9533         14.0          4.0          8.0       10.0          0.0   \n",
       "9534          3.0          4.0          1.0        1.0          0.0   \n",
       "9535          7.0         13.0          9.0        6.0          0.0   \n",
       "9536          3.0          2.0          2.0        2.0          0.0   \n",
       "9537          5.0         10.0          8.0       10.0          0.0   \n",
       "\n",
       "      No of Suicides  Percentage of cause-specific deaths out of total deaths  \\\n",
       "0               None                                           0.227273         \n",
       "1               None                                           0.983156         \n",
       "2               None                                           1.161273         \n",
       "3               None                                           3.804557         \n",
       "4               None                                           0.026743         \n",
       "...              ...                                                ...         \n",
       "9533            None                                          10.703106         \n",
       "9534            None                                           8.828921         \n",
       "9535            None                                          11.143815         \n",
       "9536            None                                           8.362872         \n",
       "9537            None                                          11.974208         \n",
       "\n",
       "      Death rate per 100 000 population  \n",
       "0                              0.000000  \n",
       "1                              0.000000  \n",
       "2                              2.395998  \n",
       "3                             28.024735  \n",
       "4                              0.074515  \n",
       "...                                 ...  \n",
       "9533                          16.744376  \n",
       "9534                           5.458483  \n",
       "9535                          16.703400  \n",
       "9536                           5.342887  \n",
       "9537                          17.383315  \n",
       "\n",
       "[9538 rows x 27 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.insert(df.columns.get_loc('Unknown Age') + 1, 'No of Suicides', None)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ac15d4d7-43c4-429d-9606-95f7a7a3fb92",
   "metadata": {},
   "outputs": [],
   "source": [
    "suicides = ['0 Year', '1-4 Years', '5-9 Years', '10-14 Years', '15-19 Years', '20-24 Years', '25-29 Years', '30-34 Years', '35-39 Years', '40-44 Years',\n",
    "            '45-49 Years', '50-54 Years', '55-59 Years', '60-64 Years', '65-69 Years', '70-74 Years', '75-79 Years', '80-84 Years', '85+ Years', 'Unknown Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "80b9c2d2-565a-445a-9244-20029f0adc5d",
   "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>Region Name</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th>0 Year</th>\n",
       "      <th>1-4 Years</th>\n",
       "      <th>5-9 Years</th>\n",
       "      <th>10-14 Years</th>\n",
       "      <th>15-19 Years</th>\n",
       "      <th>20-24 Years</th>\n",
       "      <th>...</th>\n",
       "      <th>60-64 Years</th>\n",
       "      <th>65-69 Years</th>\n",
       "      <th>70-74 Years</th>\n",
       "      <th>75-79 Years</th>\n",
       "      <th>80-84 Years</th>\n",
       "      <th>85+ Years</th>\n",
       "      <th>Unknown Age</th>\n",
       "      <th>No of Suicides</th>\n",
       "      <th>Percentage of cause-specific deaths out of total deaths</th>\n",
       "      <th>Death rate per 100 000 population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>1980</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.983156</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>2011</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.161273</td>\n",
       "      <td>2.395998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>2011</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.804557</td>\n",
       "      <td>28.024735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>1965</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.026743</td>\n",
       "      <td>0.074515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9533</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2014</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>10.703106</td>\n",
       "      <td>16.744376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9534</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2015</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>8.828921</td>\n",
       "      <td>5.458483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9535</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2015</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>11.143815</td>\n",
       "      <td>16.703400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9536</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>8.362872</td>\n",
       "      <td>5.342887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9537</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2016</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>11.974208</td>\n",
       "      <td>17.383315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9538 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region Name Country Name  Year     Sex  0 Year  1-4 Years  5-9 Years  \\\n",
