jae_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "77a5bd9e-f594-4d7b-b300-f38b4e6b92bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "bd684224-6236-4138-b363-2ccb5ea4b2ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Study data files\n",
    "fifteen = \"resources/2015.csv\"\n",
    "sixteen = \"resources/2016.csv\"\n",
    "seventeen = \"resources/2017.csv\"\n",
    "eighteen = \"resources/2018_new.csv\"\n",
    "nineteen = \"resources/2019.csv\"\n",
    "\n",
    "# Read the mouse data and the study results\n",
    "fifteen_data = pd.read_csv(fifteen)\n",
    "sixteen_data = pd.read_csv(sixteen)\n",
    "seventeen_data = pd.read_csv(seventeen)\n",
    "eighteen_data = pd.read_csv(eighteen)\n",
    "nineteen_data = pd.read_csv(nineteen)\n",
    "\n",
    "#change into dataframes\n",
    "fif_df = pd.DataFrame(fifteen_data)\n",
    "six_df = pd.DataFrame(sixteen_data)\n",
    "sev_df = pd.DataFrame(seventeen_data)\n",
    "eight_df = pd.DataFrame(eighteen_data)\n",
    "nine_df = pd.DataFrame(nineteen_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "c2bf4ceb-3287-48d0-95a9-a4420110cc69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframes showing top 5 and bottom 5 ranking on the happiness score and trust in government for each year.\n",
    "\n",
    "# 2015 top 5\n",
    "fif_top_5 = fif_df.iloc[0:5]\n",
    "fif_top_5[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]\n",
    "fif_top_5 = fif_top_5[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]\n",
    "# 2015 bottom 5\n",
    "fif_last_5 = fif_df.iloc[153:158]\n",
    "fif_last_5[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]\n",
    "fif_last_5 = fif_last_5[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "908a5ff4-2925-4526-b101-7463f8440cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>154</td>\n",
       "      <td>3.465</td>\n",
       "      <td>0.03464</td>\n",
       "      <td>0.22208</td>\n",
       "      <td>0.77370</td>\n",
       "      <td>0.42864</td>\n",
       "      <td>0.59201</td>\n",
       "      <td>0.55191</td>\n",
       "      <td>0.22628</td>\n",
       "      <td>0.67042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Benin</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>155</td>\n",
       "      <td>3.340</td>\n",
       "      <td>0.03656</td>\n",
       "      <td>0.28665</td>\n",
       "      <td>0.35386</td>\n",
       "      <td>0.31910</td>\n",
       "      <td>0.48450</td>\n",
       "      <td>0.08010</td>\n",
       "      <td>0.18260</td>\n",
       "      <td>1.63328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Syria</td>\n",
       "      <td>Middle East and Northern Africa</td>\n",
       "      <td>156</td>\n",
       "      <td>3.006</td>\n",
       "      <td>0.05015</td>\n",
       "      <td>0.66320</td>\n",
       "      <td>0.47489</td>\n",
       "      <td>0.72193</td>\n",
       "      <td>0.15684</td>\n",
       "      <td>0.18906</td>\n",
       "      <td>0.47179</td>\n",
       "      <td>0.32858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.08658</td>\n",
       "      <td>0.01530</td>\n",
       "      <td>0.41587</td>\n",
       "      <td>0.22396</td>\n",
       "      <td>0.11850</td>\n",
       "      <td>0.10062</td>\n",
       "      <td>0.19727</td>\n",
       "      <td>1.83302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Togo</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>158</td>\n",
       "      <td>2.839</td>\n",
       "      <td>0.06727</td>\n",
       "      <td>0.20868</td>\n",
       "      <td>0.13995</td>\n",
       "      <td>0.28443</td>\n",
       "      <td>0.36453</td>\n",
       "      <td>0.10731</td>\n",
       "      <td>0.16681</td>\n",
       "      <td>1.56726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country                           Region  Happiness Rank  \\\n",
       "0    Switzerland                   Western Europe               1   \n",
       "1        Iceland                   Western Europe               2   \n",
       "2        Denmark                   Western Europe               3   \n",
       "3         Norway                   Western Europe               4   \n",
       "4         Canada                    North America               5   \n",
       "..           ...                              ...             ...   \n",
       "153       Rwanda               Sub-Saharan Africa             154   \n",
       "154        Benin               Sub-Saharan Africa             155   \n",
       "155        Syria  Middle East and Northern Africa             156   \n",
       "156      Burundi               Sub-Saharan Africa             157   \n",
       "157         Togo               Sub-Saharan Africa             158   \n",
       "\n",
       "     Happiness Score  Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0              7.587         0.03411                   1.39651  1.34951   \n",
       "1              7.561         0.04884                   1.30232  1.40223   \n",
       "2              7.527         0.03328                   1.32548  1.36058   \n",
       "3              7.522         0.03880                   1.45900  1.33095   \n",
       "4              7.427         0.03553                   1.32629  1.32261   \n",
       "..               ...             ...                       ...      ...   \n",
