Update app/dashboard.py

app/dashboard.py

@@ -1,171 +1,167 @@
-###____________ Chart selection logic and dashboard generation___________
-
-
-import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-import pandas as pd
-
-class DashboardGenerator:
-    def __init__(self, df, schema):
-        self.df = df
-        self.schema = schema
-        self.charts = []
-        
-    def generate_all_charts(self):
-        """
-        Generate appropriate charts for each column type
-        """
-        print(" Generating charts...")
-        
-        # Numeric columns - Histograms
-        for col in self.schema['numeric'][:5]:  # Limit to 5 charts
-            fig = self.create_histogram(col)
-            self.charts.append({
-                'title': f'Distribution of {col}',
-                'figure': fig,
-                'type': 'histogram'
-            })
-        
-        # Categorical columns - Bar charts (top 10)
-        for col in self.schema['categorical'][:3]:
-            fig = self.create_bar_chart(col)
-            self.charts.append({
-                'title': f'Top values in {col}',
-                'figure': fig,
-                'type': 'bar'
-            })
-        
-        # Time series - Line charts
-        for date_col in self.schema['datetime']:
-            for num_col in self.schema['numeric'][:2]:
-                fig = self.create_time_series(date_col, num_col)
-                self.charts.append({
-                    'title': f'{num_col} over time',
-                    'figure': fig,
-                    'type': 'line'
-                })
-        
-        # Correlation heatmap
-        if len(self.schema['numeric']) >= 2:
-            fig = self.create_correlation_heatmap()
-            self.charts.append({
-                'title': 'Correlation Heatmap',
-                'figure': fig,
-                'type': 'heatmap'
-            })
-        
-        return self.charts
-    
-    def create_histogram(self, column):
-        """Create histogram for numeric column"""
-        fig = px.histogram(
-            self.df, 
-            x=column,
-            title=f'Distribution of {column}',
-            color_discrete_sequence=['#2E86AB'],
-            nbins=30
-        )
-        fig.update_layout(
-            showlegend=False,
-            height=400,
-            template='plotly_white'
-        )
-        return fig
-    
-    def create_bar_chart(self, column):
-        """Create bar chart for categorical column"""
-        value_counts = self.df[column].value_counts().head(10)
-        
-        fig = px.bar(
-            x=value_counts.values,
-            y=value_counts.index,
-            orientation='h',
-            title=f'Top 10 values in {column}',
-            color=value_counts.values,
-            color_continuous_scale='Blues'
-        )
-        fig.update_layout(
-            xaxis_title='Count',
-            yaxis_title=column,
-            height=400,
-            template='plotly_white'
-        )
-        return fig
-    
-    def create_time_series(self, date_col, value_col):
-        """Create time series line chart"""
-        # Group by date
-        time_data = self.df.groupby(pd.Grouper(key=date_col, freq='D'))[value_col].mean().reset_index()
-        
-        fig = px.line(
-            time_data,
-            x=date_col,
-            y=value_col,
-            title=f'{value_col} over time',
-            markers=True
-        )
-        fig.update_layout(
-            xaxis_title='Date',
-            yaxis_title=value_col,
-            height=400,
-            template='plotly_white'
-        )
-        return fig
-    
-    def create_correlation_heatmap(self):
-        """Create correlation heatmap"""
-        corr_matrix = self.df[self.schema['numeric']].corr()
-        
-        fig = px.imshow(
-            corr_matrix,
-            text_auto='.2f',
-            aspect='auto',
-            color_continuous_scale='RdBu',
-            title='Correlation Heatmap'
-        )
-        fig.update_layout(
-            height=500,
-            template='plotly_white'
-        )
-        return fig
-    
-    def create_key_metrics(self):
-        """
-        Create KPI cards for important metrics
-        """
-        metrics = []
-        
-        for col in self.schema['numeric'][:4]:  # Top 4 numeric columns
-            mean_val = self.df[col].mean()
-            std_val = self.df[col].std()
-            min_val = self.df[col].min()
-            max_val = self.df[col].max()
-            
-            metrics.append({
-                'name': col.upper(),
-                'value': f"{mean_val:,.0f}",
-                'change': f"±{std_val:,.0f}",
-                'min': f"{min_val:,.0f}",
-                'max': f"{max_val:,.0f}",
-                'type': 'average'
-            })
-        
-        return metrics
-    
-    def create_summary_table(self):
-        """
-        Create summary statistics table
-        """
-        summary = []
-        for col in self.schema['numeric']:
-            summary.append({
-                'Column': col,
-                'Mean': round(self.df[col].mean(), 2),
-                'Median': round(self.df[col].median(), 2),
-                'Std Dev': round(self.df[col].std(), 2),
-                'Min': round(self.df[col].min(), 2),
-                'Max': round(self.df[col].max(), 2)
-            })
-        
+###____________ Chart selection logic and dashboard generation___________
+
+
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import pandas as pd
+
+class DashboardGenerator:
+    def __init__(self, df, schema):
+        self.df = df
+        self.schema = schema
+        self.charts = []
+        
+    def generate_all_charts(self):
+        """
+        Generate appropriate charts for each column type
+        """
+        print(" Generating charts...")
