weather-comparation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is my goal with this project?\n",
    "As a citizen of Porto Alegre for more than 25 years I started to have the feeling that our weather is changing a little on these last years. Out of curiosity I wanted to visualize what differences are perceptible.\n",
    "\n",
    "This is a simple project intended only to consolidate my knowledge of pandas, matplotlib and seaborn as I'm currently deepening my comprehension of them.\n",
    "\n",
    "### Dataset index & columns\n",
    "0. date: from 2001-01-01 to 2022-03-10. As index.\n",
    "1. total_precip: daily total precipitation.\n",
    "2. max_temp: daily maximum temperature.\n",
    "3. avg_temp: daily average temperature.\n",
    "4. min_temp: daily minimum temperature.\n",
    "5. avg_humidity: daily average humidity.\n",
    "6. min_humidity: daily minimum humidity.\n",
    "\n",
    "### What steps will be done:\n",
    "* Data extraction: load .csv\n",
    "* Data preparation: clean useless cols & rows, simplify & translate cols, convert data types, decrease the amount of NaN, date as index, drop NaN\n",
    "* EDA: \n",
    "\n",
    "### Which questions I want to answer?\n",
    "\n",
    "#### Summer\n",
    "\n",
    "* Did the avg temp got higher in these last 7 years?\n",
    "* Did the max temp got too?\n",
    "\n",
    "#### Winter\n",
    "\n",
    "* Did the mean temperature suffered any change?\n",
    "* Did the min temperature increased?\n",
    "\n",
    "\n",
    "#### Humidity\n",
    "\n",
    "* Did the humidity averages suffered any changes along these years? Does it follows the changes of other variables?\n",
    "\n",
    "#### Precipitation\n",
    "\n",
    "* Did the monthly/yearly precipitations sums had suffered some kind of change trough the years?\n",
    "\n",
    "<!-- #### Thermal amplitude  -->\n",
    "\n",
    "<!-- * Did it change? -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data extraction\n",
    "I got this .csv file with historical data from Porto Alegre from the INMET (Instituto Nacional de Meteorologia) website. It's very simple to get the data in the way you want (specific variables, date range, etc). You can go to https://bdmep.inmet.gov.br/ (the INMET database website) and ask for it, it gives you many options to customise your data and the dataset will be sent via e-mail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data Medicao</th>\n",
       "      <th>PRECIPITACAO TOTAL, DIARIO (mm)</th>\n",
       "      <th>TEMPERATURA MAXIMA, DIARIA (C)</th>\n",
       "      <th>TEMPERATURA MEDIA, DIARIA (C)</th>\n",
       "      <th>TEMPERATURA MINIMA, DIARIA (C)</th>\n",
       "      <th>UMIDADE RELATIVA DO AR, MEDIA DIARIA (%)</th>\n",
       "      <th>UMIDADE RELATIVA DO AR, MINIMA DIARIA (%)</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>30,1</td>\n",
       "      <td>23,616667</td>\n",
       "      <td>18,4</td>\n",
       "      <td>68,458333</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>32,1</td>\n",
       "      <td>25,475</td>\n",
       "      <td>20</td>\n",
       "      <td>69,958333</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>33,4</td>\n",
       "      <td>26,345833</td>\n",
       "      <td>21</td>\n",
       "      <td>69,083333</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>35,5</td>\n",
       "      <td>27,429167</td>\n",
       "      <td>21,2</td>\n",
       "      <td>65,041667</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>35,6</td>\n",
       "      <td>27,6375</td>\n",
       "      <td>24,6</td>\n",
       "      <td>67,25</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Data Medicao PRECIPITACAO TOTAL, DIARIO (mm) TEMPERATURA MAXIMA, DIARIA (C)  \\\n",
       "0   2001-01-01                               0                           30,1   \n",
       "1   2001-01-02                               0                           32,1   \n",
       "2   2001-01-03                               0                           33,4   \n",
       "3   2001-01-04                               0                           35,5   \n",
       "4   2001-01-05                               0                           35,6   \n",
       "\n",
       "  TEMPERATURA MEDIA, DIARIA (C) TEMPERATURA MINIMA, DIARIA (C)  \\\n",
       "0                     23,616667                           18,4   \n",
       "1                        25,475                             20   \n",
       "2                     26,345833                             21   \n",
       "3                     27,429167                           21,2   \n",
       "4                       27,6375                           24,6   \n",
       "\n",
       "  UMIDADE RELATIVA DO AR, MEDIA DIARIA (%)  \\\n",
       "0                                68,458333   \n",
       "1                                69,958333   \n",
       "2                                69,083333   \n",
       "3                                65,041667   \n",
       "4                                    67,25   \n",
       "\n",
       "   UMIDADE RELATIVA DO AR, MINIMA DIARIA (%)  Unnamed: 7  \n",
       "0                                       48.0         NaN  \n",
       "1                                       45.0         NaN  \n",
       "2                                       43.0         NaN  \n",
       "3                                       37.0         NaN  \n",
       "4                                       35.0         NaN  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# [Po]rto [A]legre [W]eather dataframe being loaded from the csv with ';' as separator\n",
    "poaw = pd.read_csv('DATA_POA_2001-01-01_2022-03-11.csv',sep=';')\n",
    "poaw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7740 entries, 0 to 7739\n",
      "Data columns (total 8 columns):\n",
      " #   Column                                     Non-Null Count  Dtype  \n",
      "---  ------                                     --------------  -----  \n",
      " 0   Data Medicao                               7740 non-null   object \n",
      " 1   PRECIPITACAO TOTAL, DIARIO (mm)            7210 non-null   object \n",
      " 2   TEMPERATURA MAXIMA, DIARIA (C)             7448 non-null   object \n",
      " 3   TEMPERATURA MEDIA, DIARIA (C)              7232 non-null   object \n",
      " 4   TEMPERATURA MINIMA, DIARIA (C)             7453 non-null   object \n",
      " 5   UMIDADE RELATIVA DO AR, MEDIA DIARIA (%)   7529 non-null   object \n",
      " 6   UMIDADE RELATIVA DO AR, MINIMA DIARIA (%)  7628 non-null   float64\n",
      " 7   Unnamed: 7                                 0 non-null      float64\n",
      "dtypes: float64(2), object(6)\n",
      "memory usage: 483.9+ KB\n"
     ]
    }
   ],
   "source": [
    "poaw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation\n",
    "Steps:\n",
    "* Drop the useless cols\n",
    "* Simplify and translate cols names\n",
    "* Convert str data types to datetime (date) and to float (others)\n",
    "* Decrease the amount of NaN\n",
    "* Dropping non-useful rows\n",
    "* Creating 'year', 'month' and 'day' cols\n",
    "* Set date as index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop the useless cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the last and useless column\n",
    "poaw.drop(labels='Unnamed: 7',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simplify and translate cols names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For simplification and translation purposes I will change the columns names\n",
    "new_col_names = ['date',\n",
    "                 'total_precip',\n",
    "                 'max_temp','avg_temp',\n",
    "                 'min_temp',\n",
    "                 'avg_humidity',\n",
    "                 'min_humidity']\n",
    "\n",
    "poaw.columns = new_col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>date</th>\n",
       "      <th>total_precip</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>avg_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>avg_humidity</th>\n",
       "      <th>min_humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>30,1</td>\n",
       "      <td>23,616667</td>\n",
       "      <td>18,4</td>\n",
       "      <td>68,458333</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>32,1</td>\n",
       "      <td>25,475</td>\n",
       "      <td>20</td>\n",
       "      <td>69,958333</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>33,4</td>\n",
       "      <td>26,345833</td>\n",
       "      <td>21</td>\n",
       "      <td>69,083333</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>35,5</td>\n",
       "      <td>27,429167</td>\n",
       "      <td>21,2</td>\n",
