{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f48884e",
   "metadata": {},
   "source": [
    "# Aula 01 - Análise exploratória de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99224fcb",
   "metadata": {},
   "source": [
    "## Importanto módulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "728d8f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # para cálculos numéricos\n",
    "import matplotlib.pyplot as plt # visualização, gráficos\n",
    "import pandas as pd # leitura de tabela de dados\n",
    "\n",
    "\n",
    "plt.rc('text',usetex=True) # Para usar fontes do latex"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b38f67",
   "metadata": {},
   "source": [
    "## Alguns tipos de dados e indexação em Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12133f8",
   "metadata": {},
   "source": [
    "### A lista"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7015c630",
   "metadata": {},
   "source": [
    "Python possui um tipo de variável bastante flexível -- a lista, que é uma coleção ordenada de objetos. Numa lista é possível reunir variáveis de diferentes tipos e acessá-los de forma bastante similar como é feito Matlab. Exemplo: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1bf421a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_lista = list(range(6))\n",
    "exemplo_lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc897cac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_lista[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3541973b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_lista[-1] # acessando o último elemento"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d989678f",
   "metadata": {},
   "source": [
    "Acima temos uma lista composta puramente por inteiros. No entanto, podemos adicionar um elemento novo de tipo distinto dos demais. Veja o seguinte exemplo: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34565746",
   "metadata": {},
   "outputs": [],
   "source": [
    "exemplo_lista[0] = 'uma string' #o primeiro elemento será uma string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "14eb325b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['uma string', 1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_lista"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f81ca5a",
   "metadata": {},
   "source": [
    "E com o chamado *slice operator* podemos acessar porções da lista: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5b0c04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['uma string', 1, 2]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_lista[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5655f83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_lista[2:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be7482b",
   "metadata": {},
   "source": [
    "### O dicionário"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8acf4650",
   "metadata": {},
   "source": [
    "Já o dicionário será uma coleção de objetos não ordenada. Num dicionário, em vez de acessarmos os elementos por valores inteiros, utilizaremos pares de **key:value**. Veja abaixo um exemplo de criação de um dicionário: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af46e5cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "exemplo_dict = {'laranja':8,'banana':5}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0466f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_dict['laranja']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "309b1ac2",
   "metadata": {},
   "source": [
    "Podemos adicionar chaves também: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "911f1fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "exemplo_dict['maca'] = 22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d5525634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'laranja': 8, 'banana': 5, 'maca': 22}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57d014b",
   "metadata": {},
   "source": [
    "## Lendo a base de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a1ab358",
   "metadata": {},
   "source": [
    "Vamos analisar uma base de dados que contém informação sobre queda de meteoritos (https://data.nasa.gov/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd8e35bf",
   "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>name</th>\n",
       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>recclass</th>\n",
       "      <th>mass</th>\n",
       "      <th>fall</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "      <th>GeoLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aachen</td>\n",
       "      <td>1</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>50.77500</td>\n",
       "      <td>6.08333</td>\n",
       "      <td>(50.775, 6.08333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aarhus</td>\n",
       "      <td>2</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H6</td>\n",
       "      <td>720.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>56.18333</td>\n",
       "      <td>10.23333</td>\n",
       "      <td>(56.18333, 10.23333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abee</td>\n",
       "      <td>6</td>\n",
       "      <td>Valid</td>\n",
       "      <td>EH4</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>54.21667</td>\n",
       "      <td>-113.00000</td>\n",
       "      <td>(54.21667, -113.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acapulco</td>\n",
       "      <td>10</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Acapulcoite</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>16.88333</td>\n",
       "      <td>-99.90000</td>\n",
       "      <td>(16.88333, -99.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Achiras</td>\n",
       "      <td>370</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>780.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>-33.16667</td>\n",
       "      <td>-64.95000</td>\n",
       "      <td>(-33.16667, -64.95)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45711</th>\n",
       "      <td>Zillah 002</td>\n",
       "      <td>31356</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Eucrite</td>\n",
       "      <td>172.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>29.03700</td>\n",
       "      <td>17.01850</td>\n",
       "      <td>(29.037, 17.0185)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45712</th>\n",
       "      <td>Zinder</td>\n",
       "      <td>30409</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Pallasite, ungrouped</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>13.78333</td>\n",
       "      <td>8.96667</td>\n",
       "      <td>(13.78333, 8.96667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45713</th>\n",
       "      <td>Zlin</td>\n",
       "      <td>30410</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Found</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>49.25000</td>\n",
       "      <td>17.66667</td>\n",
       "      <td>(49.25, 17.66667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45714</th>\n",
       "      <td>Zubkovsky</td>\n",
       "      <td>31357</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>2167.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>49.78917</td>\n",
       "      <td>41.50460</td>\n",
       "      <td>(49.78917, 41.5046)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45715</th>\n",
       "      <td>Zulu Queen</td>\n",
       "      <td>30414</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L3.7</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>33.98333</td>\n",
       "      <td>-115.68333</td>\n",
       "      <td>(33.98333, -115.68333)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45716 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name     id nametype              recclass      mass   fall  \\\n",
       "0          Aachen      1    Valid                    L5      21.0   Fell   \n",
       "1          Aarhus      2    Valid                    H6     720.0   Fell   \n",
       "2            Abee      6    Valid                   EH4  107000.0   Fell   \n",
       "3        Acapulco     10    Valid           Acapulcoite    1914.0   Fell   \n",
       "4         Achiras    370    Valid                    L6     780.0   Fell   \n",
       "...           ...    ...      ...                   ...       ...    ...   \n",
       "45711  Zillah 002  31356    Valid               Eucrite     172.0  Found   \n",
       "45712      Zinder  30409    Valid  Pallasite, ungrouped      46.0  Found   \n",
       "45713        Zlin  30410    Valid                    H4       3.3  Found   \n",
       "45714   Zubkovsky  31357    Valid                    L6    2167.0  Found   \n",
       "45715  Zulu Queen  30414    Valid                  L3.7     200.0  Found   \n",
       "\n",
       "         year    reclat    reclong             GeoLocation  \n",
