From 54c5c7a7df830148caae7f2b54aafa4596fa625c Mon Sep 17 00:00:00 2001 From: Detecprof Date: Thu, 5 Sep 2024 00:07:18 -0300 Subject: [PATCH] Projeto-Guiado-II-Denise.ipynb --- .../Projeto-Denise-atividade de sala.ipynb | 2998 ++++++++++++ .../Projeto-Guiado-II-Denise.ipynb | 4089 +++++++++++++++++ exercicios/projeto-guiado/README.md | 15 +- .../projeto-guiado/dados_meteorologicos.db | Bin 0 -> 835584 bytes .../projeto_leticia_vidal.ipynb | 2 +- 5 files changed, 7096 insertions(+), 8 deletions(-) create mode 100644 exercicios/projeto-guiado/Projeto-Denise-atividade de sala.ipynb create mode 100644 exercicios/projeto-guiado/Projeto-Guiado-II-Denise.ipynb create mode 100644 exercicios/projeto-guiado/dados_meteorologicos.db diff --git a/exercicios/projeto-guiado/Projeto-Denise-atividade de sala.ipynb b/exercicios/projeto-guiado/Projeto-Denise-atividade de sala.ipynb new file mode 100644 index 0000000..2517830 --- /dev/null +++ b/exercicios/projeto-guiado/Projeto-Denise-atividade de sala.ipynb @@ -0,0 +1,2998 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('INMET_MS_ITAQUIRAI_2020.CSV', delimiter=';', skiprows=8, encoding='latin1')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data object\n", + "Hora UTC object\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) object\n", + "PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) object\n", + "PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) object\n", + "PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) object\n", + "RADIACAO GLOBAL (Kj/m²) object\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) object\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) object\n", + "TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) object\n", + "TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) object\n", + "TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) object\n", + "TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) object\n", + "UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) float64\n", + "UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) float64\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) float64\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) float64\n", + "VENTO, RAJADA MAXIMA (m/s) object\n", + "VENTO, VELOCIDADE HORARIA (m/s) object\n", + "Unnamed: 19 float64\n", + "dtype: object" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
02020/01/010000 UTC,623,122,697.0NaN11.01,9
12020/01/010100 UTC023,721,788.02,910.01,3
22020/01/010200 UTC02421,888.01,6345.0,6
32020/01/010300 UTC024,321,483.0,6332.01,5
42020/01/010400 UTC023,821,789.0NaN316.0,2
52020/01/010500 UTC023,522,393.0NaN141.00
62020/01/010600 UTC022,722,498.0NaN40.00
72020/01/010700 UTC022,9NaNNaNNaN36.0,9
82020/01/010800 UTC022,9NaNNaNNaN68.00
92020/01/010900 UTC022,922,597.02,3358.00
102020/01/011000 UTC024,722,889.0408,116.02
112020/01/011100 UTC026,422,780.01219,6341.01
122020/01/011200 UTC028,623,674.01870,5345.01,4
132020/01/011300 UTC030,323,266.02602,9346.02,5
142020/01/011400 UTC03223,159.02996,3351.02,6
152020/01/011500 UTC032,623,157.03715,37.02,8
162020/01/011600 UTC032,221,453.03284,4338.02
172020/01/011700 UTC033,523,355.03238,710.01,7
182020/01/011800 UTC029,623,269.02380,5128.01,9
192020/01/011900 UTC3,42523,290.0930,1342.02,2
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" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC ,6 \n", + "1 2020/01/01 0100 UTC 0 \n", + "2 2020/01/01 0200 UTC 0 \n", + "3 2020/01/01 0300 UTC 0 \n", + "4 2020/01/01 0400 UTC 0 \n", + "5 2020/01/01 0500 UTC 0 \n", + "6 2020/01/01 0600 UTC 0 \n", + "7 2020/01/01 0700 UTC 0 \n", + "8 2020/01/01 0800 UTC 0 \n", + "9 2020/01/01 0900 UTC 0 \n", + "10 2020/01/01 1000 UTC 0 \n", + "11 2020/01/01 1100 UTC 0 \n", + "12 2020/01/01 1200 UTC 0 \n", + "13 2020/01/01 1300 UTC 0 \n", + "14 2020/01/01 1400 UTC 0 \n", + "15 2020/01/01 1500 UTC 0 \n", + "16 2020/01/01 1600 UTC 0 \n", + "17 2020/01/01 1700 UTC 0 \n", + "18 2020/01/01 1800 UTC 0 \n", + "19 2020/01/01 1900 UTC 3,4 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 23,1 \n", + "1 23,7 \n", + "2 24 \n", + "3 24,3 \n", + "4 23,8 \n", + "5 23,5 \n", + "6 22,7 \n", + "7 22,9 \n", + "8 22,9 \n", + "9 22,9 \n", + "10 24,7 \n", + "11 26,4 \n", + "12 28,6 \n", + "13 30,3 \n", + "14 32 \n", + "15 32,6 \n", + "16 32,2 \n", + "17 33,5 \n", + "18 29,6 \n", + "19 25 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "0 22,6 97.0 \n", + "1 21,7 88.0 \n", + "2 21,8 88.0 \n", + "3 21,4 83.0 \n", + "4 21,7 89.0 \n", + "5 22,3 93.0 \n", + "6 22,4 98.0 \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 22,5 97.0 \n", + "10 22,8 89.0 \n", + "11 22,7 80.0 \n", + "12 23,6 74.0 \n", + "13 23,2 66.0 \n", + "14 23,1 59.0 \n", + "15 23,1 57.0 \n", + "16 21,4 53.0 \n", + "17 23,3 55.0 \n", + "18 23,2 69.0 \n", + "19 23,2 90.0 \n", + "\n", + " RADIACAO GLOBAL (Kj/m²) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 NaN 11.0 \n", + "1 2,9 10.0 \n", + "2 1,6 345.0 \n", + "3 ,6 332.0 \n", + "4 NaN 316.0 \n", + "5 NaN 141.0 \n", + "6 NaN 40.0 \n", + "7 NaN 36.0 \n", + "8 NaN 68.0 \n", + "9 2,3 358.0 \n", + "10 408,1 16.0 \n", + "11 1219,6 341.0 \n", + "12 1870,5 345.0 \n", + "13 2602,9 346.0 \n", + "14 2996,3 351.0 \n", + "15 3715,3 7.0 \n", + "16 3284,4 338.0 \n", + "17 3238,7 10.0 \n", + "18 2380,5 128.0 \n", + "19 930,1 342.0 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 1,9 \n", + "1 1,3 \n", + "2 ,6 \n", + "3 1,5 \n", + "4 ,2 \n", + "5 0 \n", + "6 0 \n", + "7 ,9 \n", + "8 0 \n", + "9 0 \n", + "10 2 \n", + "11 1 \n", + "12 1,4 \n", + "13 2,5 \n", + "14 2,6 \n", + "15 2,8 \n", + "16 2 \n", + "17 1,7 \n", + "18 1,9 \n", + "19 2,2 " + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data 0\n", + "Hora UTC 0\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 6\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 6\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) 466\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) 466\n", + "RADIACAO GLOBAL (Kj/m²) 4049\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 6\n", + "VENTO, VELOCIDADE HORARIA (m/s) 6\n", + "dtype: int64" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_5 = df.isnull().sum()\n", + "df_5" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8784, 9)" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
02020/01/010000 UTC,623,122,697.0NaN11.01,9
12020/01/010100 UTC023,721,788.02,910.01,3
22020/01/010200 UTC02421,888.01,6345.0,6
32020/01/010300 UTC024,321,483.0,6332.01,5
42020/01/010400 UTC023,821,789.0NaN316.0,2
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" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC ,6 \n", + "1 2020/01/01 0100 UTC 0 \n", + "2 2020/01/01 0200 UTC 0 \n", + "3 2020/01/01 0300 UTC 0 \n", + "4 2020/01/01 0400 UTC 0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 23,1 \n", + "1 23,7 \n", + "2 24 \n", + "3 24,3 \n", + "4 23,8 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "0 22,6 97.0 \n", + "1 21,7 88.0 \n", + "2 21,8 88.0 \n", + "3 21,4 83.0 \n", + "4 21,7 89.0 \n", + "\n", + " RADIACAO GLOBAL (Kj/m²) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 NaN 11.0 \n", + "1 2,9 10.0 \n", + "2 1,6 345.0 \n", + "3 ,6 332.0 \n", + "4 NaN 316.0 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 1,9 \n", + "1 1,3 \n", + "2 ,6 \n", + "3 1,5 \n", + "4 ,2 " + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)'] = pd.to_numeric(df['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)'], errors='coerce')\n", + "df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'] = pd.to_numeric(df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'], errors='coerce')\n", + "df['TEMPERATURA DO PONTO DE ORVALHO (°C)'] = pd.to_numeric(df['TEMPERATURA DO PONTO DE ORVALHO (°C)'], errors='coerce')\n", + "df['RADIACAO GLOBAL (Kj/m²)'] = pd.to_numeric(df['RADIACAO GLOBAL (Kj/m²)'], errors='coerce')\n", + "df['VENTO, VELOCIDADE HORARIA (m/s)'] = pd.to_numeric(df['VENTO, VELOCIDADE HORARIA (m/s)'], errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data object\n", + "Hora UTC object\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) float64\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) float64\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) float64\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) float64\n", + "RADIACAO GLOBAL (Kj/m²) float64\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) float64\n", + "VENTO, VELOCIDADE HORARIA (m/s) float64\n", + "dtype: object" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# df = df.dropna() # remover linhas com valores nulos ou faltantes\n", + "\n", + "df['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)'] = df['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)'].fillna(0)\n", + "df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'] = df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'].fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data 0\n", + "Hora UTC 0\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 0\n", + "PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) 6\n", + "PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) 6\n", + "PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) 6\n", + "RADIACAO GLOBAL (Kj/m²) 8312\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 0\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) 7934\n", + "TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) 6\n", + "TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) 88\n", + "TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) 495\n", + "TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) 495\n", + "UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) 492\n", + "UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) 492\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) 466\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 6\n", + "VENTO, RAJADA MAXIMA (m/s) 6\n", + "VENTO, VELOCIDADE HORARIA (m/s) 7207\n", + "Unnamed: 19 8784\n", + "dtype: int64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB)PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB)PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB)RADIACAO GLOBAL (Kj/m²)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C)TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C)UMIDADE REL. MAX. NA HORA ANT. (AUT) (%)UMIDADE REL. MIN. NA HORA ANT. (AUT) (%)UMIDADE RELATIVA DO AR, HORARIA (%)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, RAJADA MAXIMA (m/s)VENTO, VELOCIDADE HORARIA (m/s)Unnamed: 19
02020/01/010000 UTC0.0970970969,50.00.00.023,122,622,821,698.094.097.011.03,20.00.0
12020/01/010100 UTC0.0970,2970,29700.00.00.023,72322,521,697.088.088.010.04,60.00.0
22020/01/010200 UTC0.0969,8970,2969,80.024.00.024,423,721,921,288.083.088.0345.03,20.00.0
32020/01/010300 UTC0.0970,1970,1969,80.00.00.025,1242221,288.080.083.0332.04,80.00.0
42020/01/010400 UTC0.0970,2970,5970,10.00.00.024,323,721,821,489.083.089.0316.03,30.00.0
...............................................................
87792020/12/311900 UTC0.0972,6973,3972,60.00.00.023,321,7000.00.097.032.06,60.00.0
87802020/12/312000 UTC0.0970,4972,6970,40.00.00.024,423,122,822,197.089.091.0355.02,80.00.0
87812020/12/312100 UTC0.0970,7970,7970,10.00.023.024,924,123,322,593.089.089.0315.04,20.00.0
87822020/12/312200 UTC0.0972,4972,4970,70.00.00.025,124,223,122,189.087.088.0291.04,80.00.0
87832020/12/312300 UTC0.0974,1974,1972,40.00.00.024,223,422,52294.088.094.0132.03,90.00.0
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
02020/01/010000 UTC0.00.00.00.970.011.00.0
12020/01/010100 UTC0.00.00.00.880.010.00.0
22020/01/010200 UTC0.024.00.00.880.0345.00.0
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..............................
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87802020/12/312000 UTC0.00.00.00.910.0355.00.0
87812020/12/312100 UTC0.00.023.00.890.0315.00.0
87822020/12/312200 UTC0.00.00.00.880.0291.00.0
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" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC 0.0 \n", + "1 2020/01/01 0100 UTC 0.0 \n", + "2 2020/01/01 0200 UTC 0.0 \n", + "3 2020/01/01 0300 UTC 0.0 \n", + "4 2020/01/01 0400 UTC 0.0 \n", + "... ... ... ... \n", + "8779 2020/12/31 1900 UTC 0.0 \n", + "8780 2020/12/31 2000 UTC 0.0 \n", + "8781 2020/12/31 2100 UTC 0.0 \n", + "8782 2020/12/31 2200 UTC 0.0 \n", + "8783 2020/12/31 2300 UTC 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 0.0 \n", + "2 0.88 0.0 \n", + "3 0.83 0.0 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 0.0 \n", + "8780 0.91 0.0 \n", + "8781 0.89 0.0 \n", + "8782 0.88 0.0 \n", + "8783 0.94 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + "[8784 rows x 9 columns]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data 0\n", + "Hora UTC 0\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 0\n", + "PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) 0\n", + "PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) 0\n", + "PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) 0\n", + "RADIACAO GLOBAL (Kj/m²) 0\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 0\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) 0\n", + "TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) 0\n", + "TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) 0\n", + "TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) 0\n", + "TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) 0\n", + "UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) 0\n", + "UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) 0\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) 0\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 0\n", + "VENTO, RAJADA MAXIMA (m/s) 0\n", + "VENTO, VELOCIDADE HORARIA (m/s) 0\n", + "Unnamed: 19 0\n", + "dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data'] = pd.to_datetime(df['Data'], format='%Y/%m/%d')" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data'] = df['Data'].dt.strftime('%d/%m/%Y')" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
001/01/20200000 UTC0.00.00.00.970.011.00.0
101/01/20200100 UTC0.00.00.00.880.010.00.0
201/01/20200200 UTC0.024.00.00.880.0345.00.0
301/01/20200300 UTC0.00.00.00.830.0332.00.0
401/01/20200400 UTC0.00.00.00.890.0316.00.0
..............................