       "      <td>65,041667</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>35,6</td>\n",
       "      <td>27,6375</td>\n",
       "      <td>24,6</td>\n",
       "      <td>67,25</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date total_precip max_temp   avg_temp min_temp avg_humidity  \\\n",
       "0  2001-01-01            0     30,1  23,616667     18,4    68,458333   \n",
       "1  2001-01-02            0     32,1     25,475       20    69,958333   \n",
       "2  2001-01-03            0     33,4  26,345833       21    69,083333   \n",
       "3  2001-01-04            0     35,5  27,429167     21,2    65,041667   \n",
       "4  2001-01-05            0     35,6    27,6375     24,6        67,25   \n",
       "\n",
       "   min_humidity  \n",
       "0          48.0  \n",
       "1          45.0  \n",
       "2          43.0  \n",
       "3          37.0  \n",
       "4          35.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poaw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert str data types to datetime (date) and to float (others)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the \"Date\" col to datetime type and the other cols to float type (the last col is already float)\n",
    "poaw['date'] = pd.to_datetime(poaw['date'])\n",
    "\n",
    "# List with all the cols to be changed from str to float\n",
    "cols_to_float = ['total_precip', 'max_temp', 'avg_temp', 'min_temp', 'avg_humidity']\n",
    "\n",
    "poaw[cols_to_float] = poaw[cols_to_float].apply(lambda x: x.str.replace(',','.').astype(float))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decrease the amount of NaN\n",
    "In an attempt to decrease the amount of NaN and trying to lose the least amount of rows, I try to make the average from min and max temperature and replace the NaN of the avg_temp with it, in the cols where I have min & max temp but not avg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date              0\n",
       "total_precip    530\n",
       "max_temp        292\n",
       "avg_temp        508\n",
       "min_temp        287\n",
       "avg_humidity    211\n",
       "min_humidity    112\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poaw.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date              0\n",
       "total_precip    530\n",
       "max_temp        292\n",
       "avg_temp        314\n",
       "min_temp        287\n",
       "avg_humidity    211\n",
       "min_humidity    112\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here I look for the rows which avg_temp is NaN and that max_temp & min_temp exists\n",
    "# From this I replace the NaN with the arithmetic mean between max and min temperatures of that day\n",
    "poaw.loc[poaw['max_temp'].notnull() \n",
    "         & poaw['min_temp'].notnull() \n",
    "         & poaw['avg_temp'].isnull(),'avg_temp'] = (poaw['max_temp'] + poaw['min_temp']) / 2\n",
    "\n",
    "poaw.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping non-useful rows\n",
    "As I don't plan right now to apply ML models on this dataset the best choice for the rows that contain too many NaNs is to drop it commpletely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Droping rows where there are at least 4 non-NaN\n",
    "poaw.dropna(thresh=4, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date              0\n",
       "total_precip    274\n",
       "max_temp         35\n",
       "avg_temp         58\n",
       "min_temp         51\n",
       "avg_humidity      2\n",
       "min_humidity     28\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poaw.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating 'year', 'month' and 'day' cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "poaw['year'] = pd.DatetimeIndex(poaw['date']).year\n",
    "poaw['month'] = pd.DatetimeIndex(poaw['date']).month\n",
    "poaw['day'] = pd.DatetimeIndex(poaw['date']).day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set date as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the date as index\n",
    "poaw.set_index('date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_precip</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>avg_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>avg_humidity</th>\n",
       "      <th>min_humidity</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
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       "      <th>date</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>23.616667</td>\n",
       "      <td>18.4</td>\n",
       "      <td>68.458333</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>32.1</td>\n",
       "      <td>25.475000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>69.958333</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>33.4</td>\n",
       "      <td>26.345833</td>\n",
       "      <td>21.0</td>\n",
       "      <td>69.083333</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>27.429167</td>\n",
       "      <td>21.2</td>\n",
       "      <td>65.041667</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>35.6</td>\n",
       "      <td>27.637500</td>\n",
       "      <td>24.6</td>\n",
       "      <td>67.250000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_precip  max_temp   avg_temp  min_temp  avg_humidity  \\\n",
       "date                                                                    \n",
       "2001-01-01           0.0      30.1  23.616667      18.4     68.458333   \n",
       "2001-01-02           0.0      32.1  25.475000      20.0     69.958333   \n",
       "2001-01-03           0.0      33.4  26.345833      21.0     69.083333   \n",
       "2001-01-04           0.0      35.5  27.429167      21.2     65.041667   \n",
       "2001-01-05           0.0      35.6  27.637500      24.6     67.250000   \n",
       "\n",
       "            min_humidity  year  month  day  \n",
       "date                                        \n",
       "2001-01-01          48.0  2001      1    1  \n",
       "2001-01-02          45.0  2001      1    2  \n",
       "2001-01-03          43.0  2001      1    3  \n",
       "2001-01-04          37.0  2001      1    4  \n",
       "2001-01-05          35.0  2001      1    5  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poaw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 7483 entries, 2001-01-01 to 2022-03-10\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   total_precip  7209 non-null   float64\n",
      " 1   max_temp      7448 non-null   float64\n",
      " 2   avg_temp      7425 non-null   float64\n",
      " 3   min_temp      7432 non-null   float64\n",
      " 4   avg_humidity  7481 non-null   float64\n",
      " 5   min_humidity  7455 non-null   float64\n",
      " 6   year          7483 non-null   int64  \n",
      " 7   month         7483 non-null   int64  \n",
      " 8   day           7483 non-null   int64  \n",
      "dtypes: float64(6), int64(3)\n",
      "memory usage: 584.6 KB\n"
     ]
    }
   ],
   "source": [
    "poaw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "## used to get the % of NAs\n",
    "## poaw['avg_temp'].value_counts(normalize=True,dropna=False)\n",
    "\n",
    "## null_list = poaw[poaw['avg_temp'].isnull()].index.tolist()\n",
    "## filled = poaw[['max_temp','avg_temp','min_temp']].fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a summarized table\n",
    "\n",
    "In this second part of data cleaning/preparation we will summarize all the data from the poaw. We will firstly divide summer, seasons and years in different tables. After that we will put it all together in a summarized table called 'seasons'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Summers, winters and years separation\n",
    "\n",
    "# Separating by summers, winters and years (in the south hemisphere)\n",
    "summer_start, summer_end = '-12-21', '-03-20'\n",
    "winter_start, winter_end = '-05-21', '-09-22'\n",
    "year_start, year_end = '-01-01', '-12-31'\n",
    "\n",
    "\n",
    "# Initializing with 0 to make the index of years to reflect the year's number \n",
    "# as my data starts from 2001 (e.g. 2001 -> [1], 2015 -> [15])\n",
    "summers = [0]\n",
    "winters = [0]\n",
    "years = [0]\n",
    "\n",
    "year = 2000\n",
    "\n",
    "for i in range(1,23):\n",
    "    \n",
    "    year_i = str(year + i)\n",
    "    \n",
    "    summers.append(poaw.loc[year_i + summer_start:str(year+i+1) + summer_end]) #Correction bcause the summer ends in next year\n",
    "    winters.append(poaw.loc[year_i + winter_start:year_i + winter_end])\n",
    "    years.append(poaw.loc[year_i + year_start:year_i + year_end])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_precip</th>\n",
       "      <th>max_temp</th>\n",