       "0      1880.0  50.77500    6.08333       (50.775, 6.08333)  \n",
       "1      1951.0  56.18333   10.23333    (56.18333, 10.23333)  \n",
       "2      1952.0  54.21667 -113.00000      (54.21667, -113.0)  \n",
       "3      1976.0  16.88333  -99.90000       (16.88333, -99.9)  \n",
       "4      1902.0 -33.16667  -64.95000     (-33.16667, -64.95)  \n",
       "...       ...       ...        ...                     ...  \n",
       "45711  1990.0  29.03700   17.01850       (29.037, 17.0185)  \n",
       "45712  1999.0  13.78333    8.96667     (13.78333, 8.96667)  \n",
       "45713  1939.0  49.25000   17.66667       (49.25, 17.66667)  \n",
       "45714  2003.0  49.78917   41.50460     (49.78917, 41.5046)  \n",
       "45715  1976.0  33.98333 -115.68333  (33.98333, -115.68333)  \n",
       "\n",
       "[45716 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Meteorite_Landings.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af19db8b",
   "metadata": {},
   "source": [
    "Perguntas que podem surgir quando começamos a trabalhar com uma base nova: \n",
    "\n",
    "1. Quantas colunas há?\n",
    "\n",
    "2. Quantas linhas?\n",
    "\n",
    "3. Que tipo de características (*features*) há? São **categóricas** ou **numéricas**? \n",
    "   Acima quais são categóricas?\n",
    "   \n",
    "4. Há dados faltantes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3701ca19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45716, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "585d0866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45716 entries, 0 to 45715\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   name         45716 non-null  object \n",
      " 1   id           45716 non-null  int64  \n",
      " 2   nametype     45716 non-null  object \n",
      " 3   recclass     45716 non-null  object \n",
      " 4   mass         45585 non-null  float64\n",
      " 5   fall         45716 non-null  object \n",
      " 6   year         45425 non-null  float64\n",
      " 7   reclat       38401 non-null  float64\n",
      " 8   reclong      38401 non-null  float64\n",
      " 9   GeoLocation  38401 non-null  object \n",
      "dtypes: float64(4), int64(1), object(5)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f076c422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'id', 'nametype', 'recclass', 'mass', 'fall', 'year', 'reclat',\n",
       "       'reclong', 'GeoLocation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "84cc801a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>recclass</th>\n",
       "      <th>mass</th>\n",
       "      <th>fall</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "      <th>GeoLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aachen</td>\n",
       "      <td>1</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>50.77500</td>\n",
       "      <td>6.08333</td>\n",
       "      <td>(50.775, 6.08333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aarhus</td>\n",
       "      <td>2</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H6</td>\n",
       "      <td>720.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>56.18333</td>\n",
       "      <td>10.23333</td>\n",
       "      <td>(56.18333, 10.23333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abee</td>\n",
       "      <td>6</td>\n",
       "      <td>Valid</td>\n",
       "      <td>EH4</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>54.21667</td>\n",
       "      <td>-113.00000</td>\n",
       "      <td>(54.21667, -113.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acapulco</td>\n",
       "      <td>10</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Acapulcoite</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>16.88333</td>\n",
       "      <td>-99.90000</td>\n",
       "      <td>(16.88333, -99.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Achiras</td>\n",
       "      <td>370</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>780.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>-33.16667</td>\n",
       "      <td>-64.95000</td>\n",
       "      <td>(-33.16667, -64.95)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Adhi Kot</td>\n",
       "      <td>379</td>\n",
       "      <td>Valid</td>\n",
       "      <td>EH4</td>\n",
       "      <td>4239.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1919.0</td>\n",
       "      <td>32.10000</td>\n",
       "      <td>71.80000</td>\n",
       "      <td>(32.1, 71.8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adzhi-Bogdo (stone)</td>\n",
       "      <td>390</td>\n",
       "      <td>Valid</td>\n",
       "      <td>LL3-6</td>\n",
       "      <td>910.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>44.83333</td>\n",
       "      <td>95.16667</td>\n",
       "      <td>(44.83333, 95.16667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Agen</td>\n",
       "      <td>392</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H5</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1814.0</td>\n",
       "      <td>44.21667</td>\n",
       "      <td>0.61667</td>\n",
       "      <td>(44.21667, 0.61667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Aguada</td>\n",
       "      <td>398</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>-31.60000</td>\n",
       "      <td>-65.23333</td>\n",
       "      <td>(-31.6, -65.23333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Aguila Blanca</td>\n",
       "      <td>417</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>-30.86667</td>\n",
       "      <td>-64.55000</td>\n",
       "      <td>(-30.86667, -64.55)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name   id nametype     recclass      mass  fall    year  \\\n",
       "0               Aachen    1    Valid           L5      21.0  Fell  1880.0   \n",
       "1               Aarhus    2    Valid           H6     720.0  Fell  1951.0   \n",
       "2                 Abee    6    Valid          EH4  107000.0  Fell  1952.0   \n",
       "3             Acapulco   10    Valid  Acapulcoite    1914.0  Fell  1976.0   \n",
       "4              Achiras  370    Valid           L6     780.0  Fell  1902.0   \n",
       "5             Adhi Kot  379    Valid          EH4    4239.0  Fell  1919.0   \n",
       "6  Adzhi-Bogdo (stone)  390    Valid        LL3-6     910.0  Fell  1949.0   \n",
       "7                 Agen  392    Valid           H5   30000.0  Fell  1814.0   \n",
       "8               Aguada  398    Valid           L6    1620.0  Fell  1930.0   \n",
       "9        Aguila Blanca  417    Valid            L    1440.0  Fell  1920.0   \n",
       "\n",
       "     reclat    reclong           GeoLocation  \n",
       "0  50.77500    6.08333     (50.775, 6.08333)  \n",
       "1  56.18333   10.23333  (56.18333, 10.23333)  \n",
       "2  54.21667 -113.00000    (54.21667, -113.0)  \n",
       "3  16.88333  -99.90000     (16.88333, -99.9)  \n",
       "4 -33.16667  -64.95000   (-33.16667, -64.95)  \n",
       "5  32.10000   71.80000          (32.1, 71.8)  \n",
       "6  44.83333   95.16667  (44.83333, 95.16667)  \n",
       "7  44.21667    0.61667   (44.21667, 0.61667)  \n",
       "8 -31.60000  -65.23333    (-31.6, -65.23333)  \n",
       "9 -30.86667  -64.55000   (-30.86667, -64.55)  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e31a0d",
   "metadata": {},
   "source": [
    "## Detectando elementos únicos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "15d30d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fall'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "88c07f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fell', 'Found'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df['fall']) # ou utilizando o numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20c7844b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fell', 'Found'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df.fall)   # outra forma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7357742",
   "metadata": {},
   "source": [
    "## Calculando a frequência de cada entrada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5684d863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003.0    3323\n",
       "1979.0    3046\n",
       "1998.0    2697\n",
       "2006.0    2456\n",
       "1988.0    2296\n",
       "          ... \n",
       "1741.0       1\n",
       "1519.0       1\n",
       "1671.0       1\n",
       "1779.0       1\n",
       "1792.0       1\n",
       "Name: year, Length: 265, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9564071",
   "metadata": {},
   "source": [
    "Com isso, podemos até tentar fazer nosso primeiro gráfico, visualizando o número de meteoritos descobertos em função do ano. Vamos utilizar a função `plt.plot`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b9a01db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc5860d",
