np.empty: Return a new array of given shape and type, without initializing entries.
np.empty_like: Return a new array with the same shape and type as a given array.
np.eye: Return a 2-D array with ones on the diagonal and zeros elsewhere.
np.identity: Return the identity array.
np.ones: Return a new array of given shape and type, filled with ones.
np.ones_like: Return an array of ones with the same shape and type as a given array.
np.zeros: Return a new array of given shape and type, filled with zeros.
np.zeros_like: Return an array of zeros with the same shape and type as a given array.
np.full: Return a new array of given shape and type, filled with fill_value.
np.full_like: Return a full array with the same shape and type as a given array.
np.array: Create an array.
np.asarray: Convert the input to an array.
np.asanyarray: Convert the input to an ndarray, but pass ndarray subclasses through.
np.ascontiguousarray: Return a contiguous array in memory (C order).
np.asfortranarray: Return a contiguous array in memory (Fortran order).
np.copy: Return an array copy of the given object.
np.frombuffer: Interpret a buffer as a 1-dimensional array.
np.from_dlpack: Create a NumPy array from a PyCapsule object (DLPack).
np.fromfile: Construct an array from data in a text or binary file.
np.fromfunction: Construct an array by executing a function over each coordinate.
np.fromiter: Create a new 1-dimensional array from an iterable object.
np.fromstring: A new 1-D array initialized from text data in a string.
np.loadtxt: Load data from a text file.
np.genfromtxt: Load data from a text file, with missing values handled as specified.
np.savetxt: Save an array to a text file.
np.save: Save an array to a binary file in NumPy .npy format.
np.savez: Save several arrays into a single file in uncompressed .npz format.
np.savez_compressed: Save several arrays into a single file in compressed .npz format.
Array manipulation routines
np.reshape: Gives a new shape to an array without changing its data.
np.ravel: Return a contiguous flattened array.
np.transpose: Permute the dimensions of an array.
np.swapaxes: Interchange two axes of an array.
np.moveaxis: Move axes of an array to new positions.
np.broadcast_to: Broadcast an array to a new shape.
np.broadcast_arrays: Broadcast any number of arrays against each other.
np.expand_dims: Expand the shape of an array.
np.squeeze: Remove single-dimensional entries from the shape of an array.
np.concatenate: Join a sequence of arrays along an existing axis.
np.stack: Join a sequence of arrays along a new axis.
np.hstack: Stack arrays in sequence horizontally (column wise).
np.vstack: Stack arrays in sequence vertically (row wise).
np.dstack: Stack arrays in sequence depth wise (along third dimension).
np.column_stack: Stack 1-D arrays as columns into a 2-D array.
np.row_stack: Stack arrays in sequence vertically (row wise).
np.split: Split an array into multiple sub-arrays.
np.array_split: Split an array into multiple sub-arrays.
np.hsplit: Split an array into multiple sub-arrays horizontally (column-wise).
np.vsplit: Split an array into multiple sub-arrays vertically (row-wise).
np.dsplit: Split an array into multiple sub-arrays along the 3rd axis (depth-wise).
np.tile: Construct an array by repeating A the number of times given by reps.
np.repeat: Repeat elements of an array.
np.delete: Return a new array with sub-arrays along an axis deleted.
np.insert: Insert values along the given axis before the given indices.
np.append: Append values to the end of an array.
np.resize: Return a new array with the specified shape.
np.trim_zeros: Trim the leading and/or trailing zeros from a 1-D array or sequence.
np.unique: Find the unique elements of an array.
Array indexing routines
np.take: Take elements from an array along an axis.
np.choose: Construct an array from an index array and a set of arrays to choose from.
np.searchsorted: Find indices where elements should be inserted to maintain order.
np.count_nonzero: Counts the number of non-zero values in the array a.
np.nonzero: Return the indices of the elements that are non-zero.
np.where: Return elements chosen from x or y depending on condition.
np.indices: Return an array representing the indices of a grid.
np.ix_: Construct an open mesh from multiple sequences.
np.diag_indices: Return the indices to access the main diagonal of an array.
np.diag_indices_from: Return the indices to access the main diagonal of an n-dimensional array.
np.mask_indices: Return the indices to access (n_arrays, n_elements) with a mask.
np.tril_indices: Return the indices for the lower-triangle of an (n, n) array.
np.tril_indices_from: Return the indices for the lower-triangle of arr.
np.triu_indices: Return the indices for the upper-triangle of an (n, n) array.
np.triu_indices_from: Return the indices for the upper-triangle of arr.
