diff --git a/exercicios/projeto-guiado/Respostas_projeto.ipynb b/exercicios/projeto-guiado/Respostas_projeto.ipynb new file mode 100644 index 0000000..47bd256 --- /dev/null +++ b/exercicios/projeto-guiado/Respostas_projeto.ipynb @@ -0,0 +1,67 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finalização do Projeto**\n", + "\n", + "**Perguntas para Reflexão:**\n", + "\n", + "1. Qual foi a média de valores de uma coluna específica?\n", + "A Média da coluna TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) foi aproximadamente: 23°C\n", + "\n", + "2. Qual o total de registros após a limpeza dos dados?\n", + "8784 linhas e 11 colunas\n", + "\n", + "3. Quais foram os valores máximos e mínimos identificados?\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) - MÍN-0 | MÁX-44.800000\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) - MÍN-0 | MÁX-40.600000\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) - MÍN-0 | MÁX-25.800000\t\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) -MÍN-0 | MÁX-1.000000\n", + "RADIACAO GLOBAL (Kj/m²)\t- MÍN-0 | MÁX-4085.400000\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) - MÍN-0 | MÁX-360.000000\n", + "VENTO, VELOCIDADE HORARIA (m/s) - MÍN-0 | MÁX-11.900000\n", + "\n", + "4. Quantos registros tinham valores nulos antes do tratamento?\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", + "TOTAL 5005\n", + "\n", + "5. Qual foi o impacto da normalização de uma coluna específica?\n", + "Uniformidade na Escala de Dados - A conversão da coluna UMIDADE RELATIVA DO AR, HORARIA (%) nos permitiu trabalhar com todos os valores na mesma escala.\n", + "\n", + "\n", + "6. Que padrões emergiram após a análise dos dados?\n", + "A partir do gráfico, observamos que a temperatura do ar, em grande parte, esteve acima de 15°C no ano de 2020. Isso sugere que as condições durante o período analisado foram relativamente moderadas ou quentes.\n", + "Em alguns poucos casos, a temperatura caiu abaixo de 15°C, cegando até 0°C. Podemos identificar uma mudança repentina no clima.\n", + "\n", + "\n", + "7. Como os dados foram agrupados e quais insights foram gerados?\n", + "Os dados foram agrupados por ano e organizados em um arquivo CSV específico para a cidade de Itaquirai. \n", + "Insights gerados incluem: Períodos com Precipitações Acima da Média,Oscilações nas Temperaturas, Variações na Umidade, entre outros...\n", + "\n", + "8. Quais visualizações forneceram as informações mais valiosas?\n", + "A tempetatura da cidade de Itaquirai\n", + "\n", + "9. Como o uso de SQL contribuiu para a organização dos resultados?\n", + "\"SQL é extremamente eficaz para a organização e análise de dados, permitindo a filtragem e manipulação de grandes volumes de informações.\n", + "\n", + "10. De que forma os gráficos ajudaram na compreensão dos dados?\n", + "De forma bem clara, o gráfico retornou a mudança na temperatura ao longo dos meses de 2020." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercicios/projeto-guiado/clima.db b/exercicios/projeto-guiado/clima.db new file mode 100644 index 0000000..eb6c632 Binary files /dev/null and b/exercicios/projeto-guiado/clima.db differ diff --git a/exercicios/projeto-guiado/projeto-thais.ipynb b/exercicios/projeto-guiado/projeto-thais.ipynb new file mode 100644 index 0000000..4cb1341 --- /dev/null +++ b/exercicios/projeto-guiado/projeto-thais.ipynb @@ -0,0 +1,3739 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib as mp \n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Abertura e Carregamento de Dados" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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87792020/12/311900 UTC,4972,6973,3972,6775,923,122,723,321,7NaNNaNNaNNaN97.032.06,61,2NaN
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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)
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UMIDADE RELATIVA DO AR, HORARIA (%)VENTO, DIREÇÃO HORARIA (gr) (° (gr))
