To reproduce the constructor checks together, save the following as numpy_array_examples.py. It prints the attributes beside their inputs and catches the expected ragged-input error so the remaining checks can finish.<\/p>\n\n\n
\nimport numpy as np\n\nflat = np.array([10, 20, 30])\nprint("flat:", flat)\nprint("flat shape:", flat.shape, "dimensions:", flat.ndim, "dtype:", flat.dtype)\ngrid = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)\nprint("grid:", grid.tolist())\nprint("grid shape:", grid.shape, "size:", grid.size, "dtype:", grid.dtype)\nmixed = np.array([1, 2.5])\nprint("mixed values:", mixed.tolist(), "dtype:", mixed.dtype)\nsource = np.array([10, 20, 30], dtype=np.float32)\nprint("asarray reuses input:", np.asarray(source, dtype=np.float32) is source)\nprint("array shares input memory:", np.shares_memory(np.array(source), source))\ntry:\n np.array([[1, 2], [3]])\nexcept ValueError as error:\n print("ragged numeric input:", type(error).__name__)\nobjects = np.array([[1, 2], [3]], dtype=object)\nprint("object-array shape:", objects.shape)\nprint("object-array row types:", type(objects[0]).__name__, type(objects[1]).__name__)\nsequence = np.arange(6)\nreshaped = sequence.reshape(2, 3)\nprint("reshape:", sequence.shape, "to", reshaped.shape)\nprint("reshape shares input memory:", np.shares_memory(sequence, reshaped))\n<\/pre><\/div>\n\n\nThe identity check asks whether np.asarray() returns the source object, and np.shares_memory() asks whether two arrays overlap in storage. Those questions are distinct, because a view can be a different object that still shares data.<\/p>\n\n\n
\n.\/.venv\/bin\/python numpy_array_examples.py\n<\/pre><\/div>\n\n\nThe constructor checks show rectangular shapes, numeric dtypes and memory-sharing decisions.<\/figcaption><\/figure>\n\n\n\nThe flat integer dtype shown here is the observed result for this environment, so inspect your array’s dtype when its width matters. Explicit float32 construction doesn’t depend on guessing the default integer width.<\/p>\n\n\n\n
When array construction rejects the input<\/h2>\n\n\n\n Unequal row lengths can’t form the rectangular numeric table used above. Save this diagnostic as array_errors.py to compare that failure with a deliberately chosen object array and an integer that exceeds the requested dtype’s range.<\/p>\n\n\n
\nimport numpy as np\n\ntry:\n np.array([[1, 2], [3]])\nexcept ValueError as error:\n print(type(error).__name__ + ":", error)\n\nobjects = np.array([[1, 2], [3]], dtype=object)\nprint("object shape:", objects.shape)\nprint("row types:", type(objects[0]).__name__, type(objects[1]).__name__)\n\ntry:\n np.array([128], dtype=np.int8)\nexcept OverflowError as error:\n print(type(error).__name__ + ":", error)\n<\/pre><\/div>\n\n\nThe ragged numeric input raises ValueError, with a message describing an inhomogeneous shape. For this input, dtype=object produces shape (2,), with a Python list at each position. It doesn’t produce a two-column numeric matrix.<\/p>\n\n\n\n
To see the full diagnostic and the object-array result, run the saved file. The exception handlers print the errors and allow the script to continue, so a successful process exit here doesn’t mean the rejected conversions succeeded.<\/p>\n\n\n
\n.\/.venv\/bin\/python array_errors.py\n<\/pre><\/div>\n\n\nUnequal rows reject numeric construction, and the int8 conversion rejects 128.<\/figcaption><\/figure>\n\n\n\nRequirement<\/th> Action<\/th> Boundary<\/th><\/tr><\/thead> Numeric matrix calculations<\/td> Make all rows the same length<\/td> Choose any padding value according to its meaning in the calculation<\/td><\/tr> Rows that grow separately<\/td> Keep a Python list of lists<\/td> Appending to a list doesn’t require rebuilding a numeric array<\/td><\/tr> Array of Python objects<\/td> Select dtype=object deliberately<\/td> Nested structure still affects shape inference<\/td><\/tr> Integer 128<\/td> Choose an integer dtype with sufficient range<\/td> Signed int8 ends at 127<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nI’d keep independently growing rows as lists until a calculation needs a rectangular array, because choosing object dtype doesn’t resolve what the different row lengths mean.<\/p>\n\n\n\n
The int8 example raises OverflowError because 128 is outside its representable range. Check the required range before choosing a smaller integer dtype, and distinguish a constructor’s range check from overflow during arithmetic on an existing array.<\/p>\n\n\n\n
Allocate a matrix before filling its values<\/h2>\n\n\n\n When the shape is known but the measurements aren’t available yet, np.zeros() creates initialized storage instead of requiring a list of placeholder rows. Run this variation to allocate a float32 matrix and replace its first row.<\/p>\n\n\n
\nimport numpy as np\n\nmeasurements = np.zeros((2, 3), dtype=np.float32)\nmeasurements[0] = [1, 2, 3]\nprint(measurements)\nprint(measurements.dtype)\n<\/pre><\/div>\n\n\nThe second row stays zero until you assign its measurements, and the array keeps dtype float32. Use a zero only when it has an appropriate meaning in the calculation, because an unfilled measurement and a measured zero can require different treatment.<\/p>\n\n\n\n
NumPy array questions<\/h2>\n\n\n\n Creation choices also affect how you represent missing values or combine unlike inputs. Keep those decisions explicit before passing the array to another function.<\/p>\n\n\n
\n
\n
\n
Can a NumPy array contain different data types?<\/h3>\n\n\n
A numeric ndarray has one dtype, and construction converts its values to a common representation. An object array can reference Python objects of different types, but that doesn’t give it the behavior of a dense numeric matrix.<\/p>\n\n<\/div>\n<\/div>\n
\n
How do I create an empty NumPy array?<\/h3>\n\n\n
Use np.array([]) for a length-zero array. np.empty() instead allocates an array of the requested shape without initializing its entries, so fill them before reading them as data.<\/p>\n\n<\/div>\n<\/div>\n
\n
Are NumPy arrays mutable?<\/h3>\n\n\n
A writable NumPy array lets you assign new values to its elements. If another array shares its data, edits through either array can affect the shared values.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
Python lists can hold numbers, but doing math on every element means writing a loop. A NumPy array stores numbers of one type in a grid, so you can calculate on the whole array at once. Here I’ll create arrays from Python lists with np.array(), and explain how shape and dtype describe them. What is […]<\/p>\n","protected":false},"author":7,"featured_media":71770,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[93],"tags":[],"class_list":["post-1070","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-numpy"],"_links":{"self":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/1070","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/comments?post=1070"}],"version-history":[{"count":0,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/1070\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media\/71770"}],"wp:attachment":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media?parent=1070"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/categories?post=1070"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/tags?post=1070"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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