In this article we will discuss how to find unique values / rows / columns in a 1D & 2D Numpy array. Also how to find their index position & frequency count using numpy.unique().
numpy.unique()
Python’s numpy module provides a function to find the unique elements in a numpy array i.e.
numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)
Arguments
- arr : Numpy array in which we want to find the unique values.
- return_index : optional bool flag. If True returns an array of indices of first occurrence of each unique value.
- return_counts : optional bool flag. If True returns an array of occurrence count of each unique value.
- axis : If not provided then will act on flattened array. If 0 or 1 then acts on row or column wise.
It returns either one numpy array of unique values or based on arguments can also return a tuple of arrays.
Let’s understand by some examples,
Find Unique Values from a Numpy Array
To find the unique values in this array pass the complete array to numpy.unique(). It will return an array of unique values i.e.
# Get unique values in a numpy array
arr = numpy.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18])
print('Original Numpy Array : ' , arr)
# Get unique values from a numpy array
uniqueValues = numpy.unique(arr)
print('Unique Values : ',uniqueValues)
Output:
Frequently Asked:
Original Numpy Array : [11 11 12 13 14 15 16 17 12 13 11 14 18] Unique Values : [11 12 13 14 15 16 17 18]
Here we passed only one argument in the numpy.unique(). Therefore it returned only an array of unique values.
Let’s explore other arguments,
Find Unique Values & their first index position from a Numpy Array
To get the indices of unique values in numpy array, pass the return_index argument in numpy.unique(), along with array i.e.
arr = numpy.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18])
print('Original Numpy Array : ' , arr)
# Get a tuple of unique values & their first index location from a numpy array
uniqueValues, indicesList = numpy.unique(arr, return_index=True)
print('Unique Values : ', uniqueValues)
print('Indices of Unique Values : ', indicesList)
Output:
Original Numpy Array : [11 11 12 13 14 15 16 17 12 13 11 14 18] Unique Values : [11 12 13 14 15 16 17 18] Indices of Unique Values : [ 0 2 3 4 5 6 7 12]
It returns a tuple of 2 arrays i.e.
- Array of unique values
- Array of first index position of unique values in first array
Now just zipped the contents of both array to get a combination of unique value and it’s index position i.e.
# Zip both the arrays
listOfUniqueValues = zip(uniqueValues, indicesList)
print('Unique values and their first index :')
# Iterate over the zip object
for elem in listOfUniqueValues:
print(elem[0], ' at Index : ', elem[1])
Output:
Unique values and their first index : 11 at Index : 0 12 at Index : 2 13 at Index : 3 14 at Index : 4 15 at Index : 5 16 at Index : 6 17 at Index : 7 18 at Index : 12
Get Unique Values & their frequency count from a Numpy Array
To get the frequency count of unique values in numpy array, pass the return_counts argument in numpy.unique(), along with array i.e.
arr = numpy.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18])
print('Original Numpy Array : ' , arr)
# Get a tuple of unique values & their frequency in numpy array
uniqueValues, occurCount = numpy.unique(arr, return_counts=True)
print("Unique Values : " , uniqueValues)
print("Occurrence Count : ", occurCount)
Output:
Original Numpy Array : [11 11 12 13 14 15 16 17 12 13 11 14 18] Unique Values : [11 12 13 14 15 16 17 18] Occurrence Count : [3 2 2 2 1 1 1 1]
It returns a tuple of 2 arrays i.e.
- Array of unique values
- Array of frequency count of unique values in first array
Now just zipped the contents of both array to get a combination of unique value and their frequency count i.e.
# Zip both the arrays
listOfUniqueValues = zip(uniqueValues, occurCount)
print('Unique Values along with occurrence Count')
# Iterate over the zip object
for elem in listOfUniqueValues:
print(elem[0] , ' Occurs : ' , elem[1], ' times')
Output:
Unique Values along with occurrence Count 11 Occurs : 3 times 12 Occurs : 2 times 13 Occurs : 2 times 14 Occurs : 2 times 15 Occurs : 1 times 16 Occurs : 1 times 17 Occurs : 1 times 18 Occurs : 1 times
Get Unique Values , frequency count & index position from a Numpy Array
We can also pass all the arguments together i.e.
