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"children": [ { "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "This can be instantiated in several ways:" } ] }, "dd": [ { "__type": "DefList", "__tag": 4033, "children": [ { "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "csr_array(D)" } ] }, "dd": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "where D is a 2-D ndarray" } ] } ] }, { "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "csr_array(S)" } ] }, "dd": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "with another sparse array or matrix S (equivalent to S.tocsr())" } ] } ] }, { "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "csr_array((M, N), [dtype])" } ] }, "dd": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype='d'." } ] } ] }, { "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "csr_array((data, (row_ind, col_ind)), [shape=(M, N)])" } ] }, "dd": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "where " }, { "__type": "InlineCode", "__tag": 4051, "value": "data" }, { "__type": "Text", "__tag": 4046, "value": ", " }, { "__type": "InlineCode", "__tag": 4051, "value": "row_ind" }, { "__type": "Text", "__tag": 4046, "value": " and " }, { "__type": "InlineCode", "__tag": 4051, "value": "col_ind" }, { "__type": "Text", "__tag": 4046, "value": " satisfy the relationship " }, { "__type": "InlineCode", "__tag": 4051, "value": "a[row_ind[k], col_ind[k]] = data[k]" }, { "__type": "Text", "__tag": 4046, "value": "." } ] } ] }, { "__type": "DefListItem", "__tag": 4037, "dt": { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "csr_array((data, indices, indptr), [shape=(M, N)])" } ] }, "dd": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "is the standard CSR representation where the column indices for row i are stored in " }, { "__type": "InlineCode", "__tag": 4051, "value": "indices[indptr[i]:indptr[i+1]]" }, { "__type": "Text", "__tag": 4046, "value": " and their corresponding values are stored in " }, { "__type": "InlineCode", "__tag": 4051, "value": "data[indptr[i]:indptr[i+1]]" }, { "__type": "Text", "__tag": 4046, "value": ". If the shape parameter is not supplied, the array dimensions are inferred from the index arrays." } ] } ] } ] } ] } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "/scipy/sparse/_csr.py", "item_line": 324, "item_type": "ABCMeta", "aliases": [ "scipy.sparse.csr_array" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Code", "__tag": 4050, "value": "import numpy as np\nfrom scipy.sparse import csr_array\ncsr_array((3, 4), dtype=np.int8).toarray()\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "row = np.array([0, 0, 1, 2, 2, 2])\ncol = np.array([0, 2, 2, 0, 1, 2])\ndata = np.array([1, 2, 3, 4, 5, 6])\ncsr_array((data, (row, col)), shape=(3, 3)).toarray()\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "indptr = np.array([0, 2, 3, 6])\nindices = np.array([0, 2, 2, 0, 1, 2])\ndata = np.array([1, 2, 3, 4, 5, 6])\ncsr_array((data, indices, indptr), shape=(3, 3)).toarray()\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nDuplicate entries are summed together:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "row = np.array([0, 1, 2, 0])\ncol = np.array([0, 1, 1, 0])\ndata = np.array([1, 2, 4, 8])\ncsr_array((data, (row, col)), shape=(3, 3)).toarray()\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nAs an example of how to construct a CSR array incrementally,\nthe following snippet builds a term-document array from texts:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "docs = [[\"hello\", \"world\", \"hello\"], [\"goodbye\", \"cruel\", \"world\"]]\nindptr = [0]\nindices = []\ndata = []\nvocabulary = {}\nfor d in docs:\n for term in d:\n index = vocabulary.setdefault(term, len(vocabulary))\n indices.append(index)\n data.append(1)\n indptr.append(len(indices))\ncsr_array((data, indices, indptr), dtype=int).toarray()\n", "execution_status": "success" } ], "title": [], "level": 0, "target": null }, "see_also": [], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "arg1", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } }, { "__type": "SigParam", "__tag": 4030, "name": "shape", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "dtype", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "None" }, { "__type": "SigParam", "__tag": 4030, "name": "copy", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": "False" }, { "__type": "SigParam", "__tag": 4030, "name": "maxprint", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "KEYWORD_ONLY", "default": "None" } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "csr_array" }, "references": null, "qa": "scipy.sparse._csr:csr_array", "arbitrary": [], "local_refs": [ "T", "data", "dtype", "has_canonical_format", "has_sorted_indices", "indices", "indptr", "ndim", "nnz", "shape", "size" ] }