Ridgeline_vix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cftime\n",
    "from matplotlib.colors import Normalize\n",
    "from scipy.stats import gaussian_kde\n",
    "DPI = 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "planilhas/vix_mes_1990_2020.csv\n"
     ]
    }
   ],
   "source": [
    "filename = 'planilhas/vix_mes_1990_2020.csv'\n",
    "df = pd.read_csv(filename, sep=';', skiprows= 10,\n",
    "                 header=0, index_col=0)#.dropna()\n",
    "print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,\n",
       "            2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n",
       "            2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],\n",
       "           dtype='int64', name='Ano')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1970-01-01 00:00:00.000001990',\n",
       "               '1970-01-01 00:00:00.000001991',\n",
       "               '1970-01-01 00:00:00.000001992',\n",
       "               '1970-01-01 00:00:00.000001993',\n",
       "               '1970-01-01 00:00:00.000001994',\n",
       "               '1970-01-01 00:00:00.000001995',\n",
       "               '1970-01-01 00:00:00.000001996',\n",
       "               '1970-01-01 00:00:00.000001997',\n",
       "               '1970-01-01 00:00:00.000001998',\n",
       "               '1970-01-01 00:00:00.000001999',\n",
       "                  '1970-01-01 00:00:00.000002',\n",
       "               '1970-01-01 00:00:00.000002001',\n",
       "               '1970-01-01 00:00:00.000002002',\n",
       "               '1970-01-01 00:00:00.000002003',\n",
       "               '1970-01-01 00:00:00.000002004',\n",
       "               '1970-01-01 00:00:00.000002005',\n",
       "               '1970-01-01 00:00:00.000002006',\n",
       "               '1970-01-01 00:00:00.000002007',\n",
       "               '1970-01-01 00:00:00.000002008',\n",
       "               '1970-01-01 00:00:00.000002009',\n",
       "               '1970-01-01 00:00:00.000002010',\n",
       "               '1970-01-01 00:00:00.000002011',\n",
       "               '1970-01-01 00:00:00.000002012',\n",
       "               '1970-01-01 00:00:00.000002013',\n",
       "               '1970-01-01 00:00:00.000002014',\n",
       "               '1970-01-01 00:00:00.000002015',\n",
       "               '1970-01-01 00:00:00.000002016',\n",
       "               '1970-01-01 00:00:00.000002017',\n",
       "               '1970-01-01 00:00:00.000002018',\n",
       "               '1970-01-01 00:00:00.000002019',\n",
       "               '1970-01-01 00:00:00.000002020'],\n",
       "              dtype='datetime64[ns]', name='Ano', freq=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = pd.to_datetime(df.index, errors='coerce')\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\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>jan</th>\n",
       "      <th>fev</th>\n",
       "      <th>mar</th>\n",
       "      <th>abr</th>\n",
       "      <th>mai</th>\n",
       "      <th>jun</th>\n",
       "      <th>jul</th>\n",
       "      <th>ago</th>\n",
       "      <th>set</th>\n",
       "      <th>out</th>\n",
       "      <th>nov</th>\n",
       "      <th>dez</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ano</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001990</th>\n",
       "      <td>27.00</td>\n",
       "      <td>27.60</td>\n",
       "      <td>27.80</td>\n",
       "      <td>27.10</td>\n",
       "      <td>24.10</td>\n",
       "      <td>23.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001991</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.30</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.20</td>\n",
       "      <td>23.8</td>\n",
       "      <td>24.3</td>\n",
       "      <td>24.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001993</th>\n",
       "      <td>26.86</td>\n",
       "      <td>27.32</td>\n",
       "      <td>27.72</td>\n",
       "      <td>26.10</td>\n",
       "      <td>23.90</td>\n",
       "      <td>22.10</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.60</td>\n",
       "      <td>24.3</td>\n",
       "      <td>25.7</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001994</th>\n",
       "      <td>26.60</td>\n",
       "      <td>27.80</td>\n",
