diff --git a/exercicios/para-casa/Lethicia-Asevedo/README.md b/exercicios/para-casa/Lethicia-Asevedo/README.md new file mode 100644 index 0000000..e69de29 diff --git a/exercicios/para-casa/Lethicia-Asevedo/temperatura_SP.csv b/exercicios/para-casa/Lethicia-Asevedo/temperatura_SP.csv new file mode 100644 index 0000000..91cf746 --- /dev/null +++ b/exercicios/para-casa/Lethicia-Asevedo/temperatura_SP.csv @@ -0,0 +1,25 @@ +Hora,Temperatura +00,19.68040863027133 +01,19.1033788901696 +02,18.60838809814735 +03,18.18991354466859 +04,17.769490287232276 +05,17.400336173612036 +06,17.08057863650015 +07,16.779506495245748 +08,16.532882792011296 +09,16.422951922753423 +10,17.16032384722718 +11,18.966515251789904 +12,21.06089888930345 +13,22.915942600997997 +14,24.405058616155113 +15,25.551967864740792 +16,26.306136567331393 +17,26.685610640870618 +18,26.572526755045846 +19,25.989040147669588 +20,24.800900225056264 +21,22.92689564151714 +22,21.35117830004168 +23,20.365337802655556 diff --git a/exercicios/para-casa/Lethicia-Asevedo/tratamento-pandas.ipynb b/exercicios/para-casa/Lethicia-Asevedo/tratamento-pandas.ipynb new file mode 100644 index 0000000..110cfb3 --- /dev/null +++ b/exercicios/para-casa/Lethicia-Asevedo/tratamento-pandas.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: numpy in c:\\users\\samsung e20\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.0.1)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 24.0 -> 24.2\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "!pip install numpy\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: matplotlib in c:\\users\\samsung e20\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.9.1.post1)Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 24.0 -> 24.2\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\samsung e20\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\samsung e20\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\samsung e20\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (4.53.1)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\samsung e20\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy>=1.23 in c:\\users\\samsung e20\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (2.0.1)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\samsung e20\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (24.1)\n", + "Requirement already satisfied: pillow>=8 in c:\\users\\samsung e20\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (10.4.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\samsung e20\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (3.1.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\samsung e20\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (2.9.0.post0)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\samsung e20\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n" + ] + } + ], + "source": [ + "pip install matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#análise numpy Estado SP\n", + "dadosSP = np.genfromtxt('temperatura_SP.csv', delimiter = ',', skip_header = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 19.68040863]\n", + " [ 1. 19.10337889]\n", + " [ 2. 18.6083881 ]\n", + " [ 3. 18.18991354]\n", + " [ 4. 17.76949029]\n", + " [ 5. 17.40033617]\n", + " [ 6. 17.08057864]\n", + " [ 7. 16.7795065 ]\n", + " [ 8. 16.53288279]\n", + " [ 9. 16.42295192]\n", + " [10. 17.16032385]\n", + " [11. 18.96651525]\n", + " [12. 21.06089889]\n", + " [13. 22.9159426 ]\n", + " [14. 24.40505862]\n", + " [15. 25.55196786]\n", + " [16. 26.30613657]\n", + " [17. 26.68561064]\n", + " [18. 26.57252676]\n", + " [19. 25.98904015]\n", + " [20. 24.80090023]\n", + " [21. 22.92689564]\n", + " [22. 21.3511783 ]\n", + " [23. 20.3653378 ]]\n" + ] + } + ], + "source": [ + "#Primeira coluna é hora, segunda é temperatura\n", + "print(dadosSP)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "dadosSP = np.transpose(dadosSP)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 1. 2. 3. 4. 5.\n", + " 6. 7. 8. 9. 10. 11.\n", + " 12. 13. 14. 15. 16. 17.\n", + " 18. 19. 20. 21. 22. 23. ]\n", + " [19.68040863 19.10337889 18.6083881 18.18991354 17.76949029 17.40033617\n", + " 17.08057864 16.7795065 16.53288279 16.42295192 17.16032385 18.96651525\n", + " 21.06089889 22.9159426 24.40505862 25.55196786 26.30613657 26.68561064\n", + " 26.57252676 25.98904015 24.80090023 22.92689564 21.3511783 20.3653378 ]]\n" + ] + } + ], + "source": [ + "print (dadosSP)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.\n", + " 18. 19. 20. 21. 22. 23.]\n" + ] + } + ], + "source": [ + "horaSP = dadosSP[0]\n", + "print (horaSP)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[19.68040863 19.10337889 18.6083881 18.18991354 17.76949029 17.40033617\n", + " 17.08057864 16.7795065 16.53288279 16.42295192 17.16032385 18.96651525\n", + " 21.06089889 22.9159426 24.40505862 25.55196786 26.30613657 26.68561064\n", + " 26.57252676 25.98904015 24.80090023 22.92689564 21.3511783 20.3653378 ]\n" + ] + } + ], + "source": [ + "temperaturaSP = dadosSP[1]\n", + "print(temperaturaSP)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "essa é a média da amostra: 20.942757025875597\n", + "essa é a mediana da amostra: 20.022873216463445\n", + "essa é o desvio padrão da amostra: 3.5726229054553307\n", + "esse é o 1° quartil da amostra: 17.677201758827216\n", + "esse é o 2° quartil da amostra: 20.022873216463445\n", + "esse é o 3° quartil da amostra: 24.5040190183804\n", + "esse é o 4° quartil da amostra: 26.685610640870618\n" + ] + } + ], + "source": [ + "#estatiscas descritiva \n", + "\n", + "print ('Média da amostra:', np.mean (temperaturaSP))\n", + "print ('Mediana da amostra:', np.median (temperaturaSP))\n", + "print ('Desvio padrão da amostra:', np.std (temperaturaSP))\n", + "print ('1° quartil da amostra:', np.quantile(temperaturaSP,0.25))\n", + "print ('2° quartil da amostra:', np.quantile(temperaturaSP,0.50))\n", + "print ('3° quartil da amostra:', np.quantile(temperaturaSP,0.75))\n", + "print ('4° quartil da amostra:', np.quantile(temperaturaSP,1))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'whiskers': [,\n", + " ],\n", + " 'caps': [,\n", + " ],\n", + " 'boxes': [],\n", + " 'medians': [],\n", + " 'fliers': [],\n", + " 'means': []}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.boxplot(temperaturaSP)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'horas')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#bônus\n", + "\n", + "plt.plot(temperaturaSP, marker = '*', ms = 20) \n", + "plt.ylabel(\"temperatura\")\n", + "plt.xlabel (\"horas\")" + ] + } + ], + "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 +}