877931/12/20201900 UTC0.00.00.00.970.032.00.0
878031/12/20202000 UTC0.00.00.00.910.0355.00.0
878131/12/20202100 UTC0.00.023.00.890.0315.00.0
878231/12/20202200 UTC0.00.00.00.880.0291.00.0
878331/12/20202300 UTC0.00.00.00.940.0132.00.0
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8784 rows × 9 columns

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" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 0000 UTC 0.0 \n", + "1 01/01/2020 0100 UTC 0.0 \n", + "2 01/01/2020 0200 UTC 0.0 \n", + "3 01/01/2020 0300 UTC 0.0 \n", + "4 01/01/2020 0400 UTC 0.0 \n", + "... ... ... ... \n", + "8779 31/12/2020 1900 UTC 0.0 \n", + "8780 31/12/2020 2000 UTC 0.0 \n", + "8781 31/12/2020 2100 UTC 0.0 \n", + "8782 31/12/2020 2200 UTC 0.0 \n", + "8783 31/12/2020 2300 UTC 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 0.0 \n", + "2 0.88 0.0 \n", + "3 0.83 0.0 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 0.0 \n", + "8780 0.91 0.0 \n", + "8781 0.89 0.0 \n", + "8782 0.88 0.0 \n", + "8783 0.94 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + "[8784 rows x 9 columns]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "df['Hora UTC'] = pd.to_datetime(df['Hora UTC'], format='%H%M UTC', errors='coerce').dt.strftime('%H:%M')" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data e Hora'] = df['Data'] + ' ' + df['Hora UTC']" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data e Hora'] = pd.to_datetime(df['Data e Hora'], format='%d/%m/%Y %H:%M', errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data e Hora BR'] = df['Data e Hora'].dt.tz_localize('UTC').dt.tz_convert('America/Sao_Paulo')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Data e Hora BR'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\alfac\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'Data e Hora BR'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[18], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mData e Hora BR\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mData e Hora BR\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm/\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mH:\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\alfac\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 4101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 4104\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", + "File \u001b[1;32mc:\\Users\\alfac\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m 3810\u001b[0m ):\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[1;31mKeyError\u001b[0m: 'Data e Hora BR'" + ] + } + ], + "source": [ + "df['Data e Hora BR'] = df['Data e Hora BR'].dt.strftime('%d/%m/%Y %H:%M')" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)Data e HoraData e Hora BR
001/01/202000:000.00.00.00.970.011.00.02020-01-01 00:00:0031/12/2019 21:00
101/01/202001:000.00.00.00.880.010.00.02020-01-01 01:00:0031/12/2019 22:00
201/01/202002:000.024.00.00.880.0345.00.02020-01-01 02:00:0031/12/2019 23:00
301/01/202003:000.00.00.00.830.0332.00.02020-01-01 03:00:0001/01/2020 00:00
401/01/202004:000.00.00.00.890.0316.00.02020-01-01 04:00:0001/01/2020 01:00
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" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 00:00 0.0 \n", + "1 01/01/2020 01:00 0.0 \n", + "2 01/01/2020 02:00 0.0 \n", + "3 01/01/2020 03:00 0.0 \n", + "4 01/01/2020 04:00 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "0 0.0 0.97 \n", + "1 0.0 0.88 \n", + "2 0.0 0.88 \n", + "3 0.0 0.83 \n", + "4 0.0 0.89 \n", + "\n", + " RADIACAO GLOBAL (Kj/m²) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 0.0 11.0 \n", + "1 0.0 10.0 \n", + "2 0.0 345.0 \n", + "3 0.0 332.0 \n", + "4 0.0 316.0 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) Data e Hora Data e Hora BR \n", + "0 0.0 2020-01-01 00:00:00 31/12/2019 21:00 \n", + "1 0.0 2020-01-01 01:00:00 31/12/2019 22:00 \n", + "2 0.0 2020-01-01 02:00:00 31/12/2019 23:00 \n", + "3 0.0 2020-01-01 03:00:00 01/01/2020 00:00 \n", + "4 0.0 2020-01-01 04:00:00 01/01/2020 01:00 " + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Análise de Dados**" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)Data e Hora
count8784.0000008784.0000008784.0000008784.0000008784.0000008784.0000008784.0000008784
mean0.0302822.4116581.5392760.63272576.901298184.8894580.2030972020-07-01 23:30:00
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000002020-01-01 00:00:00
25%0.0000000.0000000.0000000.4900000.000000133.0000000.0000002020-04-01 11:45:00
50%0.0000000.0000000.0000000.6700000.000000171.0000000.0000002020-07-01 23:30:00
75%0.0000000.0000000.0000000.8200000.000000254.0000000.0000002020-10-01 11:15:00
max40.00000040.00000025.0000001.0000003886.000000360.0000008.0000002020-12-31 23:00:00
std0.6941427.2915064.8890040.241409414.22431181.7847190.753577NaN
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" + ], + "text/plain": [ + " PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "count 8784.000000 \n", + "mean 0.030282 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 40.000000 \n", + "std 0.694142 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "count 8784.000000 \n", + "mean 2.411658 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 40.000000 \n", + "std 7.291506 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "count 8784.000000 \n", + "mean 1.539276 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 25.000000 \n", + "std 4.889004 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "count 8784.000000 8784.000000 \n", + "mean 0.632725 76.901298 \n", + "min 0.000000 0.000000 \n", + "25% 0.490000 0.000000 \n", + "50% 0.670000 0.000000 \n", + "75% 0.820000 0.000000 \n", + "max 1.000000 3886.000000 \n", + "std 0.241409 414.224311 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \\\n", + "count 8784.000000 8784.000000 \n", + "mean 184.889458 0.203097 \n", + "min 0.000000 0.000000 \n", + "25% 133.000000 0.000000 \n", + "50% 171.000000 0.000000 \n", + "75% 254.000000 0.000000 \n", + "max 360.000000 8.000000 \n", + "std 81.784719 0.753577 \n", + "\n", + " Data e Hora \n", + "count 8784 \n", + "mean 2020-07-01 23:30:00 \n", + "min 2020-01-01 00:00:00 \n", + "25% 2020-04-01 11:45:00 \n", + "50% 2020-07-01 23:30:00 \n", + "75% 2020-10-01 11:15:00 \n", + "max 2020-12-31 23:00:00 \n", + "std NaN " + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.set_index('Data e Hora BR', inplace=True)\n", + "df[['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)', 'TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)',\n", + "'UMIDADE RELATIVA DO AR, HORARIA (%)']].plot(subplots=True)\n", + "plt.suptitle('Séries Temporais das Variáveis')\n", + "plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator(maxticks=6))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Análise do Gráfico Séries Temporais das Variáveis**\n", + "\n", + "* Preciptação Total - Após a análise foi observado que não há uma linearidade com os picos de alta e baixa.\n", + "* Temperatura Bulbo Seco - As temperaturas estão próximas de 40º, havendo uma queda de temperatura no período de junho/2020 à setembro de 2020, houve uma aumento relevante de temperatura provavelmente no mês de outubro/2020, atingindo o ponto máximo de temperatura.\n", + "* Umidade Relativa do Ar - Apresenta oscilações de picos altos e baixos, porém, os dados são mais lineares, os picos de umidade estão entre os intervalos 0,5 e 1,0.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'], df['UMIDADE RELATIVA DO AR, HORARIA (%)'], color=\"red\")\n", + "plt.title('Temperatura do Ar x Umidade Relativa do Ar')\n", + "plt.xlabel('Temperatura do Ar (°C)')\n", + "plt.ylabel('Umidade Relativa do Ar (%)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df['Data e Hora'] ,df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'],\n", + "c=df['UMIDADE RELATIVA DO AR, HORARIA (%)'],\n", + "cmap='viridis',\n", + "alpha=0.7,\n", + "edgecolors='w')\n", + "plt.colorbar(label='Umidade Relativa do Ar (%)')\n", + "plt.title('Temperatura do Ar x Umidade Relativa do Ar')\n", + "plt.xlabel('Hora e Data')\n", + "plt.ylabel('TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Análise Gráfica - Temperatura do Ar x Umidade Relativa do Ar** " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finalização do Projeto**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercicios/projeto-guiado/Projeto-Guiado-II-Denise.ipynb b/exercicios/projeto-guiado/Projeto-Guiado-II-Denise.ipynb new file mode 100644 index 0000000..8f7a0e0 --- /dev/null +++ b/exercicios/projeto-guiado/Projeto-Guiado-II-Denise.ipynb @@ -0,0 +1,4089 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Projeto Guiado: Análise de Dados Básica - Professora Letícia \n", + "\n", + "**Objetivo:**\n", + "Este projeto visa guiar as alunas no processo completo de análise de dados, desde a extração e tratamento até a análise e visualização, culminando na persistência dos resultados em um banco de dados SQLite. As alunas aprenderão a usar ferramentas e técnicas básicas de SQL, manipulação de dados com Pandas, e visualização com Matplotlib.\n", + "\n", + "### Estrutura do Projeto\n", + "\n", + "#### **Abertura e Carregamento de Dados (ETL - Extract, Transform, Load)**\n", + "\n", + "1. **Extração de Dados:** \n", + " - Inicie o projeto extraindo dados de um arquivo CSV.\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('INMET_MS_ITAQUIRAI_2020.CSV', delimiter=';', skiprows=8, encoding='latin1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. **Inspeção Inicial**\n", + "\n", + "Revise o conteúdo dos dados extraídos, observando as primeiras e últimas linhas, a forma e a descrição geral dos dados, e os tipos de dados." + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB)PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB)PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB)RADIACAO GLOBAL (Kj/m²)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C)TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C)UMIDADE REL. MAX. NA HORA ANT. (AUT) (%)UMIDADE REL. MIN. NA HORA ANT. (AUT) (%)UMIDADE RELATIVA DO AR, HORARIA (%)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, RAJADA MAXIMA (m/s)VENTO, VELOCIDADE HORARIA (m/s)Unnamed: 19
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42020/01/010400 UTC0970,2970,5970,1NaN23,821,724,323,721,821,489.083.089.0316.03,3,2NaN
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB)PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB)PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB)RADIACAO GLOBAL (Kj/m²)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C)TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C)UMIDADE REL. MAX. NA HORA ANT. (AUT) (%)UMIDADE REL. MIN. NA HORA ANT. (AUT) (%)UMIDADE RELATIVA DO AR, HORARIA (%)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, RAJADA MAXIMA (m/s)VENTO, VELOCIDADE HORARIA (m/s)Unnamed: 19
87792020/12/311900 UTC,4972,6973,3972,6775,923,122,723,321,7NaNNaNNaNNaN97.032.06,61,2NaN
87802020/12/312000 UTC0970,4972,6970,4837,824,222,724,423,122,822,197.089.091.0355.02,8,8NaN
87812020/12/312100 UTC0970,7970,7970,1524,724,92324,924,123,322,593.089.089.0315.04,21,2NaN
87822020/12/312200 UTC0972,4972,4970,7256,524,222,125,124,223,122,189.087.088.0291.04,8,9NaN
87832020/12/312300 UTC0974,1974,1972,49,623,522,524,223,422,52294.088.094.0132.03,9,9NaN
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(AUT) (mB) RADIACAO GLOBAL (Kj/m²) \\\n", + "8779 972,6 775,9 \n", + "8780 970,4 837,8 \n", + "8781 970,1 524,7 \n", + "8782 970,7 256,5 \n", + "8783 972,4 9,6 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "8779 23,1 \n", + "8780 24,2 \n", + "8781 24,9 \n", + "8782 24,2 \n", + "8783 23,5 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "8779 22,7 \n", + "8780 22,7 \n", + "8781 23 \n", + "8782 22,1 \n", + "8783 22,5 \n", + "\n", + " TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) \\\n", + "8779 23,3 \n", + "8780 24,4 \n", + "8781 24,9 \n", + "8782 25,1 \n", + "8783 24,2 \n", + "\n", + " TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) \\\n", + "8779 21,7 \n", + "8780 23,1 \n", + "8781 24,1 \n", + "8782 24,2 \n", + "8783 23,4 \n", + "\n", + " TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) \\\n", + "8779 NaN \n", + "8780 22,8 \n", + "8781 23,3 \n", + "8782 23,1 \n", + "8783 22,5 \n", + "\n", + " TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) \\\n", + "8779 NaN \n", + "8780 22,1 \n", + "8781 22,5 \n", + "8782 22,1 \n", + "8783 22 \n", + "\n", + " UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) \\\n", + "8779 NaN \n", + "8780 97.0 \n", + "8781 93.0 \n", + "8782 89.0 \n", + "8783 94.0 \n", + "\n", + " UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) \\\n", + "8779 NaN \n", + "8780 89.0 \n", + "8781 89.0 \n", + "8782 87.0 \n", + "8783 88.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "8779 97.0 \n", + "8780 91.0 \n", + "8781 89.0 \n", + "8782 88.0 \n", + "8783 94.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, RAJADA MAXIMA (m/s) \\\n", + "8779 32.0 6,6 \n", + "8780 355.0 2,8 \n", + "8781 315.0 4,2 \n", + "8782 291.0 4,8 \n", + "8783 132.0 3,9 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) Unnamed: 19 \n", + "8779 1,2 NaN \n", + "8780 ,8 NaN \n", + "8781 1,2 NaN \n", + "8782 ,9 NaN \n", + "8783 ,9 NaN " + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Imprimin as 5 últimas linhas do DataFrame \n", + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8784, 20)" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Exibir os dados do Dataframe - linhas e colunas em forma de tupla\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data object\n", + "Hora UTC object\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) object\n", + "PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) object\n", + "PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) object\n", + "PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) object\n", + "RADIACAO GLOBAL (Kj/m²) object\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) object\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) object\n", + "TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) object\n", + "TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) object\n", + "TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) object\n", + "TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) object\n", + "UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) float64\n", + "UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) float64\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) float64\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) float64\n", + "VENTO, RAJADA MAXIMA (m/s) object\n", + "VENTO, VELOCIDADE HORARIA (m/s) object\n", + "Unnamed: 19 float64\n", + "dtype: object" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Exibir os tipos de dados da coluna\n", + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. **Identificação e Tratamento de Valores Faltantes**\n", + "\n", + "Identifique a presença de valores nulos e trate-os adequadamente, seja removendo, preenchendo ou substituindo esses valores." + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 False False False \n", + "1 False False False \n", + "2 False False False \n", + "3 False False False \n", + "4 False False False \n", + "... ... ... ... \n", + "8779 False False False \n", + "8780 False False False \n", + "8781 False False False \n", + "8782 False False False \n", + "8783 False False False \n", + "\n", + " PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 False \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 False \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 