{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","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\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns  # visualization tool\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\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"data = pd.read_csv('../input/train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"ebd05e84c7a6d4ea76900e9ca10e5f9598cefdf9"},"cell_type":"code","source":"data.info()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"738dc564b53a64965b4ae92e32525969a090c0a7"},"cell_type":"code","source":"data.corr()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"88aab13e9f1b9fa10348e9fe30e332f14d986c05"},"cell_type":"code","source":"#correlation map\nf,ax = plt.subplots(figsize=(18, 18))\nsns.heatmap(data.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"5735bc34c2af3705b8934cc0a864a95d56345f39"},"cell_type":"code","source":"data.head(10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"8a2b4b751c8aa89a0dc2feb66615a442f685e797"},"cell_type":"code","source":"data.columns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"803d498a8122c04bcf081395cad16461d9e262b2"},"cell_type":"code","source":"# Line Plot\n# color = color, label = label, linewidth = width of line, alpha = opacity, grid = grid, linestyle = sytle of line\ndata.seg_id.plot(kind = 'line', color = 'g',label = 'seg_id',linewidth=1,alpha = 0.5,grid = True,linestyle = ':')\ndata.time_to_failure.plot(color = 'r',label = 'time_to_failure',linewidth=1, alpha = 0.5,grid = True,linestyle = '-.')\nplt.legend(loc='upper right')     # legend = puts label into plot\nplt.xlabel('x axis')              # label = name of label\nplt.ylabel('y axis')\nplt.title('Line Plot')            # title = title of plot\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"45b74a3b567ac6dd09e19bbc98e078f27233d53e"},"cell_type":"code","source":"# Histogram\n# bins = number of bar in figure\ndata.seg_id.plot(kind = 'hist',bins = 50,figsize = (12,12))\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"32ab35ff5af36cef8c664d8e0b41a170ca134d87"},"cell_type":"code","source":"# will be going...","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}