       "153            3.465         0.03464                   0.22208  0.77370   \n",
       "154            3.340         0.03656                   0.28665  0.35386   \n",
       "155            3.006         0.05015                   0.66320  0.47489   \n",
       "156            2.905         0.08658                   0.01530  0.41587   \n",
       "157            2.839         0.06727                   0.20868  0.13995   \n",
       "\n",
       "     Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                     0.94143  0.66557                        0.41978   \n",
       "1                     0.94784  0.62877                        0.14145   \n",
       "2                     0.87464  0.64938                        0.48357   \n",
       "3                     0.88521  0.66973                        0.36503   \n",
       "4                     0.90563  0.63297                        0.32957   \n",
       "..                        ...      ...                            ...   \n",
       "153                   0.42864  0.59201                        0.55191   \n",
       "154                   0.31910  0.48450                        0.08010   \n",
       "155                   0.72193  0.15684                        0.18906   \n",
       "156                   0.22396  0.11850                        0.10062   \n",
       "157                   0.28443  0.36453                        0.10731   \n",
       "\n",
       "     Generosity  Dystopia Residual  \n",
       "0       0.29678            2.51738  \n",
       "1       0.43630            2.70201  \n",
       "2       0.34139            2.49204  \n",
       "3       0.34699            2.46531  \n",
       "4       0.45811            2.45176  \n",
       "..          ...                ...  \n",
       "153     0.22628            0.67042  \n",
       "154     0.18260            1.63328  \n",
       "155     0.47179            0.32858  \n",
       "156     0.19727            1.83302  \n",
       "157     0.16681            1.56726  \n",
       "\n",
       "[158 rows x 12 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fif_df\n",
    "fif_corr = fif_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2092d75b-67a0-42b5-ace3-b83b430b52af",
   "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>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.41978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.14145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.48357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.36503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.32957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Rank  Happiness Score  Trust (Government Corruption)\n",
       "0  Switzerland               1            7.587                        0.41978\n",
       "1      Iceland               2            7.561                        0.14145\n",
       "2      Denmark               3            7.527                        0.48357\n",
       "3       Norway               4            7.522                        0.36503\n",
       "4       Canada               5            7.427                        0.32957"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fif_top_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "6f161393-8c31-420d-8f73-ee0cc2af3e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "fif_last_5.to_csv('images/2015_last_5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ab4cfdc7-def5-41d0-96aa-d1021ef6af99",
   "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>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>154</td>\n",
       "      <td>3.465</td>\n",
       "      <td>0.55191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Benin</td>\n",
       "      <td>155</td>\n",
       "      <td>3.340</td>\n",
       "      <td>0.08010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Syria</td>\n",
       "      <td>156</td>\n",
       "      <td>3.006</td>\n",
       "      <td>0.18906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.10062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Togo</td>\n",
       "      <td>158</td>\n",
       "      <td>2.839</td>\n",
       "      <td>0.10731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     Country  Happiness Rank  Happiness Score  Trust (Government Corruption)\n",
       "153   Rwanda             154            3.465                        0.55191\n",
       "154    Benin             155            3.340                        0.08010\n",
       "155    Syria             156            3.006                        0.18906\n",
       "156  Burundi             157            2.905                        0.10062\n",
       "157     Togo             158            2.839                        0.10731"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fif_last_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "338a261c-d768-4f00-8768-15c1b31db2c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframes showing top 5 and bottom 5 ranking on the happiness score and trust in government for each year\n",
    "\n",
    "# 2016 top 5\n",
    "six_top_5 = six_df.iloc[0:5]\n",
    "six_df[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]\n",
    "six_top_5 = six_top_5[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]\n",
    "# 2016 bottom 5\n",
    "six_last_5 = six_df.iloc[152:157]\n",
