In this Python tutorial, we will learn about “** Python Scipy Stats Mode**” where we will know the concept of mode, one of the foundational ideas in statistics, helps determine the most frequently occurring value. and cover the following topics.

- Python Scipy Stats Mode
- Python Scipy Stats Mode 2d
- Python Scipy Stats Mode Example
- Python Scipy Stats Mode Return
- Python Scipy Stats Mode Axis

## What is the mode in Statistics?

In statistics, the value that consistently appears in a particular set is referred to as the mode. The mode or modal value is the number that occurs most frequently in a data set and has a high frequency. It is among the three measures of central tendency, along with mean and median.

For instance, the set ** “4, 2, 6, 6, 8”** has a

**as its mode. Therefore, we can quickly determine the mode given a finite number of observations. There could be one mode, several modes, or none at all for a given collection of values.**

*6*- A data set is referred to as bimodal if there are two modes in it.
- A data set is referred to as trimodal if there are three modes.
- A data set is referred to as multimodal if there are four or more modes.

In this tutorial, we will calculate the mode of a given array using the method of Python Scipy.

Also, check: Python Scipy Freqz

## Scipy Stats Mode

The Python Scipy contains a method

in a module *mode()*

that the provided array should be returned as an array containing the modal value.*scipy.stats*

The syntax is given below.

`scipy.stats.mode(a, nan_policy='propagate', axis=0,)`

Where parameters are:

**a(array_data):**n-dimensional array from which to determine the mode (s).**nan_plociy():**Specifies what to do in cases when the input contains nan. (‘Propagate’ is the default) The following choices are available:

- propagate: nan is returned
- raise: throws a mistake
- omit: ignoring nan values.

**axis(int):**The direction of the axis. The default is 0. Consider the entire array an if None.

The method

returns the two value *mode()*

and *mode*

.*count*

Let’s take an example by following the below steps:

Import the necessary libraries using the below python code.

```
from scipy.stats import mode
import numpy as np
```

Create an array of data using the below code.

```
data = np.array([[3, 6, 0, 8],
[7, 1, 2, 3],
[4, 8, 1, 8],
[3, 5, 5, 0],
[9, 5, 7, 4]])
```

provide the above-created data to the method

using the below code.*mode()*

`mode(data,axis = None)`

Look at the above code output, the method returns the mode is equal to 3 and count equal to 3.

Read: Python Scipy Distance Matrix

## Python Scipy Stats Mode 2d

We already know how to use the method

from the above subsection, here we will find the mode within the two-dimensional array.*mode()*

Let’s understand with an example by following the below steps:

Import the required libraries using the below python code.

```
import numpy as np
from scipy.stats import mode
```

Create a two-dimensional array containing some elements using the below code.

```
twod_array = np.array([[ 1, 2, 7, 1, 3, 4],
[5, 4, 1, 1, 2, 1],
[3, 3, 1, 2, 2, 1]])
```

Now compute the mode of the above created two-dimensional array using the below code.

`mode(twod_array)`

Read: Python Scipy Stats Kurtosis

## Python Scipy Stats Mode Return

The method

of Python Scipy stats returns two values *mode()*

and *mode*

of type ndarray.*count*

**mode:**Collection of modal values.**count:**For each mode, an array of counts.

Let’s explore mode and count using an example by following the below steps:

Import the required libraries using the below python code.

```
from scipy import stats
import numpy as np
```

Create an array containing values using the below code.

`arr = np.array([[2,4,5,2,2],[1,1,7,4,5]])`

Pass the above-created array to a method

to compute the modal of an array using the below code.*mode()*

`mod = stats.mode(arr)`

Now check the returned mode and count of an array using the below code.

```
print("Array of mode",mod[0])
print("Count for each mode",mod[1])
```

This is how to check the return values from a method

of Python Scipy.*mode()*

Read: Python Scipy Confidence Interval

## Python Scipy Stats Mode Axis

The method

accepts a parameter *mode()*

for computing the mode, In other words, the mode can be computed on a different axis of the array by specifying the *axis*

value. A two-dimensional array has two corresponding axes, one running horizontally across columns (axis 1) and the other vertically across rows (axis 0).*axis*

Let’s take an example and compute the mode of array-based on axes by following the below steps:

Import the required libraries using the below python code.

```
from scipy import stats
import numpy as np
```

Create an array containing values using the below code.

`arr = np.array([[3,5,4,2,2],[7,4,1,4,1]])`

Pass the above-created array to a method

with *mode()*

to compute the mode of an array horizontally across columns using the below code.*axis=1*

`mod = stats.mode(arr,axis =1)`

Check the result using the below code.

`mod`

This is how to compute the mode of the array along the specified axis using the method

with parameters *mode()*

of Python Scipy.*axis*

Read: Scipy Find Peaks – Useful Tutorial

## Python Scipy Stats Mode Example

We have already learned about mode and how to calculate it using the method

of Python Scipy. In this section, we will do one more example but with a one-dimensional array.*mode()*

Let’s import the required libraries using the below python code.

`from scipy.stats import kurtosis`

Generate an array containing some values whose mode we want to calculate using the below code.

`data = [2,3,5,7,9,5,8,1]`

Compute the mode of the above created data using the below code.

`mode(data)`

The output of the above code returns the two results the mode value which is equal to 5 and the count value equal to 2. This means the mode of the whole data or array is 5 and the number 5 occurs two times in comparison to other numbers in the whole array.

Also, take a look at some more Python SciPy Tutorials.

- Python Scipy Kdtree
- Scipy Sparse Csr_matrix
- Python Scipy Load Mat File
- Python Scipy Special Module
- Scipy Linalg – Helpful Guide
- Scipy Stats Zscore + Examples
- Scipy Convolve – Complete Guide
- Scipy Integrate + Examples

So, in this tutorial, we have learned about the “** Python Scipy Stats Mode**” and covered the following topics.

- Python Scipy Stats Mode
- Python Scipy Stats Mode 2d
- Python Scipy Stats Mode Example
- Python Scipy Stats Mode Return
- Python Scipy Stats Mode Axis

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.