add some mathematical functions ; mostly from the math lessons themselfs

math/r_pkg/r_boxplot.py

@@ -0,0 +1,25 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from typing import List
+
+def r_boxplot(arr: List[float], label: str) -> None:
+    data = np.asarray(arr, dtype=float)
+    p = np.percentile(data, [0, 25, 50, 75, 100])
+
+    fig, ax = plt.subplots(figsize=(6, 2.5))
+    ax.boxplot(data, positions=[1], vert=False, patch_artist=True, widths=0.6)
+
+    labels = ["0%", "25%", "50%", "75%", "100%"]
+    for val, lab in zip(p, labels):
+        ax.axvline(val, color="#ff0000", linestyle="--", linewidth=0.8, alpha=0.9)
+
+    ax.text(0.5, 1.15, label, transform=ax.transAxes,
+            ha="center", va="bottom", fontsize=10, fontweight="bold", color="black")
+
+    ax.set_yticks([1])
+    ax.xaxis.grid(True, linestyle="--", alpha=0.3)
+
+    fig.tight_layout()
+    fig.subplots_adjust(top=0.78)
+    plt.show()
+

math/r_pkg/r_stddev.py

@@ -0,0 +1,13 @@
+# helper for standard deviation
+# book page: 235
+# AGPL-3.0
+import numpy as np
+
+def r_stddev(lst: list, r_dtype) -> float:
+    r_arr = np.asarray(lst)
+    return float(np.std(r_arr, dtype=r_dtype))
+
+def implemenentation():
+    print(r_stddev(sorted([6,4,9,5,7,11,5,3,7,5,4]),float))
+
+implemenentation()