    "six_last_5[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]\n",
    "six_last_5 = six_last_5[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "88050935-4bdd-4fb5-9c2a-163b5a041084",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>1</td>\n",
       "      <td>7.526</td>\n",
       "      <td>0.44453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2</td>\n",
       "      <td>7.509</td>\n",
       "      <td>0.41203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>3</td>\n",
       "      <td>7.501</td>\n",
       "      <td>0.14975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>4</td>\n",
       "      <td>7.498</td>\n",
       "      <td>0.35776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>5</td>\n",
       "      <td>7.413</td>\n",
       "      <td>0.41004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Rank  Happiness Score  Trust (Government Corruption)\n",
       "0      Denmark               1            7.526                        0.44453\n",
       "1  Switzerland               2            7.509                        0.41203\n",
       "2      Iceland               3            7.501                        0.14975\n",
       "3       Norway               4            7.498                        0.35776\n",
       "4      Finland               5            7.413                        0.41004"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "six_top_5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "29d24150-b392-40f5-a678-5b8aa8f05aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Benin</td>\n",
       "      <td>153</td>\n",
       "      <td>3.484</td>\n",
       "      <td>0.06681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>154</td>\n",
       "      <td>3.360</td>\n",
       "      <td>0.07112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Togo</td>\n",
       "      <td>155</td>\n",
       "      <td>3.303</td>\n",
       "      <td>0.11587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Syria</td>\n",
       "      <td>156</td>\n",
       "      <td>3.069</td>\n",
       "      <td>0.17233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>157</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.09419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Happiness Rank  Happiness Score  \\\n",
       "152        Benin             153            3.484   \n",
       "153  Afghanistan             154            3.360   \n",
       "154         Togo             155            3.303   \n",
       "155        Syria             156            3.069   \n",
       "156      Burundi             157            2.905   \n",
       "\n",
       "     Trust (Government Corruption)  \n",
       "152                        0.06681  \n",
       "153                        0.07112  \n",
       "154                        0.11587  \n",
       "155                        0.17233  \n",
       "156                        0.09419  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "six_last_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f59ae8bd-c323-4e39-8a53-928a84a65649",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframes showing top 5 and bottom 5 ranking on the happiness score and trust in government for each year\n",
    "\n",
    "# 2017 top 5\n",
    "sev_top_5 = sev_df.iloc[0:5]\n",
    "sev_df[[\"Country\", \"Happiness.Rank\", \"Happiness.Score\", \"Trust..Government.Corruption.\"]]\n",
    "sev_top_5 = sev_top_5[[\"Country\", \"Happiness.Rank\", \"Happiness.Score\", \"Trust..Government.Corruption.\"]]\n",
    "\n",
    "# 2017 bottom 5\n",
    "sev_last_5 = sev_df.iloc[150:155]\n",
    "sev_last_5[[\"Country\", \"Happiness.Rank\", \"Happiness.Score\", \"Trust..Government.Corruption.\"]]\n",
    "sev_last_5 = sev_last_5[[\"Country\", \"Happiness.Rank\", \"Happiness.Score\", \"Trust..Government.Corruption.\"]]\n",
    "#rename columns for continuity.\n",
    "clean_sev_top = sev_top_5[[\"Country\", \"Happiness.Rank\", \"Happiness.Score\",\n",
    "                           \"Trust..Government.Corruption.\"]]                                \n",
    "clean_sev_top = clean_sev_top.rename(columns={\"Happiness.Rank\":\"Happiness Rank\",\n",
    "                                              \"Happiness.Score\":\"Happiness Score\",\n",
    "                                              \"Trust..Government.Corruption.\":\"Trust (Government Corruption)\"})\n",
    "clean_sev_top = clean_sev_top.iloc[0:5]\n",
    "\n",
    "clean_sev_last = sev_last_5[[\"Country\", \"Happiness.Rank\", \"Happiness.Score\",\n",
    "                             \"Trust..Government.Corruption.\"]]                                \n",
    "clean_sev_last = clean_sev_last.rename(columns={\"Happiness.Rank\":\"Happiness Rank\",\n",
    "                                                \"Happiness.Score\":\"Happiness Score\",\n",
    "                                                \"Trust..Government.Corruption.\":\"Trust (Government Corruption)\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c98d6183-8781-41c9-80d4-db294e343314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>7.537</td>\n",
       "      <td>0.315964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.400770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>3</td>\n",
       "      <td>7.504</td>\n",
       "      <td>0.153527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>4</td>\n",