       "      <th>avg_temp</th>\n",
       "      <th>min_temp</th>\n",
       "      <th>avg_humidity</th>\n",
       "      <th>min_humidity</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2001-12-21</th>\n",
       "      <td>10.8</td>\n",
       "      <td>33.7</td>\n",
       "      <td>23.445833</td>\n",
       "      <td>16.7</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-22</th>\n",
       "      <td>16.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>20.458333</td>\n",
       "      <td>19.7</td>\n",
       "      <td>89.458333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.1</td>\n",
       "      <td>21.362500</td>\n",
       "      <td>17.0</td>\n",
       "      <td>68.833333</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>20.791667</td>\n",
       "      <td>15.7</td>\n",
       "      <td>65.875000</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-12-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>22.354167</td>\n",
       "      <td>18.2</td>\n",
       "      <td>61.625000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>12</td>\n",
       "      <td>25</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>2002-03-16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>29.450000</td>\n",
       "      <td>23.5</td>\n",
       "      <td>61.708333</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>34.8</td>\n",
       "      <td>27.404167</td>\n",
       "      <td>25.8</td>\n",
       "      <td>72.291667</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-18</th>\n",
       "      <td>4.4</td>\n",
       "      <td>30.7</td>\n",
       "      <td>25.066667</td>\n",
       "      <td>21.4</td>\n",
       "      <td>79.458333</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>29.6</td>\n",
       "      <td>25.554167</td>\n",
       "      <td>24.2</td>\n",
       "      <td>81.333333</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-20</th>\n",
       "      <td>14.6</td>\n",
       "      <td>27.8</td>\n",
       "      <td>23.791667</td>\n",
       "      <td>21.7</td>\n",
       "      <td>80.416667</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_precip  max_temp   avg_temp  min_temp  avg_humidity  \\\n",
       "date                                                                    \n",
       "2001-12-21          10.8      33.7  23.445833      16.7     75.000000   \n",
       "2001-12-22          16.0      22.8  20.458333      19.7     89.458333   \n",
       "2001-12-23           0.0      26.1  21.362500      17.0     68.833333   \n",
       "2001-12-24           0.0      25.5  20.791667      15.7     65.875000   \n",
       "2001-12-25           0.0      28.1  22.354167      18.2     61.625000   \n",
       "...                  ...       ...        ...       ...           ...   \n",
       "2002-03-16           0.0      36.0  29.450000      23.5     61.708333   \n",
       "2002-03-17           0.0      34.8  27.404167      25.8     72.291667   \n",
       "2002-03-18           4.4      30.7  25.066667      21.4     79.458333   \n",
       "2002-03-19           0.0      29.6  25.554167      24.2     81.333333   \n",
       "2002-03-20          14.6      27.8  23.791667      21.7     80.416667   \n",
       "\n",
       "            min_humidity  year  month  day  \n",
       "date                                        \n",
       "2001-12-21          45.0  2001     12   21  \n",
       "2001-12-22          80.0  2001     12   22  \n",
       "2001-12-23          38.0  2001     12   23  \n",
       "2001-12-24          42.0  2001     12   24  \n",
       "2001-12-25          40.0  2001     12   25  \n",
       "...                  ...   ...    ...  ...  \n",
       "2002-03-16          42.0  2002      3   16  \n",
       "2002-03-17          49.0  2002      3   17  \n",
       "2002-03-18          56.0  2002      3   18  \n",
       "2002-03-19          63.0  2002      3   19  \n",
       "2002-03-20          61.0  2002      3   20  \n",
       "\n",
       "[90 rows x 9 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As an example, I show the 2001's summer data\n",
    "summers[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "years_index = pd.period_range('2001',periods=21,freq='A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Creation a summarized table with the data gathered from 'summer' & 'winter'\n",
    "\n",
    "# Creating year statistics table with summers and winters data inside\n",
    "seasons = pd.DataFrame(index = years_index,columns=['SUM_temp',\n",
    "                                                       'SUM_max','SUM_max_avg',\n",
    "                                                       'SUM_min','SUM_min_avg',\n",
    "                                                       'SUM_hum','SUM_hum_min',\n",
    "                                                       'WIN_temp',\n",
    "                                                       'WIN_max','WIN_max_avg',\n",
    "                                                       'WIN_min','WIN_min_avg',\n",
    "                                                       'win_hum','WIN_hum_min'], dtype=np.float64)\n",
    "\n",
    "# My intention with using this cols var is to make possible to immediately\n",
    "# change the cols name changing themm in only one place: in the df declaration\n",
    "cols = seasons.columns\n",
    "\n",
    "for i, year in enumerate(years_index, 1):\n",
    "    \n",
    "#     print(i)\n",
    "#     print(year)\n",
    "    \n",
    "#     # Values for summer cols of 'seasons'\n",
    "    seasons.loc[year, cols[0]] = summers[i]['avg_temp'].mean()\n",
    "    seasons.loc[year, cols[1]] = summers[i]['max_temp'].max()\n",
    "    seasons.loc[year, cols[2]] = summers[i]['max_temp'].mean()\n",
    "    seasons.loc[year, cols[3]] = summers[i]['min_temp'].min()\n",
    "    seasons.loc[year, cols[4]] = summers[i]['min_temp'].mean()\n",
    "    seasons.loc[year, cols[5]] = summers[i]['avg_humidity'].mean()\n",
    "    seasons.loc[year, cols[6]] = summers[i]['avg_humidity'].min()\n",
    "\n",
    "    \n",
    "#     # Values for winters cols of 'seasons'\n",
    "    seasons.loc[year, cols[7]] = winters[i]['avg_temp'].mean()\n",
    "    seasons.loc[year, cols[8]] = winters[i]['max_temp'].max()\n",
    "    seasons.loc[year, cols[9]] = winters[i]['max_temp'].mean()\n",
    "    seasons.loc[year, cols[10]] = winters[i]['min_temp'].min()\n",
    "    seasons.loc[year, cols[11]] = winters[i]['min_temp'].mean()\n",
    "    seasons.loc[year, cols[12]] = winters[i]['avg_humidity'].mean()\n",
    "    seasons.loc[year, cols[13]] = winters[i]['avg_humidity'].min()\n",
    "\n",
    "# For simplicity reasons I round the numbers to 1 decimal place\n",
    "seasons = seasons.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUM_temp</th>\n",
       "      <th>SUM_max</th>\n",
       "      <th>SUM_max_avg</th>\n",
       "      <th>SUM_min</th>\n",
       "      <th>SUM_min_avg</th>\n",
       "      <th>SUM_hum</th>\n",
       "      <th>SUM_hum_min</th>\n",
       "      <th>WIN_temp</th>\n",
       "      <th>WIN_max</th>\n",
       "      <th>WIN_max_avg</th>\n",
       "      <th>WIN_min</th>\n",
       "      <th>WIN_min_avg</th>\n",
       "      <th>win_hum</th>\n",
       "      <th>WIN_hum_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>24.68</td>\n",
       "      <td>37.9</td>\n",
       "      <td>30.56</td>\n",
       "      <td>14.9</td>\n",
       "      <td>20.58</td>\n",
       "      <td>69.79</td>\n",
       "      <td>49.92</td>\n",
       "      <td>16.66</td>\n",
       "      <td>30.9</td>\n",
       "      <td>21.85</td>\n",
       "      <td>2.6</td>\n",
       "      <td>12.77</td>\n",
       "      <td>77.11</td>\n",
       "      <td>58.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>25.47</td>\n",
       "      <td>38.0</td>\n",
       "      <td>31.20</td>\n",
       "      <td>15.7</td>\n",
       "      <td>21.11</td>\n",
       "      <td>71.35</td>\n",
       "      <td>50.67</td>\n",
       "      <td>15.71</td>\n",
       "      <td>33.7</td>\n",
       "      <td>20.57</td>\n",
       "      <td>3.4</td>\n",
       "      <td>12.05</td>\n",
       "      <td>77.31</td>\n",
       "      <td>55.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>23.93</td>\n",
       "      <td>35.8</td>\n",
       "      <td>29.78</td>\n",
       "      <td>15.1</td>\n",
       "      <td>19.74</td>\n",
       "      <td>70.27</td>\n",
       "      <td>55.64</td>\n",
       "      <td>15.26</td>\n",
       "      <td>34.5</td>\n",
       "      <td>20.58</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.26</td>\n",
       "      <td>76.22</td>\n",
