feat: added box and histplot functionality

src/Plotter.py

 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib
 import pandas as pd
+import seaborn as sns
 from .Dataset import Dataset
 
 class Plotter:
     def __init__(self, dataset: Dataset):
+        self.ds = dataset
         self.df = dataset.get_dataframe()
+
+    
+    def customize_plot(self, fig, ax, styling_params):
+        if styling_params.get('title'):
+            ax.set_title(styling_params["title"])
+
     
-    def plotCategoricalBarChart(self, category1, category2, title):
+    def plot_categorical_bar_chart(self, category1, category2, styling_params = {}):
         ct = pd.crosstab(self.df[category1], self.df[category2])
-        ct_percent = ct.apply(lambda r: r/r.sum() * 100, axis=0) # Calculate percentages by row
+        # Calculate percentages by row
+        ct_percent = ct.apply(lambda r: r/r.sum() * 100, axis=0)                
         fig, ax = plt.subplots()
-        ax.set_title(title)
+        self.customize_plot(fig, ax, styling_params)
         ct_percent.plot(kind='bar', ax=ax)
+
+
+    def plot_categorical_boxplot(self, target, category, styling_params = {}):
+        fig, ax = plt.subplots()
+        self.customize_plot(fig, ax, styling_params)
+        sns.boxplot(x=category,y=target,data=self.df, palette='rainbow')
+
+
+    def plot_categorical_histplot(self, target, category, styling_params = {}, bins= 30):
+        uniques = self.ds.get_unique_column_values(category)
+        fig, ax = plt.subplots()
+        self.customize_plot(fig, ax, styling_params)
+        for val in uniques:
+            anx_score = self.df[self.df[category] == val][target]
+            anx_score_weights = np.ones(len(anx_score)) / len(anx_score)
+            ax.hist(
+                anx_score,
+                weights=anx_score_weights,
+                bins = bins,
+                alpha=0.5,
+            )
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