   "metadata": {},
   "source": [
    "Exemplo simples: visualizando a função $f(x) = e^{-\\frac{x}{10}}\\left[\\sin(x) + \\sin(3x)\\right]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5a78d42a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.figure(figsize=(10,6))\n",
    "x = np.linspace(0,5*np.pi,1000)\n",
    "decay = np.exp(-0.1*x)\n",
    "y = (np.sin(x) + np.sin(3*x))*decay\n",
    "plt.plot(x,y,'-',linewidth=0.7)\n",
    "plt.plot(x,decay,'-',color='C1',linewidth=0.7,label=r'Decay function')\n",
    "plt.xlabel(r'$x$',fontsize=13)\n",
    "plt.ylabel(r'$f(x) = e^{-\\frac{x}{10}}\\left[\\sin(x) + \\sin(3x)\\right]$',fontsize=13)\n",
    "plt.title(r'My plot',fontsize=13)\n",
    "plt.legend(loc='upper right',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881e13d4",
   "metadata": {},
   "source": [
    "Vejamos agora o caso da base de dados. Queremos plotar ano $\\times$ número de meteoritos encontrados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aadba277",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['year'].value_counts()\n",
    "x = np.unique(df.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "31f36f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003.0    3323\n",
       "1979.0    3046\n",
       "1998.0    2697\n",
       "2006.0    2456\n",
       "1988.0    2296\n",
       "          ... \n",
       "1741.0       1\n",
       "1519.0       1\n",
       "1671.0       1\n",
       "1779.0       1\n",
       "1792.0       1\n",
       "Name: year, Length: 265, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6a59e89d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 860.,  920., 1399., 1490., 1491., 1495., 1519., 1575., 1583.,\n",
       "       1600., 1621., 1623., 1628., 1632., 1636., 1637., 1647., 1654.,\n",
       "       1662., 1668., 1671., 1688., 1704., 1715., 1716., 1723., 1724.,\n",
       "       1740., 1741., 1749., 1750., 1751., 1753., 1766., 1768., 1769.,\n",
       "       1773., 1775., 1776., 1779., 1781., 1784., 1785., 1787., 1790.,\n",
       "       1791., 1792., 1793., 1794., 1795., 1796., 1797., 1798., 1801.,\n",
       "       1803., 1804., 1805., 1806., 1807., 1808., 1809., 1810., 1811.,\n",
       "       1812., 1813., 1814., 1815., 1817., 1818., 1819., 1820., 1821.,\n",
       "       1822., 1823., 1824., 1825., 1826., 1827., 1828., 1829., 1830.,\n",
       "       1831., 1832., 1833., 1834., 1835., 1836., 1837., 1838., 1839.,\n",
       "       1840., 1841., 1842., 1843., 1844., 1845., 1846., 1847., 1848.,\n",
       "       1849., 1850., 1851., 1852., 1853., 1854., 1855., 1856., 1857.,\n",
       "       1858., 1859., 1860., 1861., 1862., 1863., 1864., 1865., 1866.,\n",
       "       1867., 1868., 1869., 1870., 1871., 1872., 1873., 1874., 1875.,\n",
       "       1876., 1877., 1878., 1879., 1880., 1881., 1882., 1883., 1884.,\n",
       "       1885., 1886., 1887., 1888., 1889., 1890., 1891., 1892., 1893.,\n",
       "       1894., 1895., 1896., 1897., 1898., 1899., 1900., 1901., 1902.,\n",
       "       1903., 1904., 1905., 1906., 1907., 1908., 1909., 1910., 1911.,\n",
       "       1912., 1913., 1914., 1915., 1916., 1917., 1918., 1919., 1920.,\n",
       "       1921., 1922., 1923., 1924., 1925., 1926., 1927., 1928., 1929.,\n",
       "       1930., 1931., 1932., 1933., 1934., 1935., 1936., 1937., 1938.,\n",
       "       1939., 1940., 1941., 1942., 1943., 1944., 1945., 1946., 1947.,\n",
       "       1948., 1949., 1950., 1951., 1952., 1953., 1954., 1955., 1956.,\n",
       "       1957., 1958., 1959., 1960., 1961., 1962., 1963., 1964., 1965.,\n",
       "       1966., 1967., 1968., 1969., 1970., 1971., 1972., 1973., 1974.,\n",
       "       1975., 1976., 1977., 1978., 1979., 1980., 1981., 1982., 1983.,\n",
       "       1984., 1985., 1986., 1987., 1988., 1989., 1990., 1991., 1992.,\n",
       "       1993., 1994., 1995., 1996., 1997., 1998., 1999., 2000., 2001.,\n",
       "       2002., 2003., 2004., 2005., 2006., 2007., 2008., 2009., 2010.,\n",
       "       2011., 2012., 2013., 2101.,   nan])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "22fb769f",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (266,) and (265,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [36]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/pyplot.py:2769\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2767\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   2768\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;241m*\u001b[39margs, scalex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, scaley\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2769\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2770\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2771\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axes/_axes.py:1632\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1391\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1392\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1629\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1630\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1631\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1632\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1633\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1634\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axes/_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    310\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    311\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axes/_base.py:498\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m    495\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m    497\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 498\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    499\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m    501\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    502\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (266,) and (265,)"
     ]
    }
   ],
   "source": [
    "plt.plot(x,y[::-1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd42853c",
   "metadata": {},
   "source": [
    "Erro! Os vetores não têm dimensões idênticas! O que fazer? Precisamos investigar os dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d22d8582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 860.,  920., 1399., 1490., 1491., 1495., 1519., 1575., 1583.,\n",
       "       1600., 1621., 1623., 1628., 1632., 1636., 1637., 1647., 1654.,\n",
       "       1662., 1668., 1671., 1688., 1704., 1715., 1716., 1723., 1724.,\n",
       "       1740., 1741., 1749., 1750., 1751., 1753., 1766., 1768., 1769.,\n",
       "       1773., 1775., 1776., 1779., 1781., 1784., 1785., 1787., 1790.,\n",
       "       1791., 1792., 1793., 1794., 1795., 1796., 1797., 1798., 1801.,\n",
       "       1803., 1804., 1805., 1806., 1807., 1808., 1809., 1810., 1811.,\n",
       "       1812., 1813., 1814., 1815., 1817., 1818., 1819., 1820., 1821.,\n",
       "       1822., 1823., 1824., 1825., 1826., 1827., 1828., 1829., 1830.,\n",
       "       1831., 1832., 1833., 1834., 1835., 1836., 1837., 1838., 1839.,\n",
       "       1840., 1841., 1842., 1843., 1844., 1845., 1846., 1847., 1848.,\n",
       "       1849., 1850., 1851., 1852., 1853., 1854., 1855., 1856., 1857.,\n",
       "       1858., 1859., 1860., 1861., 1862., 1863., 1864., 1865., 1866.,\n",
       "       1867., 1868., 1869., 1870., 1871., 1872., 1873., 1874., 1875.,\n",
       "       1876., 1877., 1878., 1879., 1880., 1881., 1882., 1883., 1884.,\n",
       "       1885., 1886., 1887., 1888., 1889., 1890., 1891., 1892., 1893.,\n",
       "       1894., 1895., 1896., 1897., 1898., 1899., 1900., 1901., 1902.,\n",
       "       1903., 1904., 1905., 1906., 1907., 1908., 1909., 1910., 1911.,\n",
       "       1912., 1913., 1914., 1915., 1916., 1917., 1918., 1919., 1920.,\n",
       "       1921., 1922., 1923., 1924., 1925., 1926., 1927., 1928., 1929.,\n",
       "       1930., 1931., 1932., 1933., 1934., 1935., 1936., 1937., 1938.,\n",
       "       1939., 1940., 1941., 1942., 1943., 1944., 1945., 1946., 1947.,\n",
       "       1948., 1949., 1950., 1951., 1952., 1953., 1954., 1955., 1956.,\n",
       "       1957., 1958., 1959., 1960., 1961., 1962., 1963., 1964., 1965.,\n",
       "       1966., 1967., 1968., 1969., 1970., 1971., 1972., 1973., 1974.,\n",
       "       1975., 1976., 1977., 1978., 1979., 1980., 1981., 1982., 1983.,\n",
       "       1984., 1985., 1986., 1987., 1988., 1989., 1990., 1991., 1992.,\n",
       "       1993., 1994., 1995., 1996., 1997., 1998., 1999., 2000., 2001.,\n",
       "       2002., 2003., 2004., 2005., 2006., 2007., 2008., 2009., 2010.,\n",
       "       2011., 2012., 2013., 2101.,   nan])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df.year)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279f019b",
   "metadata": {},
   "source": [
