{"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":112509,"databundleVersionId":14254895,"sourceType":"competition"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\n\n# Load\ndf = pd.read_csv('/kaggle/input/csiro-biomass/train.csv')\n\nprint('Basic Information：')\ndf.info()\n\n# Head\nrows, columns = df.shape\n\nprint(df.head().to_csv(sep='\\t', na_rep='nan')) ","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\ndf['id'] = df['sample_id'].apply(lambda x: x.split('__')[0])\n\nfilter_df = df[df['target_name'].isin(['Dry_Total_g', 'Dry_Green_g', 'Dry_Clover_g', 'Dry_Dead_g', 'GDM_g'])]\n\npivot_df = filter_df.pivot(index='id', columns='target_name', values='target').reset_index()\n\n# Cal\npivot_df['Dry_Total_calc'] = pivot_df['Dry_Green_g'] + pivot_df['Dry_Clover_g'] + pivot_df['Dry_Dead_g']\npivot_df['GDM_calc'] = pivot_df['Dry_Green_g'] + pivot_df['Dry_Clover_g']\n\n# Print Verification\nprint(\"ID | Act Value of Total | Green, Clover and Dead | Act Value of GDM | Green and Clover\\n\")\nfor idx, row in pivot_df.iterrows():\n    print(\n        f\"{row['id']} | \"\n        f\"{row['Dry_Total_g']} | \"\n        f\"{row['Dry_Total_calc']:.5f} | \"\n        f\"{row['GDM_g']} | \"\n        f\"{row['GDM_calc']:.5f}\"\n    )","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}