# NumPy percentile()

The `percentile()` method computes the q-th percentile of the data along the specified axis.

### Example

``````import numpy as np

# create an array
array1 = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# calculate the 25th, 50th and 75th percentile of the array
p25 = np.percentile(array1, 25)
p50 = np.percentile(array1, 50)
p75 = np.percentile(array1, 75)

print(p25, p50, p75)

# Output: 1.75 3.5 5.25``````

## percentile() Syntax

The syntax of `percentile()` is:

``numpy.percentile(array, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, interpolation=None)``

## percentile() Arguments

The `percentile()` method takes the following arguments:

• `array` - input array(can be `array_like`)
• `q`- qth percentile to find(can be `array_like` of `float`)
• `axis`(optional)- axis or axes along which the means are computed(`int` or `tuple of int`)
• `out`(optional)- output array in which to place the result(`ndarray`)
• `keepdims`(optional)- specifies whether to preserve the shape of the original array(`bool`)
• `override_input`(optional)- `bool` value that determines if intermediate calculations can modify an array
• `method`(optional)- interpolation method to use
• `interpolation`(optional)- the deprecated name for the `method` keyword argument

Notes:

The default values of,

• `axis = None`, i.e. the percentile of the entire array is taken.
• By default, `keepdims` and `override_input` will be `False`.
• The interpolation method is `'linear'`.
• If the input contains integers or floats smaller than `float64`, the output data type is `float64`. Otherwise, the output data type is the same as that of the input.

## percentile() Return Value

The `percentile()` method returns the q-th percentile(s) of the input array along the specified axis.

## Percentile

The percentile is a statistical measure that represents the value below which a specific percentage of data falls. It helps analyze the distribution of a dataset.

In NumPy, the `percentile()` function computes the q-th percentile of data along the specified axis.

The q-th percentile represents the value below which q percent of the data falls. For example, the 50th percentile (also known as the median) divides the data into two equal halves, with 50% of the values below it.

## Example 1: Find the Percentile of a ndArray

``````import numpy as np

# create an array
array1 = np.array([[[0, 1],[2, 3]],
[[4, 5],[6, 7]]])

# find the 50th percentile of entire array
percentile1 = np.percentile(array1, q=50)

# find the 50th percentile across axis 0
percentile2 = np.percentile(array1, q=50, axis=0)

# find the 50th percentile across axis 0 and 1
percentile3 = np.percentile(array1, q=50, axis=(0, 1))

print('\n50th Percentile of the entire array:', percentile1)
print('\n50th Percentile across axis 0:\n', percentile2)
print('\n50th Percentile across axis 0 and 1:', percentile3)``````

Output

```50th Percentile of the entire array: 3.5

50th Percentile across axis 0:
[[2. 3.]
[4. 5.]]

50th Percentile across axis 0 and 1:[3. 4.]```

## Example 2: Use out to Store the Result in Desired Location

The `out` parameter allows to specify an output array where the result will be stored.

``````import numpy as np

arr = np.array([[1, 2, 3],
[4, 5, 6]])

# create an output array
output = np.zeros(3)

# compute 25th percentile and store the result in the output array
np.percentile(arr, 25, out = output, axis = 0)

print('25th Percentile:', output)``````

Output

`25th Percentile: [1.75 2.75 3.75]`

## Example 3: Using Optional keepdims Argument

If `keepdims` is set to `True`, the resultant mean array is of the same number of dimensions as the original array.

``````import numpy as np

arr = np.array([[1, 2, 3],
[4, 5, 6]])

# keepdims defaults to False
result1 = np.percentile(arr, 50 , axis = 0)

# pass keepdims as True
result2 = np.percentile(arr, 50, axis = 0, keepdims = True)

print('Dimensions in original array:', arr.ndim)
print('Without keepdims:', result1, 'with dimensions', result1.ndim)
print('With keepdims:', result2, 'with dimensions', result2.ndim)``````

Output

```Dimensions in original array: 2
Without keepdims: [2.5 3.5 4.5] with dimensions 1
With keepdims: [[2.5 3.5 4.5]] with dimensions 2```