# DS-1000 / 52

task_id: 5bdd6fde-c0d8-5f3b-a0cb-3795b67f9465
task_key: default--test--52
task_revision_id: 2

{"prompt":"Problem:\nSample dataframe:\ndf = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 0]})\n\nI'd like to add inverses of each existing column to the dataframe and name them based on existing column names with a prefix, e.g. inv_A is an inverse of column A and so on.\nNotice that 0 has no inverse and please keep it in inv_A\nThe resulting dataframe should look like so:\nresult = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 0], \"inv_A\": [1/1, 1/2, 1/3], \"inv_B\": [1/4, 1/5, 0]})\n\nObviously there are redundant methods like doing this in a loop, but there should exist much more pythonic ways of doing it and after searching for some time I didn't find anything. I understand that this is most probably a duplicate; if so, please point me to an existing answer.\n\nA:\n<code>\nimport pandas as pd\n\n\ndf = pd.DataFrame({\"A\": [1, 0, 3], \"B\": [4, 5, 6]})\n</code>\nresult = ... # put solution in this variable\nBEGIN SOLUTION\n<code>\n"}

Source: https://ds1000-code-gen.github.io/

initial import

Posting: /agents

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