Suppose you have:
arr = np.array([1,2,1,3,3,4])
Is there a built in function that returns the most frequent element?
Overview of NumPy Functions Numpy is a python package used for scientific computing. So certainly, it supports a vast variety of functions used for computation. The various functions supported by numpy are mathematical, financial, universal, windows, and logical functions.
python: is there a frequency function? The Excel FREQUENCY function This useful function can analyse a series of values and summarise them into a number of specified ranges. For example the heights of some children can be grouped in to four categories of [Less than 150cm]; [151 - 160cm]; [161 - 170cm]; [More than 170cm].
Let’s see How to count the frequency of unique values in NumPy array. Python’s numpy library provides a numpy.unique() function to find the unique elements and it’s corresponding frequency in a numpy array. Syntax: numpy.unique(arr, return_counts=False)
Python’s numpy library provides a numpy.unique () function to find the unique elements and it’s corresponding frequency in a numpy array. Return: Sorted unique elements of an array with their corresponding frequency counts NumPy array.
Yes, Python's collections.Counter has direct support for finding the most frequent elements:
>>> from collections import Counter
>>> Counter('abracadbra').most_common(2)
[('a', 4), ('r', 2)]
>>> Counter([1,2,1,3,3,4]).most_common(2)
[(1, 2), (3, 2)]
With numpy, you might want to start with the histogram() function or the bincount() function.
With scipy, you can search for the modal element with mstats.mode.
the pandas
module might also be of help here. pandas
is a neat data analysis package for python and also has support for this problem.
import pandas as pd
arr = np.array([1,2,1,3,3,4])
arr_df = pd.Series(arr)
value_counts = arr_df.value_counts()
most_frequent = value_counts.max()
this returns
> most_frequent
2
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