[+] Add stddev to scientific utils

hypy_utils/nlp_utils.py

@@ -1,6 +1,7 @@
 """
 Natual language processing utils
 """
+from __future__ import annotations
 
 
 def camel_split(camel: str) -> list[str]:

hypy_utils/scientific_utils.py

@@ -3,9 +3,7 @@ Importing this file requires numpy, matplotlib, and numba
 """
 from __future__ import annotations
 
-import time
 from dataclasses import dataclass
-from typing import Callable
 
 import numpy as np
 from matplotlib import pyplot as plt
@@ -23,13 +21,21 @@ class Statistics:
     maximum: float
     count: int
     total: float
+    stddev: float
 
     def get_metric_6(self) -> tuple[float, float, float, float, float, float]:
         return self.mean, self.median, self.minimum, self.maximum, self.lower_quartile, self.upper_quartile
 
+    def print(self, dec: int = 2):
+        print(f'> Mean: {round(self.mean, dec)}, Median: {round(self.median, dec)}')
+        print(f'> Min: {round(self.minimum, dec)}, Max: {round(self.maximum, dec)}')
+        print(f'> Q1: {round(self.lower_quartile, dec)}, Q3: {round(self.upper_quartile, dec)}')
+        print(f'> StdDev: {round(self.stddev, dec)}, IQR: {round(self.iqr, dec)}')
+        print(f'> N: {self.count}')
+
 
 @njit(cache=True)
-def _calc_col_stats_helper(col: np.ndarray) -> tuple[float, float, float, float, float, float, float, int, float]:
+def _calc_col_stats_helper(col: np.ndarray) -> tuple[float, float, float, float, float, float, float, int, float, float]:
     q1 = np.quantile(col, 0.25)
     q3 = np.quantile(col, 0.75)
     return (
@@ -41,7 +47,8 @@ def _calc_col_stats_helper(col: np.ndarray) -> tuple[float, float, float, float,
         float(np.min(col)),
         float(np.max(col)),
         len(col),
-        float(np.sum(col))
+        float(np.sum(col)),
+        float(np.std(col))
     )