For an array you plan to load back into NumPy, use np.save() and np.load() with an .npy file. Choose text or CSV when people or other tools need to inspect the values, and JSON when the array belongs in a structured application-data format. Those text formats do not automatically preserve all of NumPy’s array metadata, so choose one based on how the file will be read.
Choose a format based on how you will use the file
| Format | Use it when | Main trade-off |
|---|---|---|
.npy |
You want a NumPy-friendly file for one array and a straightforward NumPy reload. | Binary format; values are not meant to be read as ordinary text. |
.npz |
You want to save several named arrays in one NumPy archive. | Reading it back requires NumPy-compatible handling. |
| Text or numeric CSV | You want readable numeric values or a simple delimited matrix. | Text conversion can affect how values are represented; np.savetxt supports only one- or two-dimensional arrays. |
| CSV module | You need CSV row writing, quoting, or handling of textual values. | CSV is a text interchange format, not a NumPy array-preservation format. |
| JSON | You need nested data in a common structured text format. | Convert the array to ordinary Python data; record dtype or shape separately if exact reconstruction matters. |
NumPy’s I/O API index covers its binary and text functions. Its file I/O guide discusses format choice, missing values, security, and large arrays.
Save and reload one array with NPY
Use NPY when the file is primarily for NumPy. It provides the simplest NumPy save-and-load path for a single array.
import numpy as np
arr = np.array([[1, 2], [3, 4]])
np.save("array.npy", arr)
restored = np.load("array.npy", allow_pickle=False)
np.save() writes NumPy’s binary .npy format. If you pass a filename string or Path without the .npy extension, NumPy appends it. The numpy.save API reference documents the extension behavior and pickle option.
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The example disables pickle because it does not need object arrays. NumPy’s save API defaults to allow_pickle=True; pickle-enabled object arrays can pose security and portability risks. Do not load pickle-enabled files from untrusted sources. Keep the save and load settings compatible with the contents you actually need.
Save multiple arrays in one NPZ archive
When related arrays belong together, store them under names in an .npz archive. Use savez for an uncompressed archive or savez_compressed for its compressed variant.
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import numpy as np
arr = np.array([[1, 2], [3, 4]])
np.savez("arrays.npz", first=arr, second=arr * 2)
with np.load("arrays.npz", allow_pickle=False) as data:
first = data["first"]
second = data["second"]
np.savez_compressed("arrays-compressed.npz", first=arr, second=arr * 2)
The returned archive can be indexed by the names used when saving. Close it when finished; the with statement handles that in the example.
Write readable text or a simple numeric CSV
For a one- or two-dimensional numeric array, np.savetxt() writes readable text. Set delimiter="," for comma-separated columns, then use np.loadtxt() with the same delimiter to read the numeric data.
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import numpy as np
arr = np.array([[1, 2], [3, 4]])
np.savetxt("array.txt", arr)
np.savetxt("array.csv", arr, delimiter=",")
restored = np.loadtxt("array.csv", delimiter=",")
The NumPy I/O reference documents savetxt options, including formatting and delimiters. If the data contains missing values or needs more involved parsing, use genfromtxt and select its missing-value policy deliberately, as described in NumPy’s file I/O guide.
Use Python’s CSV module for general CSV rows
For CSV that may contain embedded delimiters, quoted text, or values beyond a simple numeric matrix, Python’s csv module is often a better fit than treating the file as a NumPy matrix.
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import csv
with open("rows.csv", "w", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerows(arr.tolist())
Python recommends opening CSV files with newline="" when passing the file object to a CSV writer. writerows() writes a sequence of rows, and non-string values are converted to strings. On reading, csv.reader returns strings by default; convert them explicitly if you need numbers. CSV dialects can differ between applications, so agree on delimiter, quoting, header, encoding, and line endings with the receiving tool. See the Python CSV documentation.
Save an array as JSON
Python’s built-in JSON encoder does not directly serialize a NumPy ndarray. Convert it to nested built-in lists first; when loading, the result is ordinary Python data, so construct a NumPy array explicitly if needed.
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import json
import numpy as np
arr = np.array([[1, 2], [3, 4]])
with open("array.json", "w", encoding="utf-8") as f:
json.dump(arr.tolist(), f)
with open("array.json", encoding="utf-8") as f:
nested = json.load(f)
restored = np.array(nested)
If exact reconstruction matters, especially for empty arrays or unusual dtypes, define a schema that stores the needed shape and dtype metadata and reapply it deliberately. Python’s JSON encoder also permits NaN and infinities by default, although they are outside strict JSON; pass allow_nan=False to make the encoder raise ValueError for them. Repeated calls to json.dump() on the same file do not create one valid JSON document, because JSON is not a framed protocol. See the Python JSON documentation.
Preserve data safely and deliberately
- For durable NumPy-specific storage, avoid raw
ndarray.tofile()andfromfile()when dtype portability matters: NumPy warns that this approach loses endianness and precision information. Prefer the NumPy save/load functions for NumPy persistence. - For large
.npyarrays,np.load(..., mmap_mode=...)can memory-map data. Memory mapping is not chunking or compression. - When exchanging CSV with another application, verify its dialect and conversion expectations rather than assuming every tool interprets the file identically.
- For JSON, decide how non-finite values and any dtype or shape metadata should be represented as part of the file’s schema.
These limitations and options are covered in NumPy’s file I/O guide.
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