pandas-data-cleaning

Pandas data cleaning standardizes datasets by normalizing column names, deduplicating rows, and converting types to nullable dtypes. It loads from CSV or Parquet, applies per-dtype missing value strategies, logs applied actions, and outputs a cleaned Parquet file with index=False.

Handle missing values in pandas by converting columns to nullable dtypes like Int64, Float64, and string. This approach preserves missing values during data preprocessing instead of coercing them to NaN or dropping rows unnecessarily.

The best way to deduplicate rows in pandas is by using a natural or composite key, with options to keep the first or last occurrence. This removes duplicate records while retaining the most relevant row for your dataset.

Pandas traditionally converts missing values to NaN due to default dtype limitations. Using nullable dtypes like Int64, Float64, and string resolves this by preserving true null values alongside standard data types during cleaning.

Yes, this pandas data preprocessing workflow applies across common data cleaning tasks including preparing customer data, sensor logs, and transactional records, producing a standardized Parquet output for downstream pipelines.

A limitation of this pandas cleaning approach is that it strictly outputs Parquet files with index=False and requires predefined per-dtype strategies. Users must specify natural keys for deduplication and cannot export to alternative formats directly.