# Get unique values, thier frequnecy count & first index position uniqueValues , indicesList, occurCount= numpy.unique(arr, return_index=True, return_counts=True) # Zip the contents listOfUniqueValues = zip(uniqueValues, occurCount, indicesList) # Iterate over the ziiped object and display each unique value along # with frequency count & first index position for elem in listOfUniqueValues: print(elem[0], ' Occurs : ', elem[1], ' times & first index is ', elem[2])
Output:
11 Occurs : 3 times & first index is 0 12 Occurs : 2 times & first index is 2 13 Occurs : 2 times & first index is 3 14 Occurs : 2 times & first index is 4 15 Occurs : 1 times & first index is 5 16 Occurs : 1 times & first index is 6 17 Occurs : 1 times & first index is 7 18 Occurs : 1 times & first index is 12
Find unique values, rows & columns in a 2D numpy array
We can also pass a 2D numpy array to numpy.unique() to get the unique values i.e.
# Create a 2D numpy array
arr2D = numpy.array([[11, 11, 12,11] ,[ 13, 11, 12,11] , [ 16, 11, 12, 11], [11, 11, 12, 11]])
print('Original Array :' , arr2D, sep='\n')
# Get unique values from complete 2D array
uniqueValues = numpy.unique(arr2D)
print('Unique Values : ', uniqueValues)
Output:
Original Array : [[11 11 12 11] [13 11 12 11] [16 11 12 11] [11 11 12 11]] Unique Values : [11 12 13 16]
If axis argument is not passed, 2D array will be flattened and used. To get the unique rows or columns pass axis argument i.e.
Get unique Rows :
# Get unique rows from complete 2D numpy array
uniqueRows = numpy.unique(arr2D, axis=0)
print('Unique Rows : ', uniqueRows, sep='\n')
Output:
Unique Rows : [[11 11 12 11] [13 11 12 11] [16 11 12 11]]
Get unique Columns :
# Get unique columns from 2D numpy array
uniqueColumns = numpy.unique(arr2D, axis=1)
print('Unique Columns : ', uniqueColumns, sep='\n')
Output:
Unique Columns : [[11 11 12] [11 13 12] [11 16 12] [11 11 12]]
Get unique Columns & index position :
# Get unique columns & occurrence count from a 2D numpy array
uniqueColumns, occurCount = numpy.unique(arr2D, axis=1, return_counts=True)
print('Unique Columns : ', uniqueColumns, sep='\n')
print('Unique Columns Occurrence : ', occurCount, sep='\n')
Output:
Unique Columns : [[11 11 12] [11 13 12] [11 16 12] [11 11 12]] Unique Columns Occurrence : [2 1 1]
Complete example is as follows,
import numpy as numpy
def main():
print('*** Find Unique Values from a Numpy Array ***')
arr = numpy.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18])
print('Original Numpy Array : ' , arr)
# Get unique values from a numpy array
uniqueValues = numpy.unique(arr)
print('Unique Values : ',uniqueValues)
print('*** Find Unique Values & their first index position from a Numpy Array ***')
arr = numpy.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18])
print('Original Numpy Array : ' , arr)
# Get a tuple of unique values & their first index location from a numpy array
uniqueValues, indicesList = numpy.unique(arr, return_index=True)
print('Unique Values : ', uniqueValues)
print('Indices of Unique Values : ', indicesList)
# Zip both the arrays
listOfUniqueValues = zip(uniqueValues, indicesList)
print('Unique values and their first index :')
# Iterate over the zip object
for elem in listOfUniqueValues:
print(elem[0], ' at Index : ', elem[1])
print('*** Get the occurrence count of each unique values in Numpy Array ***')
arr = numpy.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18])
print('Original Numpy Array : ' , arr)
# Get a tuple of unique values & their frequency in numpy array
uniqueValues, occurCount = numpy.unique(arr, return_counts=True)
print("Unique Values : " , uniqueValues)
