numpy.unique#

numpy.unique(ar,return_index=False,return_inverse=False,return_counts=False,axis=None,*,equal_nan=True,sorted=True)[source]#

Find the unique elements of an array.

Returns the sorted unique elements of an array. There are three optionaloutputs in addition to the unique elements:

• the indices of the input array that give the unique values

• the indices of the unique array that reconstruct the input array

• the number of times each unique value comes up in the input array

Parameters:

ararray_like

Input array. Unlessaxis is specified, this will be flattened if itis not already 1-D.

return_indexbool, optional

If True, also return the indices ofar (along the specified axis,if provided, or in the flattened array) that result in the unique array.

return_inversebool, optional

If True, also return the indices of the unique array (for the specifiedaxis, if provided) that can be used to reconstructar.

return_countsbool, optional

If True, also return the number of times each unique item appearsinar.

axisint 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 treatedas the elements of a 1-D array with the dimension of the given axis,see the notes for more details. Object arrays or structured arraysthat contain objects are not supported if theaxis kwarg is used. Thedefault is None.

equal_nanbool, optional

If True, collapses multiple NaN values in the return array into one.

New in version 1.24.

sortedbool, optional

If True, the unique elements are sorted. Elements may be sorted inpractice even ifsorted=False, but this could change withoutnotice.

New in version 2.3.

Returns:

uniquendarray

The sorted unique values.

unique_indicesndarray, optional

The indices of the first occurrences of the unique values in theoriginal array. Only provided ifreturn_index is True.

unique_inversendarray, optional

The indices to reconstruct the original array from theunique array. Only provided ifreturn_inverse is True.

unique_countsndarray, optional

The number of times each of the unique values comes up in theoriginal array. Only provided ifreturn_counts is True.

See also

repeat

Repeat elements of an array.

sort

Return a sorted copy of an array.

Notes

When an axis is specified the subarrays indexed by the axis are sorted.This is done by making the specified axis the first dimension of the array(move the axis to the first dimension to keep the order of the other axes)and then flattening the subarrays in C order. The flattened subarrays arethen viewed as a structured type with each element given a label, with theeffect that we end up with a 1-D array of structured types that can betreated in the same way as any other 1-D array. The result is that theflattened subarrays are sorted in lexicographic order starting with thefirst element.

Changed in version 1.21:Like np.sort, NaN will sort to the end of the values.For complex arrays all NaN values are considered equivalent(no matter whether the NaN is in the real or imaginary part).As the representant for the returned array the smallest one in thelexicographical order is chosen - see np.sort for how the lexicographicalorder is defined for complex arrays.

Changed in version 2.0:For multi-dimensional inputs,unique_inverse is reshapedsuch that the input can be reconstructed usingnp.take(unique,unique_inverse,axis=axis). The result isnow not 1-dimensional whenaxis=None.

Note that in NumPy 2.0.0 a higher dimensional array was returned alsowhenaxis was notNone. This was reverted, butinverse.reshape(-1) can be used to ensure compatibility with bothversions.

Examples

Try it in your browser!

>>>importnumpyasnp>>>np.unique([1,1,2,2,3,3])array([1, 2, 3])>>>a=np.array([[1,1],[2,3]])>>>np.unique(a)array([1, 2, 3])

Return the unique rows of a 2D array

>>>a=np.array([[1,0,0],[1,0,0],[2,3,4]])>>>np.unique(a,axis=0)array([[1, 0, 0], [2, 3, 4]])

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])>>>a[indices]array(['a', 'b', 'c'], dtype='<U1')

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])>>>u[indices]array([1, 2, 6, 4, 2, 3, 2])

Reconstruct the input values from the unique values and counts:

>>>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])>>>np.repeat(values,counts)array([1, 2, 2, 2, 3, 4, 6])    # original order not preserved

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