In this Python NumPy tutorial, we will learn** how to get the median using the NumPy array in Python**. With the **Python NumPy median function**, we will cover these topics.

- Python numpy median filter
- Python numpy median absolute deviation
- Python numpy median example
- Python numpy median ignore nan
- Python numpy median 2d array
- Python np.median axis
- Python numpy weighted median
- Python median without numpy
- Python numpy mean squared error
- Python numpy mean absolute error
- Python numpy mean of empty slice
- Python numpy mean of each column
- Python np.mean round
- Python numpy root mean square
- Python numpy reduce mean
- Python numpy mean and standard deviation
- Python numpy harmonic mean
- Python numpy mean ignore zero
- Python numpy average vs mean

**Table of Contents**show

## Python numpy median

- In this section, we will discuss how to find the median of a numpy array in Python.
- To do the task we are going to use the Python
**numpy.median()**function. In Python, this is a mathematical function and it is used to compute the median of the elements in an array. - This method is available in the NumPy package module and always returns the median of the numpy array value as an output. If you are using a multidimensional array then you can also get the median value of each column and row.

**Syntax:**

Let’s have a look at the Syntax and understand the working of Python **numpy.median() **function

```
numpy.median
(
a,
axis=None,
out=None,
overwrite_input=False,
keepdims=False
)
```

- This Syntax contains several parameters
**a:**This parameter specifies the input array on which we want to operate on.**axis:**This parameter indicates the axis which we want to calculate the median along with the flattened input array. If the axis is**0**then the direction down the rows and if the axis is**1**then the direction down column-wise.**out:**It is an optional parameter and it is used to store the result of numpy.median() function and by default it takes none value.**keepdims:**This parameter defines the dimension of the input array and if the value is**‘True’**the axes which are reduced are left in the output.

**Example:**

Let’s take an example and check how to use the **numpy.median()** function in Python

**Source Code:**

```
import numpy as np
new_arr=np.array([67,89,113,145,167])
new_output= np.median(new_arr)
print("Median of an array:",new_output)
```

In the above code, we imported the numpy library and then initialize an array by using the** numpy.array()** function and now we have to find the median of the input array. To do this we are going to use the **numpy.median()** function.

In the above array, we have an odd number of terms in ascending order. It will calculate the array **median=middle **term.

Here is the implementation of the following given code

Also, check: Python NumPy Count – Useful Guide

## Python numpy median filter

We had already covered this topic in the Python NumPy filter article. You can easily get all the information regarding Python numpy median filter.

## Python numpy median absolute deviation

- In Python, the numpy median absolute deviation is used to measure the observation in a given array.
- To calculate the median absolute deviation we can easily use the
**mad=median(|xi-xm|)**where xi is the data and xm is the median value.

**Example:**

```
import numpy as np
new_arr = ([17, 23, 55, 61, 63, 65, 71, 83])
new_result=np.median(np.absolute(new_arr - np.median(new_arr)))
print(new_result)
```

Here is the Screenshot of the following given code

Read: Python NumPy Replace + Examples

## Python numpy median example

- In this section, we will discuss how to use the
**numpy.median()**function in Python. - In Python, the numpy median is used to generate the median value in the NumPy array and this function involves many parameters namely axis. keepdims and it is also used for specifying the data type that a user needs to be operand on.
- In this example, we will use the axis and keepdims parameter to check how to get the median value of the numpy array.

**Example:**

Let’s take an example and check how to use the **numpy.median()** function in Python

**Source Code:**

```
import numpy as np
new_arr=np.array([[67,89,113,145,167],
[14,16,18,20,24],
[33,45,67,89,91]])
new_output= np.median(new_arr,axis=0)
new_output2= np.median(new_arr,axis=1,)
print("Axis row-wise median:",new_output)
print("Axis column-wise median",new_output2)
```

In the above program, we have used the axis parameter in **numpy.median()** function and it will calculate the row and column medians.