+        
+        #numeric columns - Histograms
+        for col in self.schema['numeric'][:5]:  # Limit to 5 charts
+            fig = self.create_histogram(col)
+            self.charts.append({
+                'title': f'Distribution of {col}',
+                'figure': fig,
+                'type': 'histogram'
+            })
+        
+        # categorical columns - Bar charts (top 10)
+        for col in self.schema['categorical'][:3]:
+            fig = self.create_bar_chart(col)
+            self.charts.append({
+                'title': f'Top values in {col}',
+                'figure': fig,
+                'type': 'bar'
+            })
+        
+        # time series - Line charts
+        for date_col in self.schema['datetime']:
+            for num_col in self.schema['numeric'][:2]:
+                fig = self.create_time_series(date_col, num_col)
+                self.charts.append({
+                    'title': f'{num_col} over time',
+                    'figure': fig,
+                    'type': 'line'
+                })
+        
+        # correlation heatmap
+        if len(self.schema['numeric']) >= 2:
+            fig = self.create_correlation_heatmap()
+            self.charts.append({
+                'title': 'Correlation Heatmap',
+                'figure': fig,
+                'type': 'heatmap'
+            })
+        
+        return self.charts
+    
+    def create_histogram(self, column):
+        """Create histogram for numeric column"""
+        fig = px.histogram(
+            self.df, 
+            x=column,
+            title=f'Distribution of {column}',
+            color_discrete_sequence=['#2E86AB'],
+            nbins=30
+        )
+        fig.update_layout(
+            showlegend=False,
+            height=400,
+            template='plotly_white'
+        )
+        return fig
+    
+    def create_bar_chart(self, column):
+        """Create bar chart for categorical column"""
+        value_counts = self.df[column].value_counts().head(10)
+        
+        fig = px.bar(
+            x=value_counts.values,
+            y=value_counts.index,
+            orientation='h',
+            title=f'Top 10 values in {column}',
+            color=value_counts.values,
+            color_continuous_scale='Blues'
+        )
+        fig.update_layout(
+            xaxis_title='Count',
+            yaxis_title=column,
+            height=400,
+            template='plotly_white'
+        )
+        return fig
+    
+    def create_time_series(self, date_col, value_col):
+        """Create time series line chart"""
+        # group by date
+        time_data = self.df.groupby(pd.Grouper(key=date_col, freq='D'))[value_col].mean().reset_index()
+        
+        fig = px.line(
+            time_data,
+            x=date_col,
+            y=value_col,
+            title=f'{value_col} over time',
+            markers=True
+        )
+        fig.update_layout(
+            xaxis_title='Date',
+            yaxis_title=value_col,
+            height=400,
+            template='plotly_white'
+        )
+        return fig
+    
+    def create_correlation_heatmap(self):
+        """Create correlation heatmap"""
+        corr_matrix = self.df[self.schema['numeric']].corr()
+        
+        fig = px.imshow(
+            corr_matrix,
+            text_auto='.2f',
+            aspect='auto',
+            color_continuous_scale='RdBu',
+            title='Correlation Heatmap'
+        )
+        fig.update_layout(
+            height=500,
+            template='plotly_white'
+        )
+        return fig
+    
+    def create_key_metrics(self):
+        """Create KPI cards for important metrics"""
+        metrics = []
+        
+        for col in self.schema['numeric'][:4]:  # Top 4 numeric columns
+            mean_val = self.df[col].mean()
+            std_val = self.df[col].std()
+            min_val = self.df[col].min()
+            max_val = self.df[col].max()
+            
+            metrics.append({
+                'name': col.upper(),
+                'value': f"{mean_val:,.0f}",
+                'change': f"±{std_val:,.0f}",
+                'min': f"{min_val:,.0f}",
+                'max': f"{max_val:,.0f}",
+                'type': 'average'
+            })
+        
+        return metrics
+    
+    def create_summary_table(self):
+        """Create summary statistics table"""
+        summary = []
+        for col in self.schema['numeric']:
+            summary.append({
+                'Column': col,
+                'Mean': round(self.df[col].mean(), 2),
+                'Median': round(self.df[col].median(), 2),
+                'Std Dev': round(self.df[col].std(), 2),
+                'Min': round(self.df[col].min(), 2),
+                'Max': round(self.df[col].max(), 2)
+            })
+        
         return pd.DataFrame(summary)