    "Há um \"not a number\". Isso ocorrerá muito frequentemente. Procuremos quem tem dados faltantes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c25ae76d",
   "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>name</th>\n",
       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>recclass</th>\n",
       "      <th>mass</th>\n",
       "      <th>fall</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "      <th>GeoLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Northwest Africa 5815</td>\n",
       "      <td>50693</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L5</td>\n",
       "      <td>256.8</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>Cacilandia</td>\n",
       "      <td>5191</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3431</th>\n",
       "      <td>Apache Junction</td>\n",
       "      <td>54566</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Iron, IIIAB</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.45000</td>\n",
       "      <td>-111.51667</td>\n",
       "      <td>(33.45, -111.51667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3462</th>\n",
       "      <td>Asarco Mexicana</td>\n",
       "      <td>2344</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Iron, IIIAB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5008</th>\n",
       "      <td>Aus</td>\n",
       "      <td>4902</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L</td>\n",
       "      <td>30.2</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-26.66667</td>\n",
       "      <td>16.25000</td>\n",
       "      <td>(-26.66667, 16.25)</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38207</th>\n",
       "      <td>Valencia</td>\n",
       "      <td>24147</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H5</td>\n",
       "      <td>33500.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.00000</td>\n",
       "      <td>-0.03333</td>\n",
       "      <td>(39.0, -0.03333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38231</th>\n",
       "      <td>Villa Regina</td>\n",
       "      <td>53827</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Iron, IIIAB</td>\n",
       "      <td>5030.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-39.10000</td>\n",
       "      <td>-67.06667</td>\n",
       "      <td>(-39.1, -67.06667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38308</th>\n",
       "      <td>Wietrzno-Bobrka</td>\n",
       "      <td>24259</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Iron</td>\n",
       "      <td>376.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.41667</td>\n",
       "      <td>21.70000</td>\n",
       "      <td>(49.41667, 21.7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38335</th>\n",
       "      <td>Wiltshire</td>\n",
       "      <td>56143</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H5</td>\n",
       "      <td>92750.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.14967</td>\n",
       "      <td>-1.81000</td>\n",
       "      <td>(51.14967, -1.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45700</th>\n",
       "      <td>Zaragoza</td>\n",
       "      <td>48916</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Iron, IVA-an</td>\n",
       "      <td>162000.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.65000</td>\n",
       "      <td>-0.86667</td>\n",
       "      <td>(41.65, -0.86667)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name     id nametype      recclass      mass   fall  \\\n",
       "37     Northwest Africa 5815  50693    Valid            L5     256.8  Found   \n",
       "1937              Cacilandia   5191    Valid            H6       NaN  Found   \n",
       "3431         Apache Junction  54566    Valid   Iron, IIIAB   25000.0  Found   \n",
       "3462         Asarco Mexicana   2344    Valid   Iron, IIIAB       NaN  Found   \n",
       "5008                     Aus   4902    Valid             L      30.2  Found   \n",
       "...                      ...    ...      ...           ...       ...    ...   \n",
       "38207               Valencia  24147    Valid            H5   33500.0  Found   \n",
       "38231           Villa Regina  53827    Valid   Iron, IIIAB    5030.0  Found   \n",
       "38308        Wietrzno-Bobrka  24259    Valid          Iron     376.0  Found   \n",
       "38335              Wiltshire  56143    Valid            H5   92750.0  Found   \n",
       "45700               Zaragoza  48916    Valid  Iron, IVA-an  162000.0  Found   \n",
       "\n",
       "       year    reclat    reclong          GeoLocation  \n",
       "37      NaN   0.00000    0.00000           (0.0, 0.0)  \n",
       "1937    NaN       NaN        NaN                  NaN  \n",
       "3431    NaN  33.45000 -111.51667  (33.45, -111.51667)  \n",
       "3462    NaN       NaN        NaN                  NaN  \n",
       "5008    NaN -26.66667   16.25000   (-26.66667, 16.25)  \n",
       "...     ...       ...        ...                  ...  \n",
       "38207   NaN  39.00000   -0.03333     (39.0, -0.03333)  \n",
       "38231   NaN -39.10000  -67.06667   (-39.1, -67.06667)  \n",
       "38308   NaN  49.41667   21.70000     (49.41667, 21.7)  \n",
       "38335   NaN  51.14967   -1.81000    (51.14967, -1.81)  \n",
       "45700   NaN  41.65000   -0.86667    (41.65, -0.86667)  \n",
       "\n",
       "[291 rows x 10 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.year.isnull(),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "07ab9711",
   "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>name</th>\n",
       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>recclass</th>\n",
       "      <th>mass</th>\n",
       "      <th>fall</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "      <th>GeoLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Aire-sur-la-Lys</td>\n",
       "      <td>425</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1769.0</td>\n",
       "      <td>50.66667</td>\n",
       "      <td>2.33333</td>\n",
       "      <td>(50.66667, 2.33333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Northwest Africa 5815</td>\n",
       "      <td>50693</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L5</td>\n",
       "      <td>256.800</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Angers</td>\n",
       "      <td>2301</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1822.0</td>\n",
       "      <td>47.46667</td>\n",
       "      <td>-0.55000</td>\n",
       "      <td>(47.46667, -0.55)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Barcelona (stone)</td>\n",
       "      <td>4944</td>\n",
       "      <td>Valid</td>\n",
       "      <td>OC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1704.0</td>\n",
       "      <td>41.36667</td>\n",
       "      <td>2.16667</td>\n",
       "      <td>(41.36667, 2.16667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Belville</td>\n",
       "      <td>5009</td>\n",
       "      <td>Valid</td>\n",
       "      <td>OC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>-32.33333</td>\n",
       "      <td>-64.86667</td>\n",
       "      <td>(-32.33333, -64.86667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45589</th>\n",
       "      <td>Yamato 984028</td>\n",
       "      <td>40648</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Martian (shergottite)</td>\n",
       "      <td>12.342</td>\n",
       "      <td>Found</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45660</th>\n",
       "      <td>Yambo no. 2</td>\n",
       "      <td>30346</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L3</td>\n",
       "      <td>3.200</td>\n",
       "      <td>Found</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45692</th>\n",
       "      <td>Zacatecas (1969)</td>\n",
       "      <td>30382</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Iron, IIIAB</td>\n",
       "      <td>6660.000</td>\n",
       "      <td>Found</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45698</th>\n",
       "      <td>Zapata County</td>\n",
       "      <td>30393</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Iron</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Found</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>-99.00000</td>\n",
       "      <td>(27.0, -99.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45700</th>\n",
       "      <td>Zaragoza</td>\n",
       "      <td>48916</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Iron, IVA-an</td>\n",
       "      <td>162000.000</td>\n",
       "      <td>Found</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.65000</td>\n",
       "      <td>-0.86667</td>\n",
       "      <td>(41.65, -0.86667)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7601 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name     id nametype               recclass  \\\n",