False \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " RADIACAO GLOBAL (Kj/m²) TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 True False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 True False \n", + "... ... ... \n", + "8779 False False \n", + "8780 False False \n", + "8781 False False \n", + "8782 False False \n", + "8783 False False \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 False \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 False \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 False \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 True \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 True \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 True \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 True \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "8779 False \n", + "8780 False \n", + "8781 False \n", + "8782 False \n", + "8783 False \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, RAJADA MAXIMA (m/s) \\\n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "8779 False False \n", + "8780 False False \n", + "8781 False False \n", + "8782 False False \n", + "8783 False False \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) Unnamed: 19 \n", + "0 False True \n", + "1 False True \n", + "2 False True \n", + "3 False True \n", + "4 False True \n", + "... ... ... \n", + "8779 False True \n", + "8780 False True \n", + "8781 False True \n", + "8782 False True \n", + "8783 False True \n", + "\n", + "[8784 rows x 20 columns]\n" + ] + } + ], + "source": [ + "#Identificar valores nulos\n", + "nulos = df.isnull()\n", + "print(nulos)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data 0\n", + "Hora UTC 0\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 6\n", + "PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) 6\n", + "PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) 6\n", + "PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) 6\n", + "RADIACAO GLOBAL (Kj/m²) 4049\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 6\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) 466\n", + "TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) 6\n", + "TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) 88\n", + "TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) 495\n", + "TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) 495\n", + "UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) 492\n", + "UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) 492\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) 466\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 6\n", + "VENTO, RAJADA MAXIMA (m/s) 6\n", + "VENTO, VELOCIDADE HORARIA (m/s) 6\n", + "Unnamed: 19 8784\n", + "dtype: int64" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Verificar a quantidade de valores nulos \n", + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('future.no_silent_downcasting', True)\n", + "df = df.fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.fillna(0).infer_objects()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB)PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB)PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB)RADIACAO GLOBAL (Kj/m²)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C)TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C)UMIDADE REL. MAX. NA HORA ANT. (AUT) (%)UMIDADE REL. MIN. NA HORA ANT. (AUT) (%)UMIDADE RELATIVA DO AR, HORARIA (%)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, RAJADA MAXIMA (m/s)VENTO, VELOCIDADE HORARIA (m/s)Unnamed: 19
02020/01/010000 UTC,6970970969,5023,122,623,122,622,821,698.094.097.011.03,21,90.0
12020/01/010100 UTC0970,2970,29702,923,721,723,72322,521,697.088.088.010.04,61,30.0
22020/01/010200 UTC0969,8970,2969,81,62421,824,423,721,921,288.083.088.0345.03,2,60.0
32020/01/010300 UTC0970,1970,1969,8,624,321,425,1242221,288.080.083.0332.04,81,50.0
42020/01/010400 UTC0970,2970,5970,1023,821,724,323,721,821,489.083.089.0316.03,3,20.0
52020/01/010500 UTC0969,7970,2969,7023,522,323,923,522,421,893.089.093.0141.01,300.0
62020/01/010600 UTC0969,2969,7969,2022,722,423,522,722,522,298.093.098.040.01,300.0
72020/01/010700 UTC0969,1969,3969,1022,9022,922,722,822,5100.099.00.036.02,2,90.0
82020/01/010800 UTC0969,2969,3969,1022,902322,8000.00.00.068.02,200.0
92020/01/010900 UTC0969,4969,4969,12,322,922,52322,9000.00.097.0358.01,300.0
102020/01/011000 UTC0970970969,4408,124,722,824,722,92322,397.089.089.016.03,720.0
112020/01/011100 UTC0970,2970,29701219,626,422,726,724,723,322,589.079.080.0341.04,510.0
122020/01/011200 UTC0970,3970,4970,21870,528,623,628,626,423,822,380.074.074.0345.03,71,40.0
132020/01/011300 UTC0970,2970,3970,22602,930,323,230,428,524,622,675.064.066.0346.05,62,50.0
142020/01/011400 UTC0970970,39702996,33223,13230,323,922,268.059.059.0351.07,12,60.0
152020/01/011500 UTC0969,5970969,53715,332,623,133,131,52422,762.056.057.07.05,42,80.0
162020/01/011600 UTC0968,7969,5968,73284,432,221,433,931,824,621,461.053.053.0338.06,520.0
172020/01/011700 UTC0967,7968,7967,73238,733,523,333,631,623,921,561.051.055.010.05,71,70.0
182020/01/011800 UTC0966,8967,8966,82380,529,623,233,526,923,720,575.054.069.0128.010,81,90.0
192020/01/011900 UTC3,4966,4966,8966,1930,12523,23024,923,622,490.065.090.0342.06,52,20.0
\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC ,6 \n", + "1 2020/01/01 0100 UTC 0 \n", + "2 2020/01/01 0200 UTC 0 \n", + "3 2020/01/01 0300 UTC 0 \n", + "4 2020/01/01 0400 UTC 0 \n", + "5 2020/01/01 0500 UTC 0 \n", + "6 2020/01/01 0600 UTC 0 \n", + "7 2020/01/01 0700 UTC 0 \n", + "8 2020/01/01 0800 UTC 0 \n", + "9 2020/01/01 0900 UTC 0 \n", + "10 2020/01/01 1000 UTC 0 \n", + "11 2020/01/01 1100 UTC 0 \n", + "12 2020/01/01 1200 UTC 0 \n", + "13 2020/01/01 1300 UTC 0 \n", + "14 2020/01/01 1400 UTC 0 \n", + "15 2020/01/01 1500 UTC 0 \n", + "16 2020/01/01 1600 UTC 0 \n", + "17 2020/01/01 1700 UTC 0 \n", + "18 2020/01/01 1800 UTC 0 \n", + "19 2020/01/01 1900 UTC 3,4 \n", + "\n", + " PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) \\\n", + "0 970 \n", + "1 970,2 \n", + "2 969,8 \n", + "3 970,1 \n", + "4 970,2 \n", + "5 969,7 \n", + "6 969,2 \n", + "7 969,1 \n", + "8 969,2 \n", + "9 969,4 \n", + "10 970 \n", + "11 970,2 \n", + "12 970,3 \n", + "13 970,2 \n", + "14 970 \n", + "15 969,5 \n", + "16 968,7 \n", + "17 967,7 \n", + "18 966,8 \n", + "19 966,4 \n", + "\n", + " PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) \\\n", + "0 970 \n", + "1 970,2 \n", + "2 970,2 \n", + "3 970,1 \n", + "4 970,5 \n", + "5 970,2 \n", + "6 969,7 \n", + "7 969,3 \n", + "8 969,3 \n", + "9 969,4 \n", + "10 970 \n", + "11 970,2 \n", + "12 970,4 \n", + "13 970,3 \n", + "14 970,3 \n", + "15 970 \n", + "16 969,5 \n", + "17 968,7 \n", + "18 967,8 \n", + "19 966,8 \n", + "\n", + " PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 969,5 0 \n", + "1 970 2,9 \n", + "2 969,8 1,6 \n", + "3 969,8 ,6 \n", + "4 970,1 0 \n", + "5 969,7 0 \n", + "6 969,2 0 \n", + "7 969,1 0 \n", + "8 969,1 0 \n", + "9 969,1 2,3 \n", + "10 969,4 408,1 \n", + "11 970 1219,6 \n", + "12 970,2 1870,5 \n", + "13 970,2 2602,9 \n", + "14 970 2996,3 \n", + "15 969,5 3715,3 \n", + "16 968,7 3284,4 \n", + "17 967,7 3238,7 \n", + "18 966,8 2380,5 \n", + "19 966,1 930,1 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 23,1 \n", + "1 23,7 \n", + "2 24 \n", + "3 24,3 \n", + "4 23,8 \n", + "5 23,5 \n", + "6 22,7 \n", + "7 22,9 \n", + "8 22,9 \n", + "9 22,9 \n", + "10 24,7 \n", + "11 26,4 \n", + "12 28,6 \n", + "13 30,3 \n", + "14 32 \n", + "15 32,6 \n", + "16 32,2 \n", + "17 33,5 \n", + "18 29,6 \n", + "19 25 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 22,6 \n", + "1 21,7 \n", + "2 21,8 \n", + "3 21,4 \n", + "4 21,7 \n", + "5 22,3 \n", + "6 22,4 \n", + "7 0 \n", + "8 0 \n", + "9 22,5 \n", + "10 22,8 \n", + "11 22,7 \n", + "12 23,6 \n", + "13 23,2 \n", + "14 23,1 \n", + "15 23,1 \n", + "16 21,4 \n", + "17 23,3 \n", + "18 23,2 \n", + "19 23,2 \n", + "\n", + " TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) \\\n", + "0 23,1 \n", + "1 23,7 \n", + "2 24,4 \n", + "3 25,1 \n", + "4 24,3 \n", + "5 23,9 \n", + "6 23,5 \n", + "7 22,9 \n", + "8 23 \n", + "9 23 \n", + "10 24,7 \n", + "11 26,7 \n", + "12 28,6 \n", + "13 30,4 \n", + "14 32 \n", + "15 33,1 \n", + "16 33,9 \n", + "17 33,6 \n", + "18 33,5 \n", + "19 30 \n", + "\n", + " TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) \\\n", + "0 22,6 \n", + "1 23 \n", + "2 23,7 \n", + "3 24 \n", + "4 23,7 \n", + "5 23,5 \n", + "6 22,7 \n", + "7 22,7 \n", + "8 22,8 \n", + "9 22,9 \n", + "10 22,9 \n", + "11 24,7 \n", + "12 26,4 \n", + "13 28,5 \n", + "14 30,3 \n", + "15 31,5 \n", + "16 31,8 \n", + "17 31,6 \n", + "18 26,9 \n", + "19 24,9 \n", + "\n", + " TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) \\\n", + "0 22,8 \n", + "1 22,5 \n", + "2 21,9 \n", + "3 22 \n", + "4 21,8 \n", + "5 22,4 \n", + "6 22,5 \n", + "7 22,8 \n", + "8 0 \n", + "9 0 \n", + "10 23 \n", + "11 23,3 \n", + "12 23,8 \n", + "13 24,6 \n", + "14 23,9 \n", + "15 24 \n", + "16 24,6 \n", + "17 23,9 \n", + "18 23,7 \n", + "19 23,6 \n", + "\n", + " TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) \\\n", + "0 21,6 \n", + "1 21,6 \n", + "2 21,2 \n", + "3 21,2 \n", + "4 21,4 \n", + "5 21,8 \n", + "6 22,2 \n", + "7 22,5 \n", + "8 0 \n", + "9 0 \n", + "10 22,3 \n", + "11 22,5 \n", + "12 22,3 \n", + "13 22,6 \n", + "14 22,2 \n", + "15 22,7 \n", + "16 21,4 \n", + "17 21,5 \n", + "18 20,5 \n", + "19 22,4 \n", + "\n", + " UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) \\\n", + "0 98.0 \n", + "1 97.0 \n", + "2 88.0 \n", + "3 88.0 \n", + "4 89.0 \n", + "5 93.0 \n", + "6 98.0 \n", + "7 100.0 \n", + "8 0.0 \n", + "9 0.0 \n", + "10 97.0 \n", + "11 89.0 \n", + "12 80.0 \n", + "13 75.0 \n", + "14 68.0 \n", + "15 62.0 \n", + "16 61.0 \n", + "17 61.0 \n", + "18 75.0 \n", + "19 90.0 \n", + "\n", + " UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) \\\n", + "0 94.0 \n", + "1 88.0 \n", + "2 83.0 \n", + "3 80.0 \n", + "4 83.0 \n", + "5 89.0 \n", + "6 93.0 \n", + "7 99.0 \n", + "8 0.0 \n", + "9 0.0 \n", + "10 89.0 \n", + "11 79.0 \n", + "12 74.0 \n", + "13 64.0 \n", + "14 59.0 \n", + "15 56.0 \n", + "16 53.0 \n", + "17 51.0 \n", + "18 54.0 \n", + "19 65.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 97.0 11.0 \n", + "1 88.0 10.0 \n", + "2 88.0 345.0 \n", + "3 83.0 332.0 \n", + "4 89.0 316.0 \n", + "5 93.0 141.0 \n", + "6 98.0 40.0 \n", + "7 0.0 36.0 \n", + "8 0.0 68.0 \n", + "9 97.0 358.0 \n", + "10 89.0 16.0 \n", + "11 80.0 341.0 \n", + "12 74.0 345.0 \n", + "13 66.0 346.0 \n", + "14 59.0 351.0 \n", + "15 57.0 7.0 \n", + "16 53.0 338.0 \n", + "17 55.0 10.0 \n", + "18 69.0 128.0 \n", + "19 90.0 342.0 \n", + "\n", + " VENTO, RAJADA MAXIMA (m/s) VENTO, VELOCIDADE HORARIA (m/s) Unnamed: 19 \n", + "0 3,2 1,9 0.0 \n", + "1 4,6 1,3 0.0 \n", + "2 3,2 ,6 0.0 \n", + "3 4,8 1,5 0.0 \n", + "4 3,3 ,2 0.0 \n", + "5 1,3 0 0.0 \n", + "6 1,3 0 0.0 \n", + "7 2,2 ,9 0.0 \n", + "8 2,2 0 0.0 \n", + "9 1,3 0 0.0 \n", + "10 3,7 2 0.0 \n", + "11 4,5 1 0.0 \n", + "12 3,7 1,4 0.0 \n", + "13 5,6 2,5 0.0 \n", + "14 7,1 2,6 0.0 \n", + "15 5,4 2,8 0.0 \n", + "16 6,5 2 0.0 \n", + "17 5,7 1,7 0.0 \n", + "18 10,8 1,9 0.0 \n", + "19 6,5 2,2 0.0 " + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Exibir as 20 primeiras linhas do Dataframe\n", + "df.head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Tratamento de Dados**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. *Ajustes e Limpeza*\n", + " \n", + "Organize e limpe os dados, removendo duplicatas e normalizando quando necessário." + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], + "source": [ + "#Reduzir o DataFrame para organizar as informações necessárias e mais relevantes \n", + "df = df[['Data','Hora UTC','PRECIPITAÇÃO TOTAL, HORÁRIO (mm)', 'TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)','UMIDADE RELATIVA DO AR, HORARIA (%)', 'RADIACAO GLOBAL (Kj/m²)', 'VENTO, DIREÇÃO HORARIA (gr) (° (gr))' ,'VENTO, VELOCIDADE HORARIA (m/s)']]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
02020/01/010000 UTC,623,197.0011.01,9
12020/01/010100 UTC023,788.02,910.01,3
22020/01/010200 UTC02488.01,6345.0,6
32020/01/010300 UTC024,383.0,6332.01,5
42020/01/010400 UTC023,889.00316.0,2
...........................
87792020/12/311900 UTC,423,197.0775,932.01,2
87802020/12/312000 UTC024,291.0837,8355.0,8
87812020/12/312100 UTC024,989.0524,7315.01,2
87822020/12/312200 UTC024,288.0256,5291.0,9
87832020/12/312300 UTC023,594.09,6132.0,9
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8784 rows × 8 columns

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" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC ,6 \n", + "1 2020/01/01 0100 UTC 0 \n", + "2 2020/01/01 0200 UTC 0 \n", + "3 2020/01/01 0300 UTC 0 \n", + "4 2020/01/01 0400 UTC 0 \n", + "... ... ... ... \n", + "8779 2020/12/31 1900 UTC ,4 \n", + "8780 2020/12/31 2000 UTC 0 \n", + "8781 2020/12/31 2100 UTC 0 \n", + "8782 2020/12/31 2200 UTC 0 \n", + "8783 2020/12/31 2300 UTC 0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 23,1 \n", + "1 23,7 \n", + "2 24 \n", + "3 24,3 \n", + "4 23,8 \n", + "... ... \n", + "8779 23,1 \n", + "8780 24,2 \n", + "8781 24,9 \n", + "8782 24,2 \n", + "8783 23,5 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 97.0 0 \n", + "1 88.0 2,9 \n", + "2 88.0 1,6 \n", + "3 83.0 ,6 \n", + "4 89.0 0 \n", + "... ... ... \n", + "8779 97.0 775,9 \n", + "8780 91.0 837,8 \n", + "8781 89.0 524,7 \n", + "8782 88.0 256,5 \n", + "8783 94.0 9,6 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 11.0 1,9 \n", + "1 10.0 1,3 \n", + "2 345.0 ,6 \n", + "3 332.0 1,5 \n", + "4 316.0 ,2 \n", + "... ... ... \n", + "8779 32.0 1,2 \n", + "8780 355.0 ,8 \n", + "8781 315.0 1,2 \n", + "8782 291.0 ,9 \n", + "8783 132.0 ,9 \n", + "\n", + "[8784 rows x 8 columns]" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Remover dados duplicados - Linhas duplicadas \n", + "df.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8784, 8)" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. *Renomeação e Ajuste de Colunas*\n", + "\n", + "Renomeie colunas e ajuste os tipos de dados conforme necessário para garantir a consistência e clareza." + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Colunas renomeadas:\n", + "Index(['Data', 'Hora UTC', 'Precipitacao_Total_mm', 'Temperatura_C',\n", + " 'Umidade_Relativa', 'Radiacao_Global_Kj_m2',\n", + " 'VENTO, DIREÇÃO HORARIA (gr) (° (gr))', 'Velocidade_Vento_m_s'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "#Renomer as colunas\n", + "df.rename(columns={\n", + " 'PRECIPITAÇÃO TOTAL, HORÁRIO (mm)': 'Precipitacao_Total_mm',\n", + " 'TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)': 'Temperatura_C',\n", + " 'UMIDADE RELATIVA DO AR, HORARIA (%)': 'Umidade_Relativa',\n", + " 'RADIACAO GLOBAL (Kj/m²)': 'Radiacao_Global_Kj_m2',\n", + " 'VENTO, DIREÇÃO HORARIA (gr)': 'Direcao_Vento_graus',\n", + " 'VENTO, VELOCIDADE HORARIA (m/s)': 'Velocidade_Vento_m_s'\n", + "}, inplace=True)\n", + "\n", + "print(\"Colunas renomeadas:\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [], + "source": [ + "#Ajustar os tipos de dados da coluna 'Data' - Alterar data para datetime e 'Hora UTC' para time\n", + "df['Data'] = pd.to_datetime(df['Data'], format='%Y/%m/%d')" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPrecipitacao_Total_mmTemperatura_CUmidade_RelativaRadiacao_Global_Kj_m2VENTO, DIREÇÃO HORARIA (gr) (° (gr))Velocidade_Vento_m_s
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101/01/20200100 UTC023,788.02,910.01,3
201/01/20200200 UTC02488.01,6345.0,6
301/01/20200300 UTC024,383.0,6332.01,5
401/01/20200400 UTC023,889.00316.0,2
...........................