       "      <td>26.30</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.80</td>\n",
       "      <td>24.1</td>\n",
       "      <td>25.2</td>\n",
       "      <td>26.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001995</th>\n",
       "      <td>27.90</td>\n",
       "      <td>27.90</td>\n",
       "      <td>27.20</td>\n",
       "      <td>25.30</td>\n",
       "      <td>24.30</td>\n",
       "      <td>23.20</td>\n",
       "      <td>23.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>23.40</td>\n",
       "      <td>24.2</td>\n",
       "      <td>24.5</td>\n",
       "      <td>25.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001996</th>\n",
       "      <td>27.50</td>\n",
       "      <td>28.20</td>\n",
       "      <td>27.90</td>\n",
       "      <td>25.60</td>\n",
       "      <td>23.10</td>\n",
       "      <td>22.30</td>\n",
       "      <td>21.8</td>\n",
       "      <td>21.6</td>\n",
       "      <td>22.60</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.1</td>\n",
       "      <td>26.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001997</th>\n",
       "      <td>26.60</td>\n",
       "      <td>26.60</td>\n",
       "      <td>25.20</td>\n",
       "      <td>24.92</td>\n",
       "      <td>23.20</td>\n",
       "      <td>23.10</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.7</td>\n",
       "      <td>24.00</td>\n",
       "      <td>24.4</td>\n",
       "      <td>26.3</td>\n",
       "      <td>27.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001998</th>\n",
       "      <td>27.90</td>\n",
       "      <td>28.10</td>\n",
       "      <td>27.80</td>\n",
       "      <td>26.70</td>\n",
       "      <td>24.80</td>\n",
       "      <td>22.30</td>\n",
       "      <td>22.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>24.34</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.7</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000001999</th>\n",
       "      <td>27.80</td>\n",
       "      <td>27.80</td>\n",
       "      <td>26.70</td>\n",
       "      <td>25.40</td>\n",
       "      <td>23.40</td>\n",
       "      <td>22.80</td>\n",
       "      <td>22.1</td>\n",
       "      <td>21.7</td>\n",
       "      <td>23.00</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>25.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002000</th>\n",
       "      <td>26.20</td>\n",
       "      <td>26.80</td>\n",
       "      <td>26.20</td>\n",
       "      <td>25.10</td>\n",
       "      <td>23.22</td>\n",
       "      <td>22.70</td>\n",
       "      <td>21.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.40</td>\n",
       "      <td>25.1</td>\n",
       "      <td>24.8</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002001</th>\n",
       "      <td>27.00</td>\n",
       "      <td>27.80</td>\n",
       "      <td>27.40</td>\n",
       "      <td>26.70</td>\n",
       "      <td>24.00</td>\n",
       "      <td>23.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>25.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002002</th>\n",
       "      <td>26.50</td>\n",
       "      <td>26.10</td>\n",
       "      <td>27.35</td>\n",
       "      <td>26.20</td>\n",
       "      <td>24.90</td>\n",
       "      <td>23.70</td>\n",
       "      <td>22.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.70</td>\n",
       "      <td>25.4</td>\n",
       "      <td>25.8</td>\n",
       "      <td>26.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002003</th>\n",
       "      <td>27.20</td>\n",
       "      <td>28.20</td>\n",
       "      <td>27.90</td>\n",
       "      <td>26.30</td>\n",
       "      <td>24.10</td>\n",
       "      <td>24.30</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>22.80</td>\n",
       "      <td>22.9</td>\n",
       "      <td>22.5</td>\n",
       "      <td>26.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002004</th>\n",
       "      <td>25.60</td>\n",
       "      <td>26.30</td>\n",
       "      <td>25.90</td>\n",
       "      <td>25.30</td>\n",
       "      <td>24.10</td>\n",
       "      <td>22.90</td>\n",
       "      <td>21.4</td>\n",
       "      <td>22.3</td>\n",
       "      <td>23.80</td>\n",
       "      <td>24.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002005</th>\n",
       "      <td>26.90</td>\n",
