{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"352f92f0-4f83-e252-dd4b-f3215e0b1626"},"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.\n\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"f9bd48e1-52c0-2e39-d700-58c6dab42f05"},"outputs":[],"source":"import matplotlib.pyplot as plt\n%matplotlib inline\nplt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"017fb0db-0735-e911-d7e5-c3dd4cb1d95e"},"outputs":[],"source":"train_photos = pd.read_csv('../input/train_photo_to_biz_ids.csv')\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"708f445f-76f8-9632-e5b8-00fdc7393963"},"outputs":[],"source":"import os\nprint(''.join([str(train_photos.photo_id[0]),'.jpg']))\n\nfrom PIL import Image\nim = Image.open(os.path.join('../input/','train_photos',''.join([str(train_photos.photo_id[5]),'.jpg'])))\nplt.imshow(im)\n\n\n#0: good_for_lunch\n#1: good_for_dinner\n#2: takes_reservations\n#3: outdoor_seating\n#4: restaurant_is_expensive\n#5: has_alcohol\n#6: has_table_service\n#7: ambience_is_classy\n#8: good_for_kids"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"c7411f48-24b8-5085-f65c-54a8551f7c52"},"outputs":[],"source":"train_attributes = pd.read_csv('../input/train.csv')\n\nlist(train_attributes)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"3f39a59e-7ee5-d9fb-b9de-5dc90ed894fd"},"outputs":[],"source":"train_attributes['labels_list'] = train_attributes['labels'].str.split(' ')\ntrain_attributes['outdoor'] = train_attributes['labels'].str.contains('3')\noutdoor_businesses = train_attributes[train_attributes.outdoor==True].business_id.tolist()\noutdoor_photos = train_photos[train_photos.business_id.isin(outdoor_businesses)].photo_id.tolist()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"806585dc-5bab-0edd-fd5b-fc5d74e15201"},"outputs":[],"source":"num_images_for_show = 5\n\nphotos_to_show = np.random.choice(outdoor_photos,num_images_for_show**2)\n\nfor x in range(num_images_for_show ** 2):\n        \n        plt.subplot(num_images_for_show, num_images_for_show, x+1)\n        im = Image.open(os.path.join('../input/','train_photos',''.join([str(photos_to_show[x]),'.jpg'])))\n        plt.imshow(im)\n        plt.axis('off')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"acd6dd76-6b67-8239-5c9d-33b310900ce8"},"outputs":[],"source":"#0: good_for_lunch\n#1: good_for_dinner\n#2: takes_reservations\n#3: outdoor_seating\n#4: restaurant_is_expensive\n#5: has_alcohol\n#6: has_table_service\n#7: ambience_is_classy\n#8: good_for_kids\n\ntrain_attributes['labels_list'] = train_attributes['labels'].str.split(' ')\ntrain_attributes['kids'] = train_attributes['labels'].str.contains('8')\nkids_businesses = train_attributes[train_attributes.kids==True].business_id.tolist()\nkidsRes_photos = train_photos[train_photos.business_id.isin(kids_businesses)].photo_id.tolist()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"1bc33182-7969-73b7-b1a7-03b4e8224161"},"outputs":[],"source":"num_images_for_show = 5\n\nphotos_to_show = np.random.choice(kidsRes_photos,num_images_for_show**2)\n\nfor x in range(num_images_for_show ** 2):\n        \n        plt.subplot(num_images_for_show, num_images_for_show, x+1)\n        im = Image.open(os.path.join('../input/','train_photos',''.join([str(photos_to_show[x]),'.jpg'])))\n        plt.imshow(im)\n        plt.axis('off')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"fbecdae1-dd60-4efa-82b4-6a868204bf26"},"outputs":[],"source":"#create a new dataset\n"}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.5.2"}},"nbformat":4,"nbformat_minor":0}