The mode often occurs in an array, which is considered as the most repeating element. An array can have one or multiple modes. This article discusses some possible methods to calculate the array mode in Numpy.

How To Calculate The Array Mode In Numpy

Method 1: Use The scipy.stats.mode() Function

The most commonly used calculating approach is the scipy.stats.mode() function. As mentioned above, the mode is the most repeating value in an array.

Besides, there are numerous statistics functions in the scipy.stats library. Inside this library, you can use the mode() option to find the array mode in Python.

An array is taken as an input argument and the function will likely return the most common values’ array inside the input one. You need to install the scipy package to employ this approach.

pip install scipy

The code as follows runs to calculate the mode of a Numpy array:

import numpy as np
from scipy import stats

number_arr = np.array([3,8,5,8,6,5,5])
mode = stats.mode(number_arr)
print(mode[0])

Output:

[5]

First of all, use the np.array() function to create an array. Then, the scipy.stats.mode() function will calculate the mode and move the result to the mode array.

Method 2: Use The numpy.unique() Function

If you only need to find the mode with the Numpy package, the numpy.unique() option makes the task more straightforward. This function works by taking an array as an input. The result is an array with all the unique elements.

The return_count the parameter can also be specified as True. This way, you can get the number of times unique elements are repeated.

Code:

import numpy as np

list_object = np.array(['Java','Ruby',5,7,7,'SQL','Python'])
vals,counts = np.unique(list_object, return_counts=True)
index = np.argmax(counts)
print(vals[index])

Output:

7

In this example, the Numpy mode of an array is calculated by using the np.argmax and np.unique() Python functions. The first step is to use the np.array() to build an array. Then, the np.unique is executed to get the unique values and store them in the vals array.

Use the np.argmax() to calculate the counts array’s maximum value and store it in the index variable.

Method 3: Use Statistics Module

The statistics and the Numpy module have statistical functions in common. Mean, mode, and median are a few functions included in these two modules.

Thus, you can use it in the same way as the numpy module to calculate the mode.

Code:

import statistics as st
import numpy as np

numAndString_arr = np.array(['Python', 'Java', 'array', 3, 4, 5, 6, 6, 6, 7,
                            9, 'Python', 1, 'Python', 1, 0, 1, 'Python'])
print(st.mode(numAndString_arr))

Output:

python

Method 4: Use User-defined Function

There is no need to use any predefined functions to get a series’ mode. Instead, you can use a user-defined one to accomplish the task.

Conclusion

The above post has explained four common methods to calculate the array mode in Numpy. Each approach is suitable for different cases, so choose the one for your purposes.