       "      <td>26.20</td>\n",
       "      <td>26.70</td>\n",
       "      <td>26.40</td>\n",
       "      <td>24.40</td>\n",
       "      <td>22.80</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.90</td>\n",
       "      <td>25.6</td>\n",
       "      <td>23.9</td>\n",
       "      <td>25.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002006</th>\n",
       "      <td>27.30</td>\n",
       "      <td>28.50</td>\n",
       "      <td>26.80</td>\n",
       "      <td>25.50</td>\n",
       "      <td>23.50</td>\n",
       "      <td>21.90</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>22.30</td>\n",
       "      <td>23.7</td>\n",
       "      <td>24.6</td>\n",
       "      <td>25.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002007</th>\n",
       "      <td>26.60</td>\n",
       "      <td>26.50</td>\n",
       "      <td>27.70</td>\n",
       "      <td>26.20</td>\n",
       "      <td>23.60</td>\n",
       "      <td>23.32</td>\n",
       "      <td>23.1</td>\n",
       "      <td>22.7</td>\n",
       "      <td>22.79</td>\n",
       "      <td>24.7</td>\n",
       "      <td>25.5</td>\n",
       "      <td>26.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002008</th>\n",
       "      <td>25.60</td>\n",
       "      <td>26.60</td>\n",
       "      <td>26.80</td>\n",
       "      <td>26.80</td>\n",
       "      <td>23.50</td>\n",
       "      <td>22.80</td>\n",
       "      <td>22.2</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.80</td>\n",
       "      <td>24.8</td>\n",
       "      <td>24.1</td>\n",
       "      <td>25.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002009</th>\n",
       "      <td>26.50</td>\n",
       "      <td>27.80</td>\n",
       "      <td>27.40</td>\n",
       "      <td>25.40</td>\n",
       "      <td>24.10</td>\n",
       "      <td>22.30</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>24.80</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002010</th>\n",
       "      <td>28.80</td>\n",
       "      <td>28.70</td>\n",
       "      <td>27.40</td>\n",
       "      <td>25.80</td>\n",
       "      <td>24.30</td>\n",
       "      <td>21.70</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.30</td>\n",
       "      <td>23.9</td>\n",
       "      <td>24.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002011</th>\n",
       "      <td>27.40</td>\n",
       "      <td>27.85</td>\n",
       "      <td>26.00</td>\n",
       "      <td>25.70</td>\n",
       "      <td>23.50</td>\n",
       "      <td>22.30</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.1</td>\n",
       "      <td>22.40</td>\n",
       "      <td>23.5</td>\n",
       "      <td>23.3</td>\n",
       "      <td>25.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002012</th>\n",
       "      <td>25.90</td>\n",
       "      <td>26.70</td>\n",
       "      <td>26.80</td>\n",
       "      <td>26.30</td>\n",
       "      <td>22.90</td>\n",
       "      <td>23.80</td>\n",
       "      <td>22.9</td>\n",
       "      <td>23.3</td>\n",
       "      <td>23.20</td>\n",
       "      <td>24.7</td>\n",
       "      <td>24.5</td>\n",
       "      <td>27.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002013</th>\n",
       "      <td>26.90</td>\n",
       "      <td>27.50</td>\n",
       "      <td>26.80</td>\n",
       "      <td>24.96</td>\n",
       "      <td>24.10</td>\n",
       "      <td>23.80</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>25.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002014</th>\n",
       "      <td>27.30</td>\n",
       "      <td>27.80</td>\n",
       "      <td>27.10</td>\n",
       "      <td>26.00</td>\n",
       "      <td>24.40</td>\n",
       "      <td>23.69</td>\n",
       "      <td>22.1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>23.90</td>\n",
       "      <td>23.7</td>\n",
       "      <td>25.3</td>\n",
       "      <td>26.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002015</th>\n",
       "      <td>28.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>27.60</td>\n",
       "      <td>26.10</td>\n",
       "      <td>23.60</td>\n",
       "      <td>23.40</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23.3</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002016</th>\n",
       "      <td>27.00</td>\n",
