{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"3d82e9c1-b718-17e8-31bb-10bcb054326c"},"outputs":[],"source":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport time \nfrom sklearn import cross_validation\n\n\n#%%\n# Load in the training data\n# This bit of memory-saving code was a Eric Couto, https://www.kaggle.com/ericcouto/grupo-bimbo-inventory-demand/using-82-less-memory/code\ntypes = {'Semana':np.uint8, 'Agencia_ID':np.uint16, 'Canal_ID':np.uint8,\n         'Ruta_SAK':np.uint16, 'Cliente_ID':np.uint32, 'Producto_ID':np.uint16,'Venta_uni_hoy':np.uint32,\n         'Dev_uni_proxima':np.uint32, 'Demanda_uni_equil':np.uint32 }\n\ntrain = pd.read_csv('../input/train.csv', usecols=types.keys(), dtype=types)\nprint(train.info(memory_usage=True))\nnew_names = [\"week\",\"depot\",\"channel\",\"route\",\"client\",\"prod\",\"sales_units\",\"return_units_next_week\",\"demand\"]\ntrain.columns = new_names\n#%%\nstart = time.time()\npt_sales = train.pivot_table(values='sales_units',index=['client','route','prod'],columns=['week'],aggfunc=np.mean)\nprint (time.time()-start)/60."}],"metadata":{"_change_revision":0,"_is_fork":false,"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":"3.5.2"}},"nbformat":4,"nbformat_minor":0}