How To Get Unique Items And Counts In Numpy ?
Table Of Contents:
- np.unique()
- Examples Of ‘np.unique( )’
(1) np.unique()
- Returns the sorted unique elements of an array.
- You can also get the unique element counts.
Syntax:
numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False,
axis=None, *, equal_nan=True)Parameters:
- ar: array_like – Input array. Unless axis is specified, this will be flattened if it is not already 1-D.
- return_index: bool, optional – If True, also return the indices of ar (along the specified axis, if provided, or in the flattened array) that result in the unique array.
- return_inverse: bool, optional – If True, also return the indices of the unique array (for the specified axis, if provided) that can be used to reconstruct ar.
- return_counts: bool, optional –
If True, also return the number of times each unique item appears in ar.
- axis: int or None, optional – The axis to operate on. If None, ar will be flattened. If an integer, the subarrays indexed by the given axis will be flattened and treated as the elements of a 1-D array with the dimension of the given axis, see the notes for more details. Object arrays or structured arrays that contain objects are not supported if the axis kwarg is used. The default is None.
- equal_nan: bool, optional – If True, collapses multiple NaN values in the return array into one.
Returns:
- unique: ndarray – The sorted unique values.
- unique_indices: ndarray, optional – The indices of the first occurrences of the unique values in the original array. Only provided if return_index is True.
- unique_inverse: ndarray, optional – The indices to reconstruct the original array from the unique array. Only provided if return_inverse is True.
- unique_counts: ndarray, optional – The number of times each of the unique values comes up in the original array. Only provided if return_counts is True.
(2) Examples Of ‘np.unique()’
Example-1
import numpy as np
data = np.array([1,3,1,5,6,2,8,8,6,7,9])
np.unique(data)Output:
array([1, 2, 3, 5, 6, 7, 8, 9])Example-2
import numpy as np
a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]])
aarray([[1, 0, 0],
[1, 0, 0],
[2, 3, 4]])Finding Unique Elements Along Rows
np.unique(a, axis=0)Output:
array([[1, 0, 0],
[2, 3, 4]])Finding Unique Elements Along Columns
np.unique(a, axis=1)Output:
array([[0, 0, 1],
[0, 0, 1],
[3, 4, 2]])Example-3: Return the indices of the original array that give the unique values:
a = np.array(['a', 'b', 'b', 'c', 'a'])
u, indices = np.unique(a, return_index=True)uarray(['a', 'b', 'c'], dtype='<U1')indicesarray([0, 1, 3], dtype=int64)Example-4: Reconstruct the input array from the unique values and inverse:
a = np.array([1, 2, 6, 4, 2, 3, 2])
u, indices = np.unique(a, return_inverse=True)uarray([1, 2, 3, 4, 6])indicesarray([0, 1, 4, 3, 1, 2, 1], dtype=int64)Example-5: Reconstruct the input values from the unique values and counts:
import numpy as np
a = np.array([1, 2, 6, 4, 2, 3, 2])
values, counts = np.unique(a, return_counts=True)valuesarray([1, 2, 3, 4, 6])countsarray([1, 3, 1, 1, 1], dtype=int64)