Distinguish repeating column names by adding an integer using pandas

Question

I have some columns that have the same names. I would like to add a 1 to the repeating column names Data Date Type hi hello stat hi hello 1/1/2022 a 0 0 1 1 0 Desired Date Type hi hello stat hi1 hello1 1/1/2022 a 0 0 1 1 0 Doing mask = df['col2'].duplicated(keep=False) I believe I can utilize mask, but not sure how to efficiently achieve this without calling out the actual column. I would like to call the full dataset and allow the algorithm to update the dupe. Any suggestion is appreciated JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

Answer 1

Use the built-in parser method _maybe_dedup_names(): df.columns = pd.io.parsers.base_parser.ParserBase({'usecols': None})._maybe_dedup_names(df.columns) # Date Type hi hello stat hi.1 hello.1 # 0 1/1/2022 a 0 0 1 1 0 This is what pandas uses to deduplicate column headers from read_csv(). Note that it scales to any number of duplicate names: cols = ['hi'] * 3 + ['hello'] * 5 pd.io.parsers.base_parser.ParserBase({'usecols': None})._maybe_dedup_names(cols) # ['hi', 'hi.1', 'hi.2', 'hello', 'hello.1', 'hello.2', 'hello.3', 'hello.4'] In pandas < 1.3: df.columns = pd.io.parsers.ParserBase({})._maybe_dedup_names(df.columns)