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" + ], + "text/plain": [ + " UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "count 8318.000000 \n", + "mean 66.817264 \n", + "std 19.456590 \n", + "min 14.000000 \n", + "25% 52.000000 \n", + "50% 69.000000 \n", + "75% 83.000000 \n", + "max 100.000000 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \n", + "count 8778.000000 \n", + "mean 185.015835 \n", + "std 81.669629 \n", + "min 1.000000 \n", + "25% 133.000000 \n", + "50% 171.000000 \n", + "75% 254.000000 \n", + "max 360.000000 " + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Estatísticas das colunas númericas\n", + "#OBS: As outras colunas devem ser tratadas para float\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 8784 entries, 0 to 8783\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Data 8784 non-null object \n", + " 1 Hora UTC 8784 non-null object \n", + " 2 PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 8778 non-null object \n", + " 3 TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 8778 non-null object \n", + " 4 TEMPERATURA DO PONTO DE ORVALHO (°C) 8318 non-null object \n", + " 5 UMIDADE RELATIVA DO AR, HORARIA (%) 8318 non-null float64\n", + " 6 RADIACAO GLOBAL (Kj/m²) 4735 non-null object \n", + " 7 VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 8778 non-null float64\n", + " 8 VENTO, VELOCIDADE HORARIA (m/s) 8778 non-null object \n", + "dtypes: float64(2), object(7)\n", + "memory usage: 617.8+ KB\n" + ] + } + ], + "source": [ + "#Imprime as quantidades de valores nao nulos e os tipos de dados em cada\n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "#Transformar as vírgulas em pontos \n", + "df=df.replace(',', '.', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "#Converter de object para float as colunas necessárias\n", + "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')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "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": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Conferindo a conversao \n", + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "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": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Retorna a quantidade de valores nulos em cada coluna\n", + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "#Subistituir valores nulos pra 0\n", + "df=df.fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "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)
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32020/01/010300 UTC0.024.321.483.00.6332.01.5
42020/01/010400 UTC0.023.821.789.00.0316.00.2
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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 UTC0.623.122.60.970.011.01.9
12020/01/010100 UTC0.023.721.70.882.910.01.3
22020/01/010200 UTC0.024.021.80.881.6345.00.6
32020/01/010300 UTC0.024.321.40.830.6332.01.5
42020/01/010400 UTC0.023.821.70.890.0316.00.2
..............................
87792020/12/311900 UTC0.423.122.70.97775.932.01.2
87802020/12/312000 UTC0.024.222.70.91837.8355.00.8
87812020/12/312100 UTC0.024.923.00.89524.7315.01.2
87822020/12/312200 UTC0.024.222.10.88256.5291.00.9
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8784 rows × 9 columns

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" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC 0.6 \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 0.4 \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 23.1 \n", + "1 23.7 \n", + "2 24.0 \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", + " 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", + "... ... \n", + "8779 22.7 \n", + "8780 22.7 \n", + "8781 23.0 \n", + "8782 22.1 \n", + "8783 22.5 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 2.9 \n", + "2 0.88 1.6 \n", + "3 0.83 0.6 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 775.9 \n", + "8780 0.91 837.8 \n", + "8781 0.89 524.7 \n", + "8782 0.88 256.5 \n", + "8783 0.94 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 0.6 \n", + "3 332.0 1.5 \n", + "4 316.0 0.2 \n", + "... ... ... \n", + "8779 32.0 1.2 \n", + "8780 355.0 0.8 \n", + "8781 315.0 1.2 \n", + "8782 291.0 0.9 \n", + "8783 132.0 0.9 \n", + "\n", + "[8784 rows x 9 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Normalizar coluna UMIDADE RELATIVA DO AR, HORARIA (%), pois é a única que está em porcentagem\n", + "df['UMIDADE RELATIVA DO AR, HORARIA (%)']=(df['UMIDADE RELATIVA DO AR, HORARIA (%)']- df['UMIDADE RELATIVA DO AR, HORARIA (%)'].min())/(df['UMIDADE RELATIVA DO AR, HORARIA (%)'].max()-df['UMIDADE RELATIVA DO AR, HORARIA (%)'].min())\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "# Convertendo a coluna 'Data' para datetime \n", + "df['Data']=pd.to_datetime(df['Data'], format='%Y/%m/%d')" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "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 UTC0.623.122.60.970.011.01.9
101/01/20200100 UTC0.023.721.70.882.910.01.3
201/01/20200200 UTC0.024.021.80.881.6345.00.6
301/01/20200300 UTC0.024.321.40.830.6332.01.5
401/01/20200400 UTC0.023.821.70.890.0316.00.2
..............................