877931/12/20201900 UTC,423,197.0775,932.01,2
878031/12/20202000 UTC024,291.0837,8355.0,8
878131/12/20202100 UTC024,989.0524,7315.01,2
878231/12/20202200 UTC024,288.0256,5291.0,9
878331/12/20202300 UTC023,594.09,6132.0,9
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8784 rows × 8 columns

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DataHora UTCPrecipitacao_Total_mmTemperatura_CUmidade_RelativaRadiacao_Global_Kj_m2VENTO, DIREÇÃO HORARIA (gr) (° (gr))Velocidade_Vento_m_s
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" + ], + "text/plain": [ + " Data Hora UTC Precipitacao_Total_mm Temperatura_C Umidade_Relativa \\\n", + "0 01/01/2020 00:00 ,6 23,1 97.0 \n", + "1 01/01/2020 01:00 0 23,7 88.0 \n", + "2 01/01/2020 02:00 0 24 88.0 \n", + "3 01/01/2020 03:00 0 24,3 83.0 \n", + "4 01/01/2020 04:00 0 23,8 89.0 \n", + "\n", + " Radiacao_Global_Kj_m2 VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 0 11.0 \n", + "1 2,9 10.0 \n", + "2 1,6 345.0 \n", + "3 ,6 332.0 \n", + "4 0 316.0 \n", + "\n", + " Velocidade_Vento_m_s \n", + "0 1,9 \n", + "1 1,3 \n", + "2 ,6 \n", + "3 1,5 \n", + "4 ,2 " + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data e Hora'] = df['Data'] + ' ' + df['Hora UTC']" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPrecipitacao_Total_mmTemperatura_CUmidade_RelativaRadiacao_Global_Kj_m2VENTO, DIREÇÃO HORARIA (gr) (° (gr))Velocidade_Vento_m_sData e Hora
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" + ], + "text/plain": [ + " Data Hora UTC Precipitacao_Total_mm Temperatura_C Umidade_Relativa \\\n", + "0 01/01/2020 00:00 ,6 23,1 97.0 \n", + "1 01/01/2020 01:00 0 23,7 88.0 \n", + "2 01/01/2020 02:00 0 24 88.0 \n", + "3 01/01/2020 03:00 0 24,3 83.0 \n", + "4 01/01/2020 04:00 0 23,8 89.0 \n", + "\n", + " Radiacao_Global_Kj_m2 VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 0 11.0 \n", + "1 2,9 10.0 \n", + "2 1,6 345.0 \n", + "3 ,6 332.0 \n", + "4 0 316.0 \n", + "\n", + " Velocidade_Vento_m_s Data e Hora \n", + "0 1,9 01/01/2020 00:00 \n", + "1 1,3 01/01/2020 01:00 \n", + "2 ,6 01/01/2020 02:00 \n", + "3 1,5 01/01/2020 03:00 \n", + "4 ,2 01/01/2020 04:00 " + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data e Hora'] = pd.to_datetime(df['Data e Hora'], format='%d/%m/%Y %H:%M', errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataHora UTCPrecipitacao_Total_mmTemperatura_CUmidade_RelativaRadiacao_Global_Kj_m2VENTO, DIREÇÃO HORARIA (gr) (° (gr))Velocidade_Vento_m_sData e Hora
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DataHora UTCPrecipitacao_Total_mmTemperatura_CUmidade_RelativaRadiacao_Global_Kj_m2VENTO, DIREÇÃO HORARIA (gr) (° (gr))Velocidade_Vento_m_sData e HoraData e Hora BR
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DataHora UTCPrecipitacao_Total_mmTemperatura_CUmidade_RelativaRadiacao_Global_Kj_m2VENTO, DIREÇÃO HORARIA (gr) (° (gr))Velocidade_Vento_m_sData e HoraData e Hora BR
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DataHora UTCPrecipitacao_Total_mmTemperatura_CUmidade_RelativaRadiacao_Global_Kj_m2VENTO, DIREÇÃO HORARIA (gr) (° (gr))Velocidade_Vento_m_sData e Hora BRTemperatura_C)CSensação_Térmica
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101/01/202001:00023,788.02,910.01,331/12/2019 22:00NaN88.0NaN
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401/01/202004:00023,889.00316.0,201/01/2020 01:00NaN89.0NaN
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Umidade_RelativaVENTO, DIREÇÃO HORARIA (gr) (° (gr))Temperatura_C)CSensação_Térmica
count8784.0000008784.000000926.0000008784.000000926.000000
mean63.272541184.88945822.87689063.27254115.351404
std24.14089581.7847196.01097824.1408956.700249
min0.0000000.0000000.0000000.000000-20.000000
25%49.000000133.00000020.00000049.00000012.800000
50%67.000000171.00000023.00000067.00000016.800000
75%82.000000254.00000027.00000082.00000019.800000
max100.000000360.00000040.000000100.00000024.800000
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" + ], + "text/plain": [ + " Umidade_Relativa VENTO, DIREÇÃO HORARIA (gr) (° (gr)) Temperatura_C) \\\n", + "count 8784.000000 8784.000000 926.000000 \n", + "mean 63.272541 184.889458 22.876890 \n", + "std 24.140895 81.784719 6.010978 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 49.000000 133.000000 20.000000 \n", + "50% 67.000000 171.000000 23.000000 \n", + "75% 82.000000 254.000000 27.000000 \n", + "max 100.000000 360.000000 40.000000 \n", + "\n", + " C Sensação_Térmica \n", + "count 8784.000000 926.000000 \n", + "mean 63.272541 15.351404 \n", + "std 24.140895 6.700249 \n", + "min 0.000000 -20.000000 \n", + "25% 49.000000 12.800000 \n", + "50% 67.000000 16.800000 \n", + "75% 82.000000 19.800000 \n", + "max 100.000000 24.800000 " + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Cálculos de soma, média, máximo e mínimo\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. *Agrupamento e Sumarização*\n", + "\n", + "Agrupe os dados para identificar padrões e tendências, gerando sumarizações que permitam uma análise mais profunda." + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Umidade_Relativa\n", + "0.0 -3.048387\n", + "14.0 NaN\n", + "15.0 NaN\n", + "16.0 21.200000\n", + "17.0 NaN\n", + " ... \n", + "96.0 16.800000\n", + "97.0 15.650000\n", + "98.0 16.266667\n", + "99.0 19.300000\n", + "100.0 19.363636\n", + "Name: Sensação_Térmica, Length: 88, dtype: float64\n" + ] + } + ], + "source": [ + "#Realizando o agrupamento da coluna umidade relativa e calculando a média da Sensação térmica'\n", + "agrupado = df.groupby('Umidade_Relativa')['Sensação_Térmica'].mean()\n", + "print(agrupado)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "#Realizando a sumarização para calcular estatísticas ou métricas de cada coluna\n", + "sumarizacao_estatistica = df.describe\n", + "print(sumarizacao_estatistica)" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Visualização de Dados com Matplotlib*\n", + "\n", + "**Análise Gráfica do DataFrame** \n", + "\n", + "![image.png](attachment:image.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. *Criação de Gráficos Básicos*\n", + "\n", + "Visualize os dados através de gráficos, como histogramas e gráficos de barras, para facilitar a compreensão das análises realizadas.\n", + "\n", + "Resposta: Elaboração de gráficos de barras para análise de umidade relativa e preicpitação total ao longo do tempo." + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [], + "source": [ + "#importando as bibliotecas para a elaboração dos gráficos\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Elaboração de gráfico de colunas - Análise da coluna 'Umidade Relativa' por horário\n", + "#plotagem do gráfico de barras \n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(df['Hora UTC'], df['Umidade_Relativa'], color='green')\n", + "\n", + "#Inserindo os dados de Título, linha horizontal é o eixo (x) com dados 'horário' linha vertical é o eixo (y) como a informação da umidade.\n", + "plt.xlabel('Hora UTC')\n", + "plt.ylabel('Umidade Relativa (%)')\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ef/xxxx5nyJAheuutty46DzvSAQBwDruXAwCAdFm3bp327t170XnatGmTSWkAAHA3SjcAAAAAAA5h9/ILJCUlae/evSpYsKA8Hk9WxwEAAAAAuJCZ6fjx4ypVqpSCgoLSnI/SfYG9e/cqKioqq2MAAAAAAALArl27VKZMmTRvp3RfoGDBgpLODVxYWFgWpwEAAAAAuFFsbKyioqK8HTItlO4LJO9SHhYWRukGAAAAAFzUvx2WnPaO5wAAAAAA4LJQugEAAAAAcAilGwAAAAAAh1C6AQAAAABwCKUbAAAAAACHULoBAAAAAHAIpRsAAAAAAIdQugEAAAAAcAilGwAAAAAAh1C6AQAAAABwCKUbAAAAAACHULoBAAAAAHAIpRsAAAAAAIdQugEAAAAAcAilGwAAAAAAh1C6AQAAAABwCKUbAAAAAACHULoBAAAAAHAIpRsAAAAAAIfkyuoAAADg0ng8mft4Zpn7eNlBZj5HPD+BjdczkHOwpRsAAAAAAIewpRsAAACOYYsugJyOLd0AAAAAADiELd0AAADZDFuXAcA9KN0AACCgUTCBy+OWEwDyWkZ2xe7lAAAAAAA4hNINAAAAAIBDKN0AAAAAADiEY7qRrbjpWCA3ZUFKPD8AACAtblpPcFMWZAxbugEAAAAAcAhbuoEcgG9IAQAAgKzBlm4AAAAAABzClm4AAC6CPUUAAMDlYEs3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA7hmG5cNo53RHpk5vLCsgIAAICsxpZuAAAAAAAcQukGAAAAAMAhlG4AAAAAABzCMd0BjGNjAQAAAGQWzuWUMWzpBgAAAADAIWzpBgB48Q02gOyM9zgAWYEt3QAAAAAAOIQt3QCQxdjyAgAAkH2xpRsAAAAAAIewpRsA4ErsAQAAALIDtnQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEM5eDgAA0o2zywMAcGnY0g0AAAAAgEMo3QAAAAAAOITSDQAAAACAQyjdAAAAAAA4hNINAAAAAIBDKN0AAAAAADiE0g0AAAAAgEMo3QAAAAAAOITSDQAAAACAQwKqdE+bNk0NGzZUwYIFVbx4cXXt2lUbN270mefMmTO67777VKRIEYWGhqpHjx46cOBAFiUGAAAAAORkAVW6f/nlF9133336448/9P333yshIUFt27bVyZMnvfMMGzZMX3zxhT766CP98ssv2rt3r7p3756FqQEAAAAAOZXHzCyrQ2TUoUOHVLx4cf3yyy+69tprFRMTo2LFiun9999Xz549JUkbNmxQjRo1tHjxYl199dUp7iMuLk5xcXHe32NjYxUVFaWYmBiFhYVl2r8lIzyezHusiy0lmZlDIktayOLeHBJZ0hIIWdySQyJLWnJqFrfkkMiSFrK4N4dElrQEShY3iI2NVXh4+L92x4Da0n2hmJgYSVJERIQkadmyZUpISFCbNm2881SvXl1ly5bV4sWLU72PadOmKTw83PsTFRXlfHAAAAAAQI4QsKU7KSlJQ4cOVbNmzXTFFVdIkvbv3688efKoUKFCPvOWKFFC+/fvT/V+xo4dq5iYGO/Prl27nI4OAAAAAMghcmV1gIy67777tGbNGi1cuPCy7ickJEQhISF+SgUAAAAAwP8JyC3d999/v7788kv99NNPKlOmjHd6ZGSk4uPjFR0d7TP/gQMHFBkZmckpAQAAAAA5XUCVbjPT/fffr08//VT/+9//VKFCBZ/b69evr9y5c+vHH3/0Ttu4caN27typJk2aZHZcAAAAAEAOF1C7l9933316//339fnnn6tgwYLe47TDw8OVL18+hYeHa+DAgRo+fLgiIiIUFhamBx54QE2aNEn1zOUAAAAAADgpoEr3Sy+9JElq2bKlz/Q33nhD/fr1kyQ9++yzCgoKUo8ePRQXF6d27drpxRdfzOSkAAAAAAAE+HW6nXCp11pzA66pmBJZUpdTs7glh0SWtARCFrfkkMiSlpyaxS05JLKkhSzuzSGRJS2BksUNcsR1ugEAAAAAcDNKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADgmo0v3rr7+qc+fOKlWqlDwejz777DOf281MEydOVMmSJZUvXz61adNGmzZtypqwAAAAAIAcL6BK98mTJ1WnTh3NmjUr1dunT5+umTNnavbs2frzzz9VoEABtWvXTmfOnMnkpAAAAAAASLmyOkB6dOjQQR06dEj1NjPTf/7zH40fP1433nijJOntt99WiRIl9Nlnn+mmm27KzKgAAAAAAATWlu6L2bZtm/bv3682bdp4p4WHh6tx48ZavHhxmn8XFxen2NhYnx8AAAAAAPwh25Tu/fv3S5JKlCjhM71EiRLe21Izbdo0hYeHe3+ioqIczQkAAAAAyDmyTenOqLFjxyomJsb7s2vXrqyOBAAAAADIJrJN6Y6MjJQkHThwwGf6gQMHvLelJiQkRGFhYT4/AAAAAAD4Q7Yp3RUqVFBkZKR+/PFH77TY2Fj9+eefatKkSRYmAwAAAADkVAF19vITJ05o8+bN3t+3bdumFStWKCIiQmXLltXQoUM1ZcoUValSRRUqVNCECRNUqlQpde3aNetCAwAAAAByrIAq3UuXLlWrVq28vw8fPlySdMcdd+jNN9/UqFGjdPLkSQ0ePFjR0dG65ppr9O233ypv3rxZFRkAAAAAkIN5zMyyOoSbxMbGKjw8XDExMa4/vtvjybzHuthSkpk5JLKkhSzuzSGRJS2BkMUtOSSypCWnZnFLDoksaSGLe3NIZElLoGRxg0vtjtnmmG4AAAAAANyG0g0AAAAAgEMo3QAAAAAAOITSDQAAAACAQyjdAAAAAAA4hNINAAAAAIBDKN0AAAAAADgkV0b/MDExUc8++6w+/PBD7dy5U/Hx8T63Hz169LLDAQAAAAAQyDK8pfuRRx7RM888oz59+igmJkbDhw9X9+7dFRQUpMmTJ/sxIgAAAAAAgSnDpfu9997TK6+8ohEjRihXrly6+eab9eqrr2rixIn6448//JkRAAAAAICAlOHSvX//ftWuXVuSFBoaqpiYGEnSDTfcoK+++so/6QAAAAAACGAZLt1lypTRvn37JEmVKlXSggULJElLlixRSEiIf9IBAAAAABDAMly6u3Xrph9//FGS9MADD2jChAmqUqWK+vbtqwEDBvgtIAAAAAAAgcpjZuaPO/rjjz+0aNEiValSRZ07d/bHXWaJ2NhYhYeHKyYmRmFhYVkd56I8nsx7rIstJZmZQyJLWsji3hwSWdISCFnckkMiS1pyaha35JDIkhayuDeHRJa0BEoWN7jU7pjhS4Zd6Oqrr9bVV1/tr7sDAAAAACDgZXj38mnTpun1119PMf3111/Xk08+eVmhAAAAAADIDjJcul9++WVVr149xfRatWpp9uzZlxUKAAAAAIDs4LIuGVayZMkU04sVK+Y9qzkAAAAAADlZhkt3VFSUfv/99xTTf//9d5UqVeqyQgEAAAAAkB1k+ERqgwYN0tChQ5WQkKDWrVtLkn788UeNGjVKI0aM8FtAAAAAAAACVYZL90MPPaQjR47o3nvvVXx8vCQpb968Gj16tMaOHeu3gAAAAAAABKrLvk73iRMntH79euXLl09VqlRRSEiIv7JlCa7TnbpAuV4fWVKXU7O4JYdElrQEQha35JDIkpacmsUtOSSypIUs7s0hkSUtgZLFDTLtOt2hoaFq2LDh5d4NAAAAAADZTrpKd/fu3fXmm28qLCxM3bt3v+i88+bNu6xgAAAAAAAEunSV7vDwcHn+/z4F4eHhjgQCAAAAACC7uOxjurMbjulOXaAc20GW1OXULG7JIZElLYGQxS05JLKkJadmcUsOiSxpIYt7c0hkSUugZHGDS+2OGb5ONwAAAAAAuLgMn0jtyJEjmjhxon766ScdPHhQSUlJPrcfPXr0ssMBAAAAABDIMly6b7/9dm3evFkDBw5UiRIlvMd6AwAAAACAczJcun/77TctXLhQderU8WceAAAAAACyjQwf0129enWdPn3an1kAAAAAAMhWMly6X3zxRY0bN06//PKLjhw5otjYWJ8fAAAAAAByugzvXl6oUCHFxsaqdevWPtPNTB6PR4mJiZcdDgAAAACAQJbh0n3rrbcqd+7cev/99zmRGgAAAAAAqchw6V6zZo2WL1+uatWq+TMPAAAAAADZRoaP6W7QoIF27drlzywAAAAAAGQrGd7S/cADD2jIkCF66KGHVLt2beXOndvn9iuvvPKywwEAAAAAEMg8ZmYZ+cOgoJQbyT0eT8CfSC02Nlbh4eGKiYlRWFhYVse5qMw8jP5iS0lmH85PltSRxb05JLKkJRCyuCWHRJa05NQsbskhkSUtZHFvDoksaQmULG5wqd0xw1u6t23bltE/BQAAAAAgR8hQ6U5ISFDr1q315ZdfqkaNGv7OBAAAAABAtpChE6nlzp1bZ86c8XcWAAAAAACylQyfvfy+++7Tk08+qbNnz/ozDwAAAAAA2UaGj+lesmSJfvzxRy1YsEC1a9dWgQIFfG6fN2/eZYcDAAAAACCQZbh0FypUSD169PBnFgAAAAAAspUMl+433njDnzkAAAAAAMh2MnxMtySdPXtWP/zwg15++WUdP35ckrR3716dOHHCL+EAAAAAAAhkl7yl+9SpU8qfP7/39x07dqh9+/bauXOn4uLidP3116tgwYJ68sknFRcXp9mzZzsSGAAAAACAQHHJW7qfffZZzZkzx/v7kCFD1KBBAx07dkz58uXzTu/WrZt+/PFH/6YEAAAAACAAXfKW7ttuu029evXS7t279eijj+q3337TokWLlCdPHp/5ypcvrz179vg9KAAAAAAAgeaSt3SXK1dOv/32m44cOSJJSkpKUmJiYor5du/erYIFC/ovIQAAAAAAASpdJ1ILCQnRrFmzJElt27bVf/7zH+9tHo9HJ06c0KRJk9SxY0e/hgQAAAAAIBB5zMzS8wfBwcHat2+f4uPj1a5dO5mZNm3apAYNGmjTpk0qWrSofv31VxUvXtypzI6KjY1VeHi4YmJiFBYWltVxLsrjybzHuthSkpk5JLKkhSzuzSGRJS2BkMUtOSSypCWnZnFLDoksaSGLe3NIZElLoGRxg0vtjum+TndyRy9TpoxWrlypuXPnatWqVTpx4oQGDhyoW2+91efEagAAAAAA5FTpLt0+f5wrl2677TZ/ZQEAAAAAIFvJUOl+9dVXFRoaetF5HnzwwQwFAgAAAAAgu0j3Md1BQUEqU6aMgoOD075Tj0dbt2697HBZgWO6Uxcox3aQJXU5NYtbckhkSUsgZHFLDoksacmpWdySQyJLWsji3hwSWdISKFncwLFjuiVp6dKlAXuiNAAAAAAAMku6LhkmnduKDQAAAAAA/l26S3c690YHAAAAACDHSnfpnjRp0r+eRO189957rw4fPpzehwEAAAAAIOBlqHTnz5//kud/9913FRsbm96HAQAAAAAg4KW7dKcXu6MDAAAAAHIqx0s3AAAAAAA5FaUbAAAAAACHULoBAAAAAHAIpRsAAAAAAIc4Xrpvu+02hYWFOf0wAAAAAAC4Tq7L+ePo6Gi99tprWr9+vSSpVq1aGjBggMLDw73zvPTSS5eXEAAAAACAAJXhLd1Lly5VpUqV9Oyzz+ro0aM6evSonnnmGVWqVEl///23PzMCAAAAABCQPJbBC2k3b95clStX1iuvvKJcuc5tMD979qzuvPNObd26Vb/++qtfg2aW2NhYhYeHKyYmxvW7xXs8mfdYF1tKMjOHRJa0kMW9OSSypCUQsrglh0SWtOTULG7JIZElLWRxbw6JLGkJlCxucKndMcO7ly9dutSncEtSrly5NGrUKDVo0CCjdwsAAAAAQLaR4d3Lw8LCtHPnzhTTd+3apYIFC15WKAAAAAAAsoMMl+4+ffpo4MCB+uCDD7Rr1y7t2rVLc+fO1Z133qmbb77ZnxkBAAAAAAhIGd69fMaMGfJ4POrbt6/Onj0rScqdO7fuuecePfHEE34LCAAAAABAoMrwidSSnTp1Slu2bJEkVapUSfnz5/dLsKzCidRSFygnVCBL6nJqFrfkkMiSlkDI4pYcElnSklOzuCWHRJa0kMW9OSSypCVQsriB4ydSS5Y/f37Vrl37cu8GAAAAAIBs57JK99KlS/Xhhx9q586dio+P97lt3rx5lxUMAAAAAIBAl64Tqd1///1atmyZJGnu3Llq2rSp1q9fr08//VQJCQlau3at/ve//yk8PNyRsAAAAAAABJJ0le6uXbt6z0w+depUPfvss/riiy+UJ08ePffcc9qwYYN69+6tsmXLOhIWAAAAAIBAkq7S/dtvv6lFixaSpC1btqhTp06SpDx58ujkyZPyeDwaNmyY5syZ4/+kAAAAAAAEmHSV7pkzZ6pbt26SpMKFC+v48eOSpNKlS2vNmjWSpOjoaJ06dcrPMQEAAAAACDzpKt2vvfaa5s6dK0m69tpr9f3330uSevXqpSFDhmjQoEG6+eabdd111/k/KQAAAAAAASbD1+k+evSozpw5o1KlSikpKUnTp0/XokWLVKVKFY0fP16FCxf2d9ZMwXW6Uxco1+sjS+pyaha35JDIkpZAyOKWHBJZ0pJTs7glh0SWtJDFvTkksqQlULK4gePX6Y6IiPD+f1BQkMaMGZPRuwIAAAAAIFtK1+7l5/v666/13XffpZi+YMECffPNN5cVCgAAAACA7CDDpXvMmDFKTExMMT0pKUljxozRP//8o+eee06rV6++rIAAAAAAAASqDJfuTZs2qWbNmimmV69eXatXr9bIkSP166+/qnPnzpcVEAAAAACAQJXh0h0eHq6tW7emmL5582ZFRERo/vz5mjFjhg4dOnRZATNq1qxZKl++vPLmzavGjRvrr7/+ypIcAAAAAICcK8Ol+8Ybb9TQoUO1ZcsW77TNmzdrxIgR6tq1qyQpNDRUn3/++WWHTK8PPvhAw4cP16RJk/T333+rTp06ateunQ4ePJjpWQAAAAAAOVeGLxkWExOj9u3ba+nSpSpTpowkaffu3WrevLnmzZunQoUK+TNnujRu3FgNGzbUCy+8IOncceZRUVF64IEHUpxlPS4uTnFxcd7fY2NjFRUVxSXDLhAolw4gS+pyaha35JDIkpZAyOKWHBJZ0pJTs7glh0SWtJDFvTkksqQlULK4geOXDAsPD9eiRYv0/fffa+XKlcqXL5+uvPJKXXvttRm9S7+Ij4/XsmXLNHbsWO+0oKAgtWnTRosXL04x/7Rp0/TII49kZkS/cftCCKSG5RbIHngtu5ubnh+yAMjpMly6Jcnj8aht27Zq27atv/JctsOHDysxMVElSpTwmV6iRAlt2LAhxfxjx47V8OHDvb8nb+kGAAAAAOByXVbpPnnypH755Rft3LlT8fHxPrc9+OCDlxUss4SEhCgkJCSrYwAAAAAAsqEMl+7ly5erY8eOOnXqlE6ePKmIiAgdPnxY+fPnV/HixbOsdBctWlTBwcE6cOCAz/QDBw4oMjIySzIBAAAAAHKmDJ+9fNiwYercubOOHTumfPny6Y8//tCOHTtUv359zZgxw58Z0yVPnjyqX7++fvzxR++0pKQk/fjjj2rSpEmW5QIAAAAA5DwZLt0rVqzQiBEjFBQUpODgYMXFxSkqKkrTp0/Xww8/7M+M6TZ8+HC98soreuutt7R+/Xrdc889OnnypPr375+luQAAAAAAOUuGdy/PnTu3goLOdfbixYtr586dqlGjhsLDw7Vr1y6/BcyIPn366NChQ5o4caL279+vq666St9++22Kk6sBAAAAAOCkDJfuunXrasmSJapSpYpatGihiRMn6vDhw3rnnXd0xRVX+DNjhtx///26//77szoGAAAAACAHy/Du5VOnTlXJkiUlSY8//rgKFy6se+65R4cOHdKcOXP8FhAAAAAAgEDlMTPL6hBuEhsbq/DwcMXExCgsLCyr4wQEjydzH+9iSyxZUuemLG7hpjEhS+rcksUtOSR3ZXETN41LZmYJlOcHqWO5zdocElnSEihZ3OBSu2OGdy8/ffq0zEz58+eXJO3YsUOffvqpatasqbZt22b0bgE4wO1vWAAuDa/l1LlpXNyUBQDgDhnevfzGG2/U22+/LUmKjo5Wo0aN9PTTT+vGG2/USy+95LeAAAAAAAAEqgyX7r///lvNmzeXJH388ceKjIzUjh079Pbbb2vmzJl+CwgAAAAAQKDKcOk+deqUChYsKElasGCBunfvrqCgIF199dXasWOH3wICAAAAABCoMly6K1eurM8++0y7du3Sd9995z2O++DBg5yADAAAAAAAXUbpnjhxokaOHKny5curcePGatKkiaRzW73r1q3rt4AAAAAAAASqy7pk2P79+7Vv3z7VqVNHQUHn+vtff/2lsLAwVa9e3W8hMxOXDEs/N106gCy4VG56fsiSOrdkcUsOANmLm95buGRYSmRJnZuyuIHjlwyTpMjISEVGRvpMa9So0eXcJQAAAAAA2UaGS/fJkyf1xBNP6Mcff9TBgweVlJTkc/vWrVsvOxwAAAAAAIEsw6X7zjvv1C+//KLbb79dJUuWlCez9zUAAAAAAMDlMly6v/nmG3311Vdq1qyZP/MAAAAAAJBtZPjs5YULF1ZERIQ/swAAAAAAkK1kuHQ/9thjmjhxok6dOuXPPAAAAAAAZBsZ3r386aef1pYtW1SiRAmVL19euXPn9rn977//vuxwAAAAAAAEsgyX7q5du/oxBgAAAAAA2U+GS/ekSZP8mQMAAAAAgGwnw8d0S1J0dLReffVVjR07VkePHpV0brfyPXv2+CUcAAAAAACBLMNbuletWqU2bdooPDxc27dv16BBgxQREaF58+Zp586devvtt/2ZEwAAAACAgJPhLd3Dhw9Xv379tGnTJuXNm9c7vWPHjvr111/9Eg4AAAAAgECW4dK9ZMkS3XXXXSmmly5dWvv377+sUAAAAAAAZAcZLt0hISGKjY1NMf2ff/5RsWLFLisUAAAAAADZQYZLd5cuXfToo48qISFBkuTxeLRz506NHj1aPXr08FtAAAAAAAACVYZL99NPP60TJ06oePHiOn36tFq0aKFKlSopNDRUjz/+uD8zAgAAAAAQkDJ89vLw8HB9//33WrhwoVatWqUTJ06ofv36uu666/yZDwAAAACAgJXuLd2LFy/Wl19+6f39mmuuUYECBfTiiy/q5ptv1uDBgxUXF+fXkAAAAAAABKJ0l+5HH31Ua9eu9f6+evVqDRo0SNdff73GjBmjL774QtOmTfNrSAAAAAAAAlG6S/eKFSt8diGfO3euGjVqpFdeeUXDhw/XzJkz9eGHH/o1JAAAAAAAgSjdpfvYsWMqUaKE9/dffvlFHTp08P7esGFD7dq1yz/pAAAAAAAIYOku3SVKlNC2bdskSfHx8fr777919dVXe28/fvy4cufO7b+EAAAAAAAEqHSX7o4dO2rMmDH67bffNHbsWOXPn1/Nmzf33r5q1SpVqlTJryEBAAAAAAhE6b5k2GOPPabu3burRYsWCg0N1VtvvaU8efJ4b3/99dfVtm1bv4YEAAAAACAQeczMMvKHMTExCg0NVXBwsM/0o0ePKjQ01KeIB5LY2FiFh4crJiZGYWFhWR0nIHg8mft4F1tiyYJL5abnhyypc0sWt+QAkL246b0lM7O4JYdElrQEShY3uNTumO4t3cnCw8NTnR4REZHRuwQAAAAAIFtJ9zHdAAAAAADg0lC6AQAAAABwCKUbAAAAAACHULoBAAAAAHAIpRsAAAAAAIdQugEAAAAAcEiGLxkGuJHbr+UHAAAgsc6SGjeNiZuyuAnjkjFs6QYAAAAAwCGUbgAAAAAAHELpBgAAAADAIZRuAAAAAAAcQukGAAAAAMAhlG4AAAAAABxC6QYAAAAAwCGUbgAAAAAAHELpBgAAAADAIZRuAAAAAAAcQukGAAAAAMAhlG4AAAAAABxC6QYAAAAAwCGUbgAAAAAAHELpBgAAAADAIZRuAAAAAAAcQukGAAAAAMAhlG4AAAAAABySK6sDANmVWVYnAAAAQKBjnTLwsaUbAAAAAACHULoBAAAAAHAIpRsAAAAAAIdQugEAAAAAcAilGwAAAAAAh1C6AQAAAABwCKUbAAAAAACHULoBAAAAAHAIpRsAAAAAAIdQugEAAAAAcAilGwAAAAAAh1C6AQAAAABwCKUbAAAAAACHULoBAAAAAHAIpRsAAAAAAIdQugEAAAAAcAilGwAAAAAAh1C6AQAAAABwCKUbAAAAAACH5MrqAACQFcyyOgEAAAByArZ0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4JCAKt2PP/64mjZtqvz586tQoUKpzrNz50516tRJ+fPnV/HixfXQQw/p7NmzmRsUAAAAAAAF2CXD4uPj1atXLzVp0kSvvfZaitsTExPVqVMnRUZGatGiRdq3b5/69u2r3Llza+rUqVmQGAAAAACQk3nMAu9qtW+++aaGDh2q6Ohon+nffPONbrjhBu3du1clSpSQJM2ePVujR4/WoUOHlCdPnhT3FRcXp7i4OO/vsbGxioqKUkxMjMLCwhz9d2QXHk/mPl7gLbHAxbnpNUQW9+YAAKdk5vsc73HITmJjYxUeHv6v3TGgdi//N4sXL1bt2rW9hVuS2rVrp9jYWK1duzbVv5k2bZrCw8O9P1FRUZkVFwAAAACQzWWr0r1//36fwi3J+/v+/ftT/ZuxY8cqJibG+7Nr1y7HcwIAAAAAcoYsL91jxoyRx+O56M+GDRsce/yQkBCFhYX5/AAAAAAA4A9ZfiK1ESNGqF+/fhedp2LFipd0X5GRkfrrr798ph04cMB7GwAAAAAAmSnLS3exYsVUrFgxv9xXkyZN9Pjjj+vgwYMqXry4JOn7779XWFiYatas6ZfHAAAAAADgUmV56U6PnTt36ujRo9q5c6cSExO1YsUKSVLlypUVGhqqtm3bqmbNmrr99ts1ffp07d+/X+PHj9d9992nkJCQrA0PAAAAAMhxAqp0T5w4UW+99Zb397p160qSfvrpJ7Vs2VLBwcH68ssvdc8996hJkyYqUKCA7rjjDj366KNZFRkAAAAAkIMF5HW6nXSp11rD/+EatsDlcdNriCzuzQEATuE63UDG5MjrdAMAAAAA4CaUbgAAAAAAHELpBgAAAADAIZRuAAAAAAAcQukGAAAAAMAhlG4AAAAAABxC6QYAAAAAwCGUbgAAAAAAHELpBgAAAADAIZRuAAAAAAAcQukGAAAAAMAhlG4AAAAAABySK6sDAEBOZ5bVCQAAAOAUtnQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAAAAAAOoXQDAAAAAOCQgCnd27dv18CBA1WhQgXly5dPlSpV0qRJkxQfH+8z36pVq9S8eXPlzZtXUVFRmj59ehYlBgAAAADkdLmyOsCl2rBhg5KSkvTyyy+rcuXKWrNmjQYNGqSTJ09qxowZkqTY2Fi1bdtWbdq00ezZs7V69WoNGDBAhQoV0uDBg7P4XwAAAAAAyGk8ZmZZHSKjnnrqKb300kvaunWrJOmll17SuHHjtH//fuXJk0eSNGbMGH322WfasGHDJd1nbGyswsPDFRMTo7CwMMeyZyceT+Y+XuAusYD7uen17JYsbskBAE7JzPc53uOQnVxqdwyY3ctTExMTo4iICO/vixcv1rXXXust3JLUrl07bdy4UceOHUv1PuLi4hQbG+vzAwAAAACAPwRs6d68ebOef/553XXXXd5p+/fvV4kSJXzmS/59//79qd7PtGnTFB4e7v2JiopyLnQ2ZZa5PwCc46bXs1uyuCUHADiF9zjAWVleuseMGSOPx3PRnwt3Dd+zZ4/at2+vXr16adCgQZf1+GPHjlVMTIz3Z9euXZd1fwAAAAAAJMvyE6mNGDFC/fr1u+g8FStW9P7/3r171apVKzVt2lRz5szxmS8yMlIHDhzwmZb8e2RkZKr3HRISopCQkAwkBwAAAADg4rK8dBcrVkzFihW7pHn37NmjVq1aqX79+nrjjTcUFOS7ob5JkyYaN26cEhISlDt3bknS999/r2rVqqlw4cJ+zw4AAAAAwMVk+e7ll2rPnj1q2bKlypYtqxkzZujQoUPav3+/z7Hat9xyi/LkyaOBAwdq7dq1+uCDD/Tcc89p+PDhWZgcAAAAAJBTZfmW7kv1/fffa/Pmzdq8ebPKlCnjc1vyVc/Cw8O1YMEC3Xfffapfv76KFi2qiRMnco1uAAAAAECWCOjrdDuB63QDAAAAAP5NjrhONwAAAAAAbkbpBgAAAADAIZRuAAAAAAAcQukGAAAAAMAhlG4AAAAAABxC6QYAAAAAwCGUbgAAAAAAHELpBgAAAADAIZRuAAAAAAAcQukGAAAAAMAhlG4AAAAAABxC6QYAAAAAwCGUbgAAAAAAHELpBgAAAADAIbmyOoDbmJkkKTY2NouTAAAAAADcKrkzJnfItFC6L3D8+HFJUlRUVBYnAQAAAAC43fHjxxUeHp7m7R77t1qewyQlJWnv3r0qWLCgPB5PVsfxq9jYWEVFRWnXrl0KCwsjC1kCIotbcpCFLIGYgyxkCcQcZCFLIOYgS2Bk8Tcz0/Hjx1WqVCkFBaV95DZbui8QFBSkMmXKZHUMR4WFhblmgSdL6sji3hwSWdJCFvfmkMiSFrK4N4dElrSQxb05JLKkxU1Z/OliW7iTcSI1AAAAAAAcQukGAAAAAMAhlO4cJCQkRJMmTVJISEhWRyELWQIuB1nIEog5yEKWQMxBFrIEYg6yBEaWrMKJ1AAAAAAAcAhbugEAAAAAcAilGwAAAAAAh1C6AQAAAABwCKUbAAAAAACHULoBAAAAAHAIpRupctNJ7cmSOrdkcUsOiSxpcUsWt+SQ3JUFAJA53PTeT5bUuSmLP1G64XXmzBmdPXtWkuTxeLI0y8mTJxUTE6OEhIQsz+KmcXFLFrfkcFsWltuU3DQmbsqSGresaLglh0SWtJAlJbfkkMiSlqzK4pbPQ8ldn0NuGhc3ZXEKpRuSpDVr1uiGG25Q8+bNVbduXb377rvatWtXlmW57rrr1KJFC1WtWlVTpkzRunXrsiyLm8bFDVncksONWVhuU+Zw05i4JYsk7d27V3/88Ye+++47nThxQtK5FY2kpKRMzbFr1y599dVXeu+997Rly5YsyyG5Z0zI4v4sLLepc9O4uCWLWz4Pk7O45XPIbePiliyOMuR4W7ZssUKFCtmgQYNszpw5dtttt1nVqlXtjjvusFWrVmVqlu3bt1uRIkXs3nvvtS+++MJGjhxpjRo1spYtW9rvv/+eqVncNC5uyeKWHG7LwnKbkpvGxE1ZzMxWrlxppUuXtiuvvNI8Ho81adLEpk2bZklJSWZmlpiYmCk5Vq1aZcWLF7fGjRtb7ty5rV69enbXXXd5Hz+zcpi5Z0zI4v4sLLepc9O4uCWLWz4Pzdz1OeSmcXFTFqdRumFPPfWUXX/99T7T5syZY82bN7eePXva+vXrMy3L22+/bc2bN/d5Q/7yyy+tS5cu1qBBA/vzzz8zLYubxsUtWdySw21ZWG5TctOYuCnLkSNHrEaNGjZy5Ejbs2eP7dy50wYNGmQNGza0gQMHelfWk//rlJiYGKtXr54NGTLEYmNj7eDBg/bEE09YnTp1rE2bNpm6cuyWMSGL+7Ow3KbOTePipixu+Tw0c9fnkJvGxU1ZnMbu5VBiYqL27NmjmJgY77RBgwZp0KBB2rNnj958802dPHkyU7LEx8drw4YN2r9/v3dap06d9OCDDyoyMlLPPPOMDhw4kClZ3DQubsnilhxuy8Jym5KbxsRNWfbv36/4+Hj17dtXpUqVUlRUlKZPn66bbrpJy5Yt09ChQyU5f0xbbGysTpw4oe7du6tgwYIqVqyYHnjgAU2ePFn79u1T9+7dZWYKCnJ+NcEtY0IW92dhuU2dm8bFTVnc8nkouetzyE3j4qYsTqN052D2/09oUbp0acXExGjz5s2S5D2Rwe23364uXbro1Vdf1eHDhzMlS6VKlRQREaFFixb5HPdz3XXXqVevXvrtt9+0c+fOTMnipnHJ6ixuyeHWLCy3KXO4aUzckCVZgQIFdPbsWa1atcqbsVChQho8eLB69eqlRYsW6YsvvnA8R1hYmCTp999/907Lnz+/OnbsqHHjxmn79u2aNWuW4zkk94wJWdyfheU2dW4aFzdkccvn4flZ3PA55MZxcUOWTJN5G9XhFklJSSl262nevLldccUVdvToUTMzS0hI8N5WsmRJe/755x3JEh8fb/Hx8T7TunXrZqVLl7a///47xfyVKlWyCRMmOJLFTePilixuyeG2LCy3KblpTNyU5ULR0dHWqlUr69q1qx08eNDntuPHj1u9evVs0KBBjuc4ffq0DRgwwK677jpbvny5z21nzpyx7t27W7du3RzPYeaeMSGL+7Ow3KbOTeOSlVnc8nlo5q7PITeNi5uyZDa2dOcw69ev14MPPqgbbrhBTzzxhL799ltJ0ty5c3X27Fm1adNGe/bsUa5cuSRJx48fV8mSJRUZGen3LGvXrtWAAQPUokULPfDAA3rrrbckSfPmzVP58uXVs2dPLVq0yPutV0JCgkqXLq0yZcr4PYubxsUtWdySw21ZWG5TctOYuCmLJEVHR2vz5s06ePCgTpw4ofDwcE2fPl3ffPONJk2apOPHj3vnDQ0NVadOnbRp0yZvPn85dOiQ/v77b23YsEFHjhxR3rx5NXLkSG3YsEGTJ0/WP//84503JCRErVq10rZt2xzZrc8tY0IW92dhuU2dm8bFLVnc8nkouetzyE3j4qYsWSKrWz8yz7p166xQoULWq1cvu+WWW6xevXpWq1Yte+KJJ8zMbOPGjVanTh2rUKGCvfTSS/bpp5/amDFjrEiRIrZ161a/Ztm4caMVKlTIBgwYYEOHDrUbb7zRihUrZg8++KCZnfumtGXLllayZEkbNWqUzZkzx4YNG2aFChWyf/75x69Z3DQubsnilhxuy8Jym5KbxsRNWczOndm4du3aVrlyZStfvrx1797dVqxYYWZmn3/+uYWEhFi/fv1sw4YN3r+57bbb7KabbrKzZ8/6NUeVKlWsUqVKVrp0aatbt679/PPPZma2YsUKCw8Pty5dutiCBQu8f3PXXXdZx44d7cyZM37LkZzFDWNCFvdnYblNO4ubxsUNWdzyeZj8WG75HHLTuLgpS1ahdOcQiYmJNmTIELv55pu9Z9L8559/bPLkyVakSBF79NFHzezc7jB9+/a1unXrWoUKFaxRo0ap7gZzuR555BHr2LGjdxeTQ4cO2WuvvWZ58+a1u+++2zvfmDFjrEOHDlatWrVUd1W6XG4aF7dkcUsOt2UxY7lNjVvGxG1Zdu/ebZGRkTZs2DD766+/7IUXXrAOHTpYWFiY/frrr2Zm9sMPP1jRokWtWbNm1rJlS7v55pstNDTUr5dJ2bdvn5UtW9ZGjRplmzZtss8//9xuvfVWCw4OtjfeeMPMzl3ep379+la3bl2rWbOmde7c2cLCwrylwl/cMiZkcX8WltvUuWlc3JLFTZ+HZu75HHLTuLgpS1aidOcgnTp1sl69evlMO3DggE2bNs3KlCljL730knf6/v377cCBA3bs2DFHsvTr189atWrlMy0uLs7ef/99y58/v89xLadPn7Zjx47ZiRMnHMnipnFxSxa35HBbFpbblNw0Jm7K8tNPP1n9+vXt8OHD3mlbtmyxW2+91fLmzWuLFy82s3MrHs8995z169fPRo8ebWvXrvVrjhUrVlitWrVsy5Yt3mmnT5+2MWPGWK5cuezjjz82M7OdO3fa/PnzbdiwYfb000/7bLHzF7eMCVncn4XlNnVuGhc3ZXHL56GZuz6H3DQubsqSVSjdOcj06dOtefPmtnHjRp/pu3btskGDBln79u19PlSc9P7771ulSpXs999/95l+/PhxmzJlitWrVy9FTqe4aVzcksUtOdyWheU2JTeNiZuyfPrppxYUFGR79+71mb53717r06ePVahQwTZv3uxzmxPX8/3111/N4/HYpk2bzMz32rgPPvighYaG2po1a/z+uKlxy5iQxf1ZWG5T56ZxcVMWt3wemrnrc8hN4+KmLFmFE6nlIA0aNNDu3bv1/vvv6+jRo97pZcqUUZ8+ffT9999n2mVzatWqpcjISL311lvauHGjd3poaKg6dOigDRs2ZFoWN42LW7K4JYfbsrDcpuSmMXFTlqZNm6phw4Z67rnnFBsb651esmRJDR8+XEWLFtWiRYsknbtOqeTM9XybNWumFi1a6OGHH9bhw4cVFBTkvVTL6NGj1aBBA3344YcyM28Op7hlTMji/iwst6lz07i4KYtbPg8ld30OuWlc3JQlq1C6s7nz3+hatWqlIUOGaMqUKZo9e7b27t3rva1atWqqWbNmpmW58sorddddd+nbb7/VCy+84L3OpSRVqVJF1apV87mOoZNZ3DQuWZnFLTncnIXlNmUON41JVmc5X/HixdWiRQstWLBAn3zyiU6fPu29rVGjRkpKSvJexzY4ONixHEFBQerVq5d27typmTNnKjo62lsISpUqpdDQUG3YsEEej8fRHJJ7xoQs7s/Ccps6N41LVmdxy+fhhVmy+nPIreOS1VncIFdWB4CzgoODZWZauHChmjdvriFDhigxMVGTJ0/Wrl271KVLF9WuXdv7hlmyZEnHs3zyySfq2bOnbr/9diUlJemJJ57Qjh071LNnT9WtW1dvv/229u7d6+gL0I3jktVZ3JDDzLwf0G7LklXLbXIOyR3P0fk53PRadkOWxMREBQcHKykpSUFBQXryySe1detWPfPMMzp16pT69++v/PnzS5LKly+vUqVKOZIj+fGT/3vvvfdq69at+vrrr3Xq1CmNGzdOhQsXliRFREQoLCxMiYmJCgoK8vsWOreMCVncn8Uty62ZKSkpyRVjIrlnXNyWxS2fh+dnccPnkBvHxQ1ZXCGz9mNH5ku+TMWAAQOscuXKtnDhQu9t77zzjl1//fUWGhpqV1xxhZUtWzZTzhA4btw4K126tM2bN8877euvv7Z+/fpZgQIFrFatWla1alVHsyQkJJiZO8YlK5+j5HEw+79jsbJqTFI7WUZWZTl48GCK4/Gyarndvn27ffPNN2Z27qyeZu5Ybs3c8VpOXm7dkCVZUlKSjRs3zn788UfvtH79+lndunWtdevWNm3aNBswYIAVLFjQ1q1b52iOwYMH2wcffOCdNmnSJLv66qutSpUqNnz4cOvdu3emHHfpljEhi/uzZOVye+HJrNwyJslZ3PR6zuosrN+mjvVbd6N0ZyN79+61P//807799luf60KuX7/e7r777hTF5uDBg7Z69WpbtmyZ7d+/369Zkh///BNrmJnt2LHDHnnkETt69KjP9Li4ONu9e7dt3brV7ydSOHz4sK1fv957htFkGzduzPRx2b17ty1YsMDefPNNn+cos7Ns2LDBRo8e7T0BSlblMDNbvny5XXPNNbZy5cosz7J69WqrVq2azZo1y2fZ3bFjh02ePDlTl9vVq1dbrly57IorrvCZntnjsmXLFnv22Wdt+PDh9ttvv9mpU6fMLGtey3v27LG//vrLvvjiCztz5oz3y5Ft27Zl+vOTlgULFlhkZKRNnjzZTp8+7Z3+zjvvWN++fa1JkybWvXv3FMu7vy1btszq169v999/v/c5Mzt38qNhw4ZZx44drX///rZ69WpHc5idu5xSVoxJUlJSimskZ1WWI0eO2MGDB12RZdOmTfbXX39leZZNmzbZvHnzLC4uzjstq5bbDRs22G233Wa7du3yTsuq5+fkyZN29OhRn8dcsmSJa17Pf/31V6ZnYf02dazfBhZKdzaxcuVKK1eunFWtWtXCw8OtevXq9v7779uBAwfMzHerptNWr15tLVu2tJ07d5rZ/70xJb8InTqrZ2pWrVpljRo1smrVqlnx4sWtXbt2Prdf+KbpdJaqVatavXr1rECBAlavXj3v1svMypKUlGSnTp2yhg0bmsfjsbvvvjvF85SZz8+KFSssd+7c9tBDD6V6e2Y+P+vXr7fChQvb8OHDbceOHSluv3Dl3UnLly+3AgUKWKdOnaxSpUr29ttvm9n/vY4za1xWrVplxYsXtw4dOljFihWtfPnyPtdXzcznZ+XKlVa+fHlr2LChlSxZ0sqXL2+zZ8+2ffv2mVnmPj9m51YkRo0aZf369bP//Oc/9s8//3hv++GHH7wrXhe+ns6cOePX9+OLXVZl+fLlFh0dneptSUlJfn+tb9261Z555hkbPny4zZ071+e2n376KdPGxOzc8zNkyBDr1KmTPfLIIz4ru5mdZcuWLVaxYkWbMGGC7dmzx+e2zM6yfPlyCwsLszlz5qS4LTOzrFy50ooVK2aDBg1KMSbLli3L1OV2xYoVli9fPvN4PN7rSyfL7OdnzZo1dsMNN1iNGjWsa9eu9uWXX3pvy+zX84YNG2zMmDF222232VNPPeVzHeklS5ZkWhbWb1PH+m3goXRnAwcPHrTq1avbww8/bFu2bLE9e/ZYnz59rEaNGjZp0qQU367PnDnTe/1Ef9u2bZtVrlzZPB6PValSxfutcVovuGeeecaeeuopR7Js2LDBihYtamPGjLHFixfbd999ZxUrVrSxY8emOr+T47J+/XorWrSojR8/3nbs2GFbt261okWL+nygZlYWM7OHH37Y+vfvb/ny5bObb77Ztm3bluk51qxZY/ny5bOJEyea2bkPqyNHjtjWrVszPUtiYqINHjzY+vfv7/39119/tddff902btyYotQ4udyuWLHCey3P+Ph4u/rqq+32229Pc36nxmXv3r1Wo0YNmzx5sneFombNmvbiiy+mOr+TY7Jr1y6rXLmyPfLII7Z3715LSkqy7t27W968eW3o0KEpVtqdzGJmtnbtWgsPD7f27dtbjx49LDw83Nq0aWOzZ89Odf7t27c7kmPdunWWJ08e69mzp8XExHinp/V+6+QusKtWrbIyZcrYddddZ02bNrWgoCCbPn16mvM7NSbJWYoXL249e/a0u+66y/LkyWOTJ0/OkixmZrNnzzaPx2N169a1xx9/3PtFUWZnSX5vGT58+CXN71SWHTt2WNmyZdP8svVCTi63yYV71KhRNnLkSGvevLnt27cvzdeQk8/P2rVrrXDhwnbffffZ7NmzrVmzZnbLLbekWeScHJe1a9daoUKFrFevXnb33XdbVFSU1atXz1544YVMzcL6bepYvw1MlO5sYO3atVa+fHlbunSpz/TRo0db7dq1bfr06Xby5EkzO7eLW4UKFax9+/Z2/Phxv+Y4ffq0jR8/3rp162Y//vijXXvttVauXLk035hiYmKsTZs21rJlyxS741yu48ePW+/eve3ee+/1TktMTLQHHnjAunTpkmJ+J8clOjraOnbsaEOHDvWZ3q5dO3vllVfsmWeesXXr1nl30zp8+LBjWZKfgyFDhtisWbNs7dq1FhISYn379rWTJ0/aU0895V2pcHJMDh8+bJUrV7a6det6p/Xv39/q169vJUuWtGuvvdaWL1/uXdlwMovZuW+pr7nmGnvrrbfMzKxFixZWv359Cw8Pt0qVKtldd93l/WY7OjraseV206ZN5vF4bNy4cd5pH330kYWEhNhPP/2UYn4nx2XhwoV2xRVX+GzB7dOnj40cOdJuu+02e/31171jcuzYMcfGxMzs22+/tcaNG9uhQ4e8u6IuWbLEihYtanXr1rVJkyZ5d8V08n3F7NyugrfddpsNGjTIO23Tpk3Wp08fu/rqq+25557zmX/GjBnWpk2bFO/Pl2v//v3WtGlTa926tRUtWtR69erlU7wvNGfOHKtatap9//33fs1hdq6IVK5c2UaNGuV9j3nttdesRIkSPstPMqfGxOzc1vby5cv7rHxOnjzZ7r33Xp8tL5mRJdnKlSvtjjvusClTplipUqXsscceS3XvBCez/PPPPxYSEuJ9b4mPj7f58+fbnDlz7PPPP0/x/uFkli+++MI6duzozTFu3Djr2rWr3Xnnnd73YLNzX8Q6udwuXbrUwsLC7OGHHzYzs//+978WHh7uPfb0wnUWJ8fk1KlT1rVrVxsyZIh32ueff27dunWzAwcO+Dw/To/L8ePHrV27djZq1CjvtN27d1uRIkWsRIkSNmXKFJ/5nczC+m1KrN8GLs5eng3Ex8crISFBp06dkiSdPn1a+fLl0xNPPKHTp0/rpZdeUrt27XTllVcqIiJCP/30kxITExUaGurXHHnz5lXNmjV1xRVXqHXr1qpUqZJuv/12XXPNNVq4cKHKlCnjPeOlmSksLExvvfWWkpKSvGe79KcCBQqoTp063t+DgoJ0zTXX6Oeff1Z8fLw8Ho9y584tM1NERIT+97//KSkpye/jEh4erhtuuMEny5QpU/Tjjz8qPj5e+/fv15NPPqmZM2eqd+/eKlKkiGPPUbL27dvr448/1r333qvffvtNzZs316+//qqEhAT16NFDkhxdVooUKaK2bdtq5cqVmjx5sr7++msVKVJEd911l4oVK6bp06era9eu+uGHH1S5cmVHs0jnzrBZrFgxRUdHa+LEiQoJCdFrr72mcuXKadasWfrwww/1xhtvaPz48QoPD9fbb7+txMREvy+3efPm1Ysvvqi7775b0rmz6DZp0kQNGjTQ/Pnz1bJlS5/XkJPjcuzYMR04cEBbtmxR2bJlNXPmTM2bN0933323jhw5opdeeknLly/X1KlTVahQIcfGRJK2b9+urVu3qmjRot5pJ06cUJMmTVS4cGHNmTNHAwYMUNmyZRUWFuZoljx58ujAgQOqUKGCpHPPUeXKlTV9+nRNmjRJH3/8sSpWrKgbbrhBklS0aFHFx8erRIkSfs2xfPlylS9fXsOGDVNSUpI6dOigO++8U6+++qrCwsJSzF+mTBldeeWVqlSpkl9zJCUlae7cuapcubIefvhhBQWduxJpw4YNlTt37lQvjePUmCQmJuqTTz5Rhw4dNGbMGO/03bt3a+3atWrWrJnq16+vjh07qnPnzo5mOZ+ZadGiRXrjjTeUmJioOXPmqGDBgvrll19Uo0YNPf74445mOXv2rF544QWFhobqqquukiR17dpVu3fvVmxsrHbu3KkePXpo7Nixqlu3rqNZJOnvv//2Xqe3Y8eOOnv2rOrUqaN169Zp6dKl2rBhg6ZOnSqPx6OoqChHltuTJ0+qRYsWGjx4sHf8b7rpJr366quaOHGivvvuO+XK5buK7OSYhISE6MiRI97xl6TffvtNy5cvV7169VStWjU1atRI06ZNk8fjcez1LJ1bVzp69Kh3WTl16pRKly6t1q1b6+jRo/r6669Vr149dejQQZJUtmxZx7IkJCTo7Nmzrli/veKKK1S7dm1XrN8WLFjQ+/xIWbt+26VLF1155ZXeaVm9futqWdf3cTkSExN9jmG85ppr7Nprr/X+fubMGe//N2jQwG666SYzc+a4x8TExFS3ICQlJdmWLVu83wju3r3bm23ZsmUWGxvrSJbk43vO3w0seYvpBx98YLVr1/b5mwvPWOrPLOefICbZr7/+apUqVbL58+d7v6Ht0qWLNWjQwPt3/s5x4fO+YMECq1atmvfbxw4dOlhQUJB16NDhors++iPL+cvmww8/bEWLFrVOnTqlOJFGrVq17I477jAz55bb8+93yJAhdsUVV9itt95qL7/8ss+8I0eOtBo1alh8fLwjx2ydPXs2xXFp5/8+ceJEK1y4sPe41OQMTi8r7du3t2LFitl1111nISEh3jOpm5k98cQTVrZs2VSPf/d3ln379lm5cuXslltusc2bN9vChQstf/789sQTT5iZWbVq1eyxxx4zM2eP7T579qzFx8db//79rWfPnt6TuSU/D1u2bLEmTZpYnz59fP7uYlugM+rgwYM+ez8sXrzYIiIirFevXj7HWp4/HsnvN/72yy+/2JgxY3ymJSYmWvny5VPdQ8PMmTExO3cowvknFXrssccsODjYxo0bZzNnzrSGDRta69atbe/evY5nOV/btm29h/FMnz7dChQoYOHh4fbdd9/5zOdUln/++ccGDx5sV199tUVFRVnHjh1t/fr1durUKVu6dKmVLl3a+vbtmylZvv/+e2vdurW9+uqrdv3113vXDaKjo+2RRx6xq6++2uckXE4tt+cfVpX8OnnllVesatWqtmzZMjNLfQumvyUmJlpMTIy1a9fOunXrZrNmzbKxY8davnz57I033rBvvvnGHnnkEatXr5599tln3r9zYlySkpLswIEDVqpUKZ9do3ft2mU1a9a0t956y6688kq78847ff7OqefIzKxhw4bWqlUr7++ZuX6blqxYv02WmJiY5eu35z/u+TJ7/TbQULoD0Nq1a+3WW2+16667zu688077+eefbdmyZVapUiXr1auXd77klfbhw4db586dHc9y1113+RzDkfyC3Lx5s/eNaevWrXbfffdZgwYN0jz5jz+y3HvvvT5Zksfio48+slq1anmnDx8+3G644Qa/v1lfbFy2bdtmW7Zs8cn11FNPWePGjVP98sJfOe6++2778ssvLTEx0Y4fP27t27c3s3O7dZcpU8befPNNCw0NtS5dung/QJzKkrzcmpm9/PLLNnfuXO/ykvxc9OjRw3r27On3HBdmGTx4sP3yyy924sQJu+aaa8zj8Xh3N0y2YMECq1Onjt+X2QuzJD9HyZK/tDl06JDVqFHDxowZ49iJWi58fn7//XczO7dr7Mcff2z169e3w4cPe5fZRYsWWeXKlW3jxo2OZrnrrrvss88+s3nz5lnNmjUtIiLCIiIifI5Lveaaa1KUPn+68P3h559/tuDgYJ9dyZPn+fnnny0oKMjWrFnj9xWMtN6nkh/njz/+8BbvmJgYi4+PtxdffNG+/fZbM/PvSX7SynL+F0IVKlSwBQsWeG/74YcfHPlSL60shw8ftqFDh/p8WbRu3TrzeDw+0zIjS8uWLb27Tg8cONDCwsIsMjLSpk+fnuKcBE5l2bx5s91+++3WqVMn27Bhg89t8+fPN4/HYxs3bvT75+GF97d+/XorVaqU1axZ09q0aeNz286dOy1//vz2/vvv+zVDallSez0cP37coqKi7L777vOZ7tSXref7448/rH379nbLLbdYtWrV7LXXXvPetn//fitbtqxNmzbN7zlSy/LCCy+Yx+OxAQMG2Pjx4y00NNR7SM1HH31k5cuX9/k88JcTJ05YbGysz5cbf//9txUvXtxuvvlm77TMWL9NLYuZb3HMrPXbS8mSWeu3aWUxO3cYQmat3wYiSneA2bBhg4WHh9tNN91kY8aMsTp16ljDhg3tnnvusffff98qVqxoXbt2tfj4eO+L8bbbbrObbrrJEhIS/PrBkVqWBg0a+Bzbkfx4W7ZssZYtW5rH47ECBQqkuFxJZmQxM/vqq6+sWrVqZmbeb5IvvNSCU1nOP1brwg+qgQMH2oABA/z6AZZWjhEjRlh8fLx17tzZihUrZiVKlLAlS5aY2blvKUuUKOH3lcDUslx11VVpnvQjKSnJevbs6XOSNaezjBw50hYtWmRNmza1qKgo+/bbb73fEo8YMcJatGjh92/zL2W5Td57o3///takSRNHPrhSy1GnTh0bOXKkmZn9/vvvVqNGDZ+/GTlypDVo0MDvx6tdmOXKK6+0Jk2a2LBhwyw2NtbWr1/v3Qpldm7LQvv27W3WrFlm5v+V440bN9qMGTN8toyanTu+MygoyF555RWf6cuWLbMaNWqkeXJCf+e40J9//mkRERHWu3dv69+/v+XOnds2b97seJbzxz0hIcFOnDhhlStXtj/++MPMzr3fejwev7+3/Nu4JL9mk/dIWLVqldWrV89WrVrl1xxpZUl+vY4ePdreeecde+CBB6xUqVK2detWmzp1quXPn9+efvppv68YpzUuO3bssG+++cabK/l5+/jjj6169ep+Lwtp5fjyyy8tV65cVrx4cVu0aJF3elxcnLVu3dr7RVFmZEmW/BzMmjXLKlWq5Ohx/mllOXHihJ09e9aaNGnicx3s+Ph4u/76673vc05nSUxMtDfffNMaNmxo7du3tyeffNJ72/PPP29169b1+3vt2rVrrW3btla3bl0rVaqUvfvuu2Z27njq//73v1a0aFHr2bNnpqzfppUltcdwev32UrNkxvptesYlmRPrt4GK0h1AkpKS7OGHH7bevXt7p8XGxtqjjz5qjRo1sltuucU+++wzq1q1qlWtWtW6du1qvXv3tgIFCvj92o1pZZkyZYpdddVVPicZMjv3QXrTTTdZRESErV27NsuyzJs3z66++mp7+OGHLU+ePD4r75mR5c477/R5c4qPj7fx48db0aJFbf369ZmSo3bt2ta/f38bN26cderUyfsBkbzCcf61QZ3OcuWVV9qgQYNSrLCPHz/eSpYsmeI64k5leeyxx6xevXo2cOBAW7VqlV1zzTVWpkwZq1OnjnXu3NkKFSrkc6ksJ7Ok9RraunWreTyeFLu+O5mjTp06dv/999uxY8esSpUq1rRpU5swYYINHDjQihQpkmlj8thjj1nt2rXtnnvu8Zk/NjbWxowZY8WLF/d+u+5PmzZtsoiICPN4PDZ27Fg7dOiQ97aTJ0/aI488Yh6Px8aPH29///23HTlyxMaMGWOVK1dOcVZdp3KkZuHChebxeCwiIsLv73GXkiUxMdFOnz7tLS6PPvqoIyujF8uS/J5y4crgww8/bI0bN/br8/NvWczMXn/9dfN4PFayZEnvl5xmZk8++WSqJ5tzMktqK8gjR460du3a+XX36X/L8d///teCgoKsXbt29t///tc2bdpkY8aMsVKlSnlP0phZWc6XvLu9EwX337IkJibaiRMnrHHjxjZhwgQ7duyYHT9+3CZMmGAlS5ZM8+oeTmQxO7c+cP7u3GZm999/v/Xs2dNOnz7tt6K7du1aK1KkiA0bNszee+89Gz58uOXOndv+/vtvMzv3fjt//nwrU6aMVa9e3dH127SynH/JtPM5uX6bniyff/65o+u36R0Xp9ZvAxmlO8D069fP59hts3Mrnk899ZQ1adLEpk+fbrGxsTZ69Gi788477f777/f7m8C/ZZkxY4Y1aNDAe7xlUlKSzZw504KDg71voJmdJXmXrA8++MA8Ho8VLlzYsW+xL3VcfvjhB+vRo4eVKVPGkXG52LLSsmVLe/DBB1Pd6uTEbnSXOibff/+9de7c2SIjI7NkWWnYsKHNnDnTzM4d2zdx4kR74oknHNmF+t+yXPgaio2NtQceeMDvK+hp5Th+/LjNmDHD6tWrZ08//bStWbPGWrVqZU2aNLFevXpl2ftK8mt5xYoVdvfdd1upUqX8vnJhdm6L04ABA6xfv342a9Ys83g89tBDD/mUtcTERHvrrbcsMjLSSpcubdWrV/d7nrRypFUa4uLi7O6777aCBQv6/TlKb5a6detaw4YNLU+ePD5FMyuyrF271saPH29hYWG2cuXKTM+yceNGGz9+vHcF1aljGy8ly/nv8WvWrLFx48ZZWFiYX7f+X+rz88MPP1iTJk2sRIkSVr16datatarf3/vTu6yYmd1xxx1WrVo1v5/H41KzJK+rVK1a1Ro3bmzlypXLknE5/9++fv16Gzp0qBUsWNCvy8qRI0esbdu29uCDD/pMb9mypT3wwAM+02JjY23UqFGOrd9eSpbzxyQxMdGef/55R9Zv05vFyfXb9GZxev02UFG6A0Tywjxz5kxr1qxZiuOxjh49anfeeac1btw4xRtCVmQZNGiQNW3a1HtJgPnz5ztSFtKTJS4uzjZv3mzNmzd3ZNfC9GSJjo62LVu22OTJk1PM53SOI0eO2J133mnNmzd3/JIN6R2TTZs22ejRox35VvRSszRq1MjxE7KkZ1zOPyHLhVscMitHs2bNvO8lcXFxqZ4gMLOyNG3a1Ls3xieffOL3LT/JTp06ZbNmzbK5c+ea2f+t0FxYvM3Onafhl19+sW+++cbv50O4WI7USsNff/1ltWrV8vtW5fRkOXv2rB05csTCw8MtODjYkffb9IzLjh07rFu3blajRg2/752RniznH57i1LkZ0jMu27Zts/bt21vFihXT3FqVGTkOHz5s//zzjy1fvvxf9+JwOkvy8/LHH3848t6SniwLFy60KVOm2OzZs/1+uEp6s8TGxtrMmTOtRYsWfl9W9u/fb40aNbJff/3VzP5vvbV///526623mtn/HR5yPifWby8ly4WcWr9NbxYn12/TkyX5JHNOrN8GOkp3gNm8ebMVLVrUBgwY4C1MyR8SO3fuNI/HY1999ZV3fqc+2C81y9dff+3Y46c3yzfffOPddSursyQfr+bkmRwvdUwyQ3rGxOnCm97lNqtfQ5nxHF1KjvNP8JZTslz4XjF37lzzeDw2cuRI70ppQkKCY2dwv5QcyWe0T0xM9O6S68T1ydOTJSEhwQ4dOmTffvutrVmzJkuznD171g4cOGC7du3yXlM3s7Mkf0mTmJjo2JdEl5rl/HE5ePCgbdu2zbHl91KXFScKZUayJCYmOnKYSnqyJL+vxMfHO/IFRHqyXPgaSkhIcOy95fzSmny+gfHjx9vtt9/uM9/5hz849dl8qVmcPEN5erMkf1Y6uX57qVmSM+T0M5WnhtIdgP73v/9ZSEiI3XfffT5vyvv27bM6der4nJSELP+XJflszG7Iklnj4pYcl5IlJz4/bsrilhxuy2J2bqUzeQXvv//9r3dr0J49e2zYsGHWvXt3O3HihKNf0FxKjq5du3ovA+i0f8vSrVs3Ry8jlJ4sXbt29fu5KjKapXv37jluXNzy+rnULCdPnnRFlm7durlmXDLrveX8ojZu3Dhr166d9/epU6fa008/nWkn5AqkLDNmzMi0S6a5aVwCDaU7QM2fP99CQkKse/fuNnfuXFu3bp2NGTPGSpYs6ei3+WQJvCxuyUEW92dxSw63ZTHz3b1x7ty5ljt3bqtWrZrlypXL77tbZjRHZh87l1aW4ODgTB2Ti2Vx07hk9rLyb1kyc1wCZUzI4o5lxexcoevQoYOZmU2YMME8Ho8jh4eQJbCzBBJKdwBbtmyZtWjRwsqVK2eVKlVy5AQkZMkeWdySgyzuz+KWHG7LYnZuRSN5ZaN169YWERHhyPFzgZKDLGQJxBxkcXeW5OI/adIkGzx4sD311FMWEhLiyAkzyRL4WQIJpTvAxcTE2LZt22zVqlWZcvwPWQI3i1tykMX9WdySw21ZzM7thjls2DDzeDx+Pwt2IOYgC1kCMQdZ3J9lypQp5vF4LDw83O9XPyBL9ssSCDxmZgIAAP8qMTFRb775purXr6+rrroqx+cgC1kCMQdZ3J9l6dKlatSokdasWaOaNWtmWQ6yBEaWQEDpBgAgHcxMHo8nq2O4JodElrSQxb05JLKkxS1ZTp48qQIFCmR1DElkSYubsrgdpRsAAAAAAIcEZXUAAAAAAACyK0o3AAAAAAAOoXQDAAAAAOAQSjcAAAAAAA6hdAMAAAAA4BBKNwAAAAAADqF0AwAAAADgEEo3AAABol+/furatWuK6T///LM8Ho+io6MzPdP27dvl8Xi0YsWKFLe1bNlSQ4cO9ea72M/PP/8sSfrkk0/UsmVLhYeHKzQ0VFdeeaUeffRRHT16NHP/YQAA+AmlGwAAKD4+3rH7btq0qfbt2+f96d27t9q3b+8zrWnTpho3bpz69Omjhg0b6ptvvtGaNWv09NNPa+XKlXrnnXccywcAgJMo3QAAZEOffPKJatWqpZCQEJUvX15PP/20z+3ly5fXY489pr59+yosLEyDBw+WJI0ePVpVq1ZV/vz5VbFiRU2YMEEJCQmXlSVPnjyKjIz0/uTLl08hISE+01asWKGpU6fq6aef1lNPPaWmTZuqfPnyuv766/XJJ5/ojjvuuKwMAABklVxZHQAAAPjXsmXL1Lt3b02ePFl9+vTRokWLdO+996pIkSLq16+fd74ZM2Zo4sSJmjRpkndawYIF9eabb6pUqVJavXq1Bg0apIIFC2rUqFGOZn7vvfcUGhqqe++9N9XbCxUq5OjjAwDgFEo3AAAB5Msvv1RoaKjPtMTERJ/fn3nmGV133XWaMGGCJKlq1apat26dnnrqKZ/S3bp1a40YMcLnb8ePH+/9//Lly2vkyJGaO3eu46V706ZNqlixonLnzu3o4wAAkNko3QAABJBWrVrppZd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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Elaboração de gráfico de colunas - Análise da coluna para análise de dados da coluna 'Precipitacao_Total' por horário\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(df['Hora UTC'], df['Sensação_Térmica'], color='blue')\n", + "\n", + "plt.title('Sensação_Térmica')\n", + "plt.xlabel('Hora UTC')\n", + "plt.ylabel('Sensação_Térmica')\n", + "\n", + "plt.xticks(rotation=45) # Rotacionando os rótulos do eixo x para melhor legibilidade\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. **Customização de Gráficos:**\n", + " \n", + "Personalize os gráficos, adicionando títulos, legendas e ajustando as cores para torná-los mais informativos.\n", + "\n", + "Resposta: Adicionei dois gráficos de colunas, utilzei cores, inseri título e informações nos eixos (x) horizontal e (y)vertical." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### **Persistência dos Resultados no SQLite**\n", + "\n", + "1. **Criação do Banco de Dados:**\n", + " - Estabeleça um banco de dados SQLite para armazenar os resultados das análises." + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [], + "source": [ + "#Importando o sqlite - Para criar o banco de dados\n", + "import sqlite3\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "#Conectar ao banco de dados (ou criando, se não existir)\n", + "#Criar um cursor para interagir com o banco de dados\n", + "conn = sqlite3.connect('dados_meteorologicos.db')\n", + "cursor = conn.cursor()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [], + "source": [ + "#Criar a tabela\n", + "cursor.execute('''\n", + "CREATE TABLE IF NOT EXISTS dados_meteorologicos (\n", + " data TEXT,\n", + " hora_utc TEXT,\n", + " precipitacao_total_mm TEXT,\n", + " pressao_atm_estacao_mb TEXT,\n", + " pressao_atm_max_mb TEXT,\n", + " pressao_atm_min_mb TEXT,\n", + " radiacao_global_kjm2 TEXT,\n", + " temperatura_ar_c TEXT,\n", + " temperatura_orvalho_c TEXT,\n", + " temperatura_max_c TEXT,\n", + " temperatura_min_c TEXT,\n", + " temperatura_orvalho_max_c TEXT,\n", + " temperatura_orvalho_min_c TEXT,\n", + " umidade_rel_max FLOAT,\n", + " umidade_rel_min FLOAT,\n", + " umidade_rel_ar FLOAT,\n", + " vento_direcao_gr FLOAT,\n", + " vento_rajada_max_ms TEXT,\n", + " vento_velocidade_ms TEXT\n", + ")\n", + "''')\n", + "conn.commit()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "2. **Salvamento dos Dados Tratados:**\n", + " - Salve os dados tratados e os resultados das análises em tabelas dentro do banco de dados." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finalização do Projeto** - **Perguntas para Reflexão**\n", + "\n", + "\n", + "\n", + "* Ao final do projeto, as alunas devem refletir sobre as seguintes questões baseadas nos dados analisados." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "1. Qual foi a média de valores de uma coluna específica?\n", + "\n", + "**Resposta: Analisando a média da coluna UMIDADE RELATIVA DO AR, HORARIA (%) = 63.272541**\n", + "\n", + "Conforme resultdo abaixo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " count mean std min \\\n", + "Umidade_Relativa 8784.0 63.272541 24.140895 0.0 \n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 8784.0 184.889458 81.784719 0.0 \n", + "Temperatura_C) 926.0 22.876890 6.010978 0.0 \n", + "Sensação_Térmica 926.0 15.351404 6.700249 -20.0 \n", + "\n", + " 25% 50% 75% max \n", + "Umidade_Relativa 49.0 67.0 82.0 100.0 \n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 133.0 171.0 254.0 360.0 \n", + "Temperatura_C) 20.0 23.0 27.0 40.0 \n", + "Sensação_Térmica 12.8 16.8 19.8 24.8 " + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe().T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Qual o total de registros após a limpeza dos dados?\n", + "\n", + "**Resposta: Após a limpeza de dados o DataFrame apresenta um total de 8784 linhas e 11 colunas.**\n", + "\n", + "Conforme resultado abaixo " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8784, 11)" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Quais foram os valores máximos e mínimos identificados?\n", + "\n", + "**Resposta: Analisando a coluna 'VENTO, DIREÇÃO HORARIA (gr) (° (gr))'\n", + "\n", + "**Valor mínimo: 0.0**\n", + "\n", + "**Valor máximo: 360.0**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Umidade_Relativa8784.063.27254124.1408950.049.067.082.0100.0
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" + ], + "text/plain": [ + " count mean std min \\\n", + "Umidade_Relativa 8784.0 63.272541 24.140895 0.0 \n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 8784.0 184.889458 81.784719 0.0 \n", + "Temperatura_C) 926.0 22.876890 6.010978 0.0 \n", + "Sensação_Térmica 926.0 15.351404 6.700249 -20.0 \n", + "\n", + " 25% 50% 75% max \n", + "Umidade_Relativa 49.0 67.0 82.0 100.0 \n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 133.0 171.0 254.0 360.0 \n", + "Temperatura_C) 20.0 23.0 27.0 40.0 \n", + "Sensação_Térmica 12.8 16.8 19.8 24.8 " + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe().T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Quantos registros tinham valores nulos antes do tratamento?\n", + "\n", + "Resposta: 4067 valores nulos " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Qual foi o impacto da normalização de uma coluna específica?\n", + "\n", + "Resposta: Normalizar os valores permitiu uma melhor análise das variações presentes no Dataframe, evitando que valores maiores e/ou muito discrepantes interfira na análise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "6. Que padrões emergiram após a análise dos dados?\n", + "\n", + "Resposta: Analisando a coluna 'Umidade Relativa em (%), com base no gráfico de barras, por horário, não houve alterações ao longo dos horários.\n", + "Quanto ao gráfico de barras de 'Sensação Térmica', observei que ao longo das mudanças de horário, houve uma grande variação, entre os intervalos com quedas e altas consideráveis, ou seja, as variações são altas com relação ao gráfico Umidade Relativa do Ar." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "7. Como os dados foram agrupados e quais insights foram gerados?\n", + "\n", + "Resposta: Realizaei um agrupamento entre a umidade relativa do ar e sensação térmica, obetndo o valor da média referente a sensação termíca da região, agrupar os dados, permitiu uma melhor análise das tendências e padrões." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "8. Quais visualizações forneceram as informações mais valiosas?\n", + "\n", + "Resposta: As variações de temperaturas (máximas e mínimas) da região, os horários em que a umidade relativa do ar é mais úmida, a média da sensação térmica e a velocidade do vento.\n", + "As informações trazidas na coleta de dados, permitiram uma melhor entendimento das variações temporais da região analisada." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "9. Como o uso de SQL contribuiu para a organização dos resultados?\n", + "\n", + "O Sql ajuda na organização dos dados do DataFrame, armazenando as informações em tabelas, permitindo consultar, filtrar e manipular os dados dos registros de uma forma organizada." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "10. De que forma os gráficos ajudaram na compreensão dos dados?\n", + "\n", + "Resposta: Um melhor análise visual do crescimento e decrescimento ao longo dos dias e horários, bem como a evolução do dados de um mode geral." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Apresentação Final:**\n", + "\n", + "**Conclusão final: O tratamento das informações das colunas foi importante, para obter melhores resultados das amostras coletadas, algumas colunas foram excluidas e renomeadas para um melhor tratamento das informações.\n", + "Os gráficos de barras permitiram analisar as maiores temperaturas, umidade do ar, entre outros dados, por datas e horários.\n", + "As informações e representações gráficas, são de grande relevância na análise de dados, permitindo uma visualização mais rápida das informações coletadas, bem como quantificar os dados da amostra.**" + ] + } + ], + "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": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercicios/projeto-guiado/README.md b/exercicios/projeto-guiado/README.md index 2c63b30..f95275c 100644 --- a/exercicios/projeto-guiado/README.md +++ b/exercicios/projeto-guiado/README.md @@ -54,6 +54,7 @@ Este projeto visa guiar as alunas no processo completo de análise de dados, des #### **Finalização do Projeto** **Perguntas para Reflexão:** +Lição de Casa Ao final do projeto, as alunas devem refletir sobre as seguintes questões baseadas nos dados analisados: 1. Qual foi a média de valores de uma coluna específica? @@ -73,10 +74,10 @@ As alunas deverão criar uma apresentação final que resuma o projeto, abordand Terminou o exercício? Dá uma olhada nessa checklist e confere se tá tudo certinho, combinado?! -- [ ] Fiz o fork do repositório. -- [ ] Clonei o fork na minha máquina (`git clone url-do-meu-fork`). -- [ ] Resolvi o exercício. -- [ ] Adicionei as mudanças. (`git add .` para adicionar todos os arquivos, ou `git add nome_do_arquivo` para adicionar um arquivo específico) -- [ ] Commitei a cada mudança significativa ou na finalização do exercício (`git commit -m "Mensagem do commit"`) -- [ ] Pushei os commits na minha branch (`git push origin nome-da-branch`) -- [ ] Criei um Pull Request seguindo as orientaçoes que estao nesse [documento](https://github.com/mflilian/repo-example/blob/main/exercicios/para-casa/instrucoes-pull-request.md). +- [x] Fiz o fork do repositório. +- [x] Clonei o fork na minha máquina (`git clone url-do-meu-fork`). +- [x] Resolvi o exercício. +- [x] Adicionei as mudanças. (`git add .` para adicionar todos os arquivos, ou `git add nome_do_arquivo` para adicionar um arquivo específico) +- [x] Commitei a cada mudança significativa ou na finalização do exercício (`git commit -m "Mensagem do commit"`) +- [x] Pushei os commits na minha branch (`git push origin nome-da-branch`) +- [x] Criei um Pull Request seguindo as orientaçoes que estao nesse [documento](https://github.com/mflilian/repo-example/blob/main/exercicios/para-casa/instrucoes-pull-request.md). diff --git a/exercicios/projeto-guiado/dados_meteorologicos.db b/exercicios/projeto-guiado/dados_meteorologicos.db new file mode 100644 index 0000000000000000000000000000000000000000..66f768e0959e0c3cc4ca15b7f4fc120b8d81fa76 GIT binary patch literal 835584 zcmeFacbr{UmFIhIsY)uTl7%qBG|H%4DKl=bs-%Q71p&;3xi-;X=O`|I+un`_&m; z_Son2@wYO4&xrW{o?rKH;2sX#!-0D^a1RIW;lMo{xQ7GxaNr&e+{1zYe{*2@@tF-< z%jL|ox7@OL`ns!@7ME_gdExr2Zn^4)n{K%7hDTg`=7yWs`n`_5!-Km=2hShfy=Q3f 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se#Az{_Y>^Jn!y_}|2?HWoKMS@Th5Wc>IyoX0AnQ9uFjr@f6*HM7gux~SO5S3 literal 0 HcmV?d00001 diff --git a/exercicios/projeto-guiado/projeto_leticia_vidal.ipynb b/exercicios/projeto-guiado/projeto_leticia_vidal.ipynb index 421437f..78b513d 100644 --- a/exercicios/projeto-guiado/projeto_leticia_vidal.ipynb +++ b/exercicios/projeto-guiado/projeto_leticia_vidal.ipynb @@ -1609,7 +1609,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.4" } }, "nbformat": 4,