       "12           Aire-sur-la-Lys    425    Valid                Unknown   \n",
       "37     Northwest Africa 5815  50693    Valid                     L5   \n",
       "38                    Angers   2301    Valid                     L6   \n",
       "76         Barcelona (stone)   4944    Valid                     OC   \n",
       "93                  Belville   5009    Valid                     OC   \n",
       "...                      ...    ...      ...                    ...   \n",
       "45589          Yamato 984028  40648    Valid  Martian (shergottite)   \n",
       "45660            Yambo no. 2  30346    Valid                     L3   \n",
       "45692       Zacatecas (1969)  30382    Valid            Iron, IIIAB   \n",
       "45698          Zapata County  30393    Valid                   Iron   \n",
       "45700               Zaragoza  48916    Valid           Iron, IVA-an   \n",
       "\n",
       "             mass   fall    year    reclat   reclong             GeoLocation  \n",
       "12            NaN   Fell  1769.0  50.66667   2.33333     (50.66667, 2.33333)  \n",
       "37        256.800  Found     NaN   0.00000   0.00000              (0.0, 0.0)  \n",
       "38            NaN   Fell  1822.0  47.46667  -0.55000       (47.46667, -0.55)  \n",
       "76            NaN   Fell  1704.0  41.36667   2.16667     (41.36667, 2.16667)  \n",
       "93            NaN   Fell  1937.0 -32.33333 -64.86667  (-32.33333, -64.86667)  \n",
       "...           ...    ...     ...       ...       ...                     ...  \n",
       "45589      12.342  Found  1998.0       NaN       NaN                     NaN  \n",
       "45660       3.200  Found  1975.0       NaN       NaN                     NaN  \n",
       "45692    6660.000  Found  1969.0       NaN       NaN                     NaN  \n",
       "45698         NaN  Found  1930.0  27.00000 -99.00000           (27.0, -99.0)  \n",
       "45700  162000.000  Found     NaN  41.65000  -0.86667       (41.65, -0.86667)  \n",
       "\n",
       "[7601 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "92dd51c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2233139d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>recclass</th>\n",
       "      <th>mass</th>\n",
       "      <th>fall</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "      <th>GeoLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aachen</td>\n",
       "      <td>1</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>50.77500</td>\n",
       "      <td>6.08333</td>\n",
       "      <td>(50.775, 6.08333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aarhus</td>\n",
       "      <td>2</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H6</td>\n",
       "      <td>720.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>56.18333</td>\n",
       "      <td>10.23333</td>\n",
       "      <td>(56.18333, 10.23333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abee</td>\n",
       "      <td>6</td>\n",
       "      <td>Valid</td>\n",
       "      <td>EH4</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>54.21667</td>\n",
       "      <td>-113.00000</td>\n",
       "      <td>(54.21667, -113.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acapulco</td>\n",
       "      <td>10</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Acapulcoite</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>16.88333</td>\n",
       "      <td>-99.90000</td>\n",
       "      <td>(16.88333, -99.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Achiras</td>\n",
       "      <td>370</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>780.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>-33.16667</td>\n",
       "      <td>-64.95000</td>\n",
       "      <td>(-33.16667, -64.95)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45711</th>\n",
       "      <td>Zillah 002</td>\n",
       "      <td>31356</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Eucrite</td>\n",
       "      <td>172.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>29.03700</td>\n",
       "      <td>17.01850</td>\n",
       "      <td>(29.037, 17.0185)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45712</th>\n",
       "      <td>Zinder</td>\n",
       "      <td>30409</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Pallasite, ungrouped</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>13.78333</td>\n",
       "      <td>8.96667</td>\n",
       "      <td>(13.78333, 8.96667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45713</th>\n",
       "      <td>Zlin</td>\n",
       "      <td>30410</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Found</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>49.25000</td>\n",
       "      <td>17.66667</td>\n",
       "      <td>(49.25, 17.66667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45714</th>\n",
       "      <td>Zubkovsky</td>\n",
       "      <td>31357</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>2167.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>49.78917</td>\n",
       "      <td>41.50460</td>\n",
       "      <td>(49.78917, 41.5046)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45715</th>\n",
       "      <td>Zulu Queen</td>\n",
       "      <td>30414</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L3.7</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>33.98333</td>\n",
       "      <td>-115.68333</td>\n",
       "      <td>(33.98333, -115.68333)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38115 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name     id nametype              recclass      mass   fall  \\\n",
       "0          Aachen      1    Valid                    L5      21.0   Fell   \n",
       "1          Aarhus      2    Valid                    H6     720.0   Fell   \n",
       "2            Abee      6    Valid                   EH4  107000.0   Fell   \n",
       "3        Acapulco     10    Valid           Acapulcoite    1914.0   Fell   \n",
       "4         Achiras    370    Valid                    L6     780.0   Fell   \n",
       "...           ...    ...      ...                   ...       ...    ...   \n",
       "45711  Zillah 002  31356    Valid               Eucrite     172.0  Found   \n",
       "45712      Zinder  30409    Valid  Pallasite, ungrouped      46.0  Found   \n",
       "45713        Zlin  30410    Valid                    H4       3.3  Found   \n",
       "45714   Zubkovsky  31357    Valid                    L6    2167.0  Found   \n",
       "45715  Zulu Queen  30414    Valid                  L3.7     200.0  Found   \n",
       "\n",
       "         year    reclat    reclong             GeoLocation  \n",
       "0      1880.0  50.77500    6.08333       (50.775, 6.08333)  \n",
       "1      1951.0  56.18333   10.23333    (56.18333, 10.23333)  \n",
       "2      1952.0  54.21667 -113.00000      (54.21667, -113.0)  \n",
       "3      1976.0  16.88333  -99.90000       (16.88333, -99.9)  \n",
       "4      1902.0 -33.16667  -64.95000     (-33.16667, -64.95)  \n",
       "...       ...       ...        ...                     ...  \n",
       "45711  1990.0  29.03700   17.01850       (29.037, 17.0185)  \n",
       "45712  1999.0  13.78333    8.96667     (13.78333, 8.96667)  \n",
       "45713  1939.0  49.25000   17.66667       (49.25, 17.66667)  \n",
       "45714  2003.0  49.78917   41.50460     (49.78917, 41.5046)  \n",
       "45715  1976.0  33.98333 -115.68333  (33.98333, -115.68333)  \n",
       "\n",
       "[38115 rows x 10 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "767ecea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1979.0    3045\n",
       "1988.0    2295\n",
       "1998.0    2147\n",
       "2003.0    1754\n",
       "2006.0    1616\n",
       "          ... \n",
       "1583.0       1\n",
       "1723.0       1\n",
       "1740.0       1\n",
       "1833.0       1\n",
       "1792.0       1\n",
       "Name: year, Length: 253, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b625ddc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(253,)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df.year).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "204851ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(np.unique(df.year)[::-1],df['year'].value_counts(),'.')\n",
    "plt.xlabel(r'Ano',fontsize=13)\n",
    "plt.ylabel(r'Número de meteoritos encontrados',fontsize=13)\n",
    "#plt.yscale('log')\n",
    "plt.tight_layout()\n",
    "# para salvar:\n",
    "# plt.savefig('meteoritos.pdf')\n",
    "plt.show()\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24725ba1",
   "metadata": {},
   "source": [
    "## Utilizando máscaras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18e270c",
   "metadata": {},
   "source": [