       "      <td>7.494</td>\n",
       "      <td>0.367007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>5</td>\n",
       "      <td>7.469</td>\n",
       "      <td>0.382612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Rank  Happiness Score  Trust (Government Corruption)\n",
       "0       Norway               1            7.537                       0.315964\n",
       "1      Denmark               2            7.522                       0.400770\n",
       "2      Iceland               3            7.504                       0.153527\n",
       "3  Switzerland               4            7.494                       0.367007\n",
       "4      Finland               5            7.469                       0.382612"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_sev_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "a8f7daa1-8872-4567-a74d-0011813251b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>151</td>\n",
       "      <td>3.471</td>\n",
       "      <td>0.455220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Syria</td>\n",
       "      <td>152</td>\n",
       "      <td>3.462</td>\n",
       "      <td>0.151347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>153</td>\n",
       "      <td>3.349</td>\n",
       "      <td>0.066035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>154</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.084148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>155</td>\n",
       "      <td>2.693</td>\n",
       "      <td>0.056565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Country  Happiness Rank  Happiness Score  \\\n",
       "150                    Rwanda             151            3.471   \n",
       "151                     Syria             152            3.462   \n",
       "152                  Tanzania             153            3.349   \n",
       "153                   Burundi             154            2.905   \n",
       "154  Central African Republic             155            2.693   \n",
       "\n",
       "     Trust (Government Corruption)  \n",
       "150                       0.455220  \n",
       "151                       0.151347  \n",
       "152                       0.066035  \n",
       "153                       0.084148  \n",
       "154                       0.056565  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_sev_last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "5606dae8-af39-4e6d-b8bf-c2014cae32c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframes showing top 5 and bottom 5 ranking on the happiness score and trust in government for each year\n",
    "\n",
    "# 2018 top 5\n",
    "eight_top_5 = eight_df.iloc[0:5]\n",
    "eight_df[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]\n",
    "eight_top_5 = eight_top_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]\n",
    "# 2018 bottom 5\n",
    "eight_last_5 = eight_df.iloc[151:156]\n",
    "eight_last_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]\n",
    "eight_last_5 = eight_last_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]\n",
    "\n",
    "#rename columns for continuity.\n",
    "clean_eight_top = eight_top_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]                                \n",
    "clean_eight_top = clean_eight_top.rename(columns={\"Country or region\":\"Country\",\n",
    "                                                  \"Score\":\"Happiness Score\",\n",
    "                                                  \"Overall rank\":\"Happiness Rank\",\n",
    "                                                  \"Perceptions of corruption\":\"Trust (Government Corruption)\"})\n",
    "\n",
    "clean_eight_last = eight_last_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]                                \n",
    "clean_eight_last = clean_eight_last.rename(columns={\"Country or region\":\"Country\", \n",
    "                                                    \"Overall rank\":\"Happiness Rank\",\n",
    "                                                    \"Score\":\"Happiness Score\",\n",
    "                                                    \"Perceptions of corruption\":\"Trust (Government Corruption)\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "773d9663-2039-4ee9-a2c5-8eb0827b666e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1</td>\n",
       "      <td>7.632</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Norway</td>\n",
       "      <td>2</td>\n",
       "      <td>7.594</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>3</td>\n",
       "      <td>7.555</td>\n",
       "      <td>0.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>4</td>\n",
       "      <td>7.495</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>5</td>\n",
       "      <td>7.487</td>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Rank  Happiness Score  Trust (Government Corruption)\n",
       "0      Finland               1            7.632                          0.393\n",
       "1       Norway               2            7.594                          0.340\n",
       "2      Denmark               3            7.555                          0.408\n",
       "3      Iceland               4            7.495                          0.138\n",