Array type routines
np.dtype: Create a data type object.
np.can_cast: Returns True if cast between data types can occur safely.
np.result_type: Returns the type that results from applying the NumPy type promotion rules to the arguments.
np.min_scalar_type: Return the data type with the smallest size and smallest scalar kind to which an array can be safely cast.
np.promote_types: Returns the data type with the smallest size and smallest scalar kind which can hold both type1 and type2.
np.common_type: Return a scalar type common to input arrays.
np.obj2sctype: Return the scalar dtype or NumPy equivalent of Python type of an object.
Array set routines
np.unique: Find the unique elements of an array.
np.intersect1d: Find the intersection of two arrays.
np.union1d: Find the union of two arrays.
np.setdiff1d: Find the set difference of two arrays.
np.setxor1d: Find the set exclusive-or of two arrays.
np.in1d: Test whether each element of an array is also present in a second array.
Array sorting, searching, and counting routines
np.sort: Return a sorted copy of an array.
np.argsort: Returns the indices that would sort an array.
np.lexsort: Perform an indirect stable sort using a sequence of keys.
np.searchsorted: Find indices where elements should be inserted to maintain order.
np.partition: Return a partitioned copy of an array.
np.argpartition: Perform an indirect partition along the given axis.
np.argmax: Returns the indices of the maximum values along an axis.
np.argmin: Returns the indices of the minimum values along an axis.
np.max: Return the maximum along a given axis.
np.min: Return the minimum along a given axis.
np.maximum: Element-wise maximum of array elements.
np.minimum: Element-wise minimum of array elements.
np.nanargmax: Returns the indices of the maximum values in the specified axis ignoring NaNs.
np.nanargmin: Returns the indices of the minimum values in the specified axis ignoring NaNs.
np.nanmax: Return the maximum of an array or maximum along an axis, ignoring any NaNs.
np.nanmin: Return the minimum of an array or minimum along an axis, ignoring any NaNs.
np.count_nonzero: Counts the number of non-zero values in the array a.
np.nonzero: Return the indices of the elements that are non-zero.
np.where: Return elements chosen from x or y depending on condition.
Array mathematics routines
np.add: Add arguments element-wise.
np.subtract: Subtract arguments element-wise.
np.multiply: Multiply arguments element-wise.
np.divide: Divide arguments element-wise.
np.floor_divide: Return the largest integer smaller or equal to the division of the inputs.
np.true_divide: Returns a true division of the inputs, element-wise.
np.mod: Return element-wise remainder of division.
np.remainder: Return element-wise remainder of division.
np.divmod: Return element-wise quotient and remainder simultaneously.
np.positive: Numerical positive, element-wise.
np.negative: Numerical negative, element-wise.
np.power: First array elements raised to powers from second array, element-wise.
np.float_power: Treat elements of arrays as floating points, element-wise.
np.remainder: Return element-wise remainder of division.
np.modf: Return the fractional and integral parts of an array, element-wise.
np.angle: Return the angle of the complex argument.
np.real: Return the real part of the complex argument.
np.imag: Return the imaginary part of the complex argument.
np.conj: Return the complex conjugate, element-wise.
np.convolve: Returns the discrete, linear convolution of two one-dimensional sequences.
np.gcd: Returns the greatest common divisor of |x1| and |x2|
np.lcm: Returns the lowest common multiple of |x1| and |x2|
Array bitwise routines
np.bitwise_and: Compute the bit-wise AND of two arrays element-wise.
np.bitwise_or: Compute the bit-wise OR of two arrays element-wise.
np.bitwise_xor: Compute the bit-wise XOR of two arrays element-wise.
np.invert: Compute bit-wise inversion, or bit-wise NOT, element-wise.
np.left_shift: Shift the bits of an integer to the left.
np.right_shift: Shift the bits of an integer to the right.
Array string operations
np.char.add: Return element-wise string concatenation for two arrays of str or unicode.
np.char.multiply: Return (a * i), that is string multiple of i copies of a , for each element.
np.char.mod: Return (a % i), that is pre-Python 2.6 string formatting (interpolation), for each element in a.
np.char.capitalize: Return a copy of a with only the first character of each element capitalized.
np.char.lower: Return an array with the elements converted to lowercase.
np.char.upper: Return an array with the elements converted to uppercase.
np.char.title: For each element in a, return a titlecased version of the string: words start with uppercase characters, all remaining cased characters are lowercase.
np.char.split: For each element in a, return a list of the words in the string, using sep as the delimiter string.
np.char.splitlines: For each element in a, return a list of the lines in the element, breaking at line boundaries.
np.char.strip: For each element in a, return a copy with the leading and trailing characters removed.
np.char.lstrip: For each element in a, return a copy with the leading characters removed.
np.char.rstrip: For each element in a, return a copy with the trailing characters removed.
np.char.join: Return a string which is the concatenation of the strings in the sequence seq.
np.char.replace: For each element in a, return a copy of the string with all occurrences of substring old replaced by new.