       "      <td>53.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>24.57</td>\n",
       "      <td>39.4</td>\n",
       "      <td>30.89</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.19</td>\n",
       "      <td>67.79</td>\n",
       "      <td>45.67</td>\n",
       "      <td>15.60</td>\n",
       "      <td>36.7</td>\n",
       "      <td>21.00</td>\n",
       "      <td>2.7</td>\n",
       "      <td>11.54</td>\n",
       "      <td>76.94</td>\n",
       "      <td>45.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>24.63</td>\n",
       "      <td>38.7</td>\n",
       "      <td>30.47</td>\n",
       "      <td>13.5</td>\n",
       "      <td>20.60</td>\n",
       "      <td>70.91</td>\n",
       "      <td>53.08</td>\n",
       "      <td>16.34</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.53</td>\n",
       "      <td>1.9</td>\n",
       "      <td>12.50</td>\n",
       "      <td>77.30</td>\n",
       "      <td>49.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>24.87</td>\n",
       "      <td>35.8</td>\n",
       "      <td>30.53</td>\n",
       "      <td>15.1</td>\n",
       "      <td>20.70</td>\n",
       "      <td>71.96</td>\n",
       "      <td>52.46</td>\n",
       "      <td>15.94</td>\n",
       "      <td>32.3</td>\n",
       "      <td>21.17</td>\n",
       "      <td>2.7</td>\n",
       "      <td>12.12</td>\n",
       "      <td>77.37</td>\n",
       "      <td>55.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>24.21</td>\n",
       "      <td>37.3</td>\n",
       "      <td>30.05</td>\n",
       "      <td>14.4</td>\n",
       "      <td>20.06</td>\n",
       "      <td>71.43</td>\n",
       "      <td>56.46</td>\n",
       "      <td>14.62</td>\n",
       "      <td>33.2</td>\n",
       "      <td>19.73</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10.78</td>\n",
       "      <td>78.32</td>\n",
       "      <td>48.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>23.66</td>\n",
       "      <td>35.3</td>\n",
       "      <td>29.16</td>\n",
       "      <td>13.9</td>\n",
       "      <td>19.82</td>\n",
       "      <td>73.34</td>\n",
       "      <td>55.25</td>\n",
       "      <td>14.92</td>\n",
       "      <td>31.8</td>\n",
       "      <td>19.80</td>\n",
       "      <td>2.3</td>\n",
       "      <td>11.40</td>\n",
       "      <td>78.33</td>\n",
       "      <td>57.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>25.17</td>\n",
       "      <td>38.5</td>\n",
       "      <td>30.71</td>\n",
       "      <td>13.7</td>\n",
       "      <td>21.47</td>\n",
       "      <td>73.96</td>\n",
       "      <td>52.38</td>\n",
       "      <td>14.76</td>\n",
       "      <td>33.4</td>\n",
       "      <td>19.87</td>\n",
       "      <td>0.3</td>\n",
       "      <td>10.92</td>\n",
       "      <td>77.64</td>\n",
       "      <td>42.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>24.55</td>\n",
       "      <td>36.2</td>\n",
       "      <td>30.12</td>\n",
       "      <td>14.1</td>\n",
       "      <td>20.99</td>\n",
       "      <td>75.54</td>\n",
       "      <td>63.04</td>\n",
       "      <td>15.34</td>\n",
       "      <td>32.9</td>\n",
       "      <td>20.23</td>\n",
       "      <td>2.8</td>\n",
       "      <td>11.79</td>\n",
       "      <td>78.64</td>\n",
       "      <td>58.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>24.85</td>\n",
       "      <td>38.4</td>\n",
       "      <td>31.08</td>\n",
       "      <td>14.7</td>\n",
       "      <td>20.55</td>\n",
       "      <td>71.39</td>\n",
       "      <td>54.67</td>\n",
       "      <td>14.29</td>\n",
       "      <td>32.4</td>\n",
       "      <td>19.36</td>\n",
       "      <td>1.9</td>\n",
       "      <td>10.71</td>\n",
       "      <td>79.40</td>\n",
       "      <td>60.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>23.61</td>\n",
       "      <td>39.0</td>\n",
       "      <td>29.35</td>\n",
       "      <td>14.4</td>\n",
       "      <td>19.69</td>\n",
       "      <td>72.25</td>\n",
       "      <td>52.29</td>\n",
       "      <td>16.46</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.20</td>\n",
       "      <td>1.1</td>\n",
       "      <td>12.34</td>\n",
       "      <td>76.24</td>\n",
       "      <td>49.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>25.76</td>\n",
       "      <td>40.6</td>\n",
       "      <td>31.93</td>\n",
       "      <td>17.4</td>\n",
       "      <td>21.60</td>\n",
       "      <td>72.23</td>\n",
       "      <td>41.71</td>\n",
       "      <td>14.89</td>\n",
       "      <td>35.1</td>\n",
       "      <td>20.31</td>\n",
       "      <td>1.4</td>\n",
       "      <td>11.08</td>\n",
       "      <td>80.03</td>\n",
       "      <td>55.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>24.86</td>\n",
       "      <td>36.5</td>\n",
       "      <td>30.35</td>\n",
       "      <td>14.4</td>\n",
       "      <td>21.13</td>\n",
       "      <td>76.41</td>\n",
       "      <td>60.62</td>\n",
       "      <td>15.88</td>\n",
       "      <td>34.3</td>\n",
       "      <td>21.16</td>\n",
       "      <td>3.6</td>\n",
       "      <td>12.16</td>\n",
       "      <td>81.22</td>\n",
       "      <td>56.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>25.07</td>\n",
       "      <td>38.9</td>\n",
       "      <td>30.73</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.17</td>\n",
       "      <td>76.59</td>\n",
       "      <td>52.71</td>\n",
       "      <td>17.21</td>\n",
       "      <td>34.8</td>\n",
       "      <td>22.16</td>\n",
       "      <td>5.7</td>\n",
       "      <td>13.74</td>\n",
       "      <td>81.11</td>\n",
       "      <td>47.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>25.32</td>\n",
       "      <td>38.3</td>\n",
       "      <td>31.09</td>\n",
       "      <td>13.6</td>\n",
       "      <td>21.47</td>\n",
       "      <td>78.17</td>\n",
       "      <td>65.25</td>\n",
       "      <td>14.72</td>\n",
       "      <td>32.9</td>\n",
       "      <td>19.84</td>\n",
       "      <td>4.1</td>\n",
       "      <td>11.14</td>\n",
       "      <td>81.85</td>\n",
       "      <td>57.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>24.10</td>\n",
       "      <td>36.8</td>\n",
       "      <td>30.10</td>\n",
       "      <td>13.9</td>\n",
       "      <td>20.06</td>\n",
       "      <td>77.08</td>\n",
       "      <td>53.08</td>\n",
       "      <td>17.66</td>\n",
       "      <td>34.8</td>\n",
       "      <td>23.49</td>\n",
       "      <td>5.5</td>\n",
       "      <td>13.89</td>\n",
       "      <td>81.34</td>\n",
       "      <td>60.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>25.28</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.03</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.42</td>\n",
       "      <td>77.38</td>\n",
       "      <td>61.17</td>\n",
       "      <td>14.92</td>\n",
       "      <td>32.9</td>\n",
       "      <td>20.27</td>\n",
       "      <td>2.6</td>\n",
       "      <td>11.19</td>\n",
       "      <td>84.34</td>\n",
       "      <td>64.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>25.20</td>\n",
       "      <td>40.3</td>\n",
       "      <td>31.94</td>\n",
       "      <td>14.6</td>\n",
       "      <td>20.58</td>\n",
       "      <td>68.51</td>\n",
       "      <td>51.62</td>\n",
       "      <td>16.11</td>\n",
       "      <td>36.1</td>\n",
       "      <td>21.81</td>\n",
       "      <td>2.2</td>\n",
       "      <td>12.24</td>\n",
       "      <td>79.47</td>\n",
       "      <td>60.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>24.67</td>\n",
       "      <td>38.3</td>\n",
       "      <td>30.71</td>\n",
       "      <td>15.7</td>\n",
       "      <td>20.58</td>\n",
       "      <td>72.95</td>\n",
       "      <td>48.71</td>\n",
       "      <td>15.56</td>\n",
       "      <td>31.3</td>\n",
       "      <td>21.19</td>\n",
       "      <td>2.7</td>\n",
       "      <td>11.52</td>\n",
       "      <td>78.93</td>\n",
       "      <td>57.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>25.52</td>\n",
       "      <td>40.3</td>\n",
       "      <td>32.17</td>\n",
       "      <td>17.4</td>\n",
       "      <td>21.01</td>\n",
       "      <td>71.67</td>\n",
       "      <td>58.08</td>\n",
       "      <td>15.35</td>\n",
       "      <td>34.5</td>\n",
       "      <td>20.68</td>\n",
       "      <td>2.3</td>\n",
       "      <td>11.66</td>\n",
       "      <td>79.13</td>\n",
       "      <td>55.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SUM_temp  SUM_max  SUM_max_avg  SUM_min  SUM_min_avg  SUM_hum  \\\n",
       "2001     24.68     37.9        30.56     14.9        20.58    69.79   \n",
