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這篇文章主要介紹了 numpy 中 ndarray 數組對象怎么用，具有一定借鑒價值，感興趣的朋友可以參考下，希望大家閱讀完這篇文章之后大有收獲，下面讓丸趣 TV 小編帶著大家一起了解一下。

1. 創建數組
1.1 自動生成數組

namedescribeempty(shape[, dtype, order])Return a new array of given shape and type, without initializing entries.empty_like(a[, dtype, order, subok])Return a new array with the same shape and type as a given array.eye(N[, M, k, dtype])Return a 2-D array with ones on the diagonal and zeros elsewhere.identity(n[, dtype])Return the identity array.ones(shape[, dtype, order])Return a new array of given shape and type, filled with ones.ones_like(a[, dtype, order, subok])Return an array of ones with the same shape and type as a given array.zeros(shape[, dtype, order])Return a new array of given shape and type, filled with zeros.zeros_like(a[, dtype, order, subok])Return an array of zeros with the same shape and type as a given array.full(shape, fill_value[, dtype, order])Return a new array of given shape and type, filled with fill_value.full_like(a, fill_value[, dtype, order, subok])Return a full array with the same shape and type as a given array.arange([start,] stop[, step,][, dtype])Return evenly spaced values within a given interval.linspace(start, stop[, num, endpoint, …])Return evenly spaced numbers over a specified interval.logspace(start, stop[, num, endpoint, base, …])Return numbers spaced evenly on a log scale.geomspace(start, stop[, num, endpoint, dtype])Return numbers spaced evenly on a log scale (a geometric progression).meshgrid(*xi, **kwargs)Return coordinate matrices from coordinate vectors.mgridnd_grid instance which returns a dense multi-dimensional“meshgrid”.)ogridnd_grid instance which returns an open multi-dimensional“meshgrid”.)

1.2 從已有數據轉化為數組

namedescribearray(object[, dtype, copy, order, subok, ndmin])Create an array.asarray(a[, dtype, order])Convert the input to an array.asanyarray(a[, dtype, order])Convert the input to an ndarray, but pass ndarray subclasses through.ascontiguousarray(a[, dtype])Return a contiguous array in memory (C order).asmatrix(data[, dtype])Interpret the input as a matrix.copy(a[, order])Return an array copy of the given object.frombuffer(buffer[, dtype, count, offset])Interpret a buffer as a 1-dimensional array.fromfile(file[, dtype, count, sep])Construct an array from data in a text or binary file.fromfunction(function, shape, **kwargs)Construct an array by executing a function over each coordinate.fromiter(iterable, dtype[, count])Create a new 1-dimensional array from an iterable object.fromstring(string[, dtype, count, sep])A new 1-D array initialized from raw binary or text data in a string.loadtxt(fname[, dtype, comments, delimiter, …])Load data from a text file.

2 數組屬性與描述

namedescribeTSame as self.transpose(), except that self is returned if self.ndim 2.dataPython buffer object pointing to the start of the array’s data.dtypeData-type of the array’s elements.flagsInformation about the memory layout of the array.flatA 1-D iterator over the array.imagThe imaginary part of the array.realThe real part of the array.sizeNumber of elements in the array.itemsizeLength of one array element in bytes.nbytesTotal bytes consumed by the elements of the array.ndimNumber of array dimensions.shapeTuple of array dimensions.stridesTuple of bytes to step in each dimension when traversing an array.ctypesAn object to simplify the interaction of the array with the ctypes module.baseBase object if memory is from some other object.