       "      <td>28.90</td>\n",
       "      <td>28.00</td>\n",
       "      <td>27.70</td>\n",
       "      <td>24.80</td>\n",
       "      <td>22.40</td>\n",
       "      <td>22.7</td>\n",
       "      <td>23.4</td>\n",
       "      <td>24.10</td>\n",
       "      <td>24.2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>26.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002017</th>\n",
       "      <td>28.00</td>\n",
       "      <td>27.63</td>\n",
       "      <td>27.10</td>\n",
       "      <td>25.60</td>\n",
       "      <td>23.70</td>\n",
       "      <td>23.70</td>\n",
       "      <td>21.1</td>\n",
       "      <td>21.7</td>\n",
       "      <td>22.60</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>26.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002018</th>\n",
       "      <td>27.50</td>\n",
       "      <td>26.60</td>\n",
       "      <td>27.50</td>\n",
       "      <td>25.10</td>\n",
       "      <td>23.70</td>\n",
       "      <td>23.40</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.6</td>\n",
       "      <td>24.00</td>\n",
       "      <td>24.6</td>\n",
       "      <td>25.3</td>\n",
       "      <td>26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002019</th>\n",
       "      <td>28.40</td>\n",
       "      <td>28.60</td>\n",
       "      <td>28.10</td>\n",
       "      <td>27.30</td>\n",
       "      <td>25.90</td>\n",
       "      <td>24.10</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.2</td>\n",
       "      <td>24.08</td>\n",
       "      <td>25.3</td>\n",
       "      <td>25.3</td>\n",
       "      <td>26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000002020</th>\n",
       "      <td>27.00</td>\n",
       "      <td>27.40</td>\n",
       "      <td>26.20</td>\n",
       "      <td>2.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.90</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>25.00</td>\n",
       "      <td>24.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 jan    fev    mar    abr    mai    jun   jul  \\\n",
       "Ano                                                                             \n",
       "1970-01-01 00:00:00.000001990  27.00  27.60  27.80  27.10  24.10  23.30   NaN   \n",
       "1970-01-01 00:00:00.000001991    NaN    NaN    NaN    NaN    NaN    NaN   NaN   \n",
       "1970-01-01 00:00:00.000001992    NaN    NaN    NaN    NaN    NaN  23.30  22.0   \n",
       "1970-01-01 00:00:00.000001993  26.86  27.32  27.72  26.10  23.90  22.10  22.8   \n",
       "1970-01-01 00:00:00.000001994  26.60  27.80  26.30  25.00  25.00  23.00  22.4   \n",
       "1970-01-01 00:00:00.000001995  27.90  27.90  27.20  25.30  24.30  23.20  23.1   \n",
       "1970-01-01 00:00:00.000001996  27.50  28.20  27.90  25.60  23.10  22.30  21.8   \n",
       "1970-01-01 00:00:00.000001997  26.60  26.60  25.20  24.92  23.20  23.10  22.4   \n",
       "1970-01-01 00:00:00.000001998  27.90  28.10  27.80  26.70  24.80  22.30  22.3   \n",
       "1970-01-01 00:00:00.000001999  27.80  27.80  26.70  25.40  23.40  22.80  22.1   \n",
       "1970-01-01 00:00:00.000002000  26.20  26.80  26.20  25.10  23.22  22.70  21.5   \n",
       "1970-01-01 00:00:00.000002001  27.00  27.80  27.40  26.70  24.00  23.10   NaN   \n",
       "1970-01-01 00:00:00.000002002  26.50  26.10  27.35  26.20  24.90  23.70  22.6   \n",
       "1970-01-01 00:00:00.000002003  27.20  28.20  27.90  26.30  24.10  24.30  23.0   \n",
       "1970-01-01 00:00:00.000002004  25.60  26.30  25.90  25.30  24.10  22.90  21.4   \n",
       "1970-01-01 00:00:00.000002005  26.90  26.20  26.70  26.40  24.40  22.80  22.0   \n",
       "1970-01-01 00:00:00.000002006  27.30  28.50  26.80  25.50  23.50  21.90  21.9   \n",
       "1970-01-01 00:00:00.000002007  26.60  26.50  27.70  26.20  23.60  23.32  23.1   \n",
       "1970-01-01 00:00:00.000002008  25.60  26.60  26.80  26.80  23.50  22.80  22.2   \n",
       "1970-01-01 00:00:00.000002009  26.50  27.80  27.40  25.40  24.10  22.30  23.0   \n",
       "1970-01-01 00:00:00.000002010  28.80  28.70  27.40  25.80  24.30  21.70  22.4   \n",
       "1970-01-01 00:00:00.000002011  27.40  27.85  26.00  25.70  23.50  22.30  23.0   \n",
       "1970-01-01 00:00:00.000002012  25.90  26.70  26.80  26.30  22.90  23.80  22.9   \n",
       "1970-01-01 00:00:00.000002013  26.90  27.50  26.80  24.96  24.10  23.80  22.6   \n",