    "Selecionando tipos distintos de asteróides:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "262330f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Acapulcoite', 'Acapulcoite/Lodranite', 'Acapulcoite/lodranite',\n",
       "       'Achondrite-prim', 'Achondrite-ung', 'Angrite', 'Aubrite',\n",
       "       'Aubrite-an', 'Brachinite', 'C', 'C1/2-ung', 'C2', 'C2-ung',\n",
       "       'C3-ung', 'C3.0-ung', 'C4', 'C4-ung', 'C4/5', 'C5/6-ung', 'C6',\n",
       "       'CB', 'CBa', 'CBb', 'CH/CBb', 'CH3', 'CI1', 'CK', 'CK3', 'CK3-an',\n",
       "       'CK4', 'CK4-an', 'CK4/5', 'CK5', 'CK5/6', 'CK6', 'CM', 'CM-an',\n",
       "       'CM1', 'CM1/2', 'CM2', 'CO3', 'CO3.0', 'CO3.1', 'CO3.2', 'CO3.3',\n",
       "       'CO3.4', 'CO3.5', 'CO3.6', 'CO3.7', 'CO3.8', 'CR', 'CR-an', 'CR1',\n",
       "       'CR2', 'CR2-an', 'CR7', 'CV2', 'CV3', 'CV3-an',\n",
       "       'Chondrite-fusion crust', 'Chondrite-ung', 'Diogenite',\n",
       "       'Diogenite-an', 'Diogenite-olivine', 'Diogenite-pm', 'E', 'E-an',\n",
       "       'E3', 'E3-an', 'E4', 'E5', 'E5-an', 'E6', 'EH', 'EH-imp melt',\n",
       "       'EH3', 'EH3/4-an', 'EH4', 'EH4/5', 'EH5', 'EH6', 'EH6-an', 'EH7',\n",
       "       'EH7-an', 'EL-melt rock', 'EL3', 'EL4', 'EL4/5', 'EL5', 'EL6',\n",
       "       'EL6/7', 'EL7', 'Enst achon-ung', 'Eucrite', 'Eucrite-Mg rich',\n",
       "       'Eucrite-an', 'Eucrite-br', 'Eucrite-cm', 'Eucrite-mmict',\n",
       "       'Eucrite-pmict', 'Eucrite-unbr', 'Fusion crust', 'H', 'H(5?)',\n",
       "       'H(?)4', 'H(L)3', 'H(L)3-an', 'H-an', 'H-imp melt',\n",
       "       'H-melt breccia', 'H-melt rock', 'H-metal', 'H/L3', 'H/L3.5',\n",
       "       'H/L3.6', 'H/L3.9', 'H/L4', 'H/L4-5', 'H/L5', 'H/L6', 'H/L~4',\n",
       "       'H3', 'H3 ', 'H3-4', 'H3-5', 'H3-6', 'H3-an', 'H3.0', 'H3.0-3.4',\n",
       "       'H3.05', 'H3.1', 'H3.10', 'H3.2', 'H3.2-3.7', 'H3.2-6', 'H3.2-an',\n",
       "       'H3.3', 'H3.4', 'H3.4-5', 'H3.4/3.5', 'H3.5', 'H3.5-4', 'H3.6',\n",
       "       'H3.6-6', 'H3.7', 'H3.7-5', 'H3.7-6', 'H3.7/3.8', 'H3.8', 'H3.8-4',\n",
       "       'H3.8-5', 'H3.8-6', 'H3.8-an', 'H3.8/3.9', 'H3.8/4', 'H3.9',\n",
       "       'H3.9-5', 'H3.9-6', 'H3.9/4', 'H3/4', 'H4', 'H4 ', 'H4(?)', 'H4-5',\n",
       "       'H4-6', 'H4-an', 'H4-melt breccia', 'H4/5', 'H4/6', 'H5', 'H5 ',\n",
       "       'H5-6', 'H5-7', 'H5-an', 'H5-melt breccia', 'H5/6', 'H6', 'H6 ',\n",
       "       'H6-melt breccia', 'H7', 'H?', 'Howardite', 'Howardite-an', 'H~4',\n",
       "       'H~4/5', 'H~5', 'H~6', 'Iron', 'Iron, IAB complex', 'Iron, IAB-MG',\n",
       "       'Iron, IAB-an', 'Iron, IAB-sHH', 'Iron, IAB-sHL',\n",
       "       'Iron, IAB-sHL-an', 'Iron, IAB-sLH', 'Iron, IAB-sLL',\n",
       "       'Iron, IAB-sLM', 'Iron, IAB-ung', 'Iron, IAB?', 'Iron, IC',\n",
       "       'Iron, IC-an', 'Iron, IIAB', 'Iron, IIAB-an', 'Iron, IIC',\n",
       "       'Iron, IID', 'Iron, IID-an', 'Iron, IIE', 'Iron, IIE-an',\n",
       "       'Iron, IIF', 'Iron, IIG', 'Iron, IIIAB', 'Iron, IIIAB-an',\n",
       "       'Iron, IIIAB?', 'Iron, IIIE', 'Iron, IIIE-an', 'Iron, IIIF',\n",
       "       'Iron, IVA', 'Iron, IVA-an', 'Iron, IVB', 'Iron, ungrouped', 'K',\n",
       "       'K3', 'L', 'L(?)3', 'L(LL)3', 'L(LL)3.05', 'L(LL)3.5-3.7',\n",
       "       'L(LL)5', 'L(LL)6', 'L-imp melt', 'L-melt breccia', 'L-melt rock',\n",
       "       'L-metal', 'L/LL', 'L/LL(?)3', 'L/LL3', 'L/LL3-5', 'L/LL3-6',\n",
       "       'L/LL3.10', 'L/LL3.2', 'L/LL3.4', 'L/LL3.6/3.7', 'L/LL4',\n",
       "       'L/LL4-6', 'L/LL4/5', 'L/LL5', 'L/LL5-6', 'L/LL5/6', 'L/LL6',\n",
       "       'L/LL6-an', 'L/LL~4', 'L/LL~5', 'L/LL~6', 'L3', 'L3-4', 'L3-5',\n",
       "       'L3-6', 'L3-melt breccia', 'L3.0', 'L3.0-3.7', 'L3.0-3.9', 'L3.00',\n",
       "       'L3.05', 'L3.1', 'L3.10', 'L3.2', 'L3.2-3.5', 'L3.2-3.6', 'L3.3',\n",
       "       'L3.3-3.5', 'L3.3-3.6', 'L3.3-3.7', 'L3.4', 'L3.4-3.7', 'L3.5',\n",
       "       'L3.5-3.7', 'L3.5-3.8', 'L3.5-3.9', 'L3.5-5', 'L3.6', 'L3.6-4',\n",
       "       'L3.7', 'L3.7-3.9', 'L3.7-4', 'L3.7-6', 'L3.7/3.8', 'L3.8',\n",
       "       'L3.8-6', 'L3.8-an', 'L3.9', 'L3.9-5', 'L3.9-6', 'L3.9/4', 'L3/4',\n",
       "       'L4', 'L4 ', 'L4-5', 'L4-6', 'L4-an', 'L4-melt breccia', 'L4/5',\n",
       "       'L5', 'L5 ', 'L5-6', 'L5-7', 'L5-melt breccia', 'L5/6', 'L6',\n",
       "       'L6 ', 'L6-melt breccia', 'L6/7', 'L7', 'LL', 'LL(L)3',\n",
       "       'LL-imp melt', 'LL-melt breccia', 'LL-melt rock', 'LL3', 'LL3-5',\n",
       "       'LL3-6', 'LL3.0', 'LL3.00', 'LL3.05', 'LL3.1', 'LL3.1-3.5',\n",
       "       'LL3.15', 'LL3.2', 'LL3.3', 'LL3.4', 'LL3.5', 'LL3.6', 'LL3.7',\n",
       "       'LL3.7-6', 'LL3.8', 'LL3.8-4', 'LL3.8-6', 'LL3.9', 'LL3.9/4',\n",
       "       'LL3/4', 'LL4', 'LL4-5', 'LL4-6', 'LL4/5', 'LL5', 'LL5-6', 'LL5-7',\n",
       "       'LL5/6', 'LL6', 'LL6 ', 'LL6(?)', 'LL6-an', 'LL6-melt breccia',\n",
       "       'LL7', 'LL7(?)', 'LL~3', 'LL~5', 'LL~6', 'Lodranite',\n",
       "       'Lodranite-an', 'Lunar', 'Lunar (anorth)', 'Lunar (bas. breccia)',\n",
       "       'Lunar (bas/anor)', 'Lunar (basalt)', 'Lunar (feldsp. breccia)',\n",
       "       'Lunar (gabbro)', 'Lunar (norite)', 'L~3', 'L~4', 'L~4-6', 'L~5',\n",
       "       'L~6', 'Martian (OPX)', 'Martian (basaltic breccia)',\n",
       "       'Martian (chassignite)', 'Martian (nakhlite)',\n",
       "       'Martian (shergottite)', 'Mesosiderite', 'Mesosiderite-A',\n",
       "       'Mesosiderite-A1', 'Mesosiderite-A2', 'Mesosiderite-A3',\n",
       "       'Mesosiderite-A3/4', 'Mesosiderite-A4', 'Mesosiderite-B',\n",
       "       'Mesosiderite-B1', 'Mesosiderite-B2', 'Mesosiderite-B4',\n",
       "       'Mesosiderite-C', 'Mesosiderite-C2', 'Mesosiderite-an',\n",
       "       'Mesosiderite?', 'OC', 'Pallasite', 'Pallasite, PES',\n",
       "       'Pallasite, PMG', 'Pallasite, PMG-an', 'Pallasite, ungrouped',\n",
       "       'Pallasite?', 'R', 'R3', 'R3-4', 'R3-5', 'R3-6', 'R3.5-4',\n",
       "       'R3.5-6', 'R3.6', 'R3.8', 'R3.8-5', 'R3.8-6', 'R3.9', 'R4', 'R5',\n",
       "       'R6', 'Relict OC', 'Relict iron', 'Stone-uncl', 'Stone-ung',\n",
       "       'Ureilite', 'Ureilite-an', 'Ureilite-pmict', 'Winonaite'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df.recclass)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a7e458",
   "metadata": {},
   "source": [
    "Criando máscara para um tipo específico: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fe37ec40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "45711    False\n",
       "45712    False\n",
       "45713    False\n",
       "45714    False\n",
       "45715    False\n",
       "Name: recclass, Length: 38115, dtype: bool"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df.recclass == 'Iron'\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6de1f11d",
   "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>name</th>\n",
       "      <th>id</th>\n",
       "      <th>nametype</th>\n",
       "      <th>recclass</th>\n",
       "      <th>mass</th>\n",
       "      <th>fall</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "      <th>GeoLocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aachen</td>\n",
       "      <td>1</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>50.77500</td>\n",
       "      <td>6.08333</td>\n",
       "      <td>(50.775, 6.08333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aarhus</td>\n",
       "      <td>2</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H6</td>\n",
       "      <td>720.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>56.18333</td>\n",
       "      <td>10.23333</td>\n",
       "      <td>(56.18333, 10.23333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abee</td>\n",
       "      <td>6</td>\n",
       "      <td>Valid</td>\n",
       "      <td>EH4</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>54.21667</td>\n",
       "      <td>-113.00000</td>\n",
       "      <td>(54.21667, -113.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Acapulco</td>\n",
       "      <td>10</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Acapulcoite</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>16.88333</td>\n",
       "      <td>-99.90000</td>\n",
       "      <td>(16.88333, -99.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Achiras</td>\n",
       "      <td>370</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>780.0</td>\n",
       "      <td>Fell</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>-33.16667</td>\n",
       "      <td>-64.95000</td>\n",
       "      <td>(-33.16667, -64.95)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45711</th>\n",
       "      <td>Zillah 002</td>\n",
       "      <td>31356</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Eucrite</td>\n",
       "      <td>172.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>29.03700</td>\n",