       "2002     25.47     38.0        31.20     15.7        21.11    71.35   \n",
       "2003     23.93     35.8        29.78     15.1        19.74    70.27   \n",
       "2004     24.57     39.4        30.89     15.0        20.19    67.79   \n",
       "2005     24.63     38.7        30.47     13.5        20.60    70.91   \n",
       "2006     24.87     35.8        30.53     15.1        20.70    71.96   \n",
       "2007     24.21     37.3        30.05     14.4        20.06    71.43   \n",
       "2008     23.66     35.3        29.16     13.9        19.82    73.34   \n",
       "2009     25.17     38.5        30.71     13.7        21.47    73.96   \n",
       "2010     24.55     36.2        30.12     14.1        20.99    75.54   \n",
       "2011     24.85     38.4        31.08     14.7        20.55    71.39   \n",
       "2012     23.61     39.0        29.35     14.4        19.69    72.25   \n",
       "2013     25.76     40.6        31.93     17.4        21.60    72.23   \n",
       "2014     24.86     36.5        30.35     14.4        21.13    76.41   \n",
       "2015     25.07     38.9        30.73     16.9        21.17    76.59   \n",
       "2016     25.32     38.3        31.09     13.6        21.47    78.17   \n",
       "2017     24.10     36.8        30.10     13.9        20.06    77.08   \n",
       "2018     25.28     38.5        31.03     16.9        21.42    77.38   \n",
       "2019     25.20     40.3        31.94     14.6        20.58    68.51   \n",
       "2020     24.67     38.3        30.71     15.7        20.58    72.95   \n",
       "2021     25.52     40.3        32.17     17.4        21.01    71.67   \n",
       "\n",
       "      SUM_hum_min  WIN_temp  WIN_max  WIN_max_avg  WIN_min  WIN_min_avg  \\\n",
       "2001        49.92     16.66     30.9        21.85      2.6        12.77   \n",
       "2002        50.67     15.71     33.7        20.57      3.4        12.05   \n",
       "2003        55.64     15.26     34.5        20.58      3.0        11.26   \n",
       "2004        45.67     15.60     36.7        21.00      2.7        11.54   \n",
       "2005        53.08     16.34     31.9        21.53      1.9        12.50   \n",
       "2006        52.46     15.94     32.3        21.17      2.7        12.12   \n",
       "2007        56.46     14.62     33.2        19.73      2.2        10.78   \n",
       "2008        55.25     14.92     31.8        19.80      2.3        11.40   \n",
       "2009        52.38     14.76     33.4        19.87      0.3        10.92   \n",
       "2010        63.04     15.34     32.9        20.23      2.8        11.79   \n",
       "2011        54.67     14.29     32.4        19.36      1.9        10.71   \n",
       "2012        52.29     16.46     33.0        22.20      1.1        12.34   \n",
       "2013        41.71     14.89     35.1        20.31      1.4        11.08   \n",
       "2014        60.62     15.88     34.3        21.16      3.6        12.16   \n",
       "2015        52.71     17.21     34.8        22.16      5.7        13.74   \n",
       "2016        65.25     14.72     32.9        19.84      4.1        11.14   \n",
       "2017        53.08     17.66     34.8        23.49      5.5        13.89   \n",
       "2018        61.17     14.92     32.9        20.27      2.6        11.19   \n",
       "2019        51.62     16.11     36.1        21.81      2.2        12.24   \n",
       "2020        48.71     15.56     31.3        21.19      2.7        11.52   \n",
       "2021        58.08     15.35     34.5        20.68      2.3        11.66   \n",
       "\n",
       "      win_hum  WIN_hum_min  \n",
       "2001    77.11        58.29  \n",
       "2002    77.31        55.75  \n",
       "2003    76.22        53.50  \n",
       "2004    76.94        45.25  \n",
       "2005    77.30        49.54  \n",
       "2006    77.37        55.08  \n",
       "2007    78.32        48.29  \n",
       "2008    78.33        57.12  \n",
       "2009    77.64        42.42  \n",
       "2010    78.64        58.00  \n",
       "2011    79.40        60.75  \n",
       "2012    76.24        49.17  \n",
       "2013    80.03        55.96  \n",
       "2014    81.22        56.17  \n",
       "2015    81.11        47.62  \n",
       "2016    81.85        57.88  \n",
       "2017    81.34        60.83  \n",
       "2018    84.34        64.25  \n",
       "2019    79.47        60.04  \n",
       "2020    78.93        57.33  \n",
       "2021    79.13        55.38  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the summarized table called 'seasons'\n",
    "seasons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Columns explanation:\n",
    "* SUM : Summer\n",
    "* WIN : Winter\n",
    "* SUM/WIN_temp --> The average for that summer's/winter's temperatures\n",
    "* SUM/WIN_max --> The highest temperature for that summer/winter\n",
    "* SUM/WIN_min --> The lowest temperature for that summer/winter\n",
    "* SUM/WIN_max_avg --> The average for that summer's/winter's daily maximum temperatures\n",
    "* SUM/WIN_min_avg --> The average for that summer's/winter's daily minimum temperatures\n",
    "* SUM/WIN_hum --> The average of the daily humidities of that summer/winter\n",
    "* SUM/WIN_hum_min --> The lowest humidity of that summer/winter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SUM_temp', 'SUM_max', 'SUM_max_avg', 'SUM_min', 'SUM_min_avg',\n",
       "       'SUM_hum', 'SUM_hum_min', 'WIN_temp', 'WIN_max', 'WIN_max_avg',\n",
       "       'WIN_min', 'WIN_min_avg', 'win_hum', 'WIN_hum_min'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import lines\n",
    "from matplotlib import patches\n",
    "from matplotlib.patheffects import withStroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Colors for ours graphs\n",
    "BROWN = \"#AD8C97\"\n",
    "BROWN_DARKER = \"#7d3a46\"\n",
    "GREEN = \"#2FC1D3\"\n",
    "BLUE = \"#076FA1\"\n",
    "GREY = \"#C7C9CB\"\n",
    "GREY_DARKER = \"#5C5B5D\"\n",
    "RED = \"#E3120B\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SUM_temp',\n",
       " 'SUM_max',\n",
       " 'SUM_max_avg',\n",
       " 'SUM_min',\n",
       " 'SUM_min_avg',\n",
       " 'SUM_hum',\n",
       " 'SUM_hum_min',\n",
       " 'WIN_temp',\n",
       " 'WIN_max',\n",
       " 'WIN_max_avg',\n",
       " 'WIN_min',\n",
       " 'WIN_min_avg',\n",
       " 'win_hum',\n",
       " 'WIN_hum_min']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_plot = seasons[['SUM_min_avg', 'SUM_temp', 'SUM_max_avg']]\n",
    "win_plot = seasons[['WIN_min_avg', 'WIN_temp', 'WIN_max_avg']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What about the summer?\n",
    "\n",
    "Questions to answer:\n",
    "* Did the avg temp increased in these last 7 years?\n",
    "* Did the max temp increased too?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.93, \"Porto Alegre's SUMMER statistics by year\")"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# These are the colors I use along this plot\n",
    "COLORS = [BLUE, BROWN_DARKER, RED]\n",
    "# These are the year indexes of seasons dataframe\n",
    "list_years = seasons.index.year\n",
    "\n",
    "# I use this list and for to make a list of the years\n",
    "# like 01, 02, 03,..., 20, 21\n",
    "two_digit_years = []\n",
    "for year in list_years:\n",
    "    two_digit_years.append(str(year)[2:])\n",
    "two_digit_years[0] = '2001' # With this it will be easier\n",
    "                            # for the user to understand\n",
    "two_digit_years[20] = '2021'# the x axis\n",
    "\n",
    "# Initialize plot -----------\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "\n",
    "# Add lines with dots\n",
    "for temperature, color in zip(sum_plot,COLORS):\n",
    "    ax.plot(list_years, seasons[temperature], color=color,lw=2)\n",
    "    ax.scatter(list_years, seasons[temperature], fc=color, s=60, lw=0.5,ec='white',zorder=12)\n",
    "ax.scatter(list_years, seasons['SUM_max'], fc=RED,s=60,lw=1.2,ec='white',zorder=12)\n",
    "ax.scatter(list_years, seasons['SUM_min'], fc=BLUE,s=60,lw=1.2,ec='white',zorder=12)\n",
    "    \n",
    "# Customize axis -----------\n",
    "# Customize y-axis ticks\n",
    "ax.yaxis.set_ticks([i * 5 for i in range(2,9)])\n",
    "ax.yaxis.set_ticklabels([i * 5 for i in range(2,9)])\n",
    "ax.yaxis.set_tick_params(labelleft=False, length=0)\n",
    "\n",
    "# Customize x-axis ticks\n",
    "ax.xaxis.set_ticks(list_years)\n",
    "ax.xaxis.set_ticklabels(two_digit_years,\n",
    "                        fontsize=12, #fontfamily=\"Econ Sans Cnd\", \n",
    "                        fontweight=50)\n",
    "ax.xaxis.set_tick_params(length=6, width=1.2)\n",
    "\n",