       "1970-01-01 00:00:00.000002014  27.30  27.80  27.10  26.00  24.40  23.69  22.1   \n",
       "1970-01-01 00:00:00.000002015  28.00  28.00  27.60  26.10  23.60  23.40  23.4   \n",
       "1970-01-01 00:00:00.000002016  27.00  28.90  28.00  27.70  24.80  22.40  22.7   \n",
       "1970-01-01 00:00:00.000002017  28.00  27.63  27.10  25.60  23.70  23.70  21.1   \n",
       "1970-01-01 00:00:00.000002018  27.50  26.60  27.50  25.10  23.70  23.40  23.4   \n",
       "1970-01-01 00:00:00.000002019  28.40  28.60  28.10  27.30  25.90  24.10  22.5   \n",
       "1970-01-01 00:00:00.000002020  27.00  27.40  26.20   2.50    NaN  23.90  23.0   \n",
       "\n",
       "                                ago    set   out   nov    dez  \n",
       "Ano                                                            \n",
       "1970-01-01 00:00:00.000001990   NaN    NaN   NaN   NaN    NaN  \n",
       "1970-01-01 00:00:00.000001991   NaN    NaN   NaN   NaN    NaN  \n",
       "1970-01-01 00:00:00.000001992  21.8  22.20  23.8  24.3  24.90  \n",
       "1970-01-01 00:00:00.000001993  22.0  23.60  24.3  25.7  26.00  \n",
       "1970-01-01 00:00:00.000001994  22.0  22.80  24.1  25.2  26.40  \n",
       "1970-01-01 00:00:00.000001995  23.7  23.40  24.2  24.5  25.40  \n",
       "1970-01-01 00:00:00.000001996  21.6  22.60  24.0  24.1  26.30  \n",
       "1970-01-01 00:00:00.000001997  22.7  24.00  24.4  26.3  27.10  \n",
       "1970-01-01 00:00:00.000001998  23.9  24.34  23.9  23.7  26.70  \n",
       "1970-01-01 00:00:00.000001999  21.7  23.00  22.4  22.9  25.70  \n",
       "1970-01-01 00:00:00.000002000  22.0  23.40  25.1  24.8  26.00  \n",
       "1970-01-01 00:00:00.000002001   NaN    NaN  23.5  24.9  25.72  \n",
       "1970-01-01 00:00:00.000002002  24.0  22.70  25.4  25.8  26.80  \n",
       "1970-01-01 00:00:00.000002003  22.2  22.80  22.9  22.5  26.20  \n",
       "1970-01-01 00:00:00.000002004  22.3  23.80  24.3  25.0  25.80  \n",
       "1970-01-01 00:00:00.000002005  23.4  22.90  25.6  23.9  25.30  \n",
       "1970-01-01 00:00:00.000002006  22.9  22.30  23.7  24.6  25.60  \n",
       "1970-01-01 00:00:00.000002007  22.7  22.79  24.7  25.5  26.30  \n",
       "1970-01-01 00:00:00.000002008  23.4  22.80  24.8  24.1  25.30  \n",
       "1970-01-01 00:00:00.000002009  22.6  24.80  25.0  26.9    NaN  \n",
       "1970-01-01 00:00:00.000002010  22.0  23.30  23.9  24.7    NaN  \n",
       "1970-01-01 00:00:00.000002011  23.1  22.40  23.5  23.3  25.10  \n",
       "1970-01-01 00:00:00.000002012  23.3  23.20  24.7  24.5  27.80  \n",
       "1970-01-01 00:00:00.000002013  22.9  23.50  23.9  24.8  25.70  \n",
       "1970-01-01 00:00:00.000002014  22.5  23.90  23.7  25.3  26.80  \n",
       "1970-01-01 00:00:00.000002015  23.3  25.00  25.7  27.3  28.00  \n",
       "1970-01-01 00:00:00.000002016  23.4  24.10  24.2  24.9  26.70  \n",
       "1970-01-01 00:00:00.000002017  21.7  22.60  25.0  24.5  26.10  \n",
       "1970-01-01 00:00:00.000002018  22.6  24.00  24.6  25.3  26.50  \n",
       "1970-01-01 00:00:00.000002019  22.2  24.08  25.3  25.3  26.50  \n",
       "1970-01-01 00:00:00.000002020  22.6  25.00  24.8   NaN  27.10  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uma pequena lista de meses\n",
    "meses = ['Jan', 'Fev', 'Mar', 'Abr', 'Maio', 'Jun',\n",
    "         'Jul', 'Ago', 'Set', 'Out', 'Nov', 'Dez']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['jan'].hist(bins=5)\n",
    "plt.xlabel(r'temperatura Média Jan, $^\\circ\\mathrm{C}$')\n",
    "plt.ylabel('Count (number of  years)')\n",
    "plt.title('Mean Jan Temperatures, 1990 – 2020')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_monthly_temperatures(filename, ax, title):\n",
    "    \"\"\"Make a ridgeline plot from the temperatures on Axes ax.\"\"\"\n",
    "\n",
    "    # Read the data into a pandas DataFrame.\n",
    "    df = pd.read_csv(filename, sep=';', skiprows=10, header=0,\n",
    "                     index_col=0).dropna()\n",
    "    # The months are identified as the first 12 column names.\n",
    "    months = df.columns[:12]\n",
    "    # Get the mean temperatures across all years for each month.\n",
    "    meanT = df[months].mean()\n",
    "    # For a single plot, we might just take the min and max for that region.\n",
    "    minT, maxT = np.min(meanT), np.max(meanT)\n",
