{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"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\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."},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"df=pd.read_csv('../input/train.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"most_frequent_place2=df.loc[df['place_id']==1623394281]"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"df['place_id'].value_counts()"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"import pylab as plot"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"plot.scatter(most_frequent_place['x'], most_frequent_place['y'], c=most_frequent_place['accuracy'])"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"plot.scatter(most_frequent_place2['x'], most_frequent_place2['y'], c=most_frequent_place2['accuracy'])"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":""}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":0}