Question:

I’m looking for best practices or a good workflow to manage DataFrame metadata in a centralized way in Python. Specifically, I need to handle column names, data types, and NaN values flexibly as I load, clean, and process data. What are common solutions / best practices?

Background:

• Data Loading: I want to assign column names dynamically when loading data from CSV files.
• Data Processing: I clean and process data in multiple steps, sometimes adding new feature columns.
• Centralized Metadata: I’m aiming for a centralized approach to managing column names, data types, etc., so that I can easily adjust them later on. Ideally, I’d like to reference columns like df[metadata.column1].

What I’ve Tried:

• Pandera: I explored using Pandera, but it seems more focused on data validation rather than metadata management or column referencing.
• Custom Classes: I experimented with creating a Column dataclass and a Schema class to store DataFrame metadata. Here’s a simplified version of what I did:

from typing import Any
from dataclasses import dataclass
import pandas as pd

@dataclass
class Column:
    name: str
    dtype: str = None
    nan_values: list[Any] | Any = None
    
class BaseSchema:
    @staticmethod
    def column_names(self):
        return [col.name for col in self.__dict__.values() if isinstance(col, Column)]
    
    @staticmethod
    def dtypes(self):
        return {col.name: col.dtype for col in self.__dict__.values() if isinstance(col, Column)}
    
    @staticmethod
    def na_values(self):
        return {col.name: col.nan_values for col in self.__dict__.values() if isinstance(col, Column)}

class ImportSchema(BaseSchema):
    region = Column(name="region", dtype="category")
    category = Column(name="category", dtype="category")
    parameter = Column(name="parameter", dtype="category")
    model = Column(name="model", dtype="category")
    powertrain = Column(name="powertrain", dtype="category")
    year = Column(name="year", dtype='Int64')
    unit = Column(name="unit", dtype="category")
    value = Column(name="value", dtype=float)

class CleanedSchema(ImportSchema):
    region = Column(name="region_cleaned", dtype="category")

class ProcessedSchema(CleanedSchema):
    region = Column(name="region_processed", dtype="category")

class FeatureSchema(ProcessedSchema):
    new_feature = Column(name="new_feature", dtype="category")

def load_data(path: str) -> pd.DataFrame:
    return pd.read_csv(path, dtype=ImportSchema.dtypes(), na_values=ImportSchema.na_values())

if __name__ == "__main__":
    df = load_data(r"data.csv")
    df = df.rename(columns={ImportSchema.region.name: CleanedSchema.region.name}).astype(CleanedSchema.region.dtype)
    df[FeatureSchema.new_feature.name] = df[CleanedSchema.region.name].apply(do_something).astype(FeatureSchema.new_feature.dtype)