    "    # But to properly compare across regions, set these to their values across\n",
    "    # both regions.\n",
    "    #minT, maxT = 24.00, 29.00\n",
    "    norm = Normalize(vmin=minT, vmax=maxT)\n",
    "\n",
    "    # The temperature grid to plot the distributions on.\n",
    "    T = np.arange(20, 31, 0.1)\n",
    "\n",
    "    # A colormap: blue (cold) to red (warm).\n",
    "    cmap = plt.get_cmap('coolwarm')\n",
    "\n",
    "    # Offset each plot vertically by this amount. It looks nice if they overlap.\n",
    "    offset = 0.45\n",
    "    # The y-axis ticks are the month names.\n",
    "    ax.yaxis.set_tick_params(length=0, width=0)\n",
    "    ax.set_ylim(-0.01, 12*offset)\n",
    "    ax.set_yticks(np.arange(0, 12*offset, offset))\n",
    "    ax.set_yticklabels(months[::-1].str.title())\n",
    "    yticklabels = ax.yaxis.get_ticklabels()\n",
    "\n",
    "    for i, month in enumerate(months[::-1]):\n",
    "        c = cmap(norm(meanT[month]))\n",
    "        dist = gaussian_kde(df[month])\n",
    "        # Plot the distribution in an white, which acts as an outline to the\n",
    "        # filled region (in an appropriate colour) under each line.\n",
    "        ax.plot(T, dist(T) + i * offset, c='w', zorder=15-i)\n",
    "        ax.fill_between(T, dist(T) + i * offset, i * offset, fc=c, zorder=15-i) \n",
    "        # Complete with a base line across the width of the plot.\n",
    "        ax.axhline(i * offset, c=c, zorder=15-i)\n",
    "        # Also set the month name to the same colour as the plot.\n",
    "        yticklabels[i].set_color(c)\n",
    "\n",
    "    ax.set_xlabel(r'Temperatura Média Mensal ($^\\circ\\mathrm{C}$)')\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "\n",
    "    ax.set_title(title)\n",
    "\n",
    "# Two plots, one above the other.\n",
    "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(800/DPI, 800/DPI), dpi=DPI)\n",
    "#filename = 'vix_mes_1990_2020.csv'\n",
    "plot_monthly_temperatures(filename, axes, 'Vitória/ES 1990-2020')\n",
    "plt.subplots_adjust(top=1, bottom=0.05, hspace=0.2)\n",
    "#plt.savefig('temps-kde.png', dpi=DPI)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "jan    27.095172\n",
       "fev    27.510345\n",
       "mar    27.081724\n",
       "abr    25.140690\n",
       "mai    24.004286\n",
       "jun    23.047000\n",
       "jul    22.453571\n",
       "ago    22.660714\n",
       "set    23.403929\n",
       "out    24.324138\n",
       "nov    24.807143\n",
       "dez    26.215556\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.max(meanT)\n",
    "months = df.columns[:12]\n",
    "meanT = df[months].mean()\n",
    "meanT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.510344827586206"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(meanT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.45357142857143"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(meanT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0639052727692846"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.944735145316669"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Data</th>\n",
       "      <th>Tx</th>\n",
       "      <th>Tn</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>UR</th>\n",
       "      <th>Prec</th>\n",
       "      <th>Evap</th>\n",
       "      <th>Eto</th>\n",
       "      <th>Insolação</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/1976</td>\n",
       "      <td>30,5</td>\n",
       "      <td>22,6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/01/1976</td>\n",
       "      <td>29,0</td>\n",
       "      <td>22,3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4,2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/01/1976</td>\n",
       "      <td>29,0</td>\n",
       "      <td>23,2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/01/1976</td>\n",
       "      <td>29,4</td>\n",
       "      <td>22,8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5,6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/01/1976</td>\n",
       "      <td>29,8</td>\n",
       "      <td>22,6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0,0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17162</th>\n",
       "      <td>27/12/2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17163</th>\n",
