{"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)\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\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":1,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"train = pd.read_csv('../input/train.csv')\ntrain.head()","execution_count":2,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"bc81575baec760a4ba212fad221591e6f48019c6"},"cell_type":"code","source":"train.info()","execution_count":3,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"22811ff1f4c1398a65a34fce11fb71423e2a7418"},"cell_type":"code","source":"train.describe()","execution_count":4,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"1793107b58b793831efb2050204bee330ff35013"},"cell_type":"code","source":"import matplotlib.pyplot as plt\ntrain.hist(bins=50, figsize=(20,15))\nplt.show()","execution_count":5,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.5","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}