3 數組方法與描述

namedescribeall([axis, out, keepdims])Returns True if all elements evaluate to True.any([axis, out, keepdims])Returns True if any of the elements of a evaluate to True.argmax([axis, out])Return indices of the maximum values along the given axis.argmin([axis, out])Return indices of the minimum values along the given axis of a.argpartition(kth[, axis, kind, order])Returns the indices that would partition this array.argsort([axis, kind, order])Returns the indices that would sort this array.astype(dtype[, order, casting, subok, copy])Copy of the array, cast to a specified type.byteswap(inplace)Swap the bytes of the array elementschoose(choices[, out, mode])Use an index array to construct a new array from a set of choices.clip([min, max, out])Return an array whose values are limited to [min, max].compress(condition[, axis, out])Return selected slices of this array along given axis.conj()Complex-conjugate all elements.conjugate()Return the complex conjugate, element-wise.copy([order])Return a copy of the array.cumprod([axis, dtype, out])Return the cumulative product of the elements along the given axis.cumsum([axis, dtype, out])Return the cumulative sum of the elements along the given axis.diagonal([offset, axis1, axis2])Return specified diagonals.dot(b[, out])Dot product of two arrays.dump(file)Dump a pickle of the array to the specified file.dumps()Returns the pickle of the array as a string.fill(value)Fill the array with a scalar value.flatten([order])Return a copy of the array collapsed into one dimension.getfield(dtype[, offset])Returns a field of the given array as a certain type.item(*args)Copy an element of an array to a standard Python scalar and return it.itemset(*args)Insert scalar into an array (scalar is cast to array’s dtype, if possible)max([axis, out])Return the maximum along a given axis.mean([axis, dtype, out, keepdims])Returns the average of the array elements along given axis.min([axis, out, keepdims])Return the minimum along a given axis.newbyteorder([new_order])Return the array with the same data viewed with a different byte order.nonzero()Return the indices of the elements that are non-zero.partition(kth[, axis, kind, order])Rearranges the elements in the array in such a way that value of the element in kth position prod([axis, dtype, out, keepdims]) Return the product of the array elements over the given axisptp([axis, out])Peak to peak (maximum – minimum) value along a given axis.put(indices, values[, mode])Set a.flat[n] = values[n] for all n in indices.ravel([order])Return a flattened array.repeat(repeats[, axis])Repeat elements of an array.reshape(shape[, order])Returns an array containing the same data with a new shape.resize(new_shape[, refcheck])Change shape and size of array in-place.round([decimals, out])Return a with each element rounded to the given number of decimals.searchsorted(v[, side, sorter])Find indices where elements of v should be inserted in a to maintain order.setfield(val, dtype[, offset])Put a value into a specified place in a field defined by a data-type.setflags([write, align, uic])Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.sort([axis, kind, order])Sort an array, in-place.squeeze([axis])Remove single-dimensional entries from the shape of a.std([axis, dtype, out, ddof, keepdims])Returns the standard deviation of the array elements along given axis.sum([axis, dtype, out, keepdims])Return the sum of the array elements over the given axis.swapaxes(axis1, axis2)Return a view of the array with axis1 and axis2 interchanged.take(indices[, axis, out, mode])Return an array formed from the elements of a at the given indices.tobytes([order])Construct Python bytes containing the raw data bytes in the array.tofile(fid[, sep, format])Write array to a file as text or binary (default).tolist()Return the array as a (possibly nested) list.tostring([order])Construct Python bytes containing the raw data bytes in the array.trace([offset, axis1, axis2, dtype, out])Return the sum along diagonals of the array.transpose(*axes)Returns a view of the array with axes transposed.var([axis, dtype, out, ddof, keepdims])Returns the variance of the array elements, along given axis.view([dtype, type])New view of array with the same data.

4 數組形態控制

namedescribendarray.reshape(shape[, order])Returns an array containing the same data with a new shape.ndarray.resize(new_shape[, refcheck])Change shape and size of array in-place.ndarray.transpose(*axes)Returns a view of the array with axes transposed.ndarray.swapaxes(axis1, axis2)Return a view of the array with axis1 and axis2 interchanged.ndarray.flatten([order])Return a copy of the array collapsed into one dimension.ndarray.ravel([order])Return a flattened array.ndarray.squeeze([axis])Remove single-dimensional entries from the shape of a.

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