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Print a regular Python list
Lists are the sequence beginners most often mean by “array.” Printing one directly shows its elements inside square brackets:
my_array = [1, 2, 3, 4]
print(my_array)
# [1, 2, 3, 4]
Python’s built-in print() converts each supplied object to text, separates multiple objects with a space by default, adds a newline by default, and writes to standard output unless you provide a text stream with file. See the Python built-in function documentation.
Print elements without brackets
Use the unpacking operator * to pass each list element as a separate argument. Set sep to choose what appears between them:
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print(*my_array, sep=", ")
# 1, 2, 3, 4
Add a label or format numbers
For custom formatting, build the text for each element explicitly. This example assumes the values are numbers:
print("Values:", ", ".join(f"{value:.2f}" for value in my_array))
The .2f format displays each number with two digits after the decimal point; it is a formatting choice, not a default applied by print().
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Identify which kind of array you have
Python has several commonly used objects that people call arrays. Their printed representations are not interchangeable.
| Type | How to print it | What to expect |
|---|---|---|
| Python list | print(values) or print(*values, sep=", ") |
A list representation with brackets and commas, or separated elements without the container brackets. |
Standard-library array.array |
print(values) or print(values.tolist()) |
The array object’s representation, or a plain list representation after conversion with .tolist(). |
NumPy ndarray |
print(arr) |
NumPy’s dimension-aware display, which uses spaces between values rather than Python-list commas. |
Print a Python array.array
The standard-library array module provides sequences constrained to a type code. Print the object directly to see its representation, or convert it to a list if that is more useful:
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print(values)
print(values.tolist())
See the Python array documentation.
Print a NumPy array or matrix
Call print(arr) for a NumPy ndarray. NumPy lays out one-dimensional arrays as rows, two-dimensional arrays as matrices, and higher-dimensional arrays as grouped slices. For example:
import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(arr)
# [[1 2]
# [3 4]]
The spaces between values are normal NumPy display formatting; printing does not convert the ndarray into nested Python lists. NumPy explains its layout in the NumPy quickstart.
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Make nested Python data easier to read
For nested built-in structures such as lists and dictionaries, use pprint.pp() when line breaks and indentation make the output easier to inspect:
from pprint import pp
nested = [[1, 2, 3], [4, 5, 6]]
pp(nested, width=20)
The pprint module keeps a structure on one line when it fits and breaks it across lines when needed. Its formatting can be configured with options such as width, indentation, depth, and compactness. Use NumPy’s own print options for ndarrays instead. See the Python pprint documentation.
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Control NumPy output for large arrays and decimals
Show more or all elements
NumPy abbreviates large arrays by displaying the edges and an ellipsis. The current stable API documentation gives a default summarization threshold of 1000 elements. You can change the threshold; to request the full representation, use sys.maxsize:
import sys
import numpy as np
np.set_printoptions(threshold=sys.maxsize)
print(np.arange(10000))
Printing every element of a very large array can overwhelm a terminal or log. For most inspection tasks, keep the summary or choose a threshold that gives enough context. See NumPy’s set_printoptions reference.
Adjust floating-point display temporarily
Use np.printoptions() as a context manager when a formatting change should apply only to a particular block:
with np.printoptions(precision=2, suppress=True):
print(arr)
precision=2 changes the displayed floating-point precision, while suppress=True avoids scientific notation for small values. NumPy also provides settings for the summarization threshold, line width, text for NaN and infinity, and type-specific formatters. These settings affect ndarray display, not the formatting of standalone scalar values. More detail is available in the NumPy printing guide.
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Choose the right printing method
- For a basic Python list with brackets, use
print(values). - For list elements separated by custom text, use
print(*values, sep=...). - For an
array.array, print the object directly or call.tolist()for a list representation. - For a NumPy vector or matrix, use
print(arr); adjust NumPy’s print options if its default layout or numeric display is not suitable. - For nested built-in data, use
pprint.pp()to make the structure easier to scan.
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