[Term Entry] Python:SciPy scipy.stats: Descriptive Stats

content/scipy/concepts/scipy-stats/terms/descriptive-stats/descriptive-stats.md

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+---
+Title: 'Descriptive Stats'
+Description: 'Summarizes and describes the essential features of a dataset.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Data'
+  - 'Functions'
+  - 'Math'
+  - 'Python'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+In SciPy, **descriptive statistics** refers to summarizing and analyzing a dataset's key characteristics. It helps summarize essential properties such as central tendency, variability, and distribution shape.
+
+The **`.describe()`** function in the `scipy.stats` module is used to calculate common descriptive statistics of a given array, such as:
+
+- Number of observations (`nobs`)
+- Minimum and maximum values (`minmax`)
+- Mean (`mean`)
+- Variance (`variance`)
+- Skewness (`skewness`)
+- Kurtosis (`kurtosis`)
+
+## Syntax
+
+```pseudo
+stats.describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate')
+```
+
+- `a`: The input data to describe.
+- `axis` (Optional): The axis along which to compute the descriptive statistics (default is `0`). If set to `None`, the statistics are calculated for the whole array.
+- `ddof` (Optional): Delta Degrees of Freedom for calculating variance (default is `1`).
+- `bias` (Optional): If set to `False`, it corrects the skewness and kurtosis calculations for statistical bias.
+- `nan_policy` (Optional): Defines the handling method to use when the input contains NaN. The options include:
+  - `propagate` (Default): Returns NaN.
+  - `raise`: Raises an error.
+  - `omit`: Ignores NaN values and performs the calculations.
+
+## Example
+
+The following example demonstrates the usage of the `.describe()` function to calculate the descriptive statistics of a given array:
+
+```py
+import numpy as np
+from scipy import stats
+
+# Define an array
+arr = np.array([12, 23, 34, 45, 56])
+
+# Calculate the descriptive statistics of the array
+res = stats.describe(arr)
+
+# Print the result
+print(res)
+```
+
+The above code produces the following output:
+
+```shell
+DescribeResult(nobs=5, minmax=(12, 56), mean=34.0, variance=302.5, skewness=0.0, kurtosis=-1.3)
+```
+
+## Codebyte Example
+
+The following codebyte example demonstrates the usage of the `.describe()` function to calculate the descriptive statistics of a given array:
+
+```codebyte/python
+import numpy as np
+from scipy import stats
+
+# Define an array
+arr = np.array([5, 10, 20, 40, 80])
+
+# Calculate the descriptive statistics of the array
+res = stats.describe(arr)
+
+# Print the result
+print(res)
+```