{"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"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":19596,"databundleVersionId":1292430,"sourceType":"competition"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"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 20GB 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sb\nimport sklearn as sk","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"a = pd.read_csv(\"/kaggle/input/birdsong-recognition/example_test_audio_metadata.csv\")\nb = pd.read_csv(\"/kaggle/input/birdsong-recognition/example_test_audio_summary.csv\")\nc = pd.read_csv(\"/kaggle/input/birdsong-recognition/sample_submission.csv\")\nd = pd.read_csv(\"/kaggle/input/birdsong-recognition/test.csv\")\ne = pd.read_csv(\"/kaggle/input/birdsong-recognition/train.csv\")\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"a.head(10)\nprint(\"\\n\")\nb.head(10)\nprint(\"\\n\")\nc.head(10)\nprint(\"\\n\")\nd.head(10)\nprint(\"\\n\")\ne.head(10)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"a.columns\nprint(\"\\n\")\nb.columns\nprint(\"\\n\")\nc.columns\nprint(\"\\n\")\nd.columns\nprint(\"\\n\")\ne.columns","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"a.shape\nprint(\"\\n\")\nb.shape\nprint(\"\\n\")\nc.shape\nprint(\"\\n\")\nd.shape\nprint(\"\\n\")\ne.shape","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(a.info())","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(b.info())","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(c.info())","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(d.info())","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(e.info())","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"a.describe\nprint(\"\\n\")\na.corr()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"b.describe\nprint(\"\\n\")\nb.corr()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"c.describe\nprint(\"\\n\")\nc.corr()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"d.describe\nprint(\"\\n\")\nd.corr()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"e.describe\nprint(\"\\n\")\ne.corr()","metadata":{},"execution_count":null,"outputs":[]}]}