Here is the execution of the following given code

Read: Python NumPy Add Tutorial

## Python numpy median ignore nan

- In this section we will learn how to calculate median of numpy array and ignore nan values in Python.
- To do this task we are going to use the
**numpy.nanmedian()**function. This function will help the user to return the median of array value while ignoring nan values. - This method is available in the NumPy package module and it involves several parameters and it calculates the median along with the axis.

**Syntax:**

Here is the Syntax of Python **numpy.nanmedian() **function

```
numpy.nanmedian
(
a,
axis=None,
out=None,
overwrite_input=False,
keepdims=<no value>
)
```

**Example:**

Let’s take an example and check how to use the **numpy.nanmedian()** function in Python

**Source Code:**

```
import numpy as np
new_arr=np.array([67,89,np.nan,145,np.nan])
new_output= np.nanmedian(new_arr)
print(new_output)
```

Here is the Output of the following given code

Read: Python NumPy diff with examples

## Python numpy median 2d array

- Here we can see how to calculate median in Python 2-dimensional array.
- In this example, we are going to calculate the median of the array, To do this task first we will create an array by using the
**numpy.array()**function. Now we will specify the axis to be**1**and it will find out the median for the input array.

**Syntax:**

Here is the Syntax of Python **numpy.median()** function

```
numpy.median
(
a,
axis=None,
out=None,
overwrite_input=False,
keepdims=False
)
```

**Example:**

```
import numpy as np
values=np.array([[10,12,14,18,26],
[24,26,28,30,40]])
print("Creation of two-dimensional array:",values)
new_result= np.nanmedian(values,axis=1)
print(new_result)
```

Here is the Screenshot of the following given code.

Also, check: Python NumPy 2d array

## Python np.median axis

- In this section we will discuss how to use axis parameter in Python
**numpy.median()**function. - In this example, we are going to compute the row and column medians by using the axis parameter. If the
**axis =0**then it effectively computes the column medians and if the**axis=1**then it will display the row medians.

**Example:**

```
import numpy as np
new_arr=np.array([[14,24,34,44,64],
[9,13,17,21,25],
[33,45,67,89,91]])
dis_row= np.median(new_arr,axis=0)
dis_col= np.median(new_arr,axis=1,)
print("Axis row-wise median:",dis_row)
print("Axis column-wise median",dis_col)
```

Read: Python NumPy Data types

## Python numpy weighted median

- In this program, we will discuss how to calculate the weighted average median of a Python NumPy array.
- To perform this particular task we are going to use the np.cumsum() method. In Python, the
**numpy.cumsum()**is used to generate the cumulative sum of numpy values. Next, we will use the**np.searchsorted()**function and it is used to find the indices of a value while inserting it into a sorted numpy array. - After that, we have declared a variable
**‘weights’**and assigned the list comprehension method. Once you will print**‘weights’**it will display the numpy array weighted median values.

**Syntax:**

Here is the Syntax of Python **numpy.cumsum()** function

```
numpy.cumsum
(
a,
axis=None,
dtype=None,
out=None
)
```

- It consists of a few parameter
**a:**This parameter indicates the input array.**axis:**By default it takes none value and it used to compute the cumsum over the array.**dtype:**It is an optional parameter and if it is not specified then it takes integer value.

**Example:**

```
import numpy as np
new_arr = np.array([[16, 278, 34, 41], [83, 38, 19, 10], [35,78,92,35], [104,245,943,145], [234,789,432,190]])
z = np.cumsum(new_arr, axis=1)
new_ind = [np.searchsorted(row, row[-1]/2.0) for row in z]
weights = [m * 10 for m in new_ind]
print("median masses is:", weights)
print(new_arr)
print(np.hstack((z, z[:, -1, np.newaxis]/2.0)))
```

You can refer to the below Screenshot

Read: Python NumPy Delete

## Python median without numpy

- In this section, we will discuss how to get the median value in Python without using numpy.
- To do this task we are going to use the Python
**median()**function. This function takes a sample value and returns its median without sorting its list. - For example, suppose we have a list that contains employees id numbers. now we want to calculate the median of a list of numbers. To do this task we are going to use
**statistics.median()**function. The mathematical formula for the median is**{(n+1)/2}**.

