chinese-social-data

To load and clean Chinese social survey data, you can use automated preprocessing workflows that handle Stata, SPSS, and SAS formats, resolve typical encoding issues, and convert survey-specific missing-value codes into standard pandas DataFrame missing values.

Variable harmonization across dataset waves involves standardizing differing variable names and coding schemes into a unified format, producing an analysis-ready pandas DataFrame that ensures consistent longitudinal tracking for downstream modeling.

Parsing Chinese administrative division codes involves mapping GB/T 2260 numeric codes to their corresponding provincial, municipal, and county-level names using Python, enabling accurate geographic joins for social science research datasets.

Yes, Chinese social media text preprocessing applies jieba segmentation, stopword removal, and custom term dictionaries to clean raw text data while emphasizing PIPL compliance and de-identification for research-oriented collection workflows.

Yes, the data preprocessing workflow supports ingesting proprietary formats like Stata, SPSS, and SAS by reading them directly into Python, bypassing manual export steps and resolving encoding conflicts natively to output clean DataFrames.

Chinese survey datasets use non-standard missing values like -1 or -2 to denote refusals or inapplicable responses, which are normalized by programmatically replacing these specific codes with standard NaN values for reproducible analysis.