       "      <td>17.01850</td>\n",
       "      <td>(29.037, 17.0185)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45712</th>\n",
       "      <td>Zinder</td>\n",
       "      <td>30409</td>\n",
       "      <td>Valid</td>\n",
       "      <td>Pallasite, ungrouped</td>\n",
       "      <td>46.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>13.78333</td>\n",
       "      <td>8.96667</td>\n",
       "      <td>(13.78333, 8.96667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45713</th>\n",
       "      <td>Zlin</td>\n",
       "      <td>30410</td>\n",
       "      <td>Valid</td>\n",
       "      <td>H4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>Found</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>49.25000</td>\n",
       "      <td>17.66667</td>\n",
       "      <td>(49.25, 17.66667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45714</th>\n",
       "      <td>Zubkovsky</td>\n",
       "      <td>31357</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L6</td>\n",
       "      <td>2167.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>49.78917</td>\n",
       "      <td>41.50460</td>\n",
       "      <td>(49.78917, 41.5046)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45715</th>\n",
       "      <td>Zulu Queen</td>\n",
       "      <td>30414</td>\n",
       "      <td>Valid</td>\n",
       "      <td>L3.7</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Found</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>33.98333</td>\n",
       "      <td>-115.68333</td>\n",
       "      <td>(33.98333, -115.68333)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38044 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name     id nametype              recclass      mass   fall  \\\n",
       "0          Aachen      1    Valid                    L5      21.0   Fell   \n",
       "1          Aarhus      2    Valid                    H6     720.0   Fell   \n",
       "2            Abee      6    Valid                   EH4  107000.0   Fell   \n",
       "3        Acapulco     10    Valid           Acapulcoite    1914.0   Fell   \n",
       "4         Achiras    370    Valid                    L6     780.0   Fell   \n",
       "...           ...    ...      ...                   ...       ...    ...   \n",
       "45711  Zillah 002  31356    Valid               Eucrite     172.0  Found   \n",
       "45712      Zinder  30409    Valid  Pallasite, ungrouped      46.0  Found   \n",
       "45713        Zlin  30410    Valid                    H4       3.3  Found   \n",
       "45714   Zubkovsky  31357    Valid                    L6    2167.0  Found   \n",
       "45715  Zulu Queen  30414    Valid                  L3.7     200.0  Found   \n",
       "\n",
       "         year    reclat    reclong             GeoLocation  \n",
       "0      1880.0  50.77500    6.08333       (50.775, 6.08333)  \n",
       "1      1951.0  56.18333   10.23333    (56.18333, 10.23333)  \n",
       "2      1952.0  54.21667 -113.00000      (54.21667, -113.0)  \n",
       "3      1976.0  16.88333  -99.90000       (16.88333, -99.9)  \n",
       "4      1902.0 -33.16667  -64.95000     (-33.16667, -64.95)  \n",
       "...       ...       ...        ...                     ...  \n",
       "45711  1990.0  29.03700   17.01850       (29.037, 17.0185)  \n",
       "45712  1999.0  13.78333    8.96667     (13.78333, 8.96667)  \n",
       "45713  1939.0  49.25000   17.66667       (49.25, 17.66667)  \n",
       "45714  2003.0  49.78917   41.50460     (49.78917, 41.5046)  \n",
       "45715  1976.0  33.98333 -115.68333  (33.98333, -115.68333)  \n",
       "\n",
       "[38044 rows x 10 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.loc[mask,:]\n",
    "df.loc[~mask,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e333eb7",
   "metadata": {},
   "source": [
    "## Calculando estatísticas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b7ec574",
   "metadata": {},
   "source": [
    "Podemos nos perguntar agora sobre a distribuição da massa dos meteoritos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4a4ea20b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            21.0\n",
       "1           720.0\n",
       "2        107000.0\n",
       "3          1914.0\n",
       "4           780.0\n",
       "           ...   \n",
       "45711       172.0\n",
       "45712        46.0\n",
       "45713         3.3\n",
       "45714      2167.0\n",
       "45715       200.0\n",
       "Name: mass, Length: 38115, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecee2c1f",
   "metadata": {},
   "source": [
    "Antes disso verifiquemos se há problemas nos dados: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f4623217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(df.mass.isna())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6644a5a4",
   "metadata": {},
   "source": [
    "## Sumário do data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8f77f2a8",
   "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>id</th>\n",
       "      <th>mass</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38115.000000</td>\n",
       "      <td>3.811500e+04</td>\n",
       "      <td>38115.000000</td>\n",
       "      <td>38115.000000</td>\n",
       "      <td>38115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25343.139000</td>\n",
       "      <td>1.560071e+04</td>\n",
       "      <td>1989.993913</td>\n",
       "      <td>-39.596529</td>\n",
       "      <td>61.309359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17395.360205</td>\n",
       "      <td>6.286817e+05</td>\n",
       "      <td>25.469892</td>\n",
       "      <td>46.175830</td>\n",
       "      <td>80.777583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>860.000000</td>\n",
       "      <td>-87.366670</td>\n",
       "      <td>-165.433330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10831.500000</td>\n",
       "      <td>6.630000e+00</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>-76.716670</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21732.000000</td>\n",
       "      <td>2.909000e+01</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>-71.500000</td>\n",
       "      <td>35.666670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39887.500000</td>\n",
       "      <td>1.872900e+02</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>157.166670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>57458.000000</td>\n",
       "      <td>6.000000e+07</td>\n",
       "      <td>2101.000000</td>\n",
       "      <td>81.166670</td>\n",
       "      <td>178.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          mass          year        reclat       reclong\n",
       "count  38115.000000  3.811500e+04  38115.000000  38115.000000  38115.000000\n",
       "mean   25343.139000  1.560071e+04   1989.993913    -39.596529     61.309359\n",
       "std    17395.360205  6.286817e+05     25.469892     46.175830     80.777583\n",
       "min        1.000000  0.000000e+00    860.000000    -87.366670   -165.433330\n",
       "25%    10831.500000  6.630000e+00   1986.000000    -76.716670      0.000000\n",
       "50%    21732.000000  2.909000e+01   1996.000000    -71.500000     35.666670\n",
       "75%    39887.500000  1.872900e+02   2002.000000      0.000000    157.166670\n",
       "max    57458.000000  6.000000e+07   2101.000000     81.166670    178.200000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "be04ace9",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_year = df.year<2023\n",
    "df = df.loc[mask_year,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cc25dafc",
   "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>id</th>\n",
       "      <th>mass</th>\n",
       "      <th>year</th>\n",
       "      <th>reclat</th>\n",
       "      <th>reclong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38114.000000</td>\n",
       "      <td>3.811400e+04</td>\n",
       "      <td>38114.000000</td>\n",
       "      <td>38114.000000</td>\n",
       "      <td>38114.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25342.304481</td>\n",
       "      <td>1.560111e+04</td>\n",
       "      <td>1989.991001</td>\n",
       "      <td>-39.597567</td>\n",
       "      <td>61.310968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17394.825419</td>\n",
       "      <td>6.286900e+05</td>\n",
       "      <td>25.463878</td>\n",
       "      <td>46.175991</td>\n",
       "      <td>80.778032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>860.000000</td>\n",
       "      <td>-87.366670</td>\n",
       "      <td>-165.433330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10831.250000</td>\n",
       "      <td>6.630000e+00</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>-76.716670</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21731.500000</td>\n",