       "4  Switzerland               5            7.487                          0.357"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_eight_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "728096e1-4e3d-479d-8b9b-e112091ba148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>152</td>\n",
       "      <td>3.355</td>\n",
       "      <td>0.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>153</td>\n",
       "      <td>3.303</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>154</td>\n",
       "      <td>3.254</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>155</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>156</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Country  Happiness Rank  Happiness Score  \\\n",
       "151                     Yemen             152            3.355   \n",
       "152                  Tanzania             153            3.303   \n",
       "153               South Sudan             154            3.254   \n",
       "154  Central African Republic             155            3.083   \n",
       "155                   Burundi             156            2.905   \n",
       "\n",
       "     Trust (Government Corruption)  \n",
       "151                          0.064  \n",
       "152                          0.097  \n",
       "153                          0.106  \n",
       "154                          0.038  \n",
       "155                          0.076  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_eight_last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "067cfc2c-bc40-4729-99c7-ca01b4fb2c6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframes showing top 5 and bottom 5 ranking on the happiness score and trust in government for each year\n",
    "\n",
    "# 2019 top 5\n",
    "nine_top_5 = nine_df.iloc[0:5]\n",
    "nine_df[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]\n",
    "nine_top_5 = nine_top_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]\n",
    "# 2019 bottom 5\n",
    "nine_last_5 = nine_df.iloc[151:156]\n",
    "nine_last_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]\n",
    "nine_last_5 = nine_last_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]\n",
    "\n",
    "#rename columns for continuity.\n",
    "clean_nine_top = nine_top_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]                                \n",
    "clean_nine_top = clean_nine_top.rename(columns={\"Country or region\":\"Country\",\n",
    "                                                \"Overall rank\":\"Happiness Rank\",\n",
    "                                                  \"Score\":\"Happiness Score\",\n",
    "                                                  \"Perceptions of corruption\":\"Trust (Government Corruption)\"})\n",
    "\n",
    "clean_nine_last = nine_last_5[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]                                \n",
    "clean_nine_last = clean_nine_last.rename(columns={\"Country or region\":\"Country\", \n",
    "                                                    \"Overall rank\":\"Happiness Rank\",\n",
    "                                                    \"Score\":\"Happiness Score\",\n",
    "                                                    \"Perceptions of corruption\":\"Trust (Government Corruption)\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7dce00b1-f541-4c87-bd4c-61c8dac26323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>1</td>\n",
       "      <td>7.769</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>2</td>\n",
       "      <td>7.600</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Norway</td>\n",
       "      <td>3</td>\n",
       "      <td>7.554</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>4</td>\n",
       "      <td>7.494</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5</td>\n",
       "      <td>7.488</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Happiness Rank  Happiness Score  Trust (Government Corruption)\n",
       "0      Finland               1            7.769                          0.393\n",
       "1      Denmark               2            7.600                          0.410\n",
       "2       Norway               3            7.554                          0.341\n",
       "3      Iceland               4            7.494                          0.118\n",
       "4  Netherlands               5            7.488                          0.298"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_nine_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "60ff9615-3713-41e4-b531-8cdc8b8bac7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>152</td>\n",
       "      <td>3.334</td>\n",
       "      <td>0.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>153</td>\n",
       "      <td>3.231</td>\n",
       "      <td>0.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>154</td>\n",
       "      <td>3.203</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>155</td>\n",
       "      <td>3.083</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>156</td>\n",
       "      <td>2.853</td>\n",
       "      <td>0.091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Country  Happiness Rank  Happiness Score  \\\n",
       "151                    Rwanda             152            3.334   \n",
       "152                  Tanzania             153            3.231   \n",
