Chap11_gapminder데이터셋으로연습하기.ipynb

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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "      <td>8425333</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "      <td>10267083</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "      <td>739.981106</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "      <td>9216418</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "      <td>10704340</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "      <td>11404948</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "      <td>11926563</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "      <td>12311143</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year  lifeExp       pop   gdpPercap\n",
       "0     Afghanistan      Asia  1952   28.801   8425333  779.445314\n",
       "1     Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "2     Afghanistan      Asia  1962   31.997  10267083  853.100710\n",
       "3     Afghanistan      Asia  1967   34.020  11537966  836.197138\n",
       "4     Afghanistan      Asia  1972   36.088  13079460  739.981106\n",
       "...           ...       ...   ...      ...       ...         ...\n",
       "1699     Zimbabwe    Africa  1987   62.351   9216418  706.157306\n",
       "1700     Zimbabwe    Africa  1992   60.377  10704340  693.420786\n",
       "1701     Zimbabwe    Africa  1997   46.809  11404948  792.449960\n",
       "1702     Zimbabwe    Africa  2002   39.989  11926563  672.038623\n",
       "1703     Zimbabwe    Africa  2007   43.487  12311143  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
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   ],
   "source": [
    "#Chap11_gapminder데이터셋으로연습하기\n",
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv(\"c:\\\\work\\\\gapminder.tsv\", sep=\"\\t\")\n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1704 entries, 0 to 1703\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   country    1704 non-null   object \n",
      " 1   continent  1704 non-null   object \n",
      " 2   year       1704 non-null   int64  \n",
      " 3   lifeExp    1704 non-null   float64\n",
      " 4   pop        1704 non-null   int64  \n",
      " 5   gdpPercap  1704 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1704, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Afghanistan\n",
       "1       Afghanistan\n",
       "2       Afghanistan\n",
       "3       Afghanistan\n",
       "4       Afghanistan\n",
       "           ...     \n",
       "1699       Zimbabwe\n",
       "1700       Zimbabwe\n",
       "1701       Zimbabwe\n",
       "1702       Zimbabwe\n",
       "1703       Zimbabwe\n",
       "Name: country, Length: 1704, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#특정 컬럼을 슬라이싱\n",
    "df[\"country\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#리스트에 열 이름을 전달하면 여러 개의 열을 추출할 수 있다.\n",
    "subset = df[[\"country\",\"continent\",\"year\"]]\n",
    "type(subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country continent  year\n",
       "0  Afghanistan      Asia  1952\n",
       "1  Afghanistan      Asia  1957\n",
       "2  Afghanistan      Asia  1962\n",
       "3  Afghanistan      Asia  1967\n",
       "4  Afghanistan      Asia  1972"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country      Afghanistan\n",
       "continent           Asia\n",
       "year                1952\n",
       "lifeExp           28.801\n",
       "pop              8425333\n",
       "gdpPercap     779.445314\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#행데이터 추출 \n",
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country      Afghanistan\n",
       "continent           Asia\n",
       "year                2002\n",
       "lifeExp           42.129\n",
       "pop             25268405\n",
       "gdpPercap     726.734055\n",
       "Name: 10, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "      <th>year</th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>pop</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "      <td>9240934</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1972</td>\n",
       "      <td>45.252</td>\n",
       "      <td>70759295</td>\n",
       "      <td>630.233627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1992</td>\n",
       "      <td>50.260</td>\n",
       "      <td>8878303</td>\n",
       "      <td>931.752773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country continent  year  lifeExp       pop   gdpPercap\n",
       "1     Afghanistan      Asia  1957   30.332   9240934  820.853030\n",
       "100    Bangladesh      Asia  1972   45.252  70759295  630.233627\n",
       "200  Burkina Faso    Africa  1992   50.260   8878303  931.752773"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#인덱스가 1, 100, 200을 한번에 추출하려면 리스트에 원하는 인덱스를 담아 전달하면 된다.\n",
    "df.loc[[1,100,200]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country      Afghanistan\n",
       "continent           Asia\n",
       "year                1957\n",
