{"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":"##图片\nprint(check_output([\"ls\", \"../input/train_photos\"]).decode(\"utf8\"))"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"%matplotlib inline\n# Sample script naive benchmark that yields 0.609 public LB score WITHOUT any image information\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Read in data, files are assumed to be in the \"../input/\" directory.\ntrain = pd.read_csv('../input/train.csv')\nsubmit = pd.read_csv('../input/sample_submission.csv')"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"train=pd.read_csv(\"../input/train_photo_to_biz_ids.csv\")\ntrain.head()"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"train.groupby(\"business_id\").photo_id.count()"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"tra=pd.read_csv(\"../input/train.csv\")\ntra.head()\ntra[\"labels\"].unique()"},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":false},"outputs":[],"source":"import matplotlib.pyplot as plt\n#%matplotlib inline\nfrom PIL import Image\nfrom PIL import ImageFilter\nplt.rcParams['figure.figsize'] = (10.0, 10.0)\nplt.subplots_adjust(wspace=0, hspace=0)\nfor x in range(25):\n        plt.subplot(5, 5, x+1)\n        im = Image.open('../input/train_photos/' + str(train.photo_id[x]) + '.jpg')\n        im = im.resize((100, 100), Image.ANTIALIAS)\n        plt.imshow(im)\n        plt.axis('off')"}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"nbformat":4,"nbformat_minor":0}