Update data_processor.py

data_processor.py

@@ -1,8 +1,10 @@
 import pandas as pd
+import numpy as np
 import plotly.express as px
 import plotly.graph_objects as go
-from typing import List, Dict, Any
 import streamlit as st
+from typing import Dict, List, Any
+
 
 class DataProcessor:
     def __init__(self):
@@ -12,7 +14,6 @@ class DataProcessor:
         self.date_columns = []
     
     def load_data(self, file) -> bool:
-        """Load and validate CSV data"""
         try:
             self.data = pd.read_csv(file)
             self._classify_columns()
@@ -22,7 +23,6 @@ class DataProcessor:
             return False
     
     def _classify_columns(self):
-        """Classify columns into numeric, categorical, and date types"""
         for col in self.data.columns:
             if pd.api.types.is_numeric_dtype(self.data[col]):
                 self.numeric_columns.append(col)
@@ -34,9 +34,8 @@ class DataProcessor:
                     self.date_columns.append(col)
                 except:
                     self.categorical_columns.append(col)
-    
+
     def get_basic_stats(self) -> Dict[str, Any]:
-        """Calculate basic statistics for numeric columns"""
         if self.data is None:
             return {}
         
@@ -47,9 +46,8 @@ class DataProcessor:
             'column_count': len(self.data.columns)
         }
         return stats
-    
+
     def create_visualization(self, chart_type: str, x_col: str, y_col: str, color_col: str = None) -> go.Figure:
-        """Create different types of visualizations based on user selection"""
         if chart_type == "Line Plot":
             fig = px.line(self.data, x=x_col, y=y_col, color=color_col)
         elif chart_type == "Bar Plot":
@@ -61,80 +59,4 @@ class DataProcessor:
         else:
             fig = px.histogram(self.data, x=x_col, color=color_col)
         
-        return fig
-    
-    def calculate_metrics(self, column: str) -> Dict[str, float]:
-        """Calculate key metrics for a selected column"""
-        if column not in self.numeric_columns:
-            return {}
-        
-        metrics = {
-            'mean': self.data[column].mean(),
-            'median': self.data[column].median(),
-            'std': self.data[column].std(),
-            'min': self.data[column].min(),
-            'max': self.data[column].max(),
-            'skew': self.data[column].skew()
-        }
-        return metrics
-
-def render_analytics_page():
-    st.title("Data Analytics Dashboard")
-    
-    # Initialize data processor
-    processor = DataProcessor()
-    
-    # File upload
-    uploaded_file = st.file_uploader("Upload your CSV data", type=['csv'])
-    if uploaded_file is not None:
-        if processor.load_data(uploaded_file):
-            st.success("Data loaded successfully!")
-            
-            # Data Preview
-            st.subheader("Data Preview")
-            st.dataframe(processor.data.head())
-            
-            # Basic Stats
-            st.subheader("Basic Statistics")
-            stats = processor.get_basic_stats()
-            st.write(stats['summary'])
-            
-            # Visualization Section
-            st.subheader("Create Visualization")
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                chart_type = st.selectbox(
-                    "Select Chart Type",
-                    ["Line Plot", "Bar Plot", "Scatter Plot", "Box Plot", "Histogram"]
-                )
-            
-            with col2:
-                x_col = st.selectbox("Select X-axis", processor.data.columns)
-            
-            with col3:
-                y_col = st.selectbox("Select Y-axis", processor.numeric_columns) if chart_type != "Histogram" else None
-            
-            color_col = st.selectbox("Select Color Variable (optional)", 
-                                   ['None'] + processor.categorical_columns)
-            color_col = None if color_col == 'None' else color_col
-            
-            # Generate and display visualization
-            fig = processor.create_visualization(
-                chart_type,
-                x_col,
-                y_col if y_col else x_col,
-                color_col
-            )
-            st.plotly_chart(fig, use_container_width=True)
-            
-            # Metrics Calculator
-            st.subheader("Metric Calculator")
-            metric_col = st.selectbox("Select column for metrics", processor.numeric_columns)
-            metrics = processor.calculate_metrics(metric_col)
-            
-            # Display metrics in columns
-            cols = st.columns(3)
-            for i, (metric, value) in enumerate(metrics.items()):
-                with cols[i % 3]:
-                    st.metric(label=metric.capitalize(), value=f"{value:.2f}")
+        return fig