       "lifeExp           30.332\n",
       "pop              9240934\n",
       "gdpPercap      820.85303\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country        Zimbabwe\n",
       "continent        Africa\n",
       "year               2007\n",
       "lifeExp          43.487\n",
       "pop            12311143\n",
       "gdpPercap    469.709298\n",
       "Name: 1703, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year       pop\n",
       "0  1952   8425333\n",
       "1  1957   9240934\n",
       "2  1962  10267083\n",
       "3  1967  11537966\n",
       "4  1972  13079460"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#슬라이싱 구문으로 데이터 추출하기 \n",
    "#다음은 모든 행(:)의 데이터에 대해 year, pop열을 추출하는 방법입니다. \n",
    "subset = df.loc[:, [\"year\",\"pop\"]]\n",
    "subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1952    49.057620\n",
       "1957    51.507401\n",
       "1962    53.609249\n",
       "1967    55.678290\n",
       "1972    57.647386\n",
       "1977    59.570157\n",
       "1982    61.533197\n",
       "1987    63.212613\n",
       "1992    64.160338\n",
       "1997    65.014676\n",
       "2002    65.694923\n",
       "2007    67.007423\n",
       "Name: lifeExp, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#기본적인 통계 사용하기\n",
    "#연도별 lifeExp열의 평균을 계산한다. 데이터를 year열로 그룹화하고 \n",
    "#lifeExp열의 평균을 구하면 된다. \n",
    "df.groupby(\"year\")[\"lifeExp\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>lifeExp</th>\n",
       "      <th>gdpPercap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1952</th>\n",
       "      <th>Africa</th>\n",
       "      <td>39.135500</td>\n",
       "      <td>1252.572466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>53.279840</td>\n",
       "      <td>4079.062552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>46.314394</td>\n",
       "      <td>5195.484004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>64.408500</td>\n",
       "      <td>5661.057435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>69.255000</td>\n",
       "      <td>10298.085650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1957</th>\n",
       "      <th>Africa</th>\n",
       "      <td>41.266346</td>\n",
       "      <td>1385.236062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>55.960280</td>\n",
       "      <td>4616.043733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>49.318544</td>\n",
       "      <td>5787.732940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>66.703067</td>\n",
       "      <td>6963.012816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>70.295000</td>\n",
       "      <td>11598.522455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1962</th>\n",
       "      <th>Africa</th>\n",
       "      <td>43.319442</td>\n",
       "      <td>1598.078825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>58.398760</td>\n",
       "      <td>4901.541870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>51.563223</td>\n",
       "      <td>5729.369625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>68.539233</td>\n",
       "      <td>8365.486814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.085000</td>\n",
       "      <td>12696.452430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1967</th>\n",
       "      <th>Africa</th>\n",
       "      <td>45.334538</td>\n",
       "      <td>2050.363801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>60.410920</td>\n",
       "      <td>5668.253496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>54.663640</td>\n",
       "      <td>5971.173374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>69.737600</td>\n",
       "      <td>10143.823757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.310000</td>\n",
       "      <td>14495.021790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1972</th>\n",
       "      <th>Africa</th>\n",
       "      <td>47.450942</td>\n",
       "      <td>2339.615674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>62.394920</td>\n",
       "      <td>6491.334139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>57.319269</td>\n",
       "      <td>8187.468699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>70.775033</td>\n",
       "      <td>12479.575246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>71.910000</td>\n",
       "      <td>16417.333380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1977</th>\n",
       "      <th>Africa</th>\n",
       "      <td>49.580423</td>\n",
       "      <td>2585.938508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>64.391560</td>\n",
       "      <td>7352.007126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>59.610556</td>\n",
       "      <td>7791.314020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>71.937767</td>\n",
       "      <td>14283.979110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>72.855000</td>\n",
       "      <td>17283.957605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1982</th>\n",
