numpy-io

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name: numpy-io description: File I/O operations including binary formats (npy/npz), text processing (csv), and memory-mapping for huge datasets. Triggers: io, load, save, npz, genfromtxt, memmap, loadtxt.

Overview

NumPy I/O handles the transition of data between system memory and persistent storage. It supports highly efficient binary formats (.npy, .npz), flexible text parsers for messy data, and memory-mapping for datasets that exceed available RAM.

When to Use

• Storing model weights or large datasets in a compressed binary format.
• Reading messy CSV files with missing data or mixed types.
• Processing multi-gigabyte datasets without loading the entire file into memory.
• Bundling multiple related arrays into a single archive file.

Decision Tree

1. Storing data for future NumPy use?
   • Use .npy for single arrays, .npz for multiple arrays.
2. Is the file larger than your RAM?
   • Use np.memmap to access disk segments lazily.
3. Reading a clean CSV?
   • Use np.loadtxt (fast).
4. Reading a messy CSV with missing values?
   • Use np.genfromtxt (feature-rich but slower).

Workflows

1. Handling Large Disk-Bound Arrays

   • Create a memory-mapped file with np.memmap(filename, dtype='float32', mode='w+', shape=shape).
   • Process or fill the array as if it were in memory.
   • Call .flush() to ensure all data is written to the physical disk.

2. Importing Messy CSV Data

   • Define a dictionary of converters for specific columns.
   • Use np.genfromtxt(csv_file, delimiter=',', skip_header=1, converters=converters).
   • Access the data which now has NaNs or default values where data was missing.

3. Bundling Multiple Arrays for Storage

   • Identify several related ndarrays.
   • Save them into a single archive with np.savez_compressed('data.npz', arr1=arr1, arr2=arr2).
   • Load them later using data = np.load('data.npz') and access via data['arr1'].

Non-Obvious Insights

• Manual Flush Requirement: Changes to a memmap array are not guaranteed to persist on disk until .flush() is called.
• NPZ Lazy Loading: .npz files are ZIP archives; np.load on an .npz does not load the data until you access a specific key.
• Parser Capability: genfromtxt handles column selection, comment characters, and missing value substitution automatically, making it the primary tool for "real-world" text data.

Evidence

• "Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory." Source
• "savez_compressed... Save several arrays into a single file in compressed .npz format." Source

Scripts

• scripts/numpy-io_tool.py: Functions for memmap creation and compressed npz saving.
• scripts/numpy-io_tool.js: Basic CSV line parser simulation.

Dependencies

• numpy (Python)

References

• references/README.md