       "153               Afghanistan             154            3.203   \n",
       "154  Central African Republic             155            3.083   \n",
       "155               South Sudan             156            2.853   \n",
       "\n",
       "     Trust (Government Corruption)  \n",
       "151                          0.411  \n",
       "152                          0.147  \n",
       "153                          0.025  \n",
       "154                          0.035  \n",
       "155                          0.091  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_nine_last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "63c2f97b-92d6-4d11-9ae4-9f7b570f4fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fif = fif_df.copy()\n",
    "six = six_df.copy()\n",
    "sev = sev_df.copy()\n",
    "eight = eight_df.copy()\n",
    "nine = nine_df.copy()\n",
    "\n",
    "#rename columns for continuity.\n",
    "sev = sev[[\"Country\", \"Happiness.Rank\", \"Happiness.Score\",\n",
    "                           \"Trust..Government.Corruption.\"]]                                \n",
    "sev = sev.rename(columns={\"Happiness.Rank\":\"Happiness Rank\",\n",
    "                                              \"Happiness.Score\":\"Happiness Score\",\n",
    "                                              \"Trust..Government.Corruption.\":\"Trust (Government Corruption)\"})\n",
    "\n",
    "eight = eight[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]                                \n",
    "eight = eight.rename(columns={\"Country or region\":\"Country\",\n",
    "                                                \"Overall rank\":\"Happiness Rank\",\n",
    "                                                  \"Score\":\"Happiness Score\",\n",
    "                                                  \"Perceptions of corruption\":\"Trust (Government Corruption)\"})\n",
    "\n",
    "nine = nine[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]                                \n",
    "nine = nine.rename(columns={\"Country or region\":\"Country\",\n",
    "                                                \"Overall rank\":\"Happiness Rank\",\n",
    "                                                  \"Score\":\"Happiness Score\",\n",
    "                                                  \"Perceptions of corruption\":\"Trust (Government Corruption)\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "b10b41c8-aea6-499d-8c27-a96be134c57e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create csv files of top 5 and bottom 5 dataframes.\n",
    "fif_top_5.to_csv('images/2015_top_5.csv')\n",
    "fif_last_5.to_csv('images/2015_last_5.csv')\n",
    "\n",
    "six_top_5.to_csv('images/2016_top_5.csv')\n",
    "six_last_5.to_csv('images/2016_last_5.csv')\n",
    "\n",
    "clean_sev_top.to_csv('images/2017_top_5.csv')\n",
    "clean_sev_last.to_csv('images/2017_last_5.csv')\n",
    "\n",
    "clean_eight_top.to_csv('images/2018_top_5.csv')\n",
    "clean_eight_last.to_csv('images/2018_last_5.csv')\n",
    "\n",
    "clean_nine_top.to_csv('images/2019_top_5.csv')\n",
    "clean_nine_last.to_csv('images/2019_last_5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "fedb34d8-47d7-4546-a3a5-f5b775812dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.39519858383691125, pvalue=2.763802425204147e-07)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#calculate mean of happiness score and trust in government columns for each year's dataset. include linear regression.\n",
    "#2015\n",
    "\n",
    "x_values = fif_df['Trust (Government Corruption)']\n",
    "y_values = fif_df['Happiness Score']\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "fig, ax = plt.subplots()\n",
    "r_squared = rvalue ** 2\n",
    "ax.text(.1, 0.95, f\"R-squared = {r_squared:.3f}\", transform=ax.transAxes)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.scatter(x_values,y_values,edgecolors=\"black\", facecolors=\"steelblue\")\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(5.8,0.8),fontsize=15,color=\"red\")\n",
    "plt.title(\"Trust of Government and Happiness Scores 2015\")\n",
    "plt.xlabel(\"Trust of Government\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.savefig('images/trust_2015.png')\n",
    "# Correlation between Trust of Government/Happiness Score in 2015.\n",
    "corr_2015 = st.pearsonr(x_values,y_values)\n",
    "corr_2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "82805deb-4fc7-4179-9d0a-16736e358a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.40203224514729263, pvalue=1.797842658402267e-07)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate mean of happiness score and trust in government columns for each year's dataset. include linear regression.\n",
    "#2016\n",
    "x_values = six_df['Trust (Government Corruption)']\n",
    "y_values = six_df['Happiness Score']\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "fig, ax = plt.subplots()\n",
    "r_squared = rvalue ** 2\n",
    "ax.text(.1, 0.95, f\"R-squared = {r_squared:.3f}\", transform=ax.transAxes)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.scatter(x_values,y_values,edgecolors=\"black\", facecolors=\"steelblue\")\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "plt.annotate(line_eq,(5.8,0.8),fontsize=15,color=\"red\")\n",
    "plt.title(\"Trust of Government and Happiness Scores 2016\")\n",