       "      <th>Africa</th>\n",
       "      <td>51.592865</td>\n",
       "      <td>2481.592960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>66.228840</td>\n",
       "      <td>7506.737088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>62.617939</td>\n",
       "      <td>7434.135157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>72.806400</td>\n",
       "      <td>15617.896551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>74.290000</td>\n",
       "      <td>18554.709840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1987</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.344788</td>\n",
       "      <td>2282.668991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>68.090720</td>\n",
       "      <td>7793.400261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>64.851182</td>\n",
       "      <td>7608.226508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>73.642167</td>\n",
       "      <td>17214.310727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>75.320000</td>\n",
       "      <td>20448.040160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1992</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.629577</td>\n",
       "      <td>2281.810333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>69.568360</td>\n",
       "      <td>8044.934406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>66.537212</td>\n",
       "      <td>8639.690248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>74.440100</td>\n",
       "      <td>17061.568084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>76.945000</td>\n",
       "      <td>20894.045885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1997</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.598269</td>\n",
       "      <td>2378.759555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>71.150480</td>\n",
       "      <td>8889.300863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>68.020515</td>\n",
       "      <td>9834.093295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>75.505167</td>\n",
       "      <td>19076.781802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>78.190000</td>\n",
       "      <td>24024.175170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2002</th>\n",
       "      <th>Africa</th>\n",
       "      <td>53.325231</td>\n",
       "      <td>2599.385159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>72.422040</td>\n",
       "      <td>9287.677107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>69.233879</td>\n",
       "      <td>10174.090397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>76.700600</td>\n",
       "      <td>21711.732422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>79.740000</td>\n",
       "      <td>26938.778040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2007</th>\n",
       "      <th>Africa</th>\n",
       "      <td>54.806038</td>\n",
       "      <td>3089.032605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>73.608120</td>\n",
       "      <td>11003.031625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>70.728485</td>\n",
       "      <td>12473.026870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>77.648600</td>\n",
       "      <td>25054.481636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>80.719500</td>\n",
       "      <td>29810.188275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lifeExp     gdpPercap\n",
       "year continent                         \n",
       "1952 Africa     39.135500   1252.572466\n",
       "     Americas   53.279840   4079.062552\n",
       "     Asia       46.314394   5195.484004\n",
       "     Europe     64.408500   5661.057435\n",
       "     Oceania    69.255000  10298.085650\n",
       "1957 Africa     41.266346   1385.236062\n",
       "     Americas   55.960280   4616.043733\n",
       "     Asia       49.318544   5787.732940\n",
       "     Europe     66.703067   6963.012816\n",
       "     Oceania    70.295000  11598.522455\n",
       "1962 Africa     43.319442   1598.078825\n",
       "     Americas   58.398760   4901.541870\n",
       "     Asia       51.563223   5729.369625\n",
       "     Europe     68.539233   8365.486814\n",
       "     Oceania    71.085000  12696.452430\n",
       "1967 Africa     45.334538   2050.363801\n",
       "     Americas   60.410920   5668.253496\n",
       "     Asia       54.663640   5971.173374\n",
       "     Europe     69.737600  10143.823757\n",
       "     Oceania    71.310000  14495.021790\n",
       "1972 Africa     47.450942   2339.615674\n",
       "     Americas   62.394920   6491.334139\n",
       "     Asia       57.319269   8187.468699\n",
       "     Europe     70.775033  12479.575246\n",
       "     Oceania    71.910000  16417.333380\n",
       "1977 Africa     49.580423   2585.938508\n",
       "     Americas   64.391560   7352.007126\n",
       "     Asia       59.610556   7791.314020\n",
       "     Europe     71.937767  14283.979110\n",
       "     Oceania    72.855000  17283.957605\n",
