{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"# Imports\n\n# pandas\nimport pandas as pd\nfrom pandas import Series,DataFrame\n\n# numpy, matplotlib, seaborn\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set_style('whitegrid')\n%matplotlib inline\n\n# machine learning\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn import cross_validation\nimport xgboost as xgb"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"# get facebook & test csv files as a DataFrame\nfacebook_df = pd.read_csv('../input/train.csv')\ntest_df     = pd.read_csv('../input/test.csv')\n\n# preview the data\nfacebook_df.head()"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"facebook_df['x'] = round(facebook_df['x'] * 100)\nfacebook_df['y'] = round(facebook_df['y'] * 100)"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"facebook_df['(x,y)'] = facebook_df[['x','y']].apply(lambda x,y: '({},{})'.format(x,y), axis=1)"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"facebook_df['x'].head()"},{"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}