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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsUse np.concatenate to join a sequence of arrays along an axis they already have. Use np.append when adding values to one array is the clearest expression—but remember that it returns a new array, not an expanded original. The most common surprise is the default axis: concatenate defaults to axis=0, while append defaults to axis=None and flattens its inputs.
What is the difference between np.concatenate and np.append?
Both functions combine array data, but their interfaces and defaults differ. NumPy describes concatenate as joining a sequence of arrays along an existing axis. append takes one array and values to add to it, and returns a new array.
| Detail | np.concatenate |
np.append |
|---|---|---|
| Inputs | A sequence of arrays | One array and the values to add |
| Default axis | axis=0 |
axis=None, which flattens both inputs |
| With an explicit axis | Dimensions must match except along the joining axis | Inputs must have compatible dimensions and matching shapes outside the joining axis |
| Changes the original array? | No; use the returned result | No; NumPy explicitly documents that it allocates and fills a new array |
For a single addition, append can read naturally. For joining several arrays, concatenate directly expresses that operation and accepts them as a sequence.
Why does np.append flatten my array?
Because its default is axis=None. With no axis specified, NumPy flattens the input array and the values being added, then appends the resulting one-dimensional data. That can be useful when a flat result is intended, but it is not the right default for preserving rows and columns.
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import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])
flat = np.append(a, b)
print(flat.shape) # (6,)
rows = np.concatenate((a, b), axis=0)
print(rows.shape) # (3, 2)To preserve a two-dimensional shape with append, specify an axis explicitly and ensure the values have the required dimensions.
How do you append rows to a 2D NumPy array?
For a two-dimensional array, rows are joined along axis=0. The arrays must have the same number of columns. A one-dimensional row needs to be reshaped into a two-dimensional, one-row array before it can be joined along that axis.
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a = np.array([[1, 2], [3, 4]])
new_row = np.array([5, 6])
rows = np.concatenate((a, new_row.reshape(1, -1)), axis=0)
# array([[1, 2],
# [3, 4],
# [5, 6]])You can also use np.append(a, new_row.reshape(1, -1), axis=0); the explicit axis prevents flattening, and the reshaping makes the dimensions compatible. If the intended result is to add columns instead, use axis=1 and provide arrays with matching row counts.
Does NumPy append modify the original array?
No. np.append returns a copy with the added values; it does not grow the original ndarray in place. Keep the returned array if you want to use the combined data:
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a = np.array([1, 2])
combined = np.append(a, 3)
# a is still array([1, 2])
# combined is array([1, 2, 3])The same practical point matters when joining arrays: each combined result occupies newly created output storage unless you provide an output buffer where supported.
Should you use concatenate, append, or stack?
- Use
concatenateto join a sequence along an axis that already exists in the arrays—for example, joining rows or columns. - Use
appendwhen adding values to one array is convenient and its flattening default is appropriate, or when you explicitly pass a compatible axis. - Use
stackwhen the result should have a new dimension. Unlike concatenate, which joins along an existing axis, stack introduces an axis; check the desired output shape before choosing.
Is np.concatenate faster than np.append?
There is no universal timing answer established here. The useful distinction is allocation behavior: append creates a new array, and repeatedly assigning each appended result can repeatedly copy the growing data. If chunks arrive over time, keep them in a Python list and concatenate once when they are ready:
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chunks = [chunk_a, chunk_b, chunk_c]
result = np.concatenate(chunks, axis=0)If the final size is known, another option is to allocate the destination once and fill its slices. NumPy 2.4.0 documentation records an out argument for concatenate and stack, which can write into a correctly shaped output buffer in applicable versions. Whether an approach is faster depends on the sizes, data type, memory layout, and workload; the documentation cited here does not establish a benchmark or speed multiplier.
Version and mask considerations
The current stable NumPy documentation identifies version 2.5. The numpy.concat shorthand was added in NumPy 2.0; consult the documentation for the NumPy version installed in your environment when relying on version-specific APIs. The append reference cited here is from the versioned NumPy 2.1 manual.
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For masked arrays, use np.ma.concatenate if the input masks need to be preserved. The ordinary np.concatenate reference warns that it does not preserve input masks.
Quick Recap
Official references
- NumPy 2.5 reference:
numpy.concatenate - NumPy 2.1 reference:
numpy.append - NumPy reference:
numpy.stack - NumPy 2.4.0 release notes
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