    "# Make gridlines be below most axis\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# Add grid lines \n",
    "ax.grid(axis = 'y', color='#A8BAC4', lw=1.2)\n",
    "ax.grid(axis = 'x' ,linestyle='--', lw=0.8)\n",
    "ax.minorticks_on()\n",
    "ax.grid(which='minor', color='#999999', linestyle='--', alpha=0.35)\n",
    "\n",
    "# Remove all spines but the one in the bottom\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "# Customize bottom spine\n",
    "ax.spines['bottom'].set_lw(4)\n",
    "ax.spines['bottom'].set_capstyle('butt')\n",
    "\n",
    "# Set custom limits\n",
    "ax.set_ylim(10,42)\n",
    "ax.set_xlim(2000.5,2021.5)\n",
    "\n",
    "# Add labels for verticcal grid lines ---------\n",
    "# The pad is equal to 1% of the vertical range (17 - 35)\n",
    "PAD = 18 * 0.01\n",
    "for label in [i * 5 for i in range(2,9)]:\n",
    "    ax.text(2022.8, label - 0.2, str(label) + '°C',\n",
    "           ha='right',va='baseline', fontsize=14,\n",
    "           fontweight=100)\n",
    "    \n",
    "# Annotate labels for variable names ---------\n",
    "\n",
    "# Note the path effecct must be a list\n",
    "path_effects = [withStroke(linewidth=10, foreground='white')]\n",
    "\n",
    "# We create a function to avoid repeating 'ax.text' many times\n",
    "def add_var_label(x, y, text, color, path_effects, ax):\n",
    "    '''This function create a label with defined position and \n",
    "    arguments so we dont need to repeat ax.text many times'''\n",
    "    ax.text(x,y,\n",
    "           text, color=color,\n",
    "           fontsize=14,\n",
    "           va='center', ha='left',\n",
    "           path_effects=path_effects)\n",
    "var_labels = [\n",
    "    {\n",
    "        'x':2006,'y':40,'text':\"Highest Temperatures\",\n",
    "        'color': RED, 'path_effects': path_effects},\n",
    "    {\n",
    "        'x':2005,'y':32,'text':\"Year's\\nMax. Temp. Avg.\",\n",
    "        'color': RED, 'path_effects': path_effects},\n",
    "    {\n",
    "        'x':2005,'y':26.5,'text':\"Year's\\nTemp. Avg.\",\n",
    "        'color': BROWN_DARKER, 'path_effects': []},\n",
    "    {\n",
    "        'x':2004,'y':22.1,'text':\"Year's\\nMin. Temp. Avg.\",\n",
    "        'color': BLUE, 'path_effects': []},\n",
    "    {\n",
    "        'x':2005,'y':16.5,'text':\"Lowest Temperatures\",\n",
    "        'color': BLUE, 'path_effects': []}\n",
    "]\n",
    "\n",
    "for label in var_labels:\n",
    "    add_var_label(**label,ax=ax)\n",
    "    \n",
    "plt.suptitle(\"Porto Alegre's SUMMER statistics by year\",y=0.93,fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions we wanted to answer about the summer:\n",
    "\n",
    "* Did the avg temp increased in these last 7 years?\n",
    "\n",
    "Looking to the graph it seems like it really rose up a bit. But let's further investigate it.\n",
    "\n",
    "* Did the max temp increased too?\n",
    "\n",
    "Yes it really look like there's a difference from the max temperatures from  the last 5 years. Let's further investigate too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are going to setup a plot that will show the difference of each year's\n",
    "# average temperature in relation to the average temperature of all two decades\n",
    "\n",
    "def nums_to_colors(num_list: list):\n",
    "    '''\n",
    "    Convert a list of numbers to a list of colors scaled between red(warm) and blue(cold)\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    num_list : A list of numbers that should be between -1.2 and 1.2. \n",
    "    \n",
    "    Returns:\n",
    "    -----------\n",
    "    color_list: A list where each num was converted to a related color.\n",
    "    \n",
    "    '''\n",
    "    color_list = []\n",
    "    for num in num_list:\n",
    "        if num < -0.8:\n",
    "            color_list.append(BLUE)\n",
    "        elif num < -0.3:\n",
    "            color_list.append(BLUE)\n",
    "        elif num < 0:\n",
    "            color_list.append(BROWN_DARKER)\n",
    "        elif num < 0.3:\n",
    "            color_list.append(BROWN_DARKER)\n",
    "        elif num < 0.8:\n",
    "            color_list.append(RED)\n",
    "        else:\n",
    "            color_list.append(RED)\n",
    "    return color_list\n",
    "\n",
    "\n",
    "# Set up seaborn theme\n",
    "sns.set_theme(style=\"whitegrid\", context=\"notebook\")\n",
    "\n",
    "# The mean of all SUM_temp of these 20 years (24.76 celsius)\n",
    "avg_summ = round(seasons['SUM_temp'].mean(), 2)\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "# Set axes values\n",
    "x = two_digit_years\n",
    "# x[0],x[-1] = '01','21'\n",
    "y = seasons['SUM_temp'] - avg_summ\n",
    "\n",
    "ax.bar(x,y,color = nums_to_colors(y))\n",
    "ax.axhline(0, color=\"k\", clip_on=False)\n",
    "\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Difference in celsius\")\n",
    "ax.set_title(\"Difference between year's summer avg temp & two decade's summer average\")\n",
    "ax.yaxis.set_ticks([-1,-0.5,0,0.5,1])\n",
    "ax.yaxis.set_ticklabels(['- 1','- 0.5','0','+ 0.5','+ 1'])\n",
    "ax.yaxis.set_tick_params(labelleft=True, length=5)\n",
    "\n",
    "# Customize bottom spine\n",
    "ax.spines['bottom'].set_lw(4)\n",
    "ax.spines['bottom'].set_capstyle('butt')\n",
    "\n",
    "# Remove all spines but the one in the bottom\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "for label in [-1,-0.5,0,0.5,1]:\n",
    "    if label == 0:\n",
    "            ax.text(21.8, label - 0.05, str(label + avg_summ) + \"°C\\n(two decade's avg)\" ,\n",
    "           ha='left',va='baseline', fontsize=12,\n",
    "           fontweight=40)\n",
    "    else:\n",
    "        ax.text(21.8, label - 0.02, str(label + avg_summ) + '°C',\n",
    "           ha='left',va='baseline', fontsize=14,\n",
    "           fontweight=40)\n",
    "\n",
    "ax.grid(axis = 'y', color='k', alpha=0.3,lw=1.2)\n",
    "ax.grid(axis = 'x' ,linestyle='--', lw=0.8)\n",
    "ax.minorticks_on()\n",
    "ax.grid(which='minor', color='#999999', linestyle='--', alpha=0.25)\n",
    "\n",
    "# seasons[['SUM_temp']] - seasons['SUM_temp'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph above it really seems like the average temperatures and max temperatures really got a little higher. There are many interesting insights that can be made from this plot, but the general idea it gives to me is that all the variables are getting higher on average.\n",
    "\n",
    "As an attempt to visualize more this change of the averages I've tried to plot a new graph with a line in the center representing the mean of all the year averages from these two decades (which is 24.76C°). After this line, I plotted on this same graph bars that represents how far each year's average is from that mean, they represent the diversion of the the values in relation to that mean/line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from matplotlib.cm import ScalarMappable\n",
    "\n",
    "# diff_from_avg = seasons['SUM_temp'] - avg_summ\n",
    "\n",
    "# diff_from_avg_norm = [x / max(diff_from_avg + 1.16) for x in diff_from_avg]\n",
    "\n",
    "# my_cmap = plt.cm.get_cmap('coolwarm')\n",
    "# colors = my_cmap(diff_from_avg_norm)\n",
    "\n",
    "# sm = ScalarMappable(cmap=my_cmap, norm=plt.Normalize(0,max(diff_from_avg_norm)))\n",
    "\n",
    "# sm.to_rgba()\n",
    "\n",
    "# cbar = plt.colorbar(sm)\n",
    "# cbar.set_label('Color', rotation=270,labelpad=25)\n",
    "\n",
    "\n",
    "# diff_from_avg\n",
    "# diff_from_avg_norm\n",
    "\n",
    "# (seasons['SUM_temp'] - avg_summ).iloc[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "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>SUM_temp</th>\n",
       "      <th>SUM_max</th>\n",
       "      <th>SUM_max_avg</th>\n",
       "      <th>SUM_min</th>\n",
       "      <th>SUM_min_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001 - 2007</th>\n",
       "      <td>24.62</td>\n",
       "      <td>37.56</td>\n",
       "      <td>30.50</td>\n",
       "      <td>14.81</td>\n",
       "      <td>20.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008 - 2014</th>\n",
       "      <td>24.64</td>\n",
       "      <td>37.79</td>\n",
       "      <td>30.39</td>\n",
       "      <td>14.66</td>\n",
       "      <td>20.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2021</th>\n",
       "      <td>25.02</td>\n",
       "      <td>38.77</td>\n",
       "      <td>31.11</td>\n",
       "      <td>15.57</td>\n",
       "      <td>20.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SUM_temp  SUM_max  SUM_max_avg  SUM_min  SUM_min_avg\n",
       "2001 - 2007     24.62    37.56        30.50    14.81        20.43\n",