877931/12/20201900 UTC0.423.122.70.97775.932.01.2
878031/12/20202000 UTC0.024.222.70.91837.8355.00.8
878131/12/20202100 UTC0.024.923.00.89524.7315.01.2
878231/12/20202200 UTC0.024.222.10.88256.5291.00.9
878331/12/20202300 UTC0.023.522.50.949.6132.00.9
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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 0.6 \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 0.4 \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 23.1 \n", + "1 23.7 \n", + "2 24.0 \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", + " 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", + "... ... \n", + "8779 22.7 \n", + "8780 22.7 \n", + "8781 23.0 \n", + "8782 22.1 \n", + "8783 22.5 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 2.9 \n", + "2 0.88 1.6 \n", + "3 0.83 0.6 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 775.9 \n", + "8780 0.91 837.8 \n", + "8781 0.89 524.7 \n", + "8782 0.88 256.5 \n", + "8783 0.94 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 0.6 \n", + "3 332.0 1.5 \n", + "4 316.0 0.2 \n", + "... ... ... \n", + "8779 32.0 1.2 \n", + "8780 355.0 0.8 \n", + "8781 315.0 1.2 \n", + "8782 291.0 0.9 \n", + "8783 132.0 0.9 \n", + "\n", + "[8784 rows x 9 columns]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convertendo a coluna 'Data para o formato brasileiro DD/MM/YYYY\n", + "df['Data']=df['Data'].dt.strftime('%d/%m/%Y')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "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)
001/01/202000:000.623.122.60.970.011.01.9
101/01/202001:000.023.721.70.882.910.01.3
201/01/202002:000.024.021.80.881.6345.00.6
301/01/202003:000.024.321.40.830.6332.01.5
401/01/202004:000.023.821.70.890.0316.00.2
..............................
877931/12/202019:000.423.122.70.97775.932.01.2
878031/12/202020:000.024.222.70.91837.8355.00.8
878131/12/202021:000.024.923.00.89524.7315.01.2
878231/12/202022:000.024.222.10.88256.5291.00.9
878331/12/202023:000.023.522.50.949.6132.00.9
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8784 rows × 9 columns

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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 Hora
001/01/202000:000.623.122.60.970.011.01.901/01/2020 00:00
101/01/202001:000.023.721.70.882.910.01.301/01/2020 01:00
201/01/202002:000.024.021.80.881.6345.00.601/01/2020 02:00
301/01/202003:000.024.321.40.830.6332.01.501/01/2020 03:00
401/01/202004:000.023.821.70.890.0316.00.201/01/2020 04:00
.................................
877931/12/202019:000.423.122.70.97775.932.01.231/12/2020 19:00
878031/12/202020:000.024.222.70.91837.8355.00.831/12/2020 20:00
878131/12/202021:000.024.923.00.89524.7315.01.231/12/2020 21:00
878231/12/202022:000.024.222.10.88256.5291.00.931/12/2020 22:00
878331/12/202023:000.023.522.50.949.6132.00.931/12/2020 23:00
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8784 rows × 10 columns

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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 Hora
001/01/202000:000.623.122.60.970.011.01.92020-01-01 00:00:00
101/01/202001:000.023.721.70.882.910.01.32020-01-01 01:00:00
201/01/202002:000.024.021.80.881.6345.00.62020-01-01 02:00:00
301/01/202003:000.024.321.40.830.6332.01.52020-01-01 03:00:00
401/01/202004:000.023.821.70.890.0316.00.22020-01-01 04:00:00
.................................
877931/12/202019:000.423.122.70.97775.932.01.22020-12-31 19:00:00
878031/12/202020:000.024.222.70.91837.8355.00.82020-12-31 20:00:00
878131/12/202021:000.024.923.00.89524.7315.01.22020-12-31 21:00:00
878231/12/202022:000.024.222.10.88256.5291.00.92020-12-31 22:00:00
878331/12/202023:000.023.522.50.949.6132.00.92020-12-31 23:00:00
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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
001/01/202000:000.623.122.60.970.011.01.92020-01-01 00:00:002019-12-31 21:00:00-03:00
101/01/202001:000.023.721.70.882.910.01.32020-01-01 01:00:002019-12-31 22:00:00-03:00
201/01/202002:000.024.021.80.881.6345.00.62020-01-01 02:00:002019-12-31 23:00:00-03:00
301/01/202003:000.024.321.40.830.6332.01.52020-01-01 03:00:002020-01-01 00:00:00-03:00
401/01/202004:000.023.821.70.890.0316.00.22020-01-01 04:00:002020-01-01 01:00:00-03:00
....................................
877931/12/202019:000.423.122.70.97775.932.01.22020-12-31 19:00:002020-12-31 16:00:00-03:00
878031/12/202020:000.024.222.70.91837.8355.00.82020-12-31 20:00:002020-12-31 17:00:00-03:00
878131/12/202021:000.024.923.00.89524.7315.01.22020-12-31 21:00:002020-12-31 18:00:00-03:00
878231/12/202022:000.024.222.10.88256.5291.00.92020-12-31 22:00:002020-12-31 19:00:00-03:00
878331/12/202023:000.023.522.50.949.6132.00.92020-12-31 23:00:002020-12-31 20:00:00-03:00
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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
001/01/202000:000.623.122.60.970.011.01.92020-01-01 00:00:0031/12/2019 21:00
101/01/202001:000.023.721.70.882.910.01.32020-01-01 01:00:0031/12/2019 22:00
201/01/202002:000.024.021.80.881.6345.00.62020-01-01 02:00:0031/12/2019 23:00
301/01/202003:000.024.321.40.830.6332.01.52020-01-01 03:00:0001/01/2020 00:00
401/01/202004:000.023.821.70.890.0316.00.22020-01-01 04:00:0001/01/2020 01:00
....................................
877931/12/202019:000.423.122.70.97775.932.01.22020-12-31 19:00:0031/12/2020 16:00
878031/12/202020:000.024.222.70.91837.8355.00.82020-12-31 20:00:0031/12/2020 17:00
878131/12/202021:000.024.923.00.89524.7315.01.22020-12-31 21:00:0031/12/2020 18:00
878231/12/202022:000.024.222.10.88256.5291.00.92020-12-31 22:00:0031/12/2020 19:00
878331/12/202023:000.023.522.50.949.6132.00.92020-12-31 23:00:0031/12/2020 20:00
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8784 rows × 11 columns

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" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 00:00 0.6 \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", + "8779 31/12/2020 19:00 0.4 \n", + "8780 31/12/2020 20:00 0.0 \n", + "8781 31/12/2020 21:00 0.0 \n", + "8782 31/12/2020 22:00 0.0 \n", + "8783 31/12/2020 23:00 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 23.1 \n", + "1 23.7 \n", + "2 24.0 \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", + " 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", + "... ... \n", + "8779 22.7 \n", + "8780 22.7 \n", + "8781 23.0 \n", + "8782 22.1 \n", + "8783 22.5 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 2.9 \n", + "2 0.88 1.6 \n", + "3 0.83 0.6 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 775.9 \n", + "8780 0.91 837.8 \n", + "8781 0.89 524.7 \n", + "8782 0.88 256.5 \n", + "8783 0.94 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 0.6 \n", + "3 332.0 1.5 \n", + "4 316.0 0.2 \n", + "... ... ... \n", + "8779 32.0 1.2 \n", + "8780 355.0 0.8 \n", + "8781 315.0 1.2 \n", + "8782 291.0 0.9 \n", + "8783 132.0 0.9 \n", + "\n", + " Data e Hora Data e Hora BR \n", + "0 2020-01-01 00:00:00 31/12/2019 21:00 \n", + "1 2020-01-01 01:00:00 31/12/2019 22:00 \n", + "2 2020-01-01 02:00:00 31/12/2019 23:00 \n", + "3 2020-01-01 03:00:00 01/01/2020 00:00 \n", + "4 2020-01-01 04:00:00 01/01/2020 01:00 \n", + "... ... ... \n", + "8779 2020-12-31 19:00:00 31/12/2020 16:00 \n", + "8780 2020-12-31 20:00:00 31/12/2020 17:00 \n", + "8781 2020-12-31 21:00:00 31/12/2020 18:00 \n", + "8782 2020-12-31 22:00:00 31/12/2020 19:00 \n", + "8783 2020-12-31 23:00:00 31/12/2020 20:00 \n", + "\n", + "[8784 rows x 11 columns]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Formataçao da coluna 'Data e Hora BR' para o formato de data e hora brasileiro.\n", + "df['Data e Hora BR'] = df['Data e Hora BR'].dt.strftime('%d/%m/%Y %H:%M')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "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
mean0.15148023.17283715.2014230.632725759.334028184.8894581.9394472020-07-01 23:30:00
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000002020-01-01 00:00:00
25%0.00000019.50000012.4000000.4900000.000000133.0000000.9000002020-04-01 11:45:00
50%0.00000023.10000016.0000000.67000023.100000171.0000001.8000002020-07-01 23:30:00
75%0.00000027.10000019.3000000.8200001434.250000254.0000002.8000002020-10-01 11:15:00
max44.80000040.60000025.8000001.0000004085.400000360.00000011.9000002020-12-31 23:00:00
std1.3756795.7259635.5925510.2414091077.03412981.7847191.405233NaN
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" + ], + "text/plain": [ + " PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "count 8784.000000 \n", + "mean 0.151480 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 44.800000 \n", + "std 1.375679 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "count 8784.000000 \n", + "mean 23.172837 \n", + "min 0.000000 \n", + "25% 19.500000 \n", + "50% 23.100000 \n", + "75% 27.100000 \n", + "max 40.600000 \n", + "std 5.725963 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "count 8784.000000 \n", + "mean 15.201423 \n", + "min 0.000000 \n", + "25% 12.400000 \n", + "50% 16.000000 \n", + "75% 19.300000 \n", + "max 25.800000 \n", + "std 5.592551 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "count 8784.000000 8784.000000 \n", + "mean 0.632725 759.334028 \n", + "min 0.000000 0.000000 \n", + "25% 0.490000 0.000000 \n", + "50% 0.670000 23.100000 \n", + "75% 0.820000 1434.250000 \n", + "max 1.000000 4085.400000 \n", + "std 0.241409 1077.034129 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \\\n", + "count 8784.000000 8784.000000 \n", + "mean 184.889458 1.939447 \n", + "min 0.000000 0.000000 \n", + "25% 133.000000 0.900000 \n", + "50% 171.000000 1.800000 \n", + "75% 254.000000 2.800000 \n", + "max 360.000000 11.900000 \n", + "std 81.784719 1.405233 \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": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Estatísticas das colunas númericas após o tratamento\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Análise de Dados" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A mediana da coluna RADIACAO GLOBAL é: 23.1\n", + "A Média da coluna RADIACAO GLOBAL é: 23.172836976320585\n", + "A Mínimo da coluna RADIACAO GLOBAL é: 0.0\n", + "A Máximo da coluna RADIACAO GLOBAL é: 40.6\n" + ] + } + ], + "source": [ + "mediana_temperatura_bulbo=df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'].median()\n", + "media_temperatura_bulbo=df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'].mean()\n", + "minimo_temperatura_bulbo=df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'].min()\n", + "maximo_temperatura_bulbo=df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'].max()\n", + "print(\"A mediana da coluna RADIACAO GLOBAL é:\" , mediana_temperatura_bulbo)\n", + "print(\"A Média da coluna RADIACAO GLOBAL é:\" , media_temperatura_bulbo)\n", + "print(\"A Mínimo da coluna RADIACAO GLOBAL é:\" , minimo_temperatura_bulbo)\n", + "print(\"A Máximo da coluna RADIACAO GLOBAL é:\" , maximo_temperatura_bulbo)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)
Data
01/01/202026.150000
01/02/202025.191667
01/03/202025.237500
01/04/202026.945833
01/05/202023.466667
......
31/05/202021.408333
31/07/202018.504167
31/08/202024.550000
31/10/202021.958333
31/12/202023.470833
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366 rows × 1 columns

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" + ], + "text/plain": [ + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)\n", + "Data \n", + "01/01/2020 26.150000\n", + "01/02/2020 25.191667\n", + "01/03/2020 25.237500\n", + "01/04/2020 26.945833\n", + "01/05/2020 23.466667\n", + "... ...\n", + "31/05/2020 21.408333\n", + "31/07/2020 18.504167\n", + "31/08/2020 24.550000\n", + "31/10/2020 21.958333\n", + "31/12/2020 23.470833\n", + "\n", + "[366 rows x 1 columns]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Agrupa a coluna DATA e RADIACAO GLOBAL (Kj/m²) e retorna a média pra cada linha\n", + "media_radiacao = df.groupby('Data').agg({'TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)': 'mean'})\n", + "media_radiacao" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualização de Dados com Matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Gráfico apresenta a \"Temperatura do Ar - Bulbo Seco\" por Mês\n", + "plt.gca().xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%b')) #Abreviaçao dos meses\n", + "plt.plot(df['Data e Hora'], df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'],color='#003300', label='TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)')\n", + "plt.xlabel('Mês')\n", + "plt.ylabel('TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)')\n", + "plt.title('Variação da Temperatura do Ar (Bulbo Seco)')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Persistência dos Resultados no SQLite" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8784" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Persistindo o DataFrame no banco de dados\n", + "import sqlite3\n", + "conn = sqlite3.connect('clima.db') #banco de dados SQLite chamado clima.db será criado.\n", + "df.to_sql('clima',conn,if_exists='replace')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "O gráfico apresenta a \"Temperatura do Ar - Bulbo Seco\" medida ao longo do tempo (em Data e hora). A temperatura do ar, medida diretamente com um termômetro de bulbo seco, indica a temperatura real do ar sem influência adicional.\n", + "A partir do gráfico, observamos que a temperatura do ar, em grande parte, esteve acima de 15°C. Isso sugere que as condições durante o período analisado foram relativamente moderadas ou quentes.\n", + "Em alguns poucos casos, a temperatura caiu abaixo de 15°C, cegando até 0°C. Podemos identificar uma mudança repentina no clima, pode ser útil investigar as causas desses eventos." + ] + }, + { + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}