       "      <td>2.908500e+01</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>-71.500000</td>\n",
       "      <td>35.666670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39886.500000</td>\n",
       "      <td>1.873350e+02</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>157.166670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>57458.000000</td>\n",
       "      <td>6.000000e+07</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>81.166670</td>\n",
       "      <td>178.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          mass          year        reclat       reclong\n",
       "count  38114.000000  3.811400e+04  38114.000000  38114.000000  38114.000000\n",
       "mean   25342.304481  1.560111e+04   1989.991001    -39.597567     61.310968\n",
       "std    17394.825419  6.286900e+05     25.463878     46.175991     80.778032\n",
       "min        1.000000  0.000000e+00    860.000000    -87.366670   -165.433330\n",
       "25%    10831.250000  6.630000e+00   1986.000000    -76.716670      0.000000\n",
       "50%    21731.500000  2.908500e+01   1996.000000    -71.500000     35.666670\n",
       "75%    39886.500000  1.873350e+02   2002.000000      0.000000    157.166670\n",
       "max    57458.000000  6.000000e+07   2013.000000     81.166670    178.200000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caee6033",
   "metadata": {},
   "source": [
    "## Criando histogramas e boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201f63a0",
   "metadata": {},
   "source": [
    "Criando histogramas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fe193aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "x = np.random.normal(size=10000)  #sorteando variáveis normais. \n",
    "\n",
    "x_theo = np.linspace(-4,4,1000)\n",
    "dist_normal = norm()\n",
    "plt.plot(x_theo,dist_normal.pdf(x_theo),color='C0',label=r'$e^{-x^2}/\\sqrt{2\\pi}$')\n",
    "\n",
    "plt.hist(x,50,density=True,edgecolor='black',\\\n",
    "         facecolor='lightgray',\\\n",
    "         linewidth=0.5,\\\n",
    "         label=r'Histograma (dados)')  #check for density=True\n",
    "plt.plot(x,len(x)*[0.01],'|')\n",
    "plt.xlabel(r'$x$',fontsize=13)\n",
    "plt.ylabel(r'Frequência',fontsize=13)\n",
    "plt.legend(loc='upper right',fontsize=13)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4354cafa",
   "metadata": {},
   "source": [
    "Box plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "2a02854b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885a54d6",
   "metadata": {},
   "source": [
    "Agora para o problema dos meteoritos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "420c99b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(df.mass,50,density=True,edgecolor='black',\\\n",
    "         facecolor='lightgray',\\\n",
    "         linewidth=0.5,\\\n",
    "         label=r'Histograma (dados)')  #check for density=True\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d5d772",
   "metadata": {},
   "source": [
    "O que deu errado?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "b92b5a98",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.811400e+04\n",
       "mean     1.560111e+04\n",
       "std      6.286900e+05\n",
       "min      0.000000e+00\n",
       "25%      6.630000e+00\n",
       "50%      2.908500e+01\n",
       "75%      1.873350e+02\n",
       "max      6.000000e+07\n",
       "Name: mass, dtype: float64"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mass.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290515ee",
   "metadata": {},
   "source": [
    "Ordens de grandeza muito discrepantes..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "a9b04644",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.figure(figsize=(10,6))\n",
    "# plt.hist(np.log10(df.mass),50,density=True,edgecolor='black',\\\n",
    "#          facecolor='lightgray',\\\n",
    "#          linewidth=0.5,\\\n",
    "#          label=r'Histograma (dados)')  #check for density=True\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "8f4dc6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_mass = df.mass>0\n",
    "masses = df.loc[mask_mass,['mass']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "801180e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(np.log10(masses),50,density=True,edgecolor='black',\\\n",
    "         facecolor='lightgray',\\\n",
    "         linewidth=0.5,\\\n",
    "         label=r'Histograma (dados)')  #check for density=True\n",
    "\n",
    "dist_normal_test = norm(loc=np.log10(masses).mean(),scale=np.log10(masses).std())\n",
    "x_theo_test = np.linspace(-2,8,1000)\n",
    "\n",
    "plt.plot(x_theo_test,dist_normal_test.pdf(x_theo_test),color='C0',label=r'$e^{-x^2/2}/\\sqrt{2\\pi}$')\n",
    "\n",
    "plt.plot(np.log10(masses),len(np.log10(masses))*[0.01],'|')\n",
    "plt.legend(loc='upper right',fontsize=13)\n",
    "plt.xlabel(r'Massa dos meteoritos (g)',fontsize=13)\n",
    "plt.ylabel(r'Frequência',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd24149",
   "metadata": {},
   "source": [
    "## Comparando populações de meteoritos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "cf7ca50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "L6                 7519\n",
       "H5                 6243\n",
       "H6                 3898\n",
       "H4                 3880\n",
       "L5                 3264\n",
       "LL5                2199\n",
       "LL6                1660\n",
       "L4                  939\n",
       "H4/5                395\n",
       "CM2                 330\n",
       "H3                  313\n",
       "CO3                 308\n",
       "Iron, IIIAB         270\n",
       "L3                  268\n",
       "LL                  223\n",
       "Ureilite            214\n",
       "E3                  205\n",
       "LL4                 198\n",
       "CV3                 184\n",
       "Howardite           179\n",
       "Diogenite           178\n",
       "Eucrite-pmict       169\n",
       "H5/6                166\n",
       "CR2                 116\n",
       "Eucrite             115\n",
       "Iron, IIAB          111\n",
       "Mesosiderite        107\n",
       "H~5                 106\n",
       "Iron, ungrouped     105\n",
       "LL3                  88\n",
       "Name: recclass, dtype: int64"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['recclass'].value_counts().head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "2e298738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mask1 = df.recclass=='Diogenite'\n",
    "mask2 = df.recclass=='Eucrite'\n",
    "\n",
    "masses1 = df.mass[mask1]\n",
    "masses2 = df.mass[mask2]\n",
    "\n",
    "dict_masses = dict()\n",
    "dict_masses['Diogenite'] = np.log10(masses1)\n",
    "dict_masses['Eucrite'] = np.log10(masses2)\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.boxplot(dict_masses.values())\n",
    "ax.set_xticklabels(dict_masses.keys(),fontsize=13)\n",
    "plt.ylabel(r'Log Massa',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e207e1",
   "metadata": {},
   "source": [
    "## Para mais tipos: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "38dbcca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             L5\n",
       "1             H6\n",
       "2            EH4\n",
       "3    Acapulcoite\n",
       "4             L6\n",
       "5            EH4\n",
       "6          LL3-6\n",
       "7             H5\n",
       "8             L6\n",
       "9              L\n",
       "Name: recclass, dtype: object"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meteoritos = df.recclass[0:10]\n",
    "meteoritos\n",
    "\n",
    "#getask1 = df.recclass=='Diogenite'\n",
    "# mask2 = df.recclass=='Eucrite'\n",
    "\n",
    "# masses1 = df.mass[mask1]\n",
    "# masses2 = df.mass[mask2]\n",
    "\n",
    "# dict_masses = dict()\n",
    "# dict_masses['Diogenite'] = np.log10(masses1)\n",
    "# dict_masses['Eucrite'] = np.log10(masses2)\n",
    "\n",
    "# fig = plt.figure(figsize=(12,8))\n",
    "# ax = fig.add_subplot(111)\n",
    "\n",
    "# ax.boxplot(dict_masses.values())\n",
    "# ax.set_xticklabels(dict_masses.keys(),fontsize=13)\n",
    "# plt.ylabel(r'Log Massa',fontsize=13)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "0069e8ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_masses = dict()\n",
    "for meteorito in meteoritos:\n",
    "    mask_meteorito = df.recclass==meteorito\n",
    "    masses = df.mass[mask_meteorito]\n",
    "    dict_masses[meteorito] = np.log10(masses)\n",
    "    \n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.boxplot(dict_masses.values())\n",
    "ax.set_xticklabels(dict_masses.keys(),fontsize=13)\n",
    "plt.ylabel(r'Log Massa',fontsize=15)\n",
    "plt.xlabel(r'Tipo do meteorito',fontsize=15)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}