# NumPy percentile() Function

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• Post category:NumPy / Python

NumPy `percentile()` function in Python is used to compute the nth percentile of the array elements along the specified axis. We basically use percentile in statistics which gives you a number that describes the value that a given percent of the values are lower than.

In this article, I will explain the syntax of NumPy percentile() and using this function to compute the percentile

## 1. Quick Examples of NumPy percentile() Function

If you are in a hurry, below are some quick examples of how to use NumPy percentile() function.

``````
# Below are the quick examples

# Example 1: # Create an 1D array
arr = np.array([2, 3, 5, 8, 9,4])
# Get the 50th percentile of 1-D array
arr2 = np.percentile(arr, 50)

# Example 2: Get the 75th percentile of 1-D array
arr2 = np.percentile(arr, 75)

# Example 3: Create 2-D array
arr = np.array([[6, 8, 4],[ 9, 5, 7]])
# Get the 50th percentile of 2-D array
arr2 = np.percentile(arr, 50)

# Example 4: Get the percentile along the axis = 0
arr2 = np.percentile(arr, 75, axis=0)

# Example 5: Get the percentile along the axis = 1
arr2 = np.percentile(arr, 75, axis=1)

# Example 6: Get the percentile of an array axis=1 and keepdims = true
arr2 = np.percentile(arr, 75, axis=1, keepdims=True)

``````

## 2. Syntax of NumPy percentile()

Following is the syntax of the numpy.percentile() function.

``````
# Syntax of numpy.percentile()
numpy.percentile(arr, percentile, axis=None, out=None, overwrite_input=False, keepdims=False)
``````

### 2.1 Parameters of percentile()

The percentile() function allows the following parameters.

• `arr -` array_like, this is the input array or object that can be converted to an array.
• `percentile` – array_like of float Percentile or sequence of percentiles to compute, which must be between 0 and 100 inclusive.
• `axis` – Axis or axes along which the percentile is computed. By default, a flattened array is used. axis = 0 means along the column and axis = 1 means working along the row.
• `out `– An alternate output array where you can place the result.
• `overwrite_input` – If the boolean value is True, you can modify the input array through intermediate calculations, to save memory.
• `keepdims` – The value is set to be True, the creates reduced axes with dimensions of one size.

### 2.2 Return Value of percentile()

It returns a scalar or array with percentile values along with the specified axis.

## 3. Usage of NumPy percentile() Function

In statistics, a percentile is a term that describes how a score compares to other scores from the same set. While there is no universal definition of percentile, it is commonly expressed as the percentage of values in a set of data scores that fall below a given value. Percentiles show how a given value compares to others. The general rule is that if a value is in the nth percentile, it is greater than nth percent of the total values.

For a better understanding, a student who scores 90 percentiles out of 100, and then it means 90% of students got less than 90 and 10% of students got more than 90.

Let’s compute the percentile value of a single dimension array using the` numpy.percentile()` function.

``````
import numpy as np
# Create an 1D array
arr = np.array([2, 3, 5, 8, 9,4])

# Get the 50th percentile of 1-D array
arr2 = np.percentile(arr, 50)
print(arr2)

# Output
# 4.5

# Get the 75th percentile of 1-D array
arr2 = np.percentile(arr, 75)
print(arr2)

# Output
# 7.25
``````

## 4. Get the Percentile Value of 2-D Array

Let’s take 2-Dimensional array and compute the percentile value using `numpy.percentile()` function. For example,

``````
# Create 2-D array
arr = np.array([[6, 8, 4],[ 9, 5, 7]])
# Get the 50th percentile of 2-D array
arr2 = np.percentile(arr, 50)
print(arr2)

# Output
# 6.5
``````

## 5. Get the Percentile along the Axis

We can compute the percentile along the axis, For example, if we set `axis=0`, then percentile is calculated along the column, and if `axis= 1`, then percentile is computed along the row.

``````
# Get the percentile along the axis = 0
arr2 = np.percentile(arr, 75, axis=0)
print(arr2)

# Output
# [8.25 7.25 6.25]

# Get the percentile along the axis = 1
arr2 = np.percentile(arr, 75, axis=1)
print(arr2)

# Output
# [7. 8.]
``````

## 6. Use axis=1 and keepdims = true

We can also compute the percentile value of an array along with specified axis and `keepdims`, `keepdims` argument keeps the dimensions in the result.

``````
# Get the percentile of an array axis=1 and keepdims = true
arr2 = np.percentile(arr, 75, axis=1, keepdims=True)
print(arr2)

# Output
# [[7.]
#  [8.]]
``````

## 7. Conclusion

In this article, I have explained how to use NumPy percentile() function and using this function how to get percentile values for 1 dimension and 2 dimension arrays along with specified parameters.

Happy Learning!!

## References 