    "plt.xlabel(\"Trust of Government\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.savefig('images/trust_2016.png')\n",
    "# Correlation between Trust of Government/Happiness Score in 2016.\n",
    "corr_2016 = st.pearsonr(x_values,y_values)\n",
    "plt.show()\n",
    "corr_2016\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "4caa26e6-f070-4e8a-8e69-e454d381215a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.4290797372221721, pvalue=2.538310699741526e-08)"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename columns of copied datasets for continuity\n",
    "clean_17 = sev_df[[\"Country\", \"Happiness.Rank\", \"Happiness.Score\",\n",
    "                           \"Trust..Government.Corruption.\"]]                                \n",
    "clean_17 = clean_17.rename(columns={\"Happiness.Rank\":\"Happiness Rank\",\n",
    "                                              \"Happiness.Score\":\"Happiness Score\",\n",
    "                                              \"Trust..Government.Corruption.\":\"Trust (Government Corruption)\"})\n",
    "clean_17\n",
    "#calculate mean of happiness score and trust in government columns for each year's dataset. include linear regression.\n",
    "#2017\n",
    "x_values = clean_17['Trust (Government Corruption)']\n",
    "y_values = clean_17['Happiness Score']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "r_squared = rvalue ** 2\n",
    "ax.text(.1, 0.95, f\"R-squared = {r_squared:.3f}\", transform=ax.transAxes)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.scatter(x_values,y_values,edgecolors=\"black\", facecolors=\"steelblue\")\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "plt.annotate(line_eq,(5.8,0.8),fontsize=15,color=\"red\")\n",
    "plt.title(\"Trust of Government and Happiness Scores 2017\")\n",
    "plt.xlabel(\"Trust of Government\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.savefig('images/trust_2017.png')\n",
    "\n",
    "# Correlation between Trust of Government/Happiness Score in 2017.\n",
    "corr_2017 = st.pearsonr(x_values,y_values)\n",
    "plt.show()\n",
    "corr_2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "a5f96764-f253-4369-ae50-c2bcf28e9dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.3921511722730108, pvalue=4.130113104185172e-07)"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename columns for continuity.\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import linregress\n",
    "\n",
    "clean_18 = eight_df[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]\n",
    "clean_18 = clean_18.rename(columns={\"Country or region\":\"Country\",\n",
    "                                                  \"Score\":\"Happiness Score\",\n",
    "                                                  \"Overall rank\":\"Happiness Rank\",\n",
    "                                                  \"Perceptions of corruption\":\"Trust (Government Corruption)\"})\n",
    "clean_18[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]\n",
    "\n",
    "# calculate mean of happiness score and trust in government columns for each year's dataset. include linear regression.\n",
    "# 2018\n",
    "x_values = clean_18['Trust (Government Corruption)']\n",
    "y_values = clean_18['Happiness Score']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "r_squared = rvalue ** 2\n",
    "ax.text(.1, 0.95, f\"R-squared = {r_squared:.3f}\", transform=ax.transAxes)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.scatter(x_values,y_values,edgecolors=\"black\", facecolors=\"steelblue\")\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "plt.annotate(line_eq,(5.8,0.8),fontsize=15,color=\"red\")\n",
    "plt.title(\"Trust of Government and Happiness Scores 2018\")\n",
    "plt.xlabel(\"Trust of Government\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.savefig('images/trust_2018.png')\n",
    "\n",
    "# Correlation between Trust of Government/Happiness Score in 2018.\n",
    "corr_2018 = st.pearsonr(x_values,y_values)\n",
    "plt.show()\n",
    "corr_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "5b9e53dc-d672-4a8c-ac27-e272faf13ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.3856130708664788, pvalue=6.654010938470258e-07)"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename columns for continuity.\n",
    "clean_19 = nine_df[[\"Country or region\", \"Overall rank\", \"Score\", \"Perceptions of corruption\"]]                                \n",
    "clean_19 = clean_19.rename(columns={\"Country or region\":\"Country\",\n",
    "                                                \"Overall rank\":\"Happiness Rank\",\n",
    "                                                  \"Score\":\"Happiness Score\",\n",
    "                                                  \"Perceptions of corruption\":\"Trust (Government Corruption)\"})\n",
    "clean_19[[\"Country\", \"Happiness Rank\", \"Happiness Score\", \"Trust (Government Corruption)\"]]\n",
    "\n",
    "#calculate mean of happiness score and trust in government columns for each year's dataset. include linear regression.\n",
    "#2019\n",
    "x_values = clean_19['Trust (Government Corruption)']\n",
    "y_values = clean_19['Happiness Score']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "r_squared = rvalue ** 2\n",
    "ax.text(.1, 0.95, f\"R-squared = {r_squared:.3f}\", transform=ax.transAxes)\n",
