{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Entrada e saída de dados\n", "\n", "Este notebook conterá nossas referências sobre entrada e saída de dados. O pandas pode ler uma variedade de tipos de arquivos usando seus métodos pd.read_. Vejamos os tipos de dados mais comuns:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mDEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\u001b[33m\n", "\u001b[0mRequirement already satisfied: numpy in /usr/local/lib/python3.8/site-packages (1.24.2)\n", "\u001b[33mDEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\u001b[33m\n", "\u001b[0m" ] } ], "source": [ "# Instale um pacote pip no kernel atual do Jupyter \n", "import sys \n", "!{ sys.executable } -m pip install numpy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CSV\n", "\n", "### CSV Input" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ " a b c d\n", "0 0 1 2 3\n", "1 4 5 6 7\n", "2 8 9 10 11\n", "3 12 13 14 15" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('exemplo.csv')\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saída de dados tipo CSV " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df.to_csv('exemplo_csv_saida.csv',index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Excel\n", "\n", "Pandas podem ler e escrever arquivos do Excel, tenha em mente, isso só importa dados. Não fórmulas nem imagens, lembrando que imagens ou macros podem bugar o método." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Entrada via Excel" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " index a b c d\n", "0 0 0 1 2 3\n", "1 1 4 5 6 7\n", "2 2 8 9 10 11\n", "3 3 12 13 14 15" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('Exemplo_Excel.xlsx',sheet_name='Sheet1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saída via Excel" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df.to_excel('Exemplo_Excel_Saida.xlsx', sheet_name='Sheet1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HTML\n", "\n", "Você pode precisar instalar htmllib5, lxml e BeautifulSoup4. No seu terminal / prompt de comando, execute:\n", "\n", " conda install lxml\n", " conda install html5lib\n", " conda install BeautifulSoup4\n", " \n", "Em seguida, reinicie o Jupyter Notebook. \n", "(Ou use instalação de pip se não estiver usando a Distribuição de Anaconda)\n", "Pandas podem ler guias de tabelas fora de html. Por exemplo:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Entrada HTML\n", "\n", "A função Pandas read_html irá ler tabelas fora de uma página da Web e retornar uma lista de objetos DataFrame:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "#df = pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html')\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from unicodedata import normalize\n", "#table_MN = pd.read_html('https://en.wikipedia.org/wiki/Minnesota', match=\"Country of origin of first and second-generation immigrants (2023)\")\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "tabs = pd.read_html(\"https://www.runnersworld.com/races-places/a20823734/these-are-the-worlds-fastest-marathoners-and-marathon-courses/\")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(tabs)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0123
0RunnerFinish TimePace/MileMarathon
1Kelvin Kiptum (Kenya)2:00:354:36.0Chicago, 2023
2Eliud Kipchoge (Kenya)2:01:094:37.2Berlin, 2022
3Kenenisa Bekele (Ethiopia)2:01:414:38.5Berlin, 2019
4Sisay Lemma (Ethiopia)2:01:484:38.7Valencia, 2023
5Birhanu Legese (Ethiopia)2:02:484:41.0Berlin, 2019
6Mosinet Geremew (Ethiopia)2:02:554:41.3London, 2019
7Dennis Kimetto (Kenya)2:02:574:41.4Berlin, 2014
8Evans Chebet (Kenya)2:03:004:41.5Valencia, 2020
9Gabriel Geay (Tanzania)2:03:004:41.5Valencia, 2022
10Lawrence Cherono (Kenya)2:03:044:41.6Valencia, 2022
\n", "
" ], "text/plain": [ " 0 1 2 3\n", "0 Runner Finish Time Pace/Mile Marathon\n", "1 Kelvin Kiptum (Kenya) 2:00:35 4:36.0 Chicago, 2023\n", "2 Eliud Kipchoge (Kenya) 2:01:09 4:37.2 Berlin, 2022\n", "3 Kenenisa Bekele (Ethiopia) 2:01:41 4:38.5 Berlin, 2019\n", "4 Sisay Lemma (Ethiopia) 2:01:48 4:38.7 Valencia, 2023\n", "5 Birhanu Legese (Ethiopia) 2:02:48 4:41.0 Berlin, 2019\n", "6 Mosinet Geremew (Ethiopia) 2:02:55 4:41.3 London, 2019\n", "7 Dennis Kimetto (Kenya) 2:02:57 4:41.4 Berlin, 2014\n", "8 Evans Chebet (Kenya) 2:03:00 4:41.5 Valencia, 2020\n", "9 Gabriel Geay (Tanzania) 2:03:00 4:41.5 Valencia, 2022\n", "10 Lawrence Cherono (Kenya) 2:03:04 4:41.6 Valencia, 2022" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabs[0]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "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": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 1 }