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Introduction to NumPy median

NumPy median function is a function in a python programming language which is used for computation of median values in the defined NumPy Array. NumPy function works well with the array like objects in Python. These Arrays can be combined with other arrays or can be restructured accordingly. NumPy is a module used for data manipulation in Python programming language. It provides user an ability to work with large set of numeric data like arrays in Python. Creation of Array and storing of numeric data with respect to NumPy median is called as NumPy Array.

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The syntax flow is represented as:

np.median( a= , axis= , keepdims= )

np: It represents the fact the NumPy modules are being imported.

np.median: This represents the fact that the NumPy median function is called once it is imported with NumPy median.

NumPy median function passes these parameters which includes:

a: This parameter represents and specifies the data that needs to be operate on.

axis: This parameter represents the axis along which the median function will revolve around and will get computed.

keepdims: This parameter is used for representing the dimensions of the output same as the input provided by the NumPy median.

How NumPy median Function works?

Every function is a working flow so does the NumPy median function. It is one of the most important function in python programming language. NumPy is a very useful module used in python programming language which is widely used for data manipulation. It gives user the flexibility and versatility to play around with the vast scale of array in terms of numeric data. Creation and simultaneous storing of data in arrays are the types of arrays which will be further used by it and are known as NumPy Arrays. NumPy Arrays religiously gets used by NumPy median function.

NumPy comprises of many computational tools which once combined with arrays of numeric data provides arrays of numeric data, when combined with other data provides split arrays into multiple arrays and then reshape and restructure the combined array or considered array into set of multiple arrays. If NumPy array is reshaped and restructured array is considered, then that NumPy Array will be considered as arrays with new set of rows and columns. Thus, function gets very well with the NumPy Array.

Let’s peek into the working where NumPy array thoroughly contributes to the NumPy median function.

NumPy median function starts working as soon as the NumPy module gets imported from the standard library and then the NumPy module gets called easily wherever is the requirement.

It is the function which is used for computing the median value in the NumPy array.

This function in the NumPy array works very well with the array-like objects in Python.

It depends on the requirement and user how they want to import this NumPy function to make it little easier to understand we have used np referring NumPy for median to work well with the function at the time of call.

The NumPy function involves many parameters which are like mandatory to get passed without them the function will start behaving in an abnormal behavior thus some parameters are considered as mandatory.

Syntax defining the parameter involves four parameters namely a, axis, keepdims, out. Without these parameters functioning won’t be smooth.

A is one of the most important and mandatory argument passed in the NumPy median function which is used for specifying the type of data that a user need to be operated on. This is the data on top of which computation and manipulation will happen.

Another argument to be considered is Axis which is again an optional argument and is used for maintaining and keeping a control along the axis using this function the median value will get computed for NumPy array. This field appears to be confusing for many new users, so coder tries to keep it as an option and make use of it in terms of complex requirement and usage.

The out parameter enables user to specify some different output array thus is an optional parameter.

Keepdims is an optional argument which is used to enable the dimensions of the output parameter same as the input parameter along with the axis.

Once import and parameter is done in the function then it is needed to compute the median using one dimensional array.

Reshaping and restructuring of data from one dimensional array help in computing using 2D array.

Computation of the numeric data can also be performed using the axis which is used for determining values with respect to median functions.

It is also used for making data compressed and easy for manipulation.


Given below are the examples mentioned:

Example #1

This program demonstrates the implementation of NumPy.median() function where the first NumPy array is the flattened array median and then all the median along the axis with value 0 and value 1 respectively.

import numpy as np m_a = [[3, 2, 5], [1, 6, 4],[5, 0, 9]] print("n With_median, axis_val = No: ", np.median(m_a)) print("n With_median, axis_val = 0 ", np.median(m_a, axis = 0)) print("n With_median, axis_val = 2 ", np.median(m_a, axis = 1))


Example #2

This program demonstrates the function which is used for creating 1D arrays by comparing with the other array as shown in the output.


import numpy as np z_a = [5, 15, 30, 55, 24, 36, 24, 17, 12] print("z_a : ", z_a) print("median of z_a : ", np.median(z_a)) z_a2 = [5,16,20,19,20] print("z_a2 : ", z_a2) print("median of z_a2 : ", np.median(z_a2))


Example #3

This program illustrates the function which is used for counting the number of balls in the given NumPy array and then counting the number of balls by calculating the median as shown in the given output.


import numpy balls = [12,18,20,65,100,86,59,13,10,88,23,63,79] x_count_balls = numpy.median(balls) print(x_count_balls)



NumPy median function is a very important function with NumPy module which gives user the flexibility and versatility to manipulate and play around with the huge values of Arrays. Even creation and restructuring of Arrays is also possible using it and its associated functionalities. It simplified the complexity present to play around with data structure naming array.

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This is a guide to the NumPy median. Here we discuss the introduction to NumPy median, how median function works along with examples respectively. You may also have a look at the following articles to learn more –

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