**Syntax:**

Here is the Syntax of Python **statistics.median()**

`statistics.median(data)`

**Example:**

Let’s take an example and check how to find the median value in Python without using numpy.

**Source Code:**

```
import statistics
employee_id = [16, 7, 15, 23, 89, 56, 67, 10, 24, 36, 57, 19]
new_median_value = statistics.median(employee_id)
print("Medain value of employees id:",new_median_value)
```

In the following given code, we imported the statistics module and then initialize a list. After that, we have used the **statistics.median()** function and within this function, we have assigned the list **’employee_id’**. Once you will print **‘new_median_value’** then the result will display the median value.

Here is the execution of the following given code

Read: Python Numpy Not Found – How to Fix

## Python numpy mean squared error

- In this section, we will discuss how to calculate the mean squared error in Python numpy array.
- In this example we have to find the mean of error squares basically square errors is between the estimated values and the true values. It is also called a regression problem.
- In this example, we have to find the regression line for the below-given values. In Python, we can easily find the regression line by using
**y=mx+c**where y values will be the predicted value. - You have to insert the random values in the
**‘x’**variable for generating the**‘y’**values. we have used**[7,8,9,4,3]**values and then use the**np.square()**function and within this function, we get the difference between true values and predicted values. - Mathematic formula to calculate the mean squared error is

```
MSE = (1/n)∑i=1n
(Xobs,i -Xmodel,i)²
```

**Source Code:**

```
import numpy as np
new_values_true= [6,12,15,17,18]
new_values_predict= [4.4,4.8,5.2,3.2,2.8]
new_result = np.square(np.subtract(new_values_true,new_values_predict)).mean()
print("Mean squared error:",new_result)
```

In the following given code, we imported the numpy library and then declare two variables** ‘new_values_true’ **and** ‘new_values_predict’**. It indicates the original value and calculated value

Here is the Screenshot of the following given code.

Read: Python NumPy Minimum tutorial

## Python numpy mean absolute error

- In this section, we will discuss how to calculate the mean of the absolute error in the Python numpy array.
- To perform this particular task we are going to use the
**np.absolute()**function to get the absolute sign in the mean absolute error and this method will calculate the absolute value element-wise. - In this example, we have used the
**np.sum()**function for summing all the elements.

**Syntax:**

Here is the Syntax of Python **numpy.absolute()** function

```
numpy.absolute
(
x,
/,
out=None,
*,
where=True,
casting='same_kind',
order='K',
dtype=None,
)
```

**Example:**

```
import numpy as np
arr1=np.array([23,34,56,78,9])
arr2= np.array([14,23,67,8,17])
result = np.sum(np.absolute((arr1 - arr2)))
print("Mean absolute error:",result.astype("int"))
```

You can refer to the below Screenshot

Read: Python NumPy Stack with Examples

## Python numpy mean of empty slice

- In this Program we will solve the runtime error warning
**‘mean of empty slice’**in Python - In this example, we have used the concept of
**numpy.nanmean()**function. In Python, this function is used to calculate the mean of the numpy array and ignore the nan values. - In this Program, we have created a simple numpy array by using the
**numpy.array()**function and assign np.nan values.

**Syntax:**

Here is the Syntax of **numpy.nanmean()** function

```
numpy.nanmean
(
a,
axis=None,
dtype=None,
out=None,
keepdims=<no value>
)
```

**Example:**

```
import numpy as np
new_arr = np.array([np.nan, np.nan,np.nan])
new_result = np.nanmean(new_arr)
print("Calculate mean:",new_result)
```

In the following given code, we have used to **np.nanmean()** function and within this function, we have passed array as an argument.

Here is the implementation of the following given code.

As you can see in the Screenshot the output displays the runtime warning **‘Mean of empty slice’**.

