Veamos cómo contar la frecuencia de valores únicos en la array NumPy. La biblioteca numpy de Python proporciona una función numpy.unique() para encontrar los elementos únicos y su frecuencia correspondiente en una array numpy.
Sintaxis: numpy.unique(arr, return_counts=False)
Retorno: Elementos únicos ordenados de una array con sus correspondientes conteos de frecuencia NumPy array.
Ahora, veamos ejemplos:
Ejemplo 1:
Python3
# import library import numpy as np ini_array = np.array([10, 20, 5, 10, 8, 20, 8, 9]) # Get a tuple of unique values # and their frequency in # numpy array unique, frequency = np.unique(ini_array, return_counts = True) # print unique values array print("Unique Values:", unique) # print frequency array print("Frequency Values:", frequency)
Producción:
Unique Values: [ 5 8 9 10 20] Frequency Values: [1 2 1 2 2]
Ejemplo 2:
Python3
# import library import numpy as np # create a 1d-array ini_array = np.array([10, 20, 5, 10, 8, 20, 8, 9]) # Get a tuple of unique values # amnd their frequency # in numpy array unique, frequency = np.unique(ini_array, return_counts = True) # convert both into one numpy array count = np.asarray((unique, frequency )) print("The values and their frequency are:\n", count)
Producción:
The values and their frequency are: [[ 5 8 9 10 20] [ 1 2 1 2 2]]
Ejemplo 3:
Python3
# import library import numpy as np # create a 1d-array ini_array = np.array([10, 20, 5, 10, 8, 20, 8, 9]) # Get a tuple of unique values # and their frequency in # numpy array unique, frequency = np.unique(ini_array, return_counts = True) # convert both into one numpy array # and then transpose it count = np.asarray((unique,frequency )).T print("The values and their frequency are in transpose form:\n", count)
Producción:
The values and their frequency are in transpose form: [[ 5 1] [ 8 2] [ 9 1] [10 2] [20 2]]