    "regress_values = x_values * slope + intercept\n",
    "line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "plt.scatter(x_values,y_values,edgecolors=\"black\", facecolors=\"steelblue\")\n",
    "plt.plot(x_values,regress_values,\"r-\")\n",
    "(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)\n",
    "plt.annotate(line_eq,(5.8,0.8),fontsize=15,color=\"red\")\n",
    "plt.title(\"Trust of Government and Happiness Scores 2019\")\n",
    "plt.xlabel(\"Trust of Government\")\n",
    "plt.ylabel(\"Happiness Score\")\n",
    "plt.savefig('images/trust_2019.png')\n",
    "# Correlation between Trust of Government/Happiness Score in 2019.\n",
    "corr_2019 = st.pearsonr(x_values,y_values)\n",
    "plt.show()\n",
    "corr_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "926fa5bb-4168-4c48-b202-1333b21d7b42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2004076369305885"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate mean of correlation between happiness and trust of government for all years.\n",
    "\n",
    "correlations = [corr_2015, corr_2016, corr_2017, corr_2018, corr_2019]\n",
    "average_corr = np.mean(correlations)\n",
    "\n",
    "average_corr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "7aea489d-10bb-4fcc-979e-a65d3e756c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge csv\n",
    "merge_2015 = pd.merge(fif_top_5, fif_last_5, how='outer')\n",
    "merge_2017 = pd.merge(clean_sev_top, clean_sev_last, how=\"outer\")\n",
    "merge_2015\n",
    "merge_2018 = pd.merge(clean_eight_top, clean_eight_last, how=\"outer\")\n",
    "merge_2019 = pd.merge(clean_nine_top, clean_nine_last, how=\"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "7ae31fe7-075c-44ef-9f85-2fd686d02981",
   "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>Variables</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Happiness Score</td>\n",
       "      <td>6.81000</td>\n",
       "      <td>6.08400</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>4.806</td>\n",
       "      <td>4.707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Economy</td>\n",
       "      <td>1.04424</td>\n",
       "      <td>1.13367</td>\n",
       "      <td>1.128431</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Life Expectancy</td>\n",
       "      <td>0.72052</td>\n",
       "      <td>0.61904</td>\n",
       "      <td>0.617144</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Freedom</td>\n",
       "      <td>0.42908</td>\n",
       "      <td>0.19847</td>\n",
       "      <td>0.153997</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Trust in Government</td>\n",
       "      <td>0.11069</td>\n",
       "      <td>0.08304</td>\n",
       "      <td>0.064491</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Variables     2015     2016      2017   2018   2019\n",
       "0      Happiness Score  6.81000  6.08400  5.250000  4.806  4.707\n",
       "1              Economy  1.04424  1.13367  1.128431  0.996  0.960\n",
       "2      Life Expectancy  0.72052  0.61904  0.617144  0.657  0.805\n",
       "3              Freedom  0.42908  0.19847  0.153997  0.133  0.154\n",
       "4  Trust in Government  0.11069  0.08304  0.064491  0.052  0.047"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Venezuela. In 2015, ranked 23 and by 2019 they ranked 108. How did other variables fluctuate during this time? What events led to these sudden and severe changes?\n",
    "# let's calculate percent change for venezuela's happiness score from 2015-2019.\n",
    "\n",
    "df = pd.DataFrame({'Variables':['Happiness Score','Economy','Life Expectancy','Freedom','Trust in Government'],\n",
    "                   '2015': [6.81, 1.04424, 0.72052, 0.42908, 0.11069],\n",
    "                   '2016': [6.084, 1.13367, 0.61904, 0.19847, 0.08304],\n",
    "                   '2017': [5.25, 1.128431, 0.617144, 0.153997, 0.064491],\n",
    "                   '2018': [4.806, 0.996, 0.657, 0.133, 0.052],\n",
    "                   '2019': [4.707, 0.96, 0.805, 0.154, 0.047]})\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "bef23386-1ed1-4313-b956-df565281d039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot Changes in scores over time for venezuela.\n",
    "import numpy as np\n",
    "time = np.arange(2015,2020,1)\n",
    "happiness = [6.81, 6.084, 5.25, 4.806, 4.707]\n",
    "gdp = [1.04424, 1.13367, 1.128431, 0.996, 0.96]\n",
    "life = [0.72052, 0.61904, 0.617144, 0.657, 0.805]\n",
    "freedom = [0.42908, 0.19847, 0.153997, 0.133, 0.154]\n",
    "trust = [0.11069, 0.08304, 0.064491, 0.052, 0.047]\n",
    "happiness, = plt.plot(time, happiness, color=\"black\", label=\"Happiness Score\")\n",
    "gdp, = plt.plot(time, gdp, color=\"steelblue\", label=\"GDP\")\n",
    "life, = plt.plot(time, life, color=\"gold\", label=\"Life Expectancy\")\n",
    "freedom, = plt.plot(time, freedom, color=\"orangered\", label=\"Freedom\")\n",
    "trust, = plt.plot(time, trust, color=\"orchid\", label=\"Corruption\")\n",
    "plt.title(\"Changes in Scores over Time\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.legend(handles=[happiness, gdp, life, freedom, trust], loc=\"best\")\n",
    "plt.show()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a06f69ac-d777-4ee0-8836-4d58e35b03ad",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}