{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Hello ,\nI am going to show you Easy generating EDA using Pandas Profiling.\n\nStatistics \n\npandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding.","metadata":{}},{"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","execution":{"iopub.status.busy":"2022-08-01T09:42:08.849077Z","iopub.execute_input":"2022-08-01T09:42:08.849553Z","iopub.status.idle":"2022-08-01T09:42:08.881921Z","shell.execute_reply.started":"2022-08-01T09:42:08.849458Z","shell.execute_reply":"2022-08-01T09:42:08.880916Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv(\"../input/tabular-playground-series-aug-2022/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-08-01T09:43:36.818306Z","iopub.execute_input":"2022-08-01T09:43:36.818720Z","iopub.status.idle":"2022-08-01T09:43:36.848041Z","shell.execute_reply.started":"2022-08-01T09:43:36.818685Z","shell.execute_reply":"2022-08-01T09:43:36.847338Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"execution":{"iopub.status.busy":"2022-08-01T09:46:35.453646Z","iopub.execute_input":"2022-08-01T09:46:35.454060Z","iopub.status.idle":"2022-08-01T09:46:36.219558Z","shell.execute_reply.started":"2022-08-01T09:46:35.454027Z","shell.execute_reply":"2022-08-01T09:46:36.218182Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install pandas-profiling","metadata":{"execution":{"iopub.status.busy":"2022-08-01T09:46:42.933371Z","iopub.execute_input":"2022-08-01T09:46:42.934185Z","iopub.status.idle":"2022-08-01T09:46:55.257844Z","shell.execute_reply.started":"2022-08-01T09:46:42.934144Z","shell.execute_reply":"2022-08-01T09:46:55.256824Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from pandas_profiling import ProfileReport as PR","metadata":{"execution":{"iopub.status.busy":"2022-08-01T09:49:27.007997Z","iopub.execute_input":"2022-08-01T09:49:27.008391Z","iopub.status.idle":"2022-08-01T09:49:27.013481Z","shell.execute_reply.started":"2022-08-01T09:49:27.008340Z","shell.execute_reply":"2022-08-01T09:49:27.012332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"profile  = PR(data,title = \"Pandas Profiling\")","metadata":{"execution":{"iopub.status.busy":"2022-08-01T09:50:09.873846Z","iopub.execute_input":"2022-08-01T09:50:09.874902Z","iopub.status.idle":"2022-08-01T09:50:09.885015Z","shell.execute_reply.started":"2022-08-01T09:50:09.874861Z","shell.execute_reply":"2022-08-01T09:50:09.883831Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"profile","metadata":{"execution":{"iopub.status.busy":"2022-08-01T09:50:14.542743Z","iopub.execute_input":"2022-08-01T09:50:14.543160Z","iopub.status.idle":"2022-08-01T09:50:17.698202Z","shell.execute_reply.started":"2022-08-01T09:50:14.543114Z","shell.execute_reply":"2022-08-01T09:50:17.697590Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}