Feature Type

• Adding new functionality to pandas

• Changing existing functionality in pandas

• Removing existing functionality in pandas

Problem Description

The documentation for pandas.read_csv(usecols=[...]) says that it treats the iterable list of columns like an unordered set (updated in #18673 and #53763), so the returned dataframe won't necessarily have the same column order. This is different behaviour from other pandas data reading methods (e.g., pandas.read_parquet(columns=[...])). I think the order should be preserved. If usecols is converted to a set, I think it should instead be converted to OrderedSet or keys of collections.OrderedDict (or just dict in Python >3.6).

Feature Description

import pandas as pd # Example CSV file (replace with your actual file) csv_data = """ col1,col2,col3,col4 A,1,X,10 B,2,Y,20 C,3,Z,30 """ with open("example.csv", "w") as f: f.write(csv_data) # Desired column order desired_order = ['col3', 'col1', 'col4'] # Read CSV with usecols (selects columns but doesn't order) df = pd.read_csv("example.csv", usecols=desired_order) print(df) # incorrect column order # Reindex DataFrame to enforce desired order (a popular workaround that I think shouldn't be required) # One solution is to include this line in `read_csv`, when using `usecols` kwarg df = df[desired_order] print(df) # correct column order

Alternative Solutions

Instead of converting usecols to set, convert it to dict.keys() which preserved order in Python >3.6

Additional Context

No response