Read ibm cloud s3 files and use on jupyter notebook pandas

ibm-cloud-s3-pandas-jupyter-notebook.ipynb

{
    "nbformat_minor": 1, 
    "cells": [
        {
            "execution_count": 21, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "import pandas as pd"
        }, 
        {
            "source": "## Connect s3 file storage", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 22, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "from botocore.client import Config\nimport ibm_boto3\nimport types\n\ndef __iter__(self): return 0\n\napi_key='xxxxxx',\nbucket='yyyyyy'\n\n\nclient = ibm_boto3.client(service_name='s3',\n    ibm_api_key_id=api_key,\n    ibm_auth_endpoint=\"https://iam.bluemix.net/oidc/token\",\n    config=Config(signature_version='oauth'),\n    endpoint_url='https://s3.eu-geo.objectstorage.service.networklayer.com')"
        }, 
        {
            "source": "## Read file", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 23, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "filename = 'aluguel.csv'\nbody = client.get_object(Bucket=bucket,Key=filename)['Body']\nif not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n\ndados = pd.read_csv(body, sep=\";\")"
        }, 
        {
            "execution_count": 24, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 24, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "pandas.core.frame.DataFrame"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "type(dados)"
        }, 
        {
            "execution_count": 25, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "execution_count": 25, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Tipo</th>\n      <th>Bairro</th>\n      <th>Quartos</th>\n      <th>Vagas</th>\n      <th>Suites</th>\n      <th>Area</th>\n      <th>Valor</th>\n      <th>Condominio</th>\n      <th>IPTU</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Quitinete</td>\n      <td>Copacabana</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>40</td>\n      <td>1700.0</td>\n      <td>500.0</td>\n      <td>60.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Casa</td>\n      <td>Jardim Bot\u00e2nico</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>100</td>\n      <td>7000.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Conjunto Comercial/Sala</td>\n      <td>Barra da Tijuca</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>150</td>\n      <td>5200.0</td>\n      <td>4020.0</td>\n      <td>1111.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Apartamento</td>\n      <td>Centro</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>15</td>\n      <td>800.0</td>\n      <td>390.0</td>\n      <td>20.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Apartamento</td>\n      <td>Higien\u00f3polis</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>48</td>\n      <td>800.0</td>\n      <td>230.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                      Tipo           Bairro  Quartos  Vagas  Suites  Area  \\\n0                Quitinete       Copacabana        1      0       0    40   \n1                     Casa  Jardim Bot\u00e2nico        2      0       1   100   \n2  Conjunto Comercial/Sala  Barra da Tijuca        0      4       0   150   \n3              Apartamento           Centro        1      0       0    15   \n4              Apartamento     Higien\u00f3polis        1      0       0    48   \n\n    Valor  Condominio    IPTU  \n0  1700.0       500.0    60.0  \n1  7000.0         NaN     NaN  \n2  5200.0      4020.0  1111.0  \n3   800.0       390.0    20.0  \n4   800.0       230.0     NaN  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "dados.head()"
        }, 
        {
            "source": "## Informa\u00e7\u00f5es Gerais Sobre a base dados", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 26, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "tipos_de_dados = pd.DataFrame(dados.dtypes, columns=['Tipos de Dados'])"
        }, 
        {
            "execution_count": 27, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "tipos_de_dados.columns.name ='Vari\u00e1veis'"
        }, 
        {
            "execution_count": 28, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "A base dados apresenta 32960 registros(im\u00f3veis) e 9 variavies\n"
                }
            ], 
            "source": "print('A base dados apresenta {} registros(im\u00f3veis) e {} variavies'.format(dados.shape[0], dados.shape[1]))"
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.5", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "version": "3.5.5", 
            "name": "python", 
            "file_extension": ".py", 
            "pygments_lexer": "ipython3", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }, 
    "nbformat": 4
}