       "2008 - 2014     24.64    37.79        30.39    14.66        20.75\n",
       "2015 - 2021     25.02    38.77        31.11    15.57        20.90"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Did the avg temp rose up in these last 7 years?\n",
    "## and did avg max temp rose up too?\n",
    "\n",
    "# To answer this with number I create a resample with a frequency of 7y to compare\n",
    "# summer variables along the years\n",
    "\n",
    "summer_7y = seasons.resample(\"7A\").agg([\n",
    "    'mean'\n",
    "]).round(2).iloc[:,0:5]\n",
    "summer_7y.index = ['2001 - 2007',\n",
    "                   '2008 - 2014',\n",
    "                   '2015 - 2021']\n",
    "summer_7y.columns = summer_7y.columns.droplevel(1)\n",
    "summer_7y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see in the plot above, the year's summer temperature average is increasing. The first decade of the century had on average lower temperatures during the summer and the second decade the opposite.\n",
    "\n",
    "This analysis reinforce the idea that Porto Alegre's summers are getting slightly hotter. My perception that our summers are being hotter seems to be in line with data.\n",
    "\n",
    "With the plots that I showed before and with this table that shows the mean of many variables of the city summers I feel confident to say that my perceptions are at least coherent with the data: Porto Alegre is having higher temperatures and higher averages during these last 7 years, but it's just a small difference.\n",
    "\n",
    "We can't confirm with certainty that my perception is related to any real changes in the city's climate, but it certainly goes in the same direction that some studies already found (I will link an interesting one below), the climate change is starting to be noticeable by people who live in the city.\n",
    "\n",
    "[Climate change in Rio Grande do Sul, by Bibiana Dávila at UFRGS](https://www.ufrgs.br/sextante/mudanca-climatica-no-rio-grande-do-sul/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What about the winter?\n",
    "\n",
    "* Did the mean temperature increased?\n",
    "* Did the minimum temperature average increased?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.93, \"Porto Alegre's WINTER statistics by year\")"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# These are the colors I use along this plot\n",
    "COLORS = [BLUE, BROWN_DARKER, RED]\n",
    "# These are the year indexes of seasons dataframe\n",
    "list_years = seasons.index.year\n",
    "\n",
    "# I use the list and for below to make a list of the years\n",
    "# like 01, 02, 03,..., 20, 21\n",
    "two_digit_years = []\n",
    "for year in list_years:\n",
    "    two_digit_years.append(str(year)[2:])\n",
    "two_digit_years[0] = '2001' # With this it will be easier\n",
    "                            # for the user to understand\n",
    "two_digit_years[20] = '2021'# the x axis\n",
    "\n",
    "# Initialize plot -----------\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "# Add lines with dots\n",
    "for temperature, color in zip(win_plot,COLORS):\n",
    "    ax.plot(list(list_years), seasons[temperature], color=color,lw=2)\n",
    "    ax.scatter(list(list_years), seasons[temperature], fc=color, s=60, lw=0.5,ec='white',zorder=12)\n",
    "ax.scatter(list(list_years), seasons['WIN_max'], fc=RED,s=60,lw=1.2,ec='white',zorder=12)\n",
    "ax.scatter(list(list_years), seasons['WIN_min'], fc=BLUE,s=60,lw=1.2,ec='white',zorder=12)\n",
    "    \n",
    "# Customize axis -----------\n",
    "# Customize y-axis ticks\n",
    "ax.yaxis.set_ticks([i * 5 for i in range(0,9)])\n",
    "ax.yaxis.set_ticklabels([i * 5 for i in range(0,9)])\n",
    "ax.yaxis.set_tick_params(labelleft=False, length=0)\n",
    "\n",
    "# Customize x-axis ticks\n",
    "ax.xaxis.set_ticks(list_years)\n",
    "ax.xaxis.set_ticklabels(two_digit_years,\n",
    "                        fontsize=12, #fontfamily=\"Econ Sans Cnd\", \n",
    "                        fontweight=50)\n",
    "ax.xaxis.set_tick_params(length=6, width=1.2)\n",
    "\n",
    "# Make gridlines be below most axis\n",
    "ax.set_axisbelow(True)\n",
    "\n",
    "# Add grid lines \n",
    "ax.grid(axis = 'y', color='#A8BAC4', lw=1.2)\n",
    "ax.grid(axis = 'x' ,linestyle='--', lw=0.8)\n",
    "ax.minorticks_on()\n",
    "ax.grid(which='minor', color='#999999', linestyle='--', alpha=0.35)\n",
    "\n",
    "# Remove all spines but the one in the bottom\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "# Customize bottom spine\n",
    "ax.spines['bottom'].set_lw(4)\n",
    "ax.spines['bottom'].set_capstyle('butt')\n",
    "\n",
    "# Set custom limits\n",
    "ax.set_ylim(0,37)\n",
    "ax.set_xlim(2000.5,2021.5)\n",
    "\n",
    "# Add labels for verticcal grid lines ---------\n",
    "# The pad is equal to 1% of the vertical range (17 - 35)\n",
    "PAD = 18 * 0.01\n",
    "for label in [i * 5 for i in range(0,8)]:\n",
    "    ax.text(2022.8, label - 0.2, str(label) + '°C',\n",
    "           ha='right',va='baseline', fontsize=14,\n",
    "           fontweight=100)\n",
    "    \n",
    "# Annotate labels for variable names ---------\n",
    "\n",
    "# Note the path effecct must be a list\n",
    "path_effects = [withStroke(linewidth=10, foreground='white')]\n",
    "\n",
    "# We create a function to avoid repeating 'ax.text' many times\n",
    "def add_var_label(x, y, text, color, path_effects, ax):\n",
    "    '''This function create a label with defined position and \n",
    "    arguments so we dont need to repeat ax.text many times'''\n",
    "    ax.text(x,y,\n",
    "           text, color=color,\n",
    "           fontsize=14,\n",
    "           va='center', ha='left',\n",
    "           path_effects=path_effects)\n",
    "var_labels = [\n",
    "    {\n",
    "        'x':2006,'y':35,'text':\"Highest Temperatures\",\n",
    "        'color': RED, 'path_effects': path_effects},\n",
    "    {\n",
    "        'x':2005,'y':23.5,'text':\"Year's\\nMax. Temp. Avg.\",\n",
    "        'color': RED, 'path_effects': path_effects},\n",
    "    {\n",
    "        'x':2005,'y':17.5,'text':\"Year's\\nTemp. Avg.\",\n",
    "        'color': BROWN_DARKER, 'path_effects': []},\n",
    "    {\n",
    "        'x':2004,'y':14,'text':\"Year's\\nMin. Temp. Avg.\",\n",
    "        'color': BLUE, 'path_effects': []},\n",
    "    {\n",
    "        'x':2005,'y':6,'text':\"Lowest Temperatures\",\n",
    "        'color': BLUE, 'path_effects': []}\n",
    "]\n",
    "\n",
    "for label in var_labels:\n",
    "    add_var_label(**label,ax=ax)\n",
    "    \n",
    "plt.suptitle(\"Porto Alegre's WINTER statistics by year\",y=0.93,fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are going to setup a plot that will show the difference of each year's\n",
    "# average temperature in relation to the average temperature of all two decades\n",
    "\n",
    "def nums_to_colors(num_list: list):\n",
    "    '''\n",
    "    Convert a list of numbers to a list of colors scaled between red(warm) and blue(cold)\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    num_list : A list of numbers that should be between -1.2 and 1.2. \n",
    "    \n",
    "    Returns:\n",
    "    -----------\n",
    "    color_list: A list where each num was converted to a related color.\n",
    "    \n",
    "    '''\n",
    "    color_list = []\n",
    "    for num in num_list:\n",
    "        if num < -0.8:\n",
    "            color_list.append(BLUE)\n",
    "        elif num < -0.3:\n",
    "            color_list.append(BLUE)\n",
    "        elif num < 0:\n",
    "            color_list.append(BROWN_DARKER)\n",
    "        elif num < 0.3:\n",
    "            color_list.append(BROWN_DARKER)\n",
    "        elif num < 0.8:\n",
    "            color_list.append(RED)\n",
    "        else:\n",
    "            color_list.append(RED)\n",
    "    return color_list\n",
    "\n",
    "\n",
    "# Set up seaborn theme\n",
    "sns.set_theme(style=\"whitegrid\", context=\"notebook\")\n",
    "\n",
    "# The mean of all SUM_temp of these 20 years (24.76 celsius)\n",
    "avg_summ = round(seasons['WIN_temp'].mean(), 2)\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "# Set axes values\n",
    "x = two_digit_years\n",
    "# x[0],x[-1] = '01','21'\n",
    "y = seasons['WIN_temp'] - avg_summ\n",
    "\n",
    "ax.bar(x,y,color = nums_to_colors(y))\n",
    "ax.axhline(0, color=\"k\", clip_on=False)\n",
    "\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Difference in celsius\")\n",
    "ax.set_title(\"Difference between year's winter avg temp & two decade's winter average\")\n",
    "ax.yaxis.set_ticks([-1,-0.5,0,0.5,1, 1.5, 2])\n",
