# Calculate the Coefficient of Variation in Python (SciPy, NumPy, Pandas)

The coefficient of variation, or CV, allows you to measure how spread out values in a dataset are, relative to their mean. In this tutorial, you’ll learn how to interpret the coefficient of variation and how to calculate it in Python, including using SciPy, NumPy, and Pandas.

## Understanding the Coefficient of Variation

The coefficient of variation is a measure of how spread out values in a dataset are, relative to their means. It’s expressed as a percentage and allows you to compare the spread of data relative to its average, making it useful for comparing the risk or uncertainty associated with different datasets.

In order to calculate the coefficient of variation, you divide the standard deviation of the dataset by the mean of the dataset. The formula for the standard coefficient of variation is shown below:

``Coefficient of Variation = Standard Deviation / Mean``

We can simplify the measure even further by describing it as the ratio between the standard deviation and the mean.

Let’s now dive into how to use Python to calculate the CV.

## How to Calculate the Coefficient of Variation in Python

In this section, we’ll explore how to calculate the coefficient of variation in Python. Because Python provides so many different helpful libraries, we can explore many different options for calculating the CV. In this section, you’ll learn how to calculate the CV using the following methods:

• Using SciPy,
• Using NumPy,
• Using Pandas, and
• Using a Python list

Let’s get started!

### How to Calculate the Coefficient of Variation in SciPy

The SciPy library can be used to calculate many different statistical values, such as exploring whether or not a distribution is normal or not. Unsurprisingly, it also allows you to easily calculate the coefficient of variation, by using the `variation()` function in the stats module. Let’s take a look at how we can calculate the coefficient of variation in SciPy:

``````# Calculating the Coefficient of Variation in SciPy
from scipy.stats import variation
data = [12, 18, 25, 30, 22, 16, 35, 28, 19, 14]
cv = variation(data, ddof=1)

print(cv)

# Returns: 0.3413307761245021``````

In the code block above, we imported the variation function from the stats module. We then created a list of values and passed this into the function. We specified a degree of freedom of 1, in order to assume we’re working with sample data.

We can see from the code block above that it returned a CV of 0.34.

In the next section, we’ll learn how to use NumPy to calculate the CV in Python.

### How to Calculate the Coefficient of Variation in NumPy

NumPy is one of the foundational libraries in Python, allowing you to work with numeric data easily and efficiently. While the NumPy library doesn’t come with a standard function of calculating the coefficient of variation, it does provide helper functions for calculating the standard deviation and the mean.

Let’s take a look at how we can use NumPy to calculate the coefficient of variation:

``````# Calculating the Coefficient of Variation in NumPy
import numpy as np
data = [12, 18, 25, 30, 22, 16, 35, 28, 19, 14]
cv = np.std(data, ddof=1) / np.mean(data)

print(cv)

# Returns: 0.3413307761245021``````

In the example above, we first imported the NumPy library using the conventional alias np. We then use both the `std()` function and the `mean()` function to calculate the coefficient of variation.

Similarly, we specified a degree of freedom of 1.

### How to Calculate the Coefficient of Variation in Pandas

Calculating the coefficient of variation in Pandas works very similarly compared to using NumPy. In fact, Pandas uses NumPy under the hood to calculate this, so the process may feel very familar.

``````# Calculating the Coefficient of Variation in Pandas
import pandas as pd
data = pd.Series([12, 18, 25, 30, 22, 16, 35, 28, 19, 14])
std_dev = data.std()
mean = data.mean()
cv = (std_dev / mean)

print(cv)

# Returns: 0.3413307761245021``````

We can see how we used Pandas to calculate the coefficient of variation of a Pandas Series. The process would be similar when using a DataFrame, except we’d select out a column.

One important thing to note is that Pandas will default to a degree of freedom of 1, meaning that we don’t need to specify it ourselves.

### How to Calculate the Coefficient of Variation from a Python List

In this final section, we’ll explore how to calculate the coefficient of variation of a Python list using only built-in libraries. Because Python comes bundled with the `math` library, we can use the functions it makes available.

Let’s take a look at how we can accomplish calculating the coefficient of variation without external libraries:

``````# How to Calculate the Coefficient of Variation without External Libraries
import math

def calculate_coefficient_of_variation(data):
n = len(data)

# Calculate the mean
mean = sum(data) / n

# Calculate the standard deviation
std_dev = math.sqrt(sum((x - mean) ** 2 for x in data) / (n - 1))

# Calculate the coefficient of variation
cv = std_dev / mean

return cv

# Example usage:
data = [23, 45, 67, 89, 12, 56, 34]
cv = calculate_coefficient_of_variation(data)
print(cv)

# Returns: 0.3413307761245021``````

While the example above is a little overkill, it’s helpful for when you need to work with pure Python.

## Conclusion

In this post, we explored the concept of the coefficient of variation (CV), a statistical measure that helps assess the variability of data relative to its mean. This parameter is especially useful for comparing the spread and risk associated with different datasets. We explained the CV formula, which involves dividing the standard deviation by the mean, and then delved into various methods to calculate it using Python.

We first demonstrated how to compute the CV using specialized libraries such as SciPy, NumPy, and Pandas. These libraries provide convenient functions for statistical calculations, streamlining the process. Additionally, we discussed the flexibility of a pure Python solution for those who prefer to work without external dependencies.

By following the guidelines and examples provided, you can confidently calculate the coefficient of variation, tailoring your approach to suit your specific data and library preferences. Whether you opt for the efficiency of libraries or the simplicity of pure Python, the CV offers a powerful tool for understanding the variability and risk within your datasets.