Numpy File I/O: Practical Application of save and load for Data Persistence
This article introduces Numpy data persistence methods for storing/reading array data. A single array is saved as a `.npy` binary file using `np.save()`, and loaded with `np.load()`. The file automatically appends the extension, so ensure the path is correct. Multiple arrays are saved as a `.npz` compressed file using `np.savez()`, and loading returns a dictionary object accessible via key names. For text format, use `np.savetxt()`/`loadtxt()` to save as CSV or other text files, which are human-readable. However, binary formats (`.npy`/`.npz`) are more efficient and preserve data types. In summary: use `save()`/`load()` for single arrays, `savez()` for multiple arrays, and `savetxt()`/`loadtxt()` for text format, choosing based on specific needs.
Read MoreData Storage Fundamentals: How Python Web Saves User Information with SQLite
This article introduces the basic method of implementing web data storage using SQLite and Flask. SQLite is lightweight and easy to use, built into Python, and requires no additional server, making it suitable for beginners. First, the Flask environment needs to be installed. The core steps include creating a user table (with auto-incrementing id, unique username, password, and email fields), implementing registration (parameterized data insertion) and user list display (querying and returning dictionary results) through Python operations. During operations, attention should be paid to password encryption (to prevent plaintext storage), SQL injection prevention, and proper connection closure. The article demonstrates the data persistence process with sample code, emphasizing that SQLite is suitable for small projects and serves as an entry-level tool for learning data storage, with potential for future expansion of functions such as login authentication and ORM.
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