       "1982 Africa     51.592865   2481.592960\n",
       "     Americas   66.228840   7506.737088\n",
       "     Asia       62.617939   7434.135157\n",
       "     Europe     72.806400  15617.896551\n",
       "     Oceania    74.290000  18554.709840\n",
       "1987 Africa     53.344788   2282.668991\n",
       "     Americas   68.090720   7793.400261\n",
       "     Asia       64.851182   7608.226508\n",
       "     Europe     73.642167  17214.310727\n",
       "     Oceania    75.320000  20448.040160\n",
       "1992 Africa     53.629577   2281.810333\n",
       "     Americas   69.568360   8044.934406\n",
       "     Asia       66.537212   8639.690248\n",
       "     Europe     74.440100  17061.568084\n",
       "     Oceania    76.945000  20894.045885\n",
       "1997 Africa     53.598269   2378.759555\n",
       "     Americas   71.150480   8889.300863\n",
       "     Asia       68.020515   9834.093295\n",
       "     Europe     75.505167  19076.781802\n",
       "     Oceania    78.190000  24024.175170\n",
       "2002 Africa     53.325231   2599.385159\n",
       "     Americas   72.422040   9287.677107\n",
       "     Asia       69.233879  10174.090397\n",
       "     Europe     76.700600  21711.732422\n",
       "     Oceania    79.740000  26938.778040\n",
       "2007 Africa     54.806038   3089.032605\n",
       "     Americas   73.608120  11003.031625\n",
       "     Asia       70.728485  12473.026870\n",
       "     Europe     77.648600  25054.481636\n",
       "     Oceania    80.719500  29810.188275"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lifeExp, gdpPercap열의 평균값을 연도, 지역별로 그룹화하여 한번에 계산하기 \n",
    "multi_group = df.groupby([\"year\",\"continent\"])[[\"lifeExp\",\"gdpPercap\"]].mean()\n",
    "multi_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1952    49.057620\n",
       "1957    51.507401\n",
       "1962    53.609249\n",
       "1967    55.678290\n",
       "1972    57.647386\n",
       "1977    59.570157\n",
       "1982    61.533197\n",
       "1987    63.212613\n",
       "1992    64.160338\n",
       "1997    65.014676\n",
       "2002    65.694923\n",
       "2007    67.007423\n",
       "Name: lifeExp, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "global_yearly_lifeExp = df.groupby(\"year\")[\"lifeExp\"].mean()\n",
    "global_yearly_lifeExp "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='year'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cvFCsfTqylZkMj/p54LEgBQYq7Dm5G90xBhYiIrpjV6/XYGfKVexMzUVBRa12uX9Xe8wIUuChAR6wMedXDd09nkVERKQXjUbAkUvFiDyWjd8ySvDHbF72VqaY7N8FjwUp0NvNVtwiyegwsBARUbNUqRqwM+UqouOykV1ao10+sqcTpgcqMMbXFeYmnNyNWgcDCxERNelKaTWi4rKxMyUXVaqb86bYWphgRpACTwzphq6OViJXSB0BAwsREd1CEAQcyyhF5LHLOJRWrB326e5sjdnDvTDZvzOseW0KtSGebUREpHWjTo2YE3mIiruMS0VV2uX393bG7OFeGNnDifOmkCgYWIiICHnlN7A5Phvbkq6i4kY9gJu3JE8L6IJZw7rBm8/0IZExsBARdVCCICDlShkij13GvnNFUP8+d4rCwRKzhnbD9CAF7CxMRa6S6CYGFiKiDkbVoMYPpwoQFXdZ57k+w7o7YvawbhjVxxUyDvuQgWFgISLqIIora/FlQg62Jl5BSVUdAMDcRIpJ/p0xe3g3+LjZiVwh0e0xsBARGblTV8sRFZeNH0/no159c9jHzc4CTwz1RMjgrnyuD7ULDCxEREaoXq3B3rOFiDx2GcdzyrXLAzw7IXx4N4z1dYOpTCpegUR6YmAhIjIi16vr8HVSDrbEX0Gh8uazfUxlEjwywAOzh3fDgC724hZIdIcYWIiIjMCFAiWijmXj+5N5UDVoAABONmYIDfZE6JCucLG1ELlCorvDwEJE1E6pNQIOXChC5LHLSMi6rl3er7Mdwod54WE/dz7bh4wGAwsRUTtTcaP+5kMI47Nx9foNAIBMKsE4XzeED++GAM9OkEh4WzIZFwYWIqJ2IvNaFaKOZePb47moqVMDAOytTDEjqCueGOqJzvaWIldI1HoYWIiIDJhGI+CX9GuIPJaNo5euaZf3crVB+HAvTBzYGZZmHPYh48fAQkRkgKpVDfj2eC6i4rKRda0aACCRAKN8XBA+3AvDujty2Ic6FAYWIiIDE5dZgkVfn0RJlQoAYGNugumBCoQN9UQ3J2uRqyMSBwMLEZGB0GgEfHo0E+//nAaNAHR1sMKc4d0wNVABG3P+uaaOjZ8AIiIDUF5Th8U7TuHQxWIAwJRBXbBqYj9en0L0OwYWIiKRnc4tx/wvjyOv/AbMTKR4c4IvpgcqeI0K0V8wsBARiUQQBHyZmIM3fziPOrUGXR2s8EnoIPTrLBe7NCKDw8BCRCSCmroGvPLdGXx/Mh8A8GBfV7w3zQ9yS1ORKyMyTAwsRERtLKO4CvO/TEV6cRVkUgmWju2Nefd4cwiIqAkMLEREbSj2VD5e/vY0aurUcLY1x0ch/gj2dhS7LCKDx8BCRNQGVA1qvL37AqLjrwAAhng74MMQfz5FmaiZGFiIiFpZblkNFm49gVNXywEAC+7rjsUP9oKJTCpuYUTtCAMLEVErOpJWjOe2n0R5TT3sLEzwwWMDMaqPq9hlEbU7esf7vLw8zJw5E46OjrC0tET//v2RkpICAKivr8dLL72E/v37w9raGh4eHggLC0N+fn6Tfa5YsQISiUTn5ePjc2d7RERkANQaAWv3X0J4VDLKa+rRv7McuxeNZFghukN6/cJSVlaG4cOH4/7778eePXvg7OyM9PR0dOrUCQBQU1OD48eP47XXXoOfnx/Kysrw7LPP4tFHH9WGmtvx9fXFgQMH/izMhD/+EFH7VFqlwrPbTuK3jBIAQGhwV7z2cF9YmHLWWqI7pVcqWLNmDRQKBSIjI7XLvLy8tP8tl8uxf/9+nW0++ugjDB48GDk5OejatevtCzExgZubW7PqUKlUUKlU2vdKpbK5u0BE1KpSr1zHwq9OoFBZC0tTGd6a1A+TB3URuyyidk+vIaHY2FgEBgZi2rRpcHFxgb+/PzZs2NDkNhUVFZBIJLC3t2+yXXp6Ojw8PODt7Y3Q0FDk5OTctu3q1ashl8u1L4VCoc9uEBG1OEEQ8MWvWXjsswQUKmvh7WyN7xcOZ1ghaiESQRCE5ja2sLh5+93ixYsxbdo0JCcn49lnn8X69esxa9asW9rX1tZi+PDh8PHxwVdffXXbfvfs2YOqqir07t0bBQUFWLlyJfLy8nD27FnY2tre0r6xX1gUCgUqKipgZ2fX3N0hImoRlbX1WPrNaew5WwgAeGiAO9ZMGcAnLBP9A6VSCblc3qzvb70Ci5mZGQIDAxEXF6ddtmjRIiQnJyM+Pl6nbX19PaZMmYLc3FwcOXJEryBRXl4OT09PrF27FhEREf/YXp8dJiJqSRcLlZj/5XFcLqmGqUyCVx/qi7Chnpy1lqgZ9Pn+1mtIyN3dHX379tVZ1qdPn1uGb+rr6zF9+nRcuXIF+/fv1ztE2Nvbo1evXsjIyNBrOyKitvRNai4mfnwMl0uq4SG3wPanhmLWsG4MK0StQK/AMnz4cKSlpeksu3TpEjw9PbXv/wgr6enpOHDgABwd9Z9yuqqqCpmZmXB3d9d7WyKi1lZbr8bL357Gkp2nUFuvwT29nPHjopEY1LWT2KURGS29Asvzzz+PhIQEvP3228jIyMDWrVvx+eefY+HChQBuhpWpU6ciJSUFX331FdRqNQoLC1FYWIi6ujptP6NGjcJHH32kfb9kyRIcPXoU2dnZiIuLw6RJkyCTyRASEtJCu0lE1DKulFZjyqdx2JZ8FRIJ8PzoXoicHQQHazOxSyMyanpdERYUFISYmBgsW7YMb7zxBry8vLBu3TqEhoYCuDmpXGxsLABg4MCBOtsePnwY9913HwAgMzMTJSUl2nW5ubkICQlBaWkpnJ2dMWLECCQkJMDZ2fkudo2IqGX9fK4QL+w8hcraBjhYm+G/MwZiZE/+nSJqC3pddGuoeNEtEbWmBrUG7/6chs+OZgEABnW1x0ePD4KHvaXIlRG1b/p8f/OeOyKiJhQra/HM1yeQdPk6AGDOcC+8PN4HZiZ8cCFRW2JgISK6jfjMUvz76xMoqVLBxtwE70wdgH/1580ARGJgYCEi+huNRsD6XzLx3r40aATAx80Wn4QOgrezjdilEXVYDCxERH9RUVOPxTtO4uDFYgDA5EGd8dbE/rA044MLicTEwEJE9LszuRWY/1UqcstuwMxEipWP+mJGkIITwREZAAYWIurwBEHA1qQcrIw9jzq1BgoHS3waGoB+neVil0ZEv2NgIaIOraauAa/GnMV3J/IAAKP7uOL9aX6QW5mKXBkR/RUDCxF1WJnXqjD/y1RcKqqCTCrBi2N746l7vDkERGSAGFiIqEP68XQ+XvrmNKrr1HC2Ncf/QvwxxFv/Z58RUdtgYCGiDkWtEbBq93lEHssGAAR7OeB/j/vDxdZC3MKIqEkMLETUYdTWq7Ho6xP4+XwRAGD+fd3xwoO9YCLjrLVEho6BhYg6hPKaOjwZnYKUK2UwM5Fi3WMDOWstUTvCwEJERi+v/AZmbUpCRnEVbC1M8EVYIIJ5vQpRu8LAQkRG7WKhErM3JaNQWQs3OwtEzxmM3m62YpdFRHpiYCEio5WQVYq5m1NQWduAni42iJ4zGB72lmKXRUR3gIGFiIzST2cK8Ny2k6hTaxDUrRM2hAXC3spM7LKI6A4xsBCR0YmOy8aKH85BEICxvq747wx/WJjy4YVE7RkDCxEZDUEQ8O6+NHxyJBMAMHNIV6x8tB9kUs5cS9TeMbAQkVGoV2vw8rdn8O3xXADAkjG9sPD+Hpxmn8hIMLAQUbtXrWrAgq+O4+ila5BJJXh7Uj88FtRV7LKIqAUxsBBRu1ZSpcKcqGSczq2AhakUn4QOwgM+rmKXRUQtjIGFiNqtnNIahG1KRHZpDTpZmWLj7CAM6tpJ7LKIqBUwsBBRu3QmtwLhUUkoqapDl06WiJ4zGN2dbcQui4haCQMLEbU7v1y6hvlfpqK6To2+7naICg+Cix2ftkxkzBhYiKhdiTmRixd3nkaDRsDwHo5YPzMAthamYpdFRK2MgYWI2gVBEPD5L1lYveciAOBRPw+8N80PZiZSkSsjorbAwEJEBk+jEbBq9wVsOnYZAPDkCC+88q8+kHJCOKIOg4GFiAyaqkGNF3acwo+nCwAA//lXH8y9x1vkqoiorTGwEJHBUtbW46nNqYjPKoWpTIJ3p/phon9nscsiIhEwsBCRQSpS1mLWpiRcLKyEtZkMnz0RiBE9ncQui4hEwsBCRAYn81oVwjYmIa/8BpxszBEVHoR+neVil0VEImJgISKDknqlDBHRySivqYeXkzWiwwejq6OV2GURkcgYWIjIYBw4X4Rnvj6O2noN/LrIsWl2EBxtzMUui4gMAAMLERmEbUk5eCXmDDQCcH9vZ3wcOghWZvwTRUQ38a8BEYlKEAR8eDADHxy4BACYGtAFqyf3h6mME8IR0Z/0/ouQl5eHmTNnwtHREZaWlujfvz9SUlK06wVBwPLly+Hu7g5LS0uMHj0a6enp/9jvxx9/jG7dusHCwgLBwcFISkrStzQiamfUGgH/+f6sNqw8c38PvDt1AMMKEd1Cr78KZWVlGD58OExNTbFnzx6cP38e77//Pjp1+vNx7u+88w4+/PBDrF+/HomJibC2tsbYsWNRW1t72363b9+OxYsX4/XXX8fx48fh5+eHsWPHori4+M73jIgMWm29Gk9/mYqtiTmQSIA3J/hiydjekEg4ey0R3UoiCILQ3MYvv/wyjh07hl9//bXR9YIgwMPDAy+88AKWLFkCAKioqICrqyuioqIwY8aMRrcLDg5GUFAQPvroIwCARqOBQqHAv//9b7z88sv/WJdSqYRcLkdFRQXs7OyauztEJJLymjpERKcg9UoZzEyk+HDGQIzr5y52WUTUxvT5/tbrF5bY2FgEBgZi2rRpcHFxgb+/PzZs2KBdf/nyZRQWFmL06NHaZXK5HMHBwYiPj2+0z7q6OqSmpupsI5VKMXr06Ntuo1KpoFQqdV5E1D7kld/A1PXxSL1SBjsLE3wZEcywQkT/SK/AkpWVhU8//RQ9e/bEvn37MH/+fCxatAjR0dEAgMLCQgCAq6urznaurq7adX9XUlICtVqt1zarV6+GXC7XvhQKhT67QUQiuVioxORPjiGjuApudhbY+fQwDPZyELssImoH9AosGo0GgwYNwttvvw1/f3/MmzcPc+fOxfr161urvkYtW7YMFRUV2tfVq1fb9N8nIv0lZJVi2vp4FClV6Olig+8WDENvN1uxyyKidkKvwOLu7o6+ffvqLOvTpw9ycnIAAG5ubgCAoqIinTZFRUXadX/n5OQEmUym1zbm5uaws7PTeRGR4dp9ugBhG5NQWduAoG6d8M3Tw+Bhbyl2WUTUjugVWIYPH460tDSdZZcuXYKnpycAwMvLC25ubjh48KB2vVKpRGJiIoYOHdpon2ZmZggICNDZRqPR4ODBg7fdhojaj6hjl/HM18dRp9ZgrK8rtkQEQ25lKnZZRNTO6BVYnn/+eSQkJODtt99GRkYGtm7dis8//xwLFy4EAEgkEjz33HNYtWoVYmNjcebMGYSFhcHDwwMTJ07U9jNq1CjtHUEAsHjxYmzYsAHR0dG4cOEC5s+fj+rqaoSHh7fMXhJRmxMEAWv2XsSKH85DEIAnhnjik9AAWJjKxC6NiNohvWa6DQoKQkxMDJYtW4Y33ngDXl5eWLduHUJDQ7Vtli5diurqasybNw/l5eUYMWIE9u7dCwsLC22bzMxMlJSUaN8/9thjuHbtGpYvX47CwkIMHDgQe/fuveVCXCJqH+rVGrz07Wl8dzwPALBkTC8svL8H51ghojum1zwshorzsBAZjmpVAxZ8dRxHL12DTCrB6kn9MT2Id/IR0a30+f7ms4SIqMVcq1QhIjoZp3MrYGEqxSehg/CAD38pJaK7x8BCRC0i81oVZkcm4er1G+hkZYpNs4Pg37XTP29IRNQMDCxEdNdSsq/jyc0pKK+ph6ejFaLCB8PLyVrssojIiDCwENFd+elMAZ7bfhJ1DRoMVNjji1mBcLIxF7ssIjIyDCxEdEcEQcDG3y7jrZ8uQBCAB/u64sMZ/rA0423LRNTyGFiISG9qjYA3fzyPqLhsAEDYUE+8/ogvZFLetkxErYOBhYj0UluvxrPbTmDfuZuP03jlXz6YO9Kbc6wQUatiYCGiZrteXYeI6GScyCmHmUyK96f74RE/D7HLIqIOgIGFiJolu6QasyOTkF1aAzsLE2wIC0Swt6PYZRFRB8HAQkT/6EROGSKiU3C9ug6d7S0RPScIPVxsxS6LiDoQBhYiatLP5wqxaNsJ1NZr0K+zHTbNDoKLrcU/b0hE1IIYWIjotjbHZ+P12HMQBOD+3s746PFBsDbnnw0ianv8y0NEt9BoBKzZexGf/ZIFAAgZrMCbE/rBRCYVuTIi6qgYWIhIR229Gkt2nsKPpwsAAC+O7Y0F93XnbctEJCoGFiLSKq+pw7zNqUjKvg5TmQTvTB2ASf5dxC6LiIiBhYhuunq9BrMjk5B5rRq25ib47IkADOvhJHZZREQAGFiICMCZ3AqERyWjpEoFd7kFIsOD4ONmJ3ZZRERaDCxEHdzhi8VYuPU4aurU8HGzRVT4YLjJedsyERkWBhaiDuzrpBy8+v1ZqDUCRvZ0wiehg2BrYSp2WUREt2BgIeqABEHA+z9fwkeHMwAAUwO6YPXk/jDlbctEZKAYWIg6mLoGDV769jRiTuQBAJ4d1RPPje7J25aJyKAxsBB1IMraejy9JRVxmaWQSSVYPak/pgcpxC6LiOgfMbAQdRD55TcQHpmMtKJKWJvJ8MnMANzby1nssoiImoWBhagDOJ+vRHhUEoqUKrjYmmPT7CD06ywXuywiomZjYCEycr+mX8P8L4+jStWAni42iJozGJ3tLcUui4hILwwsREZsZ8pVLPvuDBo0AoZ4O+CzmYGQW/G2ZSJqfxhYiIyQIAj48GAGPjhwCQAwYaAH3pk6AOYmMpErIyK6MwwsREamXq3Bf2LOYEdKLgBg/n3d8eKY3pBKedsyEbVfDCxERqRK1YAFXx3HL5euQSoB3pjQDzOHeIpdFhHRXWNgITISRcpahEcm43yBEpamMnz0uD9G9XEVuywiohbBwEJkBC4VVWL2piTkV9TCycYMG2cFwU9hL3ZZREQthoGFqJ2LyyzBU1tSUVnbAG8na0SFD0ZXRyuxyyIialEMLETt2K6TeViy8xTq1QICPTthQ1ggOlmbiV0WEVGLY2AhaocEQcCnRzPxzt40AMC/+rth7fSBsDDlbctEZJwYWIjamQa1Bq/HnsNXiTkAgCdHeOGVf/XhbctEZNQYWIjakZq6Bvx76wkcvFgMiQRY/nBfhA/3ErssIqJWJ9Wn8YoVKyCRSHRePj4+AIDs7Oxb1v3x2rlz5237nD179i3tx40bd3d7RWSESqpUCPk8AQcvFsPcRIpPQwMYVoiow9D7FxZfX18cOHDgzw5MbnahUChQUFCg0/bzzz/Hu+++i/HjxzfZ57hx4xAZGal9b25urm9ZREYtp7QGYZsSkV1ag05WpvhiVhACPDuJXRYRUZvRO7CYmJjAzc3tluUymeyW5TExMZg+fTpsbGya7NPc3LzRPokIOJtXgdmRSSipqkOXTpbYPGcwvJ2b/kwRERkbvYaEACA9PR0eHh7w9vZGaGgocnJyGm2XmpqKkydPIiIi4h/7PHLkCFxcXNC7d2/Mnz8fpaWlTbZXqVRQKpU6LyJj9Ft6CR77LB4lVXXo426H7+YPY1ghog5JIgiC0NzGe/bsQVVVFXr37o2CggKsXLkSeXl5OHv2LGxtbXXaLliwAEeOHMH58+eb7HPbtm2wsrKCl5cXMjMz8corr8DGxgbx8fGQyRq/RXPFihVYuXLlLcsrKipgZ2fX3N0hMmh/nWNlWHdHrH8iAHYWpmKXRUTUYpRKJeRyebO+v/UKLH9XXl4OT09PrF27VueXlBs3bsDd3R2vvfYaXnjhBb36zMrKQvfu3XHgwAGMGjWq0TYqlQoqlUr7XqlUQqFQMLCQ0fji1yys2n0BAPDwAHe8P90P5iacY4WIjIs+geWubmu2t7dHr169kJGRobP8m2++QU1NDcLCwvTu09vbG05OTsjIyLhtYDE3N+eFuWSUNBoBq/dcwIZfLwMAwod3w2sP9eUcK0TU4el9DctfVVVVITMzE+7u7jrLN27ciEcffRTOzs5695mbm4vS0tJb+iQydnUNGizecVIbVl4e74PlDzOsEBEBegaWJUuW4OjRo8jOzkZcXBwmTZoEmUyGkJAQbZuMjAz88ssvePLJJxvtw8fHBzExMQBuBp4XX3wRCQkJyM7OxsGDBzFhwgT06NEDY8eOvYvdImpfqlQNiIhOxvcn82EileD9aX54+t7ukEgYVoiIAD2HhHJzcxESEoLS0lI4OztjxIgRSEhI0PklZdOmTejSpQvGjBnTaB9paWmoqKgAcPNW6NOnTyM6Ohrl5eXw8PDAmDFj8Oabb3LIhzqMa5UqzIlKxpm8CliZyfBJ6CDc19tF7LKIiAzKXV10ayj0uWiHyJBcKa1G2KYkXCmtgYO1GSJnB8FPYS92WUREbaLNLrolojt3JrcC4VE3J4RTOFhi85xgeDlZi10WEZFBYmAhEsEvl67h6S9TUVOnhq+HHSLDg+BiayF2WUREBouBhaiNfX/i5oRwDRoBw3s4Yv3MANhyQjgioiYxsBC1oQ2/ZOGtn25OCPeonwfem+YHM5O7ml2AiKhDYGAhagMajYC3f7qAL367OcdKxAgv/OdffTjHChFRMzGwELWyugYNluw8hdhT+QCAV/7lg3n3dBe5KiKi9oWBhagVVdbWY/6Xx/FbRglMpBK8O20AJvl3EbssIqJ2h4GFqJUUV9YiPDIZ5/KVsDKTYf3MANzTS//HVRAREQMLUau4XFKNsE2JuHr9BhytzRAZHoQBXezFLouIqN1iYCFqYaeulmNOVDJKq+vQ1cEKm+cMRjdOCEdEdFcYWIha0JG0Yiz46jhq6tTo19kOkbMHw9mWz8UiIrpbDCxELeS747lY+s1pNGgEjOjhhPVPBMDGnB8xIqKWwL+mRHdJEAR8/ksWVu+5CACYMNAD707lhHBERC2JgYXoLmg0AlbtvoBNx25OCDd3pBeWjeeEcERELY2BhegOqRrUeGHHKfx4ugAA8J9/9cHce7xFroqIyDgxsBDdgcraejy1JRVxmaUwlUnw3jQ/TBjYWeyyiIiMFgMLkZ6KlbWYHZmM8wVKWJvJsP6JAIzsyQnhiIhaEwMLkR6yrlUhbFMScstuwMnGDFHhg9Gvs1zssoiIjB4DC1Eznfx9Qrjr1XXwdLw5IZynIyeEIyJqCwwsRM1wOK0YC748jhv1avTvLEdkeBCcbDghHBFRW2FgIfoH36Tm4qVvT0OtETCypxPWzwyANSeEIyJqU/yrS3QbgiDg06OZeGdvGgBgkn9nrJkygBPCERGJgIGFqBEajYA3fjyPqLhsAMBT93jjpXE+nBCOiEgkDCxEf6NqUGPxjlPY/fuEcK8+1AdPjuSEcEREYmJgIfoLZW095m1OQULWdZjKJHh/+kA86uchdllERB0eAwvR74qVtZgVmYwLv08I99kTgRjR00nssoiICAwsRACAK6XVCP0i8fcJ4cwRFR7ECeGIiAwIAwt1eJeKKjHzi0QUV6rQzdEKm+cEo6ujldhlERHRXzCwUId2OrccYZuSUF5TDx83W2yOGAwXWwuxyyIior9hYKEOKzGrFBHRKahSNcBPYY/o8CDYW5mJXRYRETWCgYU6pCNpxXhqSypUDRoM8XbAF7OCYMPZa4mIDBb/QlOH89OZAjy77QTq1QIe8HHBJ6GDYGEqE7ssIiJqAgMLdSg7U67ipW9PQyMADw1wxwfTB3KqfSKidoCBhTqMqGOXseKH8wCAxwIVeHtyf8g41T4RUbvAwEJGTxAEfHIkE+/uu/kQwznDvfDaw30gkTCsEBG1FwwsZNQEQcD/7b2Iz45mAQCeHdUTz43uybBCRNTO6DV4v2LFCkgkEp2Xj4+Pdv199913y/qnn366yT4FQcDy5cvh7u4OS0tLjB49Gunp6Xe2N0R/odEIePX7s9qw8p9/9cHzD/ZiWCEiaof0/oXF19cXBw4c+LMDE90u5s6dizfeeEP73sqq6RlD33nnHXz44YeIjo6Gl5cXXnvtNYwdOxbnz5+HhQUn8KI706DW4MVvTiPmRB4kEuCtif3xeHBXscsiIqI7pHdgMTExgZub223XW1lZNbn+rwRBwLp16/Dqq69iwoQJAIDNmzfD1dUV33//PWbMmKFveURQNajx760n8PP5IphIJXh/uh8mDOwsdllERHQX9L6fMz09HR4eHvD29kZoaChycnJ01n/11VdwcnJCv379sGzZMtTU1Ny2r8uXL6OwsBCjR4/WLpPL5QgODkZ8fPxtt1OpVFAqlTovIgCoqWvAk9Ep+Pl8EcxMpFg/M4BhhYjICOj1C0twcDCioqLQu3dvFBQUYOXKlRg5ciTOnj0LW1tbPP744/D09ISHhwdOnz6Nl156CWlpafjuu+8a7a+wsBAA4OrqqrPc1dVVu64xq1evxsqVK/UpnTqAihv1mBOVjNQrZbAyk+GLsEAM6+EkdllERNQCJIIgCHe6cXl5OTw9PbF27VpERETcsv7QoUMYNWoUMjIy0L1791vWx8XFYfjw4cjPz4e7u7t2+fTp0yGRSLB9+/ZG/12VSgWVSqV9r1QqoVAoUFFRATs7uzvdHWrHSqtUeGJjEs4XKGFnYYKoOYMxqGsnscsiIqImKJVKyOXyZn1/39UUn/b29ujVqxcyMjIaXR8cHAwAt13/x7UuRUVFOsuLioqavA7G3NwcdnZ2Oi/quAoqbmD6Z/E4X6CEo7UZvp43hGGFiMjI3FVgqaqqQmZmps6vI3918uRJALjtei8vL7i5ueHgwYPaZUqlEomJiRg6dOjdlEYdxJXSakxbH4/Ma9Vwl1tgx9ND4eshF7ssIiJqYXoFliVLluDo0aPIzs5GXFwcJk2aBJlMhpCQEGRmZuLNN99EamoqsrOzERsbi7CwMNxzzz0YMGCAtg8fHx/ExMQAACQSCZ577jmsWrUKsbGxOHPmDMLCwuDh4YGJEye26I6S8blUVIlp6+ORW3YDno5W2Pn0UHR3thG7LCIiagV6XXSbm5uLkJAQlJaWwtnZGSNGjEBCQgKcnZ1RW1uLAwcOYN26daiuroZCocCUKVPw6quv6vSRlpaGiooK7fulS5eiuroa8+bNQ3l5OUaMGIG9e/dyDhZq0pncCoRtSkRZTT16u9piS8RguNjxnCEiMlZ3ddGtodDnoh1q/5IuX8ecqGRUqRrg10WOqPDB6GRtJnZZRESkJ32+v/ksIWpXjl66hqe2pKC2XoPBXg7YOCsQthamYpdFREStjIGF2o29Zwvw769PoF4t4L7ezvg0NACWZjKxyyIiojbAwELtwrepuXjxm1PQCMBD/d3xwWMDYWZyVze5ERFRO8LAQgZvc3w2lu86BwCYFtAF/zdlAGRSPnGZiKgjYWAhg/bx4Qy8uy8NADB7WDcsf7gvpAwrREQdDgMLGSRBEPDOvjR8eiQTALDogR54/sFekEgYVoiIOiIGFjI4Go2A12PPYUvCFQDAsvE+eOreW59FRUREHQcDCxmUBrUGS785je9O5EEiAVZN7IfQYE+xyyIiIpExsJDBUDWosejrE9h3rggyqQRrp/thwsDOYpdFREQGgIGFDEJNXQOe2pKKX9NLYCaT4qPH/THG9/ZP7CYioo6FgYVEp6ytx5zIZKRcKYOlqQwbwgIxoqeT2GUREZEBYWAhUZVWqTArMgln85SwtTBBVHgQAjwdxC6LiIgMDAMLiaawohYzNyYio7gKjtZm2BwxGL4ecrHLIiIiA8TAQqLIKa1B6MYEXL1+A+5yC2yJCEYPFxuxyyIiIgPFwEJtLr2oEjM3JqJIqYKnoxW+jAiGwsFK7LKIiMiAMbBQmzqbV4GwTUm4Xl2HXq42+DIiGC52FmKXRUREBo6BhdpMcvZ1zIlMRqWqAQO6yBEdPhidrM3ELouIiNoBBhZqE79cuoZ5W1JQW6/B4G4O2Dg7ELYWpmKXRURE7QQDC7W6n88V4pmtJ1Cn1uDeXs5YPzMAlmYyscsiIqJ2hIGFWtUPp/Lx3PaTUGsEjO/nhv/O8IeZiVTssoiIqJ1hYKFWszPlKl769jQ0AjDJvzPenToAJjKGFSIi0h8DC7WKLfHZeG3XOQBAyGAF3prYH1KpROSqiIiovWJgoRa34ZcsvPXTBQDA7GHd8PojfSGRMKwQEdGdY2ChFiMIAv53KANr918CACy4rzteHNubYYWIiO4aAwu1CEEQsGZvGtYfzQQALBnTC8880FPkqoiIyFgwsNBd02gEvPHjeUTFZQMAXn2oD54c6S1uUUREZFQYWOiuqDUC/hNzBtuSrwIAVk3sh5lDPEWuioiIjA0DC92xBrUGL+w8hV0n8yGVAO9M9cPUgC5il0VEREaIgYXuSF2DBou+PoG95wphIpVg3YyBeHiAh9hlERGRkWJgIb3V1qsx/8tUHE67BjOZFB+HDsKDfV3FLouIiIwYAwvppVrVgLmbUxCXWQoLUyk+fyIQ9/RyFrssIiIycgws1GzK2nqERyYj9UoZrM1k2DQ7CMHejmKXRUREHQADCzVLWXUdwjYl4UxeBewsTBA9ZzD8u3YSuywiIuogGFjoH12rVOGJjYm4WFgJB2szbIkYDF8PudhlERFRB8LAQk0qqLiB0C8SkXWtGs625tj6ZDB6utqKXRYREXUwDCx0W1ev1+DxLxJw9foNeMgt8NXcIfBysha7LCIi6oCk+jResWIFJBKJzsvHxwcAcP36dfz73/9G7969YWlpia5du2LRokWoqKhoss/Zs2ff0ue4cePufI+oRWRdq8L0z+Jx9foNeDpaYcfTQxlWiIhINHr/wuLr64sDBw782YHJzS7y8/ORn5+P9957D3379sWVK1fw9NNPIz8/H998802TfY4bNw6RkZHa9+bm5vqWRS0orbASoV8koqRKhe7O1tg6dwhc7SzELouIiDowvQOLiYkJ3Nzcblner18/fPvtt9r33bt3x1tvvYWZM2eioaFBG2waY25u3mift6NSqaBSqbTvlUpls7elpp3Nq8ATGxNRVlOPPu522BIxGE42DJBERCQuvYaEACA9PR0eHh7w9vZGaGgocnJybtu2oqICdnZ2TYYVADhy5AhcXFzQu3dvzJ8/H6WlpU22X716NeRyufalUCj03Q1qROqVMoRsSEBZTT38usjx9dxghhUiIjIIEkEQhOY23rNnD6qqqtC7d28UFBRg5cqVyMvLw9mzZ2Frq3vnSElJCQICAjBz5ky89dZbt+1z27ZtsLKygpeXFzIzM/HKK6/AxsYG8fHxkMlkjW7T2C8sCoVCG5BIf/GZpYiITkZNnRqDuzlg4+xA2FqYil0WEREZMaVSCblc3qzvb70Cy9+Vl5fD09MTa9euRUREhE4BDz74IBwcHBAbGwtT0+Z/8WVlZaF79+44cOAARo0a1axt9NlhutWRtGI8tSUVqgYNRvRwwudhAbAy4w1kRETUuvT5/tZ7SOiv7O3t0atXL2RkZGiXVVZWYty4cbC1tUVMTIxeYQUAvL294eTkpNMntZ595woxd3MKVA0ajPJxwRezAhlWiIjI4NxVYKmqqkJmZibc3d0B3ExKY8aMgZmZGWJjY2Fhof+dJbm5uSgtLdX2Sa1n18k8LPjqOOrVAh7q745PZwbAwrTxYTgiIiIx6RVYlixZgqNHjyI7OxtxcXGYNGkSZDIZQkJCtGGluroaGzduhFKpRGFhIQoLC6FWq7V9+Pj4ICYmBsDNwPPiiy8iISEB2dnZOHjwICZMmIAePXpg7NixLbunpGNH8lU8t/0k1BoBk/07478zBsLM5K7yKxERUavR67f/3NxchISEoLS0FM7OzhgxYgQSEhLg7OyMI0eOIDExEQDQo0cPne0uX76Mbt26AQDS0tK0k8nJZDKcPn0a0dHRKC8vh4eHB8aMGYM333yTc7G0os3x2Vi+6xwAIGRwV7w1sR+kUonIVREREd3eXV10ayh40W3zfXY0E6v3XAQAzBnuhdce7gOJhGGFiIjanj7f37y6soMQBAH/PZiOdQfSAQDP3N8DL4zpxbBCRETtAgNLByAIAv5v70V8djQLALBkTC8880BPkasiIiJqPgYWI6fRCFj5wzlEx18BALz2cF9EjPASuSoiIiL9MLAYMbVGwLLvTmNHSi4kEmDVxH4IDfYUuywiIiK9MbAYqXq1Bi/sOIXYU/mQSoD3pvlh8qAuYpdFRER0RxhYjJCqQY1FX5/AvnNFMJFK8N8Z/nhoACfiIyKi9ouBxcjU1qvx1JZUHL10DWYyKT4JHYTRfV3FLouIiOiuMLAYkWpVA56MTkF8ViksTKXYEBaIkT2dxS6LiIjorjGwGAllbT3CI5OReqUMNuYm2DQ7CIO9HMQui4iIqEUwsBiBsuo6hG1Kwpm8CthZmGBzRDAGKuzFLouIiKjFMLC0cxU19QjZkICLhZVwsDbDlojB8PWQi10WERFRi2Jgacdq69WYuyUFFwsr4Wxrjq1PBqOnq63YZREREbU4qdgF0J1RawQs3nESSZevw9bcBJvnDGZYISIio8XA0g4JgoA3fzyPn84UwlQmwWdhAejjzqdUExGR8WJgaYfWH81CVFw2AOD96QMxrLuTuAURERG1MgaWdua747lYs/ciAODVh/rgUT8PkSsiIiJqfQws7cjRS9ew9JvTAIC5I73w5EhvkSsiIiJqGwws7cSZ3ArM/zIVDRoBEwZ6YNn4PmKXRERE1GYYWNqBnNIahEcloaZOjeE9HPHuVD9IpRKxyyIiImozDCwGrrRKhbBNiSipqkMfdzusnxkAMxP+byMioo6F33wGrKauAXOikpFdWoPO9paIDg+CrYWp2GURERG1OQYWA1Wv1mDhV8dxKrcC9lam2BwxGC52FmKXRUREJAoGFgMkCAL+E3MGh9OuwcJUio2zgtDd2UbssoiIiETDwGKAPth/CTtSciGVAP8LGYQAz05il0RERCQqBhYD81XiFXx4KAMAsGpifzzY11XkioiIiMTHwGJA9p0rxGvfnwUALBrVE48HdxW5IiIiIsPAwGIgUrKvY9HXJ6ARgBlBCjw/uqfYJRERERkMBhYDkFFciYjoFKgaNBjl44JVE/tBIuHEcERERH9gYBFZkbIWszYlo+JGPQYq7PG/x/1hIuP/FiIior/iN6OIlLX1mLUpCXnlN+DlZI1Ns4NgZWYidllEREQGh4FFJKoGNZ7anIqLhZVwsjHH5jmD4WBtJnZZREREBomBRQQajYAXdpxCfFYprM1kiAoPgsLBSuyyiIiIDBYDiwje+ukCfjxdABOpBOufCEC/znKxSyIiIjJoDCxtbMMvWdj422UAwHvT/DCyp7PIFRERERk+BpY2tOtkHt766QIAYNl4H0z07yxyRURERO0DA0sbOZZRgiU7TwEAZg/rhnn3eItcERERUfuhV2BZsWIFJBKJzsvHx0e7vra2FgsXLoSjoyNsbGwwZcoUFBUVNdmnIAhYvnw53N3dYWlpidGjRyM9Pf3O9sZAncuvwFNbUlGvFvBQf3csf7gvJ4YjIiLSg96/sPj6+qKgoED7+u2337Trnn/+efzwww/YuXMnjh49ivz8fEyePLnJ/t555x18+OGHWL9+PRITE2FtbY2xY8eitrZW/70xQFev12B2ZDKqVA0I9nLA+9P9IJUyrBAREelD71nKTExM4ObmdsvyiooKbNy4EVu3bsUDDzwAAIiMjESfPn2QkJCAIUOG3LKNIAhYt24dXn31VUyYMAEAsHnzZri6uuL777/HjBkzGq1BpVJBpVJp3yuVSn13o02UVddhVmQSrlWq4ONmi8/DAmFhKhO7LCIionZH719Y0tPT4eHhAW9vb4SGhiInJwcAkJqaivr6eowePVrb1sfHB127dkV8fHyjfV2+fBmFhYU628jlcgQHB992GwBYvXo15HK59qVQKPTdjVZ3o06NOdHJyLpWDQ+5BaLCB0NuaSp2WURERO2SXoElODgYUVFR2Lt3Lz799FNcvnwZI0eORGVlJQoLC2FmZgZ7e3udbVxdXVFYWNhof38sd3V1bfY2ALBs2TJUVFRoX1evXtVnN1pdg1qDf399AidyymFnYYLoOYPhJrcQuywiIqJ2S68hofHjx2v/e8CAAQgODoanpyd27NgBS0vLFi/udszNzWFubt5m/54+BEHAa7vO4cCFIpiZSLFxdhB6utqKXRYREVG7dle3Ndvb26NXr17IyMiAm5sb6urqUF5ertOmqKio0WteAGiX//1Ooqa2MXQfHszA10k5kEiAD2cMRFA3B7FLIiIiavfuKrBUVVUhMzMT7u7uCAgIgKmpKQ4ePKhdn5aWhpycHAwdOrTR7b28vODm5qazjVKpRGJi4m23MWTbknLwwYFLAIA3HvXFuH7uIldERERkHPQKLEuWLMHRo0eRnZ2NuLg4TJo0CTKZDCEhIZDL5YiIiMDixYtx+PBhpKamIjw8HEOHDtW5Q8jHxwcxMTEAAIlEgueeew6rVq1CbGwszpw5g7CwMHh4eGDixIktuqOt7eCFIvzn+7MAgIX3d8cTQ7uJWxAREZER0esaltzcXISEhKC0tBTOzs4YMWIEEhIS4Ox883k4H3zwAaRSKaZMmQKVSoWxY8fik08+0ekjLS0NFRUV2vdLly5FdXU15s2bh/LycowYMQJ79+6FhUX7uUj1eE4ZFm49DrVGwJRBXbBkTG+xSyIiIjIqEkEQBLGLuFtKpRJyuRwVFRWws7Nr038761oVpnwah7KaetzbyxlfzAqEqYxPPCAiIvon+nx/85v1LhRX1iJsUxLKauoxoIscn4QOYlghIiJqBfx2vUOVtfUIj0xGbtkNeDpaYdPsIFib6z1xMBERETUDA8sdqGvQYP6Xx3EuXwlHazNsnjMYTjaGOS8MERGRMWBg0ZNGI2DpN6fwW0YJrMxkiAwPgqejtdhlERERGTUGFj2t2XcR35/Mh0wqwSehgzCgi73YJRERERk9BhY9RB67jM+OZgEA/m9yf9zX20XkioiIiDoGBpZm+vF0Pt748TwA4MWxvTEt0PCeEE1ERGSsGFiaIT6zFIu3n4IgAE8M8cSC+7qLXRIREVGHwsDyDy4WKjFvSwrq1BqM83XDikd9IZFIxC6LiIioQ2FgaUJe+Q3M2pSEytoGBHXrhHUzBkImZVghIiJqa5zprAmmMgmcbMxha2GKDWGBsDCViV0SERFRh8TA0gQXWwtsmzcE1So17K3MxC6HiIiow2Jg+Qe2FqawtTAVuwwiIqIOjdewEBERkcFjYCEiIiKDx8BCREREBo+BhYiIiAweAwsREREZPAYWIiIiMngMLERERGTwGFiIiIjI4DGwEBERkcFjYCEiIiKDx8BCREREBo+BhYiIiAweAwsREREZPKN4WrMgCAAApVIpciVERETUXH98b//xPd4UowgslZWVAACFQiFyJURERKSvyspKyOXyJttIhObEGgOn0WiQn58PW1tbSCQSnXVKpRIKhQJXr16FnZ2dSBW2fzyOLYPHsWXwOLYMHseWweN45wRBQGVlJTw8PCCVNn2VilH8wiKVStGlS5cm29jZ2fFEagE8ji2