8000 DOC: add explanation of dtype to parameter values for np.append by tuhinsharma121 · Pull Request #26303 · numpy/numpy · GitHub
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DOC: add explanation of dtype to parameter values for np.append #26303

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merged 6 commits into from
Apr 22, 2024
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DOC: add explanation of dtype to parameter values for np.append #26303

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1807e08
DOC: add explanation of dtype to parameter values for np.append
tuhinsharma121 Apr 18, 2024
1dbc253
DOC: added markdown formatting for values and arr [skip azp][skip cir…
tuhinsharma121 Apr 19, 2024
c016301
DOC: added one example with mixed dtype [skip azp][skip cirrus][skip …
tuhinsharma121 Apr 21, 2024
0489082
DOC: added Notes section to document mixed dtype effect on np.append …
tuhinsharma121 Apr 21, 2024
e02cb02
DOC: enhanced example with c.dtype [skip azp][skip cirrus][skip actions]
tuhinsharma121 Apr 21, 2024
853c42b
DOC: remove Notes section and refine Example [skip azp][skip cirrus][…
tuhinsharma121 Apr 22, 2024
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tuhinsharma121 committed Apr 21, 2024
commit 04890824f0d1d2de70947ed8185b0760e882cac0
12 changes: 8 additions & 4 deletions numpy/lib/_function_base_impl.py
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Expand Up @@ -5715,10 +5715,7 @@ def append(arr, values, axis=None):
These values are appended to a copy of `arr`. It must be of the
correct shape (the same shape as `arr`, excluding `axis`). If
`axis` is not specified, `values` can be any shape and will be
flattened before use. If dtype of `values` is different from the
dtype of `arr` then their dtypes are compared to figure out the
common dtype they can both be safely coerced to. For e.g. `int64`
and `float64` can both go to `float64`.
flattened before use.
axis : int, optional
The axis along which `values` are appended. If `axis` is not
given, both `arr` and `values` are flattened before use.
Expand All @@ -5735,6 +5732,13 @@ def append(arr, values, axis=None):
insert : Insert elements into an array.
delete : Delete elements from an array.

Notes
-----
If dtype of `values` is different from the dtype of `arr` then
their dtypes are compared to figure out the common dtype they can
both be safely coerced to. For e.g. `int64` and `float64` can both
go to `float64`. See examples section below.

Examples
--------
>>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
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