       "0         Africa   Cabo Verde  1980  Female     0.0        0.0        0.0   \n",
       "1         Africa   Cabo Verde  1980    Male     0.0        0.0        0.0   \n",
       "2         Africa   Cabo Verde  2011  Female     0.0        0.0        0.0   \n",
       "3         Africa   Cabo Verde  2011    Male     0.0        0.0        0.0   \n",
       "4         Africa        Egypt  1965  Female     0.0        0.0        0.0   \n",
       "...          ...          ...   ...     ...     ...        ...        ...   \n",
       "9533     Oceania  New Zealand  2014    Male     0.0        0.0        0.0   \n",
       "9534     Oceania  New Zealand  2015  Female     0.0        0.0        0.0   \n",
       "9535     Oceania  New Zealand  2015    Male     0.0        0.0        0.0   \n",
       "9536     Oceania  New Zealand  2016  Female     0.0        0.0        0.0   \n",
       "9537     Oceania  New Zealand  2016    Male     0.0        0.0        0.0   \n",
       "\n",
       "      10-14 Years  15-19 Years  20-24 Years  ...  60-64 Years  65-69 Years  \\\n",
       "0             0.0          1.0          0.0  ...          0.0          0.0   \n",
       "1             0.0          2.0          0.0  ...          0.0          1.0   \n",
       "2             0.0          0.0          1.0  ...          0.0          0.0   \n",
       "3             3.0          1.0          4.0  ...          0.0          0.0   \n",
       "4             0.0          6.0          1.0  ...          2.0          0.0   \n",
       "...           ...          ...          ...  ...          ...          ...   \n",
       "9533          2.0         27.0         41.0  ...         26.0         13.0   \n",
       "9534          4.0         25.0         16.0  ...         12.0          7.0   \n",
       "9535          5.0         32.0         38.0  ...         24.0         14.0   \n",
       "9536          3.0         10.0         16.0  ...          4.0          5.0   \n",
       "9537          5.0         34.0         52.0  ...         23.0         18.0   \n",
       "\n",
       "      70-74 Years  75-79 Years  80-84 Years  85+ Years  Unknown Age  \\\n",
       "0             0.0          0.0          0.0        0.0          0.0   \n",
       "1             0.0          1.0          0.0        0.0          0.0   \n",
       "2             0.0          0.0          0.0        0.0          0.0   \n",
       "3             1.0          0.0          1.0        1.0          0.0   \n",
       "4             0.0          0.0          0.0        0.0          0.0   \n",
       "...           ...          ...          ...        ...          ...   \n",
       "9533         14.0          4.0          8.0       10.0          0.0   \n",
       "9534          3.0          4.0          1.0        1.0          0.0   \n",
       "9535          7.0         13.0          9.0        6.0          0.0   \n",
       "9536          3.0          2.0          2.0        2.0          0.0   \n",
       "9537          5.0         10.0          8.0       10.0          0.0   \n",
       "\n",
       "      No of Suicides  Percentage of cause-specific deaths out of total deaths  \\\n",
       "0                1.0                                           0.227273         \n",
       "1                6.0                                           0.983156         \n",
       "2                5.0                                           1.161273         \n",
       "3               37.0                                           3.804557         \n",
       "4               11.0                                           0.026743         \n",
       "...              ...                                                ...         \n",
       "9533           379.0                                          10.703106         \n",
       "9534           144.0                                           8.828921         \n",
       "9535           386.0                                          11.143815         \n",
       "9536           145.0                                           8.362872         \n",
       "9537           416.0                                          11.974208         \n",
       "\n",
       "      Death rate per 100 000 population  \n",
       "0                              0.000000  \n",
       "1                              0.000000  \n",
       "2                              2.395998  \n",
       "3                             28.024735  \n",
       "4                              0.074515  \n",
       "...                                 ...  \n",
       "9533                          16.744376  \n",
       "9534                           5.458483  \n",
       "9535                          16.703400  \n",
       "9536                           5.342887  \n",
       "9537                          17.383315  \n",
       "\n",
       "[9538 rows x 27 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['No of Suicides'] = df[suicides].sum(axis=1)\n",
    "df = df.fillna(0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3d0c0b66-e763-4770-ad49-448ecb467669",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eporting data\n",
    "df.to_csv('who_suicide_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852b989f-b538-4933-951b-ee43da45bfff",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5fd1c1b4-c27f-447b-8a3f-9f65dbace752",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb686f96-6c86-4a57-bd86-a47bf5ab8fb8",
