Rename visualizer.py to visualization.py

visualization.py

@@ -0,0 +1,41 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+import numpy as np
+
+class VisualizationSelector:
+    def select_visualizations(self, data):
+        visualizations = []
+        
+        # Histogram for numerical columns
+        numeric_columns = data.select_dtypes(include=[np.number]).columns
+        for column in numeric_columns:
+            fig, ax = plt.subplots()
+            sns.histplot(data[column], kde=True, ax=ax)
+            ax.set_title(f'Distribution of {column}')
+            visualizations.append(fig)
+
+        # Correlation heatmap
+        if len(numeric_columns) > 1:
+            fig, ax = plt.subplots(figsize=(10, 8))
+            sns.heatmap(data[numeric_columns].corr(), annot=True, cmap='coolwarm', ax=ax)
+            ax.set_title('Correlation Heatmap')
+            visualizations.append(fig)
+
+        # Scatter plot matrix
+        if len(numeric_columns) > 1:
+            fig = sns.pairplot(data[numeric_columns])
+            fig.fig.suptitle('Scatter Plot Matrix', y=1.02)
+            visualizations.append(fig)
+
+        # Box plots for categorical vs numerical
+        categorical_columns = data.select_dtypes(include=['object']).columns
+        for cat_col in categorical_columns:
+            for num_col in numeric_columns:
+                fig, ax = plt.subplots()
+                sns.boxplot(x=cat_col, y=num_col, data=data, ax=ax)
+                ax.set_title(f'{cat_col} vs {num_col}')
+                ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
+                visualizations.append(fig)
+
+        return visualizations

visualizer.py

@@ -1,47 +0,0 @@
-import streamlit as st
-import plotly.express as px
-import plotly.graph_objects as go
-
-class Visualizer:
-    def create_visualizations(self, df):
-        chart_type = st.selectbox("Select chart type", 
-                                  ["Scatter", "Line", "Bar", "Histogram", "Box Plot", "Heatmap", "Pie Chart", "3D Scatter"])
-        
-        if chart_type in ["Scatter", "Line", "Bar"]:
-            x_axis = st.selectbox("Select X-axis", df.columns)
-            y_axis = st.selectbox("Select Y-axis", df.columns)
-            color = st.selectbox("Select color (optional)", ["None"] + df.columns.tolist())
-            
-            if chart_type == "Scatter":
-                fig = px.scatter(df, x=x_axis, y=y_axis, color=None if color == "None" else color)
-            elif chart_type == "Line":
-                fig = px.line(df, x=x_axis, y=y_axis, color=None if color == "None" else color)
-            else:  # Bar
-                fig = px.bar(df, x=x_axis, y=y_axis, color=None if color == "None" else color)
-        
-        elif chart_type == "Histogram":
-            column = st.selectbox("Select column", df.columns)
-            fig = px.histogram(df, x=column)
-        
-        elif chart_type == "Box Plot":
-            y_axis = st.selectbox("Select Y-axis", df.columns)
-            x_axis = st.selectbox("Select X-axis (optional)", ["None"] + df.columns.tolist())
-            fig = px.box(df, y=y_axis, x=None if x_axis == "None" else x_axis)
-        
-        elif chart_type == "Heatmap":
-            corr_matrix = df.corr()
-            fig = px.imshow(corr_matrix, color_continuous_scale='RdBu_r')
-        
-        elif chart_type == "Pie Chart":
-            values = st.selectbox("Select values", df.columns)
-            names = st.selectbox("Select names", df.columns)
-            fig = px.pie(df, values=values, names=names)
-        
-        elif chart_type == "3D Scatter":
-            x_axis = st.selectbox("Select X-axis", df.columns)
-            y_axis = st.selectbox("Select Y-axis", df.columns)
-            z_axis = st.selectbox("Select Z-axis", df.columns)
-            color = st.selectbox("Select color (optional)", ["None"] + df.columns.tolist())
-            fig = px.scatter_3d(df, x=x_axis, y=y_axis, z=z_axis, color=None if color == "None" else color)
-
-        st.plotly_chart(fig)