# ds1000 / 925 - taskset: [ds1000](https://harnessreport.com/tasks/ds1000.md) - difficulty: - category: - language: - runnable from the site: no - agent timeout: 1800s ## Results by harness _none yet_ ## Instruction ``` # 925: DS-1000 Task ## Prompt Problem: I would like to apply minmax scaler to column A2 and A3 in dataframe myData and add columns new_A2 and new_A3 for each month. myData = pd.DataFrame({ 'Month': [3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8], 'A1': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2], 'A2': [31, 13, 13, 13, 33, 33, 81, 38, 18, 38, 18, 18, 118], 'A3': [81, 38, 18, 38, 18, 18, 118, 31, 13, 13, 13, 33, 33], 'A4': [1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8], }) Below code is what I tried but got en error. from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() cols = myData.columns[2:4] myData['new_' + cols] = myData.groupby('Month')[cols].scaler.fit_transform(myData[cols]) How can I do this? Thank you. A: corrected, runnable code <code> import numpy as np from sklearn.preprocessing import MinMaxScaler import pandas as pd myData = pd.DataFrame({ 'Month': [3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8], 'A1': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2], 'A2': [31, 13, 13, 13, 33, 33, 81, 38, 18, 38, 18, 18, 118], 'A3': [81, 38, 18, 38, 18, 18, 118, 31, 13, 13, 13, 33, 33], 'A4': [1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8], }) scaler = MinMaxScaler() </code> myData = ... # put solution in this variable BEGIN SOLUTION <code> ## What to do - Edit `solution/solution.py` so the code passes the DS-1000 tests. - Do not access the internet or install new packages; required libraries are preinstalled in the Docker image. - Run tests locally via `bash tests/test.sh`. ## Notes - Keep the variable names/signatures implied by the prompt/code_context. - The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`). ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp