{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"545fa4a8-4817-e5a5-8d97-c9c8f3301239"},"source":"# Studying correlations of sea lions"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"8978fc74-ed0e-299c-3c67-e3b0d6f953f8"},"outputs":[],"source":"\n# 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)\nimport seaborn as sns\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\nfrom subprocess import check_output\nimport itertools\nimport matplotlib.pyplot as plt\nimport cv2\nimport glob\nfrom PIL import Image, ImageDraw, ImageFilter"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"0b2fe196-d34f-09b2-7d26-374784b2c17e"},"outputs":[],"source":"df = pd.read_csv('../input/Train/train.csv')\ntrain1 = glob.glob('../input/Train/*.jpg')\ntrain2 = glob.glob('../input/TrainDotted/*.jpg')\nsubmission = pd.read_csv('../input/sample_submission.csv')\ndf.head()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"85f2afeb-a60c-9f20-4a19-87fd03ace5ce"},"outputs":[],"source":"df[['adult_males', 'subadult_males', 'adult_females', 'juveniles', 'pups']].sum(axis=0).plot.barh()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"c7217454-5639-c06a-22f8-ca5671ba0849"},"outputs":[],"source":"corr = df[['adult_males', 'subadult_males', 'adult_females', 'juveniles', 'pups']].corr()\ncorr\n# High correlation between adult_females, adult_males and pups"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"6014414b-0376-b2fd-c869-5a238a43c81f"},"outputs":[],"source":"corx = sns.PairGrid(df[['adult_males', 'subadult_males', 'adult_females', 'juveniles', 'pups']], palette=[\"red\"])\ncorx.map_lower(plt.scatter, s=10)\ncorx.map_diag(sns.distplot, kde=False)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"a5be64e7-b515-9e06-e6cd-74b6576ee520"},"outputs":[],"source":" def get_im_cv2(path):\n    img = cv2.imread(path)\n    #new = cv2.resize(img, (1032, 1032), cv2.INTER_LINEAR)\n    return img"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"e69dc31f-a71a-520f-e3a0-57dd01feb640"},"outputs":[],"source":"im2 = Image.open(train2[4])\nplt.imshow(im2); plt.axis('off')\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"db6b7fd6-b876-f51b-9bf5-51f4fceabe8d"},"outputs":[],"source":"submission.head()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"e14904b9-32a7-a747-cc49-73ebb4d279b1"},"outputs":[],"source":""}],"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":"3.6.0"}},"nbformat":4,"nbformat_minor":0}