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output", "scratch": true } }, { "file": "seaborn-descr.py", "settings": { "buffer_size": 2079, "line_ending": "Unix" } }, { "contents": "import pandas as pd\nimport os\nimport matplotlib\nimport matplotlib.pyplot as plt\n# from sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nfrom scipy.stats import trim_mean, kurtosis\nfrom scipy.stats.mstats import mode, gmean, hmean\n\n\ndef linebreak():\n \"\"\"prints a line break to split up functions\"\"\"\n print('\\n ============================================== \\n')\n\n\nmatplotlib.rcParams['backend'] = 'TkAgg'\nplt.style.use('seaborn-dark-palette')\n\npath = os.getcwd()\ndata_file = str('/data/Social_Network_Ads.csv')\n\ndf = pd.read_csv(path + data_file)\n# df = pd.DataFrame(df)\n\ndf = df.sample(frac=1).reset_index(drop=True)\n\nprint('{} rows. {} cols.'.format(df.shape[0], df.shape[1]))\n\nlinebreak()\nprint(df.iloc[0:10, :])\n\nlinebreak()\nX = df[['Age', 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