refactor: added func documentation

src/Plotter.py

@@ -10,11 +10,32 @@ class Plotter:
         self.ds = dataset
         self.df = dataset.get_dataframe()
 
-    def customize_plot(self, fig, ax, styling_params):
+    def customize_plot(self, fig, ax, styling_params) -> None:
+        """ customize_plot
+
+        Args:
+            fig (plt.figure.Figure),
+            ax (plt.axes.Axes),
+            styling_params (dict)
+
+
+        Returns:
+            None
+        """
         if styling_params.get("title"):
             ax.set_title(styling_params["title"])
 
-    def distribution_plot(self, target):
+    def distribution_plot(self, target) -> None:
+        """ plot a distribution plot.
+
+        Args:
+            target (str, must be present as a column in the dataset),
+            styling_params (dict)
+
+
+        Returns:
+            None
+        """
         grouped_data = self.df.groupby(target).size()
         plt.barh(grouped_data.index, grouped_data.values)
         print(
@@ -28,7 +49,18 @@ class Plotter:
 
     def plot_categorical_bar_chart(
         self, category1, category2, styling_params={}
-    ):
+    ) -> None:
+        """ plot a categorical bar chart.
+
+        Args:
+            category1 (str, must be present as a column in the dataset),
+            category2 (str, must be present as a column in the dataset),
+            styling_params (dict)
+
+
+        Returns:
+            None
+        """
         ct = pd.crosstab(self.df[category1], self.df[category2])
         # Calculate percentages by row
         ct_percent = ct.apply(lambda r: r / r.sum() * 100, axis=0)
@@ -36,14 +68,39 @@ class Plotter:
         self.customize_plot(fig, ax, styling_params)
         ct_percent.plot(kind="bar", ax=ax)
 
-    def plot_categorical_boxplot(self, target, category, styling_params={}):
+    def plot_categorical_boxplot(
+        self, target, category, styling_params={}
+    ) -> None:
+        """ plot a categorical boxplot.
+
+        Args:
+            target (str, must be present as a column in the dataset),
+            category (str, must be present as a column in the dataset),
+            styling_params (dict)
+
+
+        Returns:
+            None
+        """
+        
         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
-    ):
+    ) -> None:
+        """ plot a categorical hisplot.
+
+        Args:
+            target (str, must be present as a column in the dataset),
+            category (str, must be present as a column in the dataset),
+            styling_params (dict)
+
+
+        Returns:
+            None
+        """
         uniques = self.ds.get_unique_column_values(category)
         fig, ax = plt.subplots()
         self.customize_plot(fig, ax, styling_params)
@@ -57,7 +114,18 @@ class Plotter:
                 alpha=0.5,
             )
 
-    def plot_scatterplot(self, target1, target2, styling_params={}):
+    def plot_scatterplot(self, target1, target2, styling_params={}) -> None:
+        """ plot a scatterplot.
+
+        Args:
+            target1 (str, must be present as a column in the dataset),
+            target2 (str, must be present as a column in the dataset),
+            styling_params (dict)
+
+
+        Returns:
+            None
+        """
         fig, ax = plt.subplots()
         self.customize_plot(fig, ax, styling_params)
-        ax.scatter(self.df[target1], self.df[target2])
+        ax.scatter(self.df[target1], self.df[target2])
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