The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →To convert a NumPy array to a string in Python, first decide whether you need readable display text, structured data for JSON, a string for each element, one custom joined string, or raw bytes. For a quick display, use str(arr); for JSON, serialize arr.tolist(); for binary data, use arr.tobytes(). These outputs are not interchangeable.
Examples below use arr = np.array([[1, 2], [3, 4]]). The right method depends on what you intend to do with the result:
As an Amazon Associate I earn from qualifying purchases.
- Show the values to a person: use NumPy’s display formatting.
- Exchange structured data: convert to Python values and serialize them, for example as JSON.
- Make each value text: convert elements individually, or join them into one string.
- Store or transmit the underlying data as binary: use a bytes-producing method, not a text formatter.
1. Use str(arr) for a quick display
str(arr) returns NumPy’s usual readable representation. For example:
import numpy as np
arr = np.array([[1, 2], [3, 4]])
text = str(arr)
print(text)This is convenient for logs, debugging, or display. It is presentation text, not a stable serialization format: NumPy’s print settings, precision, and line wrapping can affect how arrays are shown, and large arrays may be summarized. See NumPy’s print options and array formatting documentation.
#1 Best Overall
2. Choose a NumPy representation function for display
NumPy provides functions for returning formatted text directly. They differ in how much control they offer and what information their output emphasizes.
| Method | What it returns | Best suited to |
|---|---|---|
np.array_str(arr) |
A string focused on the array’s data | A concise data display |
np.array_repr(arr) |
A representation that can include array and dtype details | Inspecting what the object is |
np.array2string(arr, ...) |
A string with configurable formatting | Controlling separators, precision, or line width |
np.array_str(arr): focus on the data
np.array_str(arr) returns the array data as a single string. It is similar to array_repr, but does not include the same array-kind information. This remains display text rather than JSON or a general interchange format. See NumPy’s array_str documentation.
np.array_repr(arr): include representation details
np.array_repr(arr) returns a representation of the array that may show its type or dtype. This can help when inspecting an object, but the result is not JSON. For example, a NumPy-style representation such as array([1, 2]) uses syntax that JSON does not accept. See NumPy’s array_repr documentation.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesnp.array2string(arr, ...): control formatting explicitly
Use np.array2string when you need to set formatting options such as the separator, precision, or line width:
text = np.array2string(arr, separator=', ', precision=2)The function also supports custom formatters and a summarization threshold. Its default precision is tied to NumPy’s print options; rounding a displayed float to a low precision can hide detail, so do not use formatted output as a lossless numeric record. See the array2string reference.
3. Convert to nested Python values for JSON
For JSON text, convert the array to Python lists and scalars with tolist(), then pass the result to Python’s JSON serializer:
import json
json_text = json.dumps(arr.tolist())For the example array, the serialized data has nested lists, preserving its two-dimensional arrangement. NumPy documents tolist() as returning a nested list with one level per array dimension and Python scalars at the leaves. The array’s NumPy dtype is not thereby preserved as metadata. Check the scalar types and handling of non-finite values for your application rather than assuming every array can be represented losslessly in JSON. See NumPy’s tolist documentation and Python’s JSON documentation.
4. Convert each element to text or join values into one string
These are two different goals: element-wise conversion retains an array structure, while joining produces one scalar string.
Element-wise string conversion
Use astype(str) when you want a string value for each element:
string_array = arr.astype(str)The result is still an array, now containing strings; it is not one Python string containing the whole array. NumPy string dtypes can have fixed widths, so inspect the resulting dtype and confirm behavior for the NumPy version and values you use. See the astype documentation.
One custom joined string
For a simple one-off string, flatten the values for iteration and join their text with a delimiter:
Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →joined = ', '.join(map(str, arr.flat))For the example, this yields 1, 2, 3, 4. Joining discards the original shape, and a delimiter can make the result ambiguous if values themselves contain that delimiter. If you need to reconstruct the data, preserve the shape and define an escaping or encoding scheme instead of relying on a bare joined string.
5. Use tobytes() for raw binary data, not readable text
arr.tobytes() returns Python bytes containing the array’s raw data; it does not turn numbers into readable numerals. Its default traversal order is C order, and the order argument controls the traversal. Binary decoding requires compatible dtype, byte order, shape, and layout information; the bytes alone do not describe all of that. NumPy documents frombuffer for constructing an array from a buffer when the correct interpretation is known. See the tobytes documentation and frombuffer documentation.
Use tobytes() in new code. The older tostring() spelling has been deprecated since NumPy 1.19; see the NumPy 2.0 documentation.
Quick Recap
Which method should you use?
| Your goal | Use | What to keep in mind |
|---|---|---|
| Display an array quickly | str(arr) |
Output is for presentation and can depend on formatting settings. |
| Display data with explicit formatting | np.array_str(arr) or np.array2string(arr, ...) |
array2string offers formatting controls. |
| Inspect an array representation | np.array_repr(arr) |
Representation syntax is not JSON. |
| Produce JSON text | json.dumps(arr.tolist()) |
Check type and non-finite value handling for your use case. |
| Convert values individually to strings | arr.astype(str) |
The result remains an array; check string dtype width. |
| Make one delimited text field | ', '.join(map(str, arr.flat)) |
Shape is lost unless stored separately. |
| Get raw binary data | arr.tobytes() |
The result is bytes, and decoding needs metadata. |
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
