CLN: Enforce deprecation of using alias for builtin/NumPy funcs (pandas-dev#57444)

* CLN: Enforce deprecation of using alias for builtin/NumPy funcs

* GH# and whatsnew

* Fixup docs

* More tests

* Restore docstring

* Test fixes

* Test fixups

* Test fixes

* Test fixup

* Test fixes

- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

Author: Chisholm6192

scripts/handle_outliers.py

@@ -0,0 +1,30 @@
+import pandas as pd
+import numpy as np
+
+
+"""
+Detect and handle outliers in a DataFrame.
+
+Parameters:
+    data: DataFrame
+    method: str, default 'z-score'. The method used for outlier detection. Options: 'z-score' or 'IQR' (Interquartile Range).
+    threshold: float, default 3. The threshold for identifying outliers. Data points beyond this threshold are considered outliers.
+
+Returns:
+    DataFrame: DataFrame with outliers handled (replaced or removed).
+"""
+def handle_outliers(data, method='z-score', threshold=3):
+    if method == 'z-score':
+        z_scores = np.abs((data - data.mean()) / data.std())
+        data_no_outliers = data[(z_scores < threshold).all(axis=1)]
+    
+    elif method == 'IQR':
+        Q1 = data.quantile(0.25)
+        Q3 = data.quantile(0.75)
+        IQR = Q3 - Q1
+        data_no_outliers = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis=1)]
+
+    else:
+        raise ValueError("Invalid method. Use z-score or IQR")
+    
+    return data_no_outliers