Merge CSV Files With Different Column Names Using a Mapping
Combine CSV exports with different column names into one tidy table using a mapping dictionary, type fixes and a report of unmapped columns.
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Write a Python (pandas) script that combines CSV files whose column names differ, such as exports from different tools. Folder: [FOLDER_PATH]. Systems or sources the files come from: [SOURCE_SYSTEMS]. For each source, its column names and what they mean: [COLUMN_MEANINGS]. The final column names and types I want: [TARGET_COLUMNS_AND_TYPES]. Date formats seen: [DATE_FORMATS]. Number formats seen (commas, currency symbols): [NUMBER_FORMATS]. Design the script with: 1. A mapping dictionary at the top (source name to target name), one block per source, which I can edit. 2. A function that detects which source a file belongs to by its header, with a clear error if nothing matches. 3. Cleaning functions for dates, numbers, text trimming and empty strings, each applied only to the columns I list. 4. A final check that prints unmapped columns, rows with failed conversions and the count of each (do not silently drop rows; write them to a separate rejects file). 5. A summary with the arithmetic: rows in per file, rows rejected, rows out. After the code, add a one-page explanation of the mapping approach for a beginner, and a testing plan using two tiny invented sample files that you provide as text I can save. Never modify the originals, and never overwrite output without a flag. Say clearly which assumptions you made about my data. Do not claim to have executed the code. If my column meanings are unclear, ask me before writing the mapping.
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