{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
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  "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\nimport matplotlib.pyplot as plt\nimport os\nfrom IPython.display import display_markdown as mkdown # as print\n\ndef nl():\n    print('\\n')\n    \nfor f in os.listdir('../input'):\n    print(f.ljust(30) + str(round(os.path.getsize('../input/' + f) / 1000000, 2)) + 'MB')\n"
 }
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