### Solution

Here is the Solution of runtime warning** ‘Mean of empty slice’**

- In this example, we are going to update the input array like we will insert the integer number in it.
- The reason for the run-time error is we have not inserted the integer values. While using
**numpy.nanmean()**function you have to use at least one non-nan value.

**Source Code:**

```
import numpy as np
new_arr = np.array([12,13,np.nan, np.nan,np.nan])
new_result = np.nanmean(new_arr)
print("Calculate mean:",new_result)
```

Here is the implementation of the following given code.

Read: Python NumPy round + 13 Examples

## Python numpy mean of each column

- In this section, we will discuss how to calculate the mean of each column in Python numpy array.
- In Python to compute the mean of values in a Numpy array then we can easily use the
**numpy.mean()**function. This function takes the sum of all elements available in an array along with the axis and is divided by the number of elements. - This method is available in the Numpy package module and returns the mean of the array elements. In this example we will use the axis parameter enables to calculate the mean of the column. If the axis is
**1**then the direction goes horizontally across the column.

**Syntax:**

Here is the Syntax of Python **numpy.mean()** function

```
numpy.mean
(
a,
axis=None,
dtype=None,
out=None,
keepdims=<no value>,
where=<no value>
)
```

**Example:**

Let’s take an example and check how to calculate the mean of each column in Python numpy array

**Source Code:**

```
import numpy as np
new_arr = np.array([[14,25,36,37],
[15,16,27,37]])
new_output = np.mean(new_arr,axis=1)
print("Calculate mean column-wise:",new_output)
```

Here is the implementation of the following given code

As you can see in the Screenshot the output displays the mean value of the array column-wise.

Read: Python NumPy Repeat

## Python np.mean round

- In this section, we will discuss how to round off the mean value in the Python NumPy array.
- To perform this particular task firstly we will create an array and use the
**np.mean()**function. Once you will print**‘new_output’**then the result will display the mean value of the array. - After that, we are going to use the
**numpy.round()**function and get the rounded value of mean values.

**Example:**

Let’s take an example and check how to use the Python **numpy.round()** function.

**Source Code:**

```
import numpy as np
new_arr = np.array([12,34,56,78,54])
new_output = np.mean(new_arr)
print("Calculate mean of array:",new_output)
d= np.round(new_output)
print("Rounded value of mean:",d)
```

In the above program, we have created an array by using the **numpy.array()** function that contains integer value. After that, we have assigned a mean value result in **numpy.round()** function as an argument.

Here is the Screenshot of the following given code.

Read: Python NumPy 3d array + Examples

## Python numpy root mean square

- In this section, we will discuss how to calculate the root mean square value in Python numpy array.
- To do this task we are going to use the
**numpy.sqrt()**function. In this example, this function takes the numpy array to mean as an argument and calculates the square root of elements in the array.

**Example:**

```
import numpy as np
new_arr= np.array([12,14,16,18,20])
result=np.mean(new_arr**2)
print("Mean array:",result)
new_roo= np.sqrt(result)
print(new_roo)
```

You can refer to the below Screenshot

As you can see in the Screenshot the output displays the square root of a mean value.

Read: Python NumPy Split + 11 Examples

## Python numpy reduce mean

- In this Program, we will learn how to use the
**numpy.mean()**function in Python. - In Python the
**numpy.mean()**function is used to find the mean of values in a Numpy array and it also sums all the elements available in an array along with the axis and is divided by the number of elements.

**Syntax:**

Here is the Syntax of Python **numpy.mean()** function

```
numpy.mean
(
a,
axis=None,
dtype=None,
out=None,
keepdims=<no value>,
where=<no value>
)
```

**Example:**

```
import numpy as np
new_values= np.array([16,23,26,29,30])
new_output =np.mean(new_values)
print("Mean value:",new_output)
```

Here is the output of the following given code

Read: Python NumPy Normalize

## Python numpy mean and standard deviation

- In this section, we will learn how to calculate the standard deviation in Python numpy array by using the
**numpy.std()**function. - In Python, this function will help the user to measure the amount of variance in data and also the square root of the mean square deviation.
- This method is available in the Python numpy package module and always returns the standard deviation of the input array.

