Untested Early Stage Chart Module

Chart.py

@@ -1 +1,112 @@
-# Modul Buat Membuat Statistik
+# Chart.py
+import matplotlib.pyplot as plt
+from matplotlib_venn import venn2, venn3
+
+class Chart:
+    def __init__(self, title="", xlabel="", ylabel=""):
+        # Initialize the Chart class with common plotting properties.
+        # title: The title of the chart.
+        # xlabel: Label for the x-axis.
+        # ylabel: Label for the y-axis.
+        self.title = title
+        self.xlabel = xlabel
+        self.ylabel = ylabel
+        self.figure = None  # Placeholder for the plot figure.
+
+    def _apply_common_properties(self):
+        # Apply common properties like title, xlabel, and ylabel to the current plot.
+        if self.title:
+            plt.title(self.title)
+        if self.xlabel:
+            plt.xlabel(self.xlabel)
+        if self.ylabel:
+            plt.ylabel(self.ylabel)
+
+    def box(self, data):
+        # Prepare a box diagram for visualizing the distribution of a dataset.
+        self.figure = plt.figure(figsize=(10, 6))
+        plt.boxplot(data)
+        self._apply_common_properties()
+
+    def pie(self, data, labels):
+        # Prepare a pie chart to show percentage distribution.
+        self.figure = plt.figure(figsize=(8, 8))
+        plt.pie(data, labels=labels, autopct='%1.1f%%', startangle=140)
+        if self.title:
+            plt.title(self.title)
+
+    def line(self, x_values, y_values):
+        # Prepare a line chart to plot the relationship between x and y values.
+        self.figure = plt.figure(figsize=(10, 6))
+        plt.plot(x_values, y_values, marker='o')
+        self._apply_common_properties()
+        plt.grid()  # Display a grid for better readability.
+
+    def histogram(self, data, bins=10):
+        # Prepare a histogram for visualizing the distribution of a dataset.
+        self.figure = plt.figure(figsize=(10, 6))
+        plt.hist(data, bins=bins, alpha=0.7, color='b')
+        self._apply_common_properties()
+
+    def boxplot(self, data):
+        # Prepare a boxplot using seaborn-like aesthetics for enhanced visualization.
+        self.figure = plt.figure(figsize=(10, 6))
+        plt.boxplot(data)
+        self._apply_common_properties()
+
+    def scatter(self, x_values, y_values):
+        # Prepare a scatter plot to visualize the relationship between two variables.
+        self.figure = plt.figure(figsize=(10, 6))
+        plt.scatter(x_values, y_values, alpha=0.6, edgecolors='w', s=100)
+        self._apply_common_properties()
+        plt.grid()  # Display a grid for better readability.
+
+    def heatmap(self, data, annot=True, cmap='viridis'):
+        # Prepare a heatmap for visualizing a 2D matrix data.
+        self.figure = plt.figure(figsize=(12, 8))
+        plt.imshow(data, cmap=cmap, aspect='auto')
+        if annot:
+            for (i, j), val in np.ndenumerate(data):
+                plt.text(j, i, f'{val}', ha='center', va='center', color='white')
+        self._apply_common_properties()
+        plt.colorbar()  # Show color scale.
+
+    def venn(self, sets, set_labels):
+        # Prepare a Venn diagram for visualizing the overlap between two or three sets.
+        self.figure = plt.figure(figsize=(8, 8))
+        if len(sets) == 2:
+            venn2(sets, set_labels)
+        elif len(sets) == 3:
+            venn3(sets, set_labels)
+        else:
+            raise ValueError("Only 2 or 3 sets can be displayed in a Venn diagram.")
+        if self.title:
+            plt.title(self.title)
+
+    def pareto(self, data, labels):
+        # Prepare a Pareto chart with bars representing values and a line showing the cumulative percentage.
+        # Sort data in descending order.
+        sorted_data = sorted(zip(data, labels), reverse=True)
+        data, labels = zip(*sorted_data)
+        cumulative_percentage = [sum(data[:i + 1]) / sum(data) * 100 for i in range(len(data))]
+
+        self.figure, ax1 = plt.subplots(figsize=(12, 8))
+        ax1.bar(labels, data, color='b', alpha=0.6)
+        ax1.set_xlabel(self.xlabel)
+        ax1.set_ylabel(self.ylabel)
+
+        # Plot cumulative percentage line.
+        ax2 = ax1.twinx()
+        ax2.plot(labels, cumulative_percentage, color='r', marker='D', linestyle='-', linewidth=2)
+        ax2.set_ylabel('Cumulative Percentage')
+        ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0f}%'))
+
+        if self.title:
+            ax1.set_title(self.title)
+
+    def show(self):
+        # Display the prepared chart.
+        if self.figure:
+            plt.show()
+        else:
+            print("No chart has been prepared. Please call a chart method first.")

README.md

@@ -9,6 +9,8 @@
 
 ### Required
 - Matplotlib
+- Matplotlib_Venn
+- Seaborn
 - Numpy
 - Tabulate
 - Pandas

Transformation.py

@@ -0,0 +1 @@
+# Data Transformation Module