data-cache-inspector

To query market data, you filter rows based on specific dates, columns, and conditions directly from the 'data_cache' directory without manual file manipulation. This allows efficient retrieval of specific data points for analysis.

You can compare financial market turnover across different dates by using the comparison analysis feature. It evaluates data points across varying conditions, enabling you to identify market trends between specific dates.

Yes, data analysis using polars and pandas works with akshare and bao_stock dependencies. These libraries are required to query and analyze the market data stored in your local data cache.

The best way to analyze market data without manual file manipulation is to query and filter directly from the 'data_cache' directory. This approach supports filtering by date, column, and conditions for efficient data access.

Yes, you can filter specific data columns from the data_cache for comparison analysis. The system supports retrieving specific data rows based on column filters, allowing targeted comparison across different data points.

You need Python libraries like polars and pandas for market data querying because they provide the underlying data processing capabilities required to filter, retrieve, and compare market data stored in the data_cache.