Webcolumn_indices = [1,4,5,6] new_names = ['a','b','c','d'] old_names = df.columns[column_indices] df.rename(columns=dict(zip(old_names, new_names)), … WebMay 25, 2024 · Way 1: Using rename () method. Import pandas. Create a data frame with multiple columns. Create a dictionary and set key = old name, value= new name of columns header. Assign the dictionary in columns. Call the rename method and pass columns that contain dictionary and inplace=true as an argument.

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WebJul 30, 2024 · Zip two lists and convert to dictionary using dict () function >>> L1 = ['a','b','c','d'] >>> L2 = [1,2,3,4] >>> d = dict(zip(L1,L2)) >>> d {'a': 1, 'b': 2, 'c': 3, 'd': 4} Using dictionary comprehension syntax >>> L1 = ['a','b','c','d'] >>> L2 = [1,2,3,4] >>> d = {k:v for k,v in zip(L1,L2)} >>> d {'a': 1, 'b': 2, 'c': 3, 'd': 4} Pythonic Webdf.rename(columns=dict(zip(df.columns[[1,2]], new_cols)),inplace=True) say you have a dictionary of the new column names and the name of the column they should replace: df.rename(columns={'old_col':'new_col', 'old_col_2':'new_col_2'}, inplace=True) But, if you don't have that, and you only have the indices, you can do this: dermtheory amazon

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WebSep 30, 2024 · Python zip () method takes iterable or containers and returns a single iterator object, having mapped values from all the containers. It is used to map the similar index of multiple containers so that they can be … WebDec 2, 2024 · def dict_zip(*dicts): return {k: [d[k] for d in dicts] for k in args[0].keys()} That's assuming all dicts have the same keys, or more exactly, all dicts have at least all the … WebTake two lists and use zip () to create a dictionary with old being keys and new being values. Rename all the columns and make permanent with inplace=True … dermtech pigmented lesion assay