{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"df = pd.read_csv('../input/siim-isic-melanoma-classification/train.csv')\ndf.info() \ndf.head(20)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df['benign_malignant'].unique() ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df['diagnosis'].unique() ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\n\n#sns.countplot(df['sex'])\n\n# Get the counts for each class\ncases_count = df['diagnosis'].value_counts()\n\n\n# Plot the results \nplt.figure(figsize=(25,10))\nsns.barplot(x=cases_count.index, y= cases_count.values)\nplt.title('Nombre de cas', fontsize=14)\nplt.xlabel('Diagnosis', fontsize=12)\nplt.ylabel('Nombre', fontsize=12)\nplt.xticks(range(len(cases_count.index)), \n           ['unknown', \n            'nevus'\n            'melanoma', \n            'seborrheic keratosis',\n            'lentigo NOS', \n            'lichenoid keratosis', \n            'solar lentigo',\n            'cafe-au-lait macule', \n            'atypical melanocytic proliferation'\n           ])\nplt.show()\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"img = plt.imread('../input/siim-isic-melanoma-classification/jpeg/test/ISIC_0052060.jpg')\nprint(img)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(img)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df[df['diagnosis'] == 'nevus']\ndf['benign_malignant'].value_counts() ","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}