Dx7Fl8Di2DB7HlsHjeGf+6ZeVP/CiWyIiIjJ4DCxERERk8Iw+sJibm+P111+Hubm52KW0azyOLYPHsWXwOLYMHseWwePYNoziolsiIiIybkb/CwsRERG1fwwsREREZPAYWIiIiMjgMbAQERGRwWsXgeWXX37BI488Ag8PD0gkEnz//fc664uKijB79mx4eHjAysoK48aNQ3p6uk6b++67DxKJROf19NNP67TJycnBQw89BCsrK7i4uODFF19EQ0NDa+9em2iJYwgA8fHxeOCBB2BtbQ07Ozvcc889uHHjhnb99evXERoaCjs7O9jb2yMiIgJVVVWtvXtt6m6PZXZ29i3n4h+vnTt3atvxfGz6fCwsLMQTTzwBNzc3WFtbY9CgQfj222912hj7+dgSxzEzMxOTJk2Cs7Mz7OzsMH36dBQVFem0MfbjuHr1agQFBcHW1hYuLi6YOHEi0tLSdNrU1tZi4cKFcHR0hI2NDaZMmXLLcWrOZ/bIkSMYNGgQzM3N0aNHD0RFRbX27hmNdhFYqqur4efnh48//viWdYIgYOLEicjKysKuXbtw4sQJeHp6YvTo0aiurtZpO3fuXBQUFGhf77zzjnadWq3GQw89hLq6OsTFxSE6OhpRUVFYvnx5q+9fW2iJYxgfH49x48ZhzJgxSEpKQnJyMp555hmd6ZRDQ0Nx7tw57N+/Hz/++CN++eUXzJs3r032sa3c7bFUKBQ652FBQQFWrlwJGxsbjB8/HgDPx+acj2FhYUhLS0NsbCzOnDmDyZMnY/r06Thx4oS2jbGfj3d7HKurqzFmzBhIJBIcOnQIx44dQ11dHR555BFoNBptX8Z+HI8ePYqFCxciISEB+/fvR319PcaMGaNzvj3//PP44YcfsHPnThw9ehT5+fmYPHmydn1zPrOXL1/GQw89hPvvvx8nT57Ec889hyeffBL79u1r0/1tt4R2BoAQExOjfZ+WliYAEM6ePatdplarBWdnZ2HDhg3aZffee6/w7LPP3rbfn376SZBKpUJhYaF22aeffirY2dkJKpWqRfdBbHd6DIODg4VXX331tv2eP39eACAkJydrl+3Zs0eQSCRCXl5ey+6EgbjTY/l3AwcOFObMmaN9z/Pxn4+htbW1sHnzZp2+HBwctG062vl4J8dx3759glQqFSoqKrRtysvLBYlEIuzfv18QhI53HAVBEIqLiwUAwtGjRwVBuHlMTE1NhZ07d2rbXLhwQQAgxMfHC4LQvM/s0qVLBV9fX51/67HHHhPGjh3b2rtkFNrFLyxNUalUAAALCwvtMqlUCnNzc/z22286bb/66is4OTmhX79+WLZsGWpqarTr4uPj0b9/f7i6umqXjR07FkqlEufOnWvlvRBXc45hcXExEhMT4eLigmHDhsHV1RX33nuvzjGOj4+Hvb09AgMDtctGjx4NqVSKxMTENtobcelzPv4hNTUVJ0+eREREhHYZz8d/PobDhg3D9u3bcf36dWg0Gmzbtg21tbW47777APB8bM5xVKlUkEgkOhOeWVhYQCqVatt0xONYUVEBAHBwcABw8zNaX1+P0aNHa9v4+Piga9euiI+PB9C8z2x8fLxOH3+0+aMPalq7Dyx/nDTLli1DWVkZ6urqsGbNGuTm5qKgoEDb7vHHH8eXX36Jw4cPY9myZdiyZQtmzpypXV9YWKhzogHQvi8sLGybnRFJc45hVlYWAGDFihWYO3cu9u7di0GDBmHUqFHaMfHCwkK4uLjo9G1iYgIHBwejP4Z/aO75+FcbN25Enz59MGzYMO0yno//fAx37NiB+vp6ODo6wtzcHE899RRiYmLQo0cPADwfm3MchwwZAmtra7z00kuoqalBdXU1lixZArVarW3T0Y6jRqPBc889h+HDh6Nfv34Abh4DMzMz2Nvb67R1dXXVHoPmfGZv10apVOpcC0iNa/eBxdTUFN999x0uXboEBwcHWFlZ4fDhwxg/frzOtRXz5s3D2LFj0b9/f4SGhmLz5s2IiYlBZmamiNUbhuYcwz/Gs5966imEh4fD398fH3zwAXr37o1NmzaJWb5Bae75+IcbN25g69atOr+udHTNPYavvfYaysvLceDAAaSkpGDx4sWYPn06zpw5I2L1hqM5x9HZ2Rk7d+7EDz/8ABsbG8jlcpSXl2PQoEGNnq8dwcKFC3H27Fls27ZN7FLob0zELqAlBAQE4OTJk6ioqEBdXR2cnZ0RHBys8xPm3wUHBwMAMjIy0L17d7i5uSEpKUmnzR9XgLu5ubVe8Qbin46hu7s7AKBv37462/Xp0wc5OTkAbh6n4uJinfUNDQ24fv16hziGf9DnfPzmm29QU1ODsLAwneU8H5s+hpmZmfjoo49w9uxZ+Pr6AgD8/Pzw66+/4uOPP8b69et5PqJ55+KYMWOQmZmJkpISmJiYwN7eHm5ubvD29gbQsT7XzzzzjPai4i5dumiXu7m5oa6uDuXl5Tq/shQVFWmPQXM+s25ubrfcWVRUVAQ7OztYWlq2xi4ZFaOK0HK5HM7OzkhPT0dKSgomTJhw27YnT54E8OcX8dChQ3HmzBmdD+b+/fthZ2d3y5e0MbvdMezWrRs8PDxuudXv0qVL8PT0BHDzGJaXlyM1NVW7/tChQ9BoNNqA2JE053zcuHEjHn30UTg7O+ss5/l40+2O4R/Xn/39VwCZTKb9NZDn45+acy46OTnB3t4ehw4dQnFxMR599FEAHeM4CoKAZ555BjExMTh06BC8vLx01gcEBMDU1BQHDx7ULktLS0NOTg6GDh0KoHmf2aFDh+r08UebP/qgfyD2Vb/NUVlZKZw4cUI4ceKEAEBYu3atcOLECeHKlSuCIAjCjh07hMOHDwuZmZnC999/L3h6egqTJ0/Wbp+RkSG88cYbQkpKinD58mVh165dgre3t3DPPfdo2zQ0NAj9+vUTxowZI5w8eVLYu3ev4OzsLCxbtqzN97c13O0xFARB+OCDDwQ7Ozth586dQnp6uvDqq68KFhYWQkZGhrbNuHHjBH9/fyExMVH47bffhJ49ewohISFtuq+trSWOpSAIQnp6uiCRSIQ9e/bcso7nY9PHsK6uTujRo4cwcuRIITExUcjIyBDee+89QSKRCLt379a2M/bzsSXOxU2bNgnx8fFCRkaGsGXLFsHBwUFYvHixThtjP47z588X5HK5cOTIEaGgoED7qqmp0bZ5+umnha5duwqHDh0SUlJShKFDhwpDhw7Vrm/OZzYrK0uwsrISXnzxReHChQvCxx9/LMhkMmHv3r1tur/tVbsILIcPHxYA3PKaNWuWIAiC8N///lfo0qWLYGpqKnTt2lV49dVXdW79zMnJEe655x7BwcFBMDc3F3r06CG8+OKLOrfyCYIgZGdnC+PHjxcsLS0FJycn4YUXXhDq6+vbcldbzd0ewz+sXr1a6NKli2BlZSUMHTpU+PXXX3XWl5aWCiEhIYKNjY1gZ2cnhIeHC5WVlW2xi22mpY7lsmXLBIVCIajV6kb/HZ6PTR/DS5cuCZMnTxZcXFwEKysrYcCAAbfc5mzs52NLHMeXXnpJcHV1FUxNTYWePXsK77//vqDRaHTaGPtxbOwYAhAiIyO1bW7cuCEsWLBA6NSpk2BlZSVMmjRJKCgo0OmnOZ/Zw4cPCwMHDhTMzMwEb29vnX+DmiYRBEFo3d9wiIiIiO6OUV3DQkRERMaJgYWIiIgMHgMLERERGTwGFiIiIjJ4DCxERERk8BhYiIiIyOAxsBAREZHBY2AhIiIig8fAQkRERAaPgYWIiIgMHgMLERkttVqtfXozEbVvDCxE1CY2b94MR0dHqFQqneUTJ07EE088AQDYtWsXBg0aBAsLC3h7e2PlypVoaGjQtl27di369+8Pa2trKBQKLFiwAFVVVdr1UVFRsLe3R2xsLPr27Qtzc3Pk5OS0zQ4SUatiYCGiNjFt2jSo1WrExsZqlxUXF2P37t2YM2cOfv31V4SFheHZZ5/F+fPn8dlnnyEqKgpvvfWWtr1UKsWHH36Ic+fOITo6GocOHcLSpUt1/p2amhqsWbMGX3zxBc6dOwcXF5c220ciaj18WjMRtZkFCxYgOzsbP/30E4Cbv5h8/PHHyMjIwIMPPohRo0Zh2bJl2vZffvklli5divz8/Eb7++abb/D000+jpKQEwM1fWMLDw3Hy5En4+fm1/g4RUZthYCGiNnPixAkEBQXhypUr6Ny5MwYMGIBp06bhtddeg7OzM6qqqiCTybTt1Wo1amtrUV1dDSsrKxw4cACrV6/GxYsXoVQq0dDQoLM+KioKTz31FGprayGRSETcUyJqaSZiF0BEHYe/vz/8/PywefNmjBkzBufOncPu3bsBAFVVVVi5ciUmT558y3YWFhbIzs7Gww8/jPnz5+Ott96Cg4MDfvvtN0RERKCurg5WVlYAAEtLS4YVIiPEwEJEberJJ5/EunXrkJeXh9GjR0OhUAAABg0ahLS0NPTo0aPR7VJTU6HRaPD+++9DKr15+d2OHTvarG4iEhcDCxG1qccffxxLlizBhg0bsHnzZu3y5cuX4+GHH0bXrl0xdepUSKVSnDp1CmfPnsWqVavQo0cP1NfX43//+x8eeeQRHDt2DOvXrxdxT4ioLfEuISJqU3K5HFOmTIGNjQ0mTpyoXT527Fj8+OOP+PnnnxEUFIQhQ4bggw8+gKenJwDAz88Pa9euxZo1a9CvXz989dVXWL16tUh7QURtjRfdElGbGzVqFHx9ffHhhx+KXQoRtRMMLETUZsrKynDkyBFMnToV58+fR+/evcUuiYjaCV7DQkRtxt/fH2VlZVizZg3DChHphb+wEBERkcHjRbdERERk8BhYiIiIyOAxsBAREZHBY2AhIiIig8fAQkRERAaPgYWIiIgMHgMLERERGTwGFiIiIjJ4/w9Kd8uKFzpq+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_yearly_lifeExp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}