   "metadata": {},
   "source": [
    "## Region-wise average death rate from suicide per 100000 population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "98d3951e-75d4-4f8b-a630-e5317e71daaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "regional_suicides = df.groupby('Region Name')['Death rate per 100 000 population'].mean().reset_index()\n",
    "regional_suicides = regional_suicides.sort_values(by='Death rate per 100 000 population', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "741e6c12-058f-447e-be3c-7044cec98a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting a Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "29dc04e3-e732-4bd9-83a2-59c264e97a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(regional_suicides['Death rate per 100 000 population'], labels=regional_suicides['Region Name'], autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Region-wise distribution of average death from suicide per 100000 population')\n",
    "plt.axis('equal')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc3db92-2f1c-4f97-bdc6-90a726a96f3b",
   "metadata": {},
   "source": [
    "## Top 10 Most Suicide prone countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3a520a3f-b73e-4090-92b7-664803ddc525",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_suicides = df.groupby('Country Name')['Death rate per 100 000 population'].mean().reset_index()\n",
    "country_suicides = country_suicides.sort_values(by='Death rate per 100 000 population', ascending=False)\n",
    "top_10_countries = country_suicides.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a3bb033a-36fe-4628-a307-e19408c06043",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting bar graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c11e9d3a-5979-42be-8c4b-309aaa32d711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(top_10_countries['Country Name'], top_10_countries['Death rate per 100 000 population'],color='red')\n",
    "plt.title('Top 10 Countries with the Most Suicide Deaths per 100 000 population')\n",
    "plt.xlabel('Country Name')\n",
    "plt.ylabel('Total Number of Suicides')\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43da8b1c-9ca3-43ed-b800-927126d3fe02",
   "metadata": {},
   "source": [
    "## Suicides & Age Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5bcc5906-0447-41b3-ad87-9e84e3f0e72e",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_groups = ['0 Year', '1-4 Years', '5-9 Years', '10-14 Years', '15-19 Years',\n",
    "              '20-24 Years', '25-29 Years', '30-34 Years', '35-39 Years', '40-44 Years',\n",
    "              '45-49 Years', '50-54 Years', '55-59 Years', '60-64 Years', '65-69 Years',\n",
    "              '70-74 Years', '75-79 Years', '80-84 Years', '85+ Years', 'Unknown Age']\n",
    "total_suicides_by_age_group = df[age_groups].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cc47cbcd-2da4-44d9-ac69-58fe2e7d63c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a270c38a-2f72-46a8-9ab7-26e6d02d1503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(total_suicides_by_age_group.index, total_suicides_by_age_group.values)\n",
    "\n",
    "plt.title('Distribution of Suicide Deaths Across Age Groups (All Years)')\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Total Number of Suicides')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Disable scientific notation on the y-axis\n",
    "plt.ticklabel_format(style='plain', axis='y')\n",
    "\n",
    "plt.grid(axis='y')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466ae25f-1771-4160-91bc-a72ee8bb8ca0",
   "metadata": {},
   "source": [
    "## Distribution of suicide numbers by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6d1fd34d-3aef-4a75-bc73-727df3031d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_suicides_by_gender = df.groupby('Sex')['Death rate per 100 000 population'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "831dc427-0c80-427b-8e44-7000d6870e1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9fc22c75-1f0a-4315-a11d-1cab9cdd67fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(total_suicides_by_gender, labels=total_suicides_by_gender.index, autopct='%1.1f%%', startangle=140)\n",
    "plt.title('Gender-wise Distribution of death from suicides per 100,000 Population')\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "12d65582-9146-40ae-93ba-8b612655884f",
   "metadata": {},
   "outputs": [],
   "source": [
    "suicides_by_year_gender = df.groupby(['Year', 'Sex'])['No of Suicides'].sum().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2d80dbd3-c413-4cff-95da-38184d607b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting line graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fffb3cf5-1584-4688-a6cb-6b690086f977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(suicides_by_year_gender.index, suicides_by_year_gender['Male'], label='Male', marker='o')\n",
    "plt.plot(suicides_by_year_gender.index, suicides_by_year_gender['Female'], label='Female', marker='o')\n",
    "plt.title('Number of death from suicides over the years')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Suicides')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f009faca-cd3c-45c3-b6de-a7ca88edd4ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}