print("Occurrence Count : ", occurCount)
# Zip both the arrays
listOfUniqueValues = zip(uniqueValues, occurCount)
print('Unique Values along with occurrence Count')
# Iterate over the zip object
for elem in listOfUniqueValues:
print(elem[0] , ' Occurs : ' , elem[1], ' times')
print('*** Get the first index & occurrence count of each unique values in Numpy Array ***')
arr = numpy.array([11, 11, 12, 13, 14, 15, 16, 17, 12, 13, 11, 14, 18])
print('Original Numpy Array : ' , arr)
# Get unique values, thier frequnecy count & first index position
uniqueValues , indicesList, occurCount= numpy.unique(arr, return_index=True, return_counts=True)
# Zip the contents
listOfUniqueValues = zip(uniqueValues, occurCount, indicesList)
# Iterate over the ziiped object and display each unique value along
# with frequency count & first index position
for elem in listOfUniqueValues:
print(elem[0], ' Occurs : ', elem[1], ' times & first index is ', elem[2])
print('*** Find unique values in 2D Numpy Array ***')
# Create a 2D numpy array
arr2D = numpy.array([[11, 11, 12,11] ,[ 13, 11, 12,11] , [ 16, 11, 12, 11], [11, 11, 12, 11]])
print('Original Array :' , arr2D, sep='\n')
# Get unique values from complete 2D array
uniqueValues = numpy.unique(arr2D)
print('Unique Values : ', uniqueValues)
# Get unique rows from complete 2D numpy array
uniqueRows = numpy.unique(arr2D, axis=0)
print('Unique Rows : ', uniqueRows, sep='\n')
# Get unique columns from 2D numpy array
uniqueColumns = numpy.unique(arr2D, axis=1)
print('Unique Columns : ', uniqueColumns, sep='\n')
# Get unique columns & occurrence count from a 2D numpy array
uniqueColumns, occurCount = numpy.unique(arr2D, axis=1, return_counts=True)
print('Unique Columns : ', uniqueColumns, sep='\n')
print('Unique Columns Occurrence : ', occurCount, sep='\n')
if __name__ == '__main__':
main()
Output:
*** Find Unique Values from a Numpy Array *** Original Numpy Array : [11 11 12 13 14 15 16 17 12 13 11 14 18] Unique Values : [11 12 13 14 15 16 17 18] *** Find Unique Values & their first index position from a Numpy Array *** Original Numpy Array : [11 11 12 13 14 15 16 17 12 13 11 14 18] Unique Values : [11 12 13 14 15 16 17 18] Indices of Unique Values : [ 0 2 3 4 5 6 7 12] Unique values and their first index : 11 at Index : 0 12 at Index : 2 13 at Index : 3 14 at Index : 4 15 at Index : 5 16 at Index : 6 17 at Index : 7 18 at Index : 12 *** Get the occurrence count of each unique values in Numpy Array *** Original Numpy Array : [11 11 12 13 14 15 16 17 12 13 11 14 18] Unique Values : [11 12 13 14 15 16 17 18] Occurrence Count : [3 2 2 2 1 1 1 1] Unique Values along with occurrence Count 11 Occurs : 3 times 12 Occurs : 2 times 13 Occurs : 2 times 14 Occurs : 2 times 15 Occurs : 1 times 16 Occurs : 1 times 17 Occurs : 1 times 18 Occurs : 1 times *** Get the first index & occurrence count of each unique values in Numpy Array *** Original Numpy Array : [11 11 12 13 14 15 16 17 12 13 11 14 18] 11 Occurs : 3 times & first index is 0 12 Occurs : 2 times & first index is 2 13 Occurs : 2 times & first index is 3 14 Occurs : 2 times & first index is 4 15 Occurs : 1 times & first index is 5 16 Occurs : 1 times & first index is 6 17 Occurs : 1 times & first index is 7 18 Occurs : 1 times & first index is 12 *** Find unique values in 2D Numpy Array *** Original Array : [[11 11 12 11] [13 11 12 11] [16 11 12 11] [11 11 12 11]] Unique Values : [11 12 13 16] Unique Rows : [[11 11 12 11] [13 11 12 11] [16 11 12 11]] Unique Columns : [[11 11 12] [11 13 12] [11 16 12] [11 11 12]] Unique Columns : [[11 11 12] [11 13 12] [11 16 12] [11 11 12]] Unique Columns Occurrence : [2 1 1]