    "ax.yaxis.set_ticklabels(['- 1','- 0.5','0','+ 0.5','+ 1', '+ 1.5', '+ 2'])\n",
    "ax.yaxis.set_tick_params(labelleft=True, length=5)\n",
    "\n",
    "# Customize bottom spine\n",
    "ax.spines['bottom'].set_lw(4)\n",
    "ax.spines['bottom'].set_capstyle('butt')\n",
    "\n",
    "# Remove all spines but the one in the bottom\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "for label in [-1,-0.5,0,0.5,1,1.5,2]:\n",
    "    if label == 0:\n",
    "            ax.text(21.8, label - 0.05, str(label + avg_summ) + \"°C\\n(two decade's avg)\" ,\n",
    "           ha='left',va='baseline', fontsize=12,\n",
    "           fontweight=40)\n",
    "    else:\n",
    "        ax.text(21.8, label - 0.02, str(round((label + avg_summ), 2)) + '°C',\n",
    "           ha='left',va='baseline', fontsize=14,\n",
    "           fontweight=40)\n",
    "\n",
    "ax.grid(axis = 'y', color='k', alpha=0.3,lw=1.2)\n",
    "ax.grid(axis = 'x' ,linestyle='--', lw=0.8)\n",
    "ax.minorticks_on()\n",
    "ax.grid(which='minor', color='#999999', linestyle='--', alpha=0.25)\n",
    "\n",
    "# seasons[['SUM_temp']] - seasons['SUM_temp'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>WIN_temp</th>\n",
       "      <th>WIN_max</th>\n",
       "      <th>WIN_max_avg</th>\n",
       "      <th>WIN_min</th>\n",
       "      <th>WIN_min_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001 - 2007</th>\n",
       "      <td>15.73</td>\n",
       "      <td>33.31</td>\n",
       "      <td>20.92</td>\n",
       "      <td>2.64</td>\n",
       "      <td>11.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008 - 2014</th>\n",
       "      <td>15.22</td>\n",
       "      <td>33.27</td>\n",
       "      <td>20.42</td>\n",
       "      <td>1.91</td>\n",
       "      <td>11.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2021</th>\n",
       "      <td>15.93</td>\n",
       "      <td>33.90</td>\n",
       "      <td>21.35</td>\n",
       "      <td>3.59</td>\n",
       "      <td>12.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             WIN_temp  WIN_max  WIN_max_avg  WIN_min  WIN_min_avg\n",
       "2001 - 2007     15.73    33.31        20.92     2.64        11.86\n",
       "2008 - 2014     15.22    33.27        20.42     1.91        11.49\n",
       "2015 - 2021     15.93    33.90        21.35     3.59        12.20"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winter_7y = seasons.resample('7A')\n",
    "winter_7y = winter_7y.agg([\n",
    "    'mean'\n",
    "]).round(2).iloc[:,7:-2]\n",
    "winter_7y.index = ['2001 - 2007',\n",
    "                    '2008 - 2014',\n",
    "                    '2015 - 2021']\n",
    "winter_7y.columns = winter_7y.columns.droplevel(1)\n",
    "winter_7y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the winter we can see a similar pattern to the one that our summer is following: average temperatues, maximum and minimum variables are all (somewhat) increasing. All the variables had some increase, but the maximum averages and the two minimum variables (average and lowest temperature) had highest increase.\n",
    "\n",
    "The average temperatures seems to be getting higher and all the other metrics too. Answering our questions: Yes, the mean temperature average suffered an increase and the miniminum temperatures (and averages) suffered too.\n",
    "\n",
    "Again, we, citizens, can feel a difference and this difference can be felt by our sensations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Humidity\n",
    "\n",
    "Questions we want to answer:\n",
    "\n",
    "* Did the humidity averages suffered any changes along these years?\n",
    "* Does it follows the changes of other variables?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>avg_humidity</th>\n",
       "      <th>min_humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001 - 2007</th>\n",
       "      <td>74.09</td>\n",
       "      <td>51.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008 - 2014</th>\n",
       "      <td>75.47</td>\n",
       "      <td>52.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2021</th>\n",
       "      <td>77.85</td>\n",
       "      <td>54.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             avg_humidity  min_humidity\n",
       "2001 - 2007         74.09         51.16\n",
       "2008 - 2014         75.47         52.34\n",
       "2015 - 2021         77.85         54.83"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poaw_7y = poaw.loc[:'2021-12-31'].resample('7AS').agg([\n",
    "    'mean'\n",
    "])\n",
    "\n",
    "poaw_7y_hum = poaw_7y.round(2).loc[:,['avg_humidity','min_humidity']]\n",
    "poaw_7y_hum.index = ['2001 - 2007',\n",
    "                       '2008 - 2014',\n",
    "                       '2015 - 2021']\n",
    "poaw_7y_hum.columns = poaw_7y_hum.columns.droplevel(1)\n",
    "poaw_7y_hum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average humidity and minimum humidity mantained themselves in a similar averages along these 20 years although they seem to be increasing too. I'm no specialist, so I can't affirm anything, even if there's a small increase on it. It can be only a common difference or something else, only a little bit of more research from my part can understand it further.\n",
    "\n",
    "Answering the questions: they seems to be increasing although it's a subtle difference. As all the other variables seems to be increasing too I can say that this change is following the changes of other metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precipitation\n",
    "\n",
    "Questions we want to answer:\n",
    "\n",
    "* Did the monthly precipitations sums had suffered some kind of change trough the years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Sum of precipitation (mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001 - 2007</th>\n",
       "      <td>7532.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008 - 2014</th>\n",
       "      <td>10567.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015 - 2021</th>\n",
       "      <td>10809.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sum of precipitation (mm)\n",
       "2001 - 2007                     7532.4\n",
       "2008 - 2014                    10567.8\n",
       "2015 - 2021                    10809.8"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poaw_7y = poaw.loc[:'2021-12-31'].resample('7AS').agg([\n",
    "    'sum'\n",
    "])\n",
    "poaw_7y.index = ['2001 - 2007',\n",
    "                       '2008 - 2014',\n",
    "                       '2015 - 2021']\n",
    "poaw_7y_prec = poaw_7y.iloc[:,0]\n",
    "poaw_7y_prec.name = ('Sum of precipitation (mm)')\n",
    "poaw_7y_prec.to_frame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Sum of precipitation (mm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001 - 2010</th>\n",
       "      <td>12258.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011 - 2020</th>\n",
       "      <td>15431.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021 - 2021</th>\n",
       "      <td>1220.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sum of precipitation (mm)\n",
       "2001 - 2010                    12258.6\n",
       "2011 - 2020                    15431.4\n",
       "2021 - 2021                     1220.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poaw_10y = poaw.loc[:'2021-12-31'].resample('10AS').agg([\n",
    "    'sum'\n",
    "])\n",
    "poaw_10y.index = ['2001 - 2010',\n",
    "                       '2011 - 2020',\n",
    "                       '2021 - 2021']\n",
    "poaw_10y_prec = poaw_10y.iloc[:,0]\n",
    "poaw_10y_prec.name = ('Sum of precipitation (mm)')\n",
    "poaw_10y_prec.to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this last table we are able to see that we having considerable more rain in this last decade of 2011 to 2020. Being specific: the difference between 2001-2010 sum and the 2011-2020 sum is 3172.8mm more precipitation for the latter.\n",
    "\n",
    "This tendency towards a higher amount of precipitation is something expected in the climate change context of the state we are in. Rio Grande do Sul suffers from the climate changes that are being made trough the amazon forest deforestation, in more than one way it intensifies the precipitations on our region."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What's next?\n",
    "\n",
    "After this EDA I'm planning to explore other parts of this data (like thermal amplitude) and if I can get more data (other years) I will thing about doing some forecasting.\n",
    "\n",
    "This will only happen in the future, so see you next time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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