       "      <td>28/12/2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17164</th>\n",
       "      <td>29/12/2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17165</th>\n",
       "      <td>30/12/2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17166</th>\n",
       "      <td>31/12/2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17167 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Data    Tx    Tn Tmed   UR Prec Evap  Eto Insolação\n",
       "0      01/01/1976  30,5  22,6  NaN  NaN  1,4  NaN  NaN       NaN\n",
       "1      02/01/1976  29,0  22,3  NaN  NaN  4,2  NaN  NaN       NaN\n",
       "2      03/01/1976  29,0  23,2  NaN  NaN  1,6  NaN  NaN       NaN\n",
       "3      04/01/1976  29,4  22,8  NaN  NaN  5,6  NaN  NaN       NaN\n",
       "4      05/01/1976  29,8  22,6  NaN  NaN  0,0  NaN  NaN       NaN\n",
       "...           ...   ...   ...  ...  ...  ...  ...  ...       ...\n",
       "17162  27/12/2022   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "17163  28/12/2022   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "17164  29/12/2022   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "17165  30/12/2022   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "17166  31/12/2022   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "\n",
       "[17167 rows x 9 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'planilhas/Planilha_Vitoria.csv'\n",
    "df = pd.read_csv(filename, sep=';', encoding='utf-8') #header=0, index_col=0)#.dropna()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Data'] = pd.to_datetime(df['Data'],format='%d/%m/%Y', dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1976-01-01\n",
       "1       1976-01-02\n",
       "2       1976-01-03\n",
       "3       1976-01-04\n",
       "4       1976-01-05\n",
       "           ...    \n",
       "17162   2022-12-27\n",
       "17163   2022-12-28\n",
       "17164   2022-12-29\n",
       "17165   2022-12-30\n",
       "17166   2022-12-31\n",
       "Name: Data, Length: 17167, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Tx</th>\n",
       "      <th>Tn</th>\n",
       "      <th>Tmed</th>\n",
       "      <th>UR</th>\n",
       "      <th>Prec</th>\n",
       "      <th>Evap</th>\n",
       "      <th>Eto</th>\n",
       "      <th>Insolação</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>30,5</td>\n",
       "      <td>22,6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-02</th>\n",
       "      <td>29,0</td>\n",
       "      <td>22,3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4,2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-03</th>\n",
       "      <td>29,0</td>\n",
       "      <td>23,2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-04</th>\n",
       "      <td>29,4</td>\n",
       "      <td>22,8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5,6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-05</th>\n",
       "      <td>29,8</td>\n",
       "      <td>22,6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0,0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17167 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Tx    Tn Tmed   UR Prec Evap  Eto Insolação\n",
       "Data                                                     \n",
       "1976-01-01  30,5  22,6  NaN  NaN  1,4  NaN  NaN       NaN\n",
       "1976-01-02  29,0  22,3  NaN  NaN  4,2  NaN  NaN       NaN\n",
       "1976-01-03  29,0  23,2  NaN  NaN  1,6  NaN  NaN       NaN\n",
       "1976-01-04  29,4  22,8  NaN  NaN  5,6  NaN  NaN       NaN\n",
       "1976-01-05  29,8  22,6  NaN  NaN  0,0  NaN  NaN       NaN\n",
       "...          ...   ...  ...  ...  ...  ...  ...       ...\n",
       "2022-12-27   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "2022-12-28   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "2022-12-29   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "2022-12-30   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "2022-12-31   NaN   NaN  NaN  NaN  NaN  NaN  NaN       NaN\n",
       "\n",
       "[17167 rows x 8 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Data', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[42], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "df[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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