Pandas scenarios to read CSV with inconsistent casing in column headers
I'm reviewing some code and I've been struggling with this for a few days now and could really use some help. I am trying to read a CSV file in Pandas where the column headers have inconsistent casing. For example, the headers are defined as `Name`, `age`, and `Location`. When I attempt to read the CSV using `pd.read_csv()`, it seems that operations like `df['name']` return a `KeyError`, and I need to access the columns as expected. Here's the code I'm using: ```python import pandas as pd df = pd.read_csv('data.csv') print(df.head()) ``` The CSV file looks like this: ``` Name,age,Location Alice,30,New York bob,25,Los Angeles CHARLIE,35,Chicago ``` I tried using `df.columns = df.columns.str.lower()` to standardize the headers, but that doesn’t help in maintaining the original casing which might be important later for user queries. Is there a way to read the CSV while preserving the casing of the headers and still being able to access the columns reliably? Also, are there any best practices for handling such inconsistencies in CSV files when using Pandas? I’m on Pandas version 1.3.4. For context: I'm using Python on CentOS. I'd be grateful for any help.