{"cells": [{"cell_type": "code", "execution_count": null, "outputs": [], "metadata": {"_cell_guid": "1bea2b60-f037-422c-84ed-a8a5ad217873", "_uuid": "9982351ab47138a2c7677b771da4c81dec053ebc"}, "source": ["# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output."]}, {"cell_type": "code", "execution_count": null, "outputs": [], "metadata": {"_cell_guid": "b793368d-1b14-4495-8568-ba5bbbd7096a", "_uuid": "1f456943b3881f5b82571f32324cab1863a8bcf2"}, "source": ["df_trainData = pd.read_csv('../input/train.csv')\n", "df_item=  pd.read_csv('../input/items.csv')\n", "df_oil = pd.read_csv('../input/oil.csv')\n", "df_stores = pd.read_csv('../input/stores.csv')\n", "df_transactions = pd.read_csv('../input/transactions.csv')"]}, {"cell_type": "code", "execution_count": null, "outputs": [], "metadata": {}, "source": ["df_trainData.head(10)"]}, {"cell_type": "code", "execution_count": null, "outputs": [], "metadata": {}, "source": ["df_item.head(10)"]}, {"cell_type": "code", "execution_count": null, "outputs": [], "metadata": {}, "source": ["df_oil.head(10)"]}, {"cell_type": "code", "execution_count": null, "outputs": [], "metadata": {"_cell_guid": "18b2e87e-b51a-4d6c-a83a-74ac302cffbb", "collapsed": true, "_uuid": "b818f377fcd47879b74a48b42b20a7b8e5a93860"}, "source": ["df_transactions.head(10)"]}, {"cell_type": "code", "execution_count": null, "outputs": [], "metadata": {"_kg_hide-output": false, "_cell_guid": "a80cc5bd-980e-4132-a8fd-2747e2807e2a", "collapsed": true, "_uuid": "e302bc36360857976fa92ab6a736729d7deead17"}, "source": []}, {"cell_type": "code", "execution_count": null, "outputs": [], "metadata": {"_cell_guid": "2f1a9246-fbb7-42e3-813c-ee2631866f8a", "collapsed": true, "_uuid": "0f3de5da342bd6f280b7b59dd52a93278cae6270"}, "source": ["data"]}, {"cell_type": "code", "execution_count": null, "outputs": [], "metadata": {"collapsed": true}, "source": []}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 3", "name": "python3", "language": "python"}, "language_info": {"version": "3.6.3", "name": "python", "codemirror_mode": {"version": 3, "name": "ipython"}, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "mimetype": "text/x-python", "file_extension": ".py"}}, "nbformat_minor": 1}