**Syntax:**

Let’s have a look at the Syntax and understand the working of Python** numpy.std()** function.

```
numpy.std
(
a,
axis=None,
dtype=None,
out=None,
ddof=0,
keepdims=<no value>,
where=<no value>
)
```

- It consists of a few parameters
**a:**This parameter indicates the input array whose values standard deviation is calculated.**axis:**By default, it takes none value and it is an optional parameter to calculate the standard deviation. It indicates the axis along which the std is calculated.**dtype:**This parameter indicates the data type and by default, it takes float64 value.**out:**This parameter specifies the output array where the result is to be stored.**ddof:**It represents the delta degree of freedom and the divisor is used in compute in N-ddof and by default, ddof takes zero value.

**Example:**

Let’s take an example and check how to find the standard deviation in the Python NumPy array

**Source Code:**

```
import numpy as np
new_values= np.array([7,18,16,14,12])
new_output =np.mean(new_values)
new_output2= np.std(new_values)
print("Mean value:",new_output)
print("Standard deviation:",new_output2)
```

Here is the execution of the following given code

Read: Python NumPy Random [30 Examples]

## Python numpy harmonic mean

- In this section, we will discuss how to use harmonic mean in Python numpy array.
- To perform this particular task we are going to use
**statistics.harmonic_mean()**function. In Python, this function is used to get the sum of the reciprocals of the mean and it is also a type of numerical average.

**Example:**

```
import numpy as np
import statistics
arr= np.array([12,23,34,56])
new_output= statistics.harmonic_mean(arr)
print(new_output)
```

In the following given code, we have imported the numpy and statistics library and then initialize an array by using the** numpy.array() **function. After that, we have used the **statistics.harmonic_mean()** function and it will compute the harmonic mean of the provided element.

Here is the Screenshot of the following given code

Read: Python NumPy absolute value with examples

## Python numpy mean ignore zero

- In this section we will discuss how to ignore the zero value in mean array by using NumPy Python.
- To do this task first we will create an array by using the
**numpy.array()**function. Next, we will declare a variable**‘new_output’**and assign a**numpy.mean()**function. Once you get the mean value from the array. - You can easily use the
**np.trim_zero()**function for ignoring the zero value in the mean result.

**Syntax:**

Here is the Syntax of Python numpy.trim_zeros() function

```
numpy.trim_zeros
(
filt,
trim='fb'
)
```

**Example:**

```
import numpy as np
new_arr=np.array([
[18, 89, 12, 145, 167],
[0, 0, 0, 0, 0]
])
new_output= np.mean(new_arr,axis=1)
print("Mean of an array:",new_output)
d = np.trim_zeros(new_output)
print("Ignore zero values:",d)
```

Here is the execution of the following given code

## Python numpy average vs mean

We had already covered this topic in Python NumPy Average article. You get all the information regarding the difference between NumPy average and NumPy mean in Python.

Also, take a look at some more Python NumPy tutorials.

In this Python NumPy tutorial, we have learned** how to get the median using the NumPy array in Python**. Moreover, with the **Python NumPy median function**, we have covered these topics.

- Python numpy median filter
- Python numpy median absolute deviation
- Python numpy median example
- Python numpy median ignore nan
- Python numpy median 2d array
- Python numpy median image
- Python np.median axis
- Python numpy weighted median
- Python median without numpy
- Python numpy mean squared error
- Python numpy mean absolute error
- Python numpy mean of empty slice
- Python numpy mean of each column
- Python np.mean round
- Python numpy rolling median
- Python numpy root mean square
- Python numpy reduce mean
- Python numpy mean and standard deviation
- Python numpy harmonic mean
- Python numpy mean ignore zero
- Python numpy average vs mean

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