[Term Entry] Python NumPy Built-in Function: .Quantile()

content/numpy/concepts/built-in-functions/terms/quantile/quantile.md

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+---
+Title: '.quantile()'
+Description: 'Computes the qth quantile of the input array along the specified axis.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Arrays'
+  - 'Data'
+  - 'Functions'
+  - 'Methods'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/data-science'
+---
+
+The **`.quantile()`** function in NumPy returns the qth quantile of an array along a specified axis. Quantiles are the division points that separate a data set into equal probabilities. For example, the 25th quantile is the point which 25% of the data set falls below.
+
+## Syntax
+
+```pseudo
+numpy.quantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, weights=None)
+```
+
+**Parameters:**
+
+- `a`: The input array containing the data to compute the quantiles from.
+- `q`: The quantile(s) to compute. This can be a float or array-like of floats between `0` and `1`, where `0.5` represents the median.
+- `axis` (Optional): The axis or axes on which to calculate the quantile. `axis=0` computes along columns, and `axis=1` computes along rows. If set to `None` (default), the input is flattened before computation.
+- `out` (Optional): Specifies a different array in which to place the result. It must have the same shape as the expected result.
+- `overwrite_input` (Optional): If `True`, the input array `a` may be modified to save memory. Default is `False`.
+- `method` (Optional): The method used to calculate the quantile. The default is `'linear'`. Valid options include: `'inverted_cdf'`, `'averaged_inverted_cdf'`, `'closest_observation'`, `'interpolated_inverted_cdf'`, `'hazen'`, `'weibull'`, `'median_unbiased'`, and `'normal_unbiased'`.
+  `keepdims` (Optional): If `True`, the reduced axes are retained with size one, maintaining the number of dimensions in the output.
+  `weights` (Optional): An array of weights corresponding to values in `a`, used to influence the quantile calculation. This parameter is only supported by the `'inverted_cdf'` method. The shape of `weights` must either match `a`, or be 1-dimensional with a length equal to `a` when flattened.
+
+**Return value:**
+
+The `.quantile()` function returns the qth quantile(s) of an array as a NumPy array ([`ndarray`](https://www.codecademy.com/resources/docs/numpy/ndarray)) or a scalar (`float64`) if the result is a single value.
+
+## Example: Computing multiple quantiles from data
+
+The following example creates an array and then uses `.quantile()` to calculate various quantiles from the data:
+
+```py
+import numpy as np
+
+a = np.array([[0,1,2],[3,4,5]])
+
+print(np.quantile(a, .25))
+# Computes the 25th quantile along a flattened axis
+
+print(np.quantile(a, .5, axis=0))
+# Computes the 50th quantile along the vertical axis
+
+print(np.quantile(a, .5, axis=1))
+# Computes the 50th quantile along the horizontal axis
+
+print(np.quantile(a, .75, axis=1, keepdims=True))
+# Computes the 75th quantile along the horizontal axis, while retaining the original dimensions of the input array
+```
+
+This code produces the following output:
+
+```shell
+1.25
+[1.5 2.5 3.5]
+[1. 4.]
+[[1.5]
+ [4.5]]
+```
+
+## Codebyte Example
+
+The following codebyte example computes various quantiles for an input array, `a`:
+
+```codebyte/python
+import numpy as np
+
+a = np.array([[0,2,4],[6,8,10],[12,14,16]])
+
+print("Array:")
+print(a)
+
+print("\nThe 30th quantile of the array:")
+print(np.quantile(a, .3))
+
+print("\nThe 50th quantile along the horizontal axis (axis=1):")
+print(np.quantile(a, .5, axis=1))
+
+print("\nThe 50th quantile along the vertical axis (axis=0):")
+print(np.quantile(a, .5, axis=0))
+
+print("\nThe 90th quantile along the horizontal axis with keepdims=True:")
+print(np.quantile(a, .9, axis=1, keepdims=True))
+```