final

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+from io import BytesIO
+import numpy as np
+
+# Set the style for all plots - using a built-in style
+plt.style.use('fivethirtyeight')  
+
+def configure_plot_style(fig, ax):
+    """Configure common plot styling elements"""
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.grid(True, linestyle='--', alpha=0.7)
+    fig.patch.set_facecolor('white')
+    ax.set_facecolor('white')
+
+
+st.title("Interactive Dataset Plotting Tool")
+
+# Upload Dataset
+uploaded_file = st.file_uploader("Upload your CSV dataset", type=["csv"])
+
+if uploaded_file:
+    try:
+        # Load dataset
+        df = pd.read_csv(uploaded_file)
+        st.write("Dataset Preview:")
+        st.dataframe(df)
+
+        # Plot type selection
+        plot_types = ["Line Plot", "Bar Plot", "Scatter Plot", "Histogram", "Box Plot", "Correlation Matrix"]
+        plot_type = st.selectbox("Select Plot Type:", plot_types)
+
+        # Color scheme selection
+        color_schemes = ['viridis', 'magma', 'plasma', 'inferno', 'cividis']
+        color_scheme = st.selectbox("Select Color Scheme:", color_schemes)
+
+        # Common figure creation
+        fig, ax = plt.subplots(figsize=(10, 6))
+        configure_plot_style(fig, ax)
+
+        if plot_type in ["Line Plot", "Bar Plot"]:
+            x_column = st.selectbox("Select X-axis column:", df.columns)
+            y_column = st.selectbox("Select Y-axis column:", df.columns)
+
+            if not pd.api.types.is_numeric_dtype(df[y_column]):
+                st.warning("Y-axis column must be numeric for this plot type.")
+            else:
+                if plot_type == "Line Plot":
+                    ax.plot(df[x_column], df[y_column], marker='o', linewidth=2, 
+                           color=plt.cm.get_cmap(color_scheme)(0.6))
+                else:  
+                    ax.bar(df[x_column], df[y_column], color=plt.cm.get_cmap(color_scheme)(0.6))
+                    
+                    
+                ax.set_title(f"{plot_type} of {y_column} vs {x_column}", pad=20, fontsize=14)
+                ax.set_xlabel(x_column, fontsize=12)
+                ax.set_ylabel(y_column, fontsize=12)
+                plt.xticks(rotation=45 if len(df[x_column].unique()) > 10 else 0)
+
+        elif plot_type == "Scatter Plot":
+            x_column = st.selectbox("Select X-axis column:", df.columns)
+            y_column = st.selectbox("Select Y-axis column:", df.columns)
+            
+            if not pd.api.types.is_numeric_dtype(df[x_column]) or not pd.api.types.is_numeric_dtype(df[y_column]):
+                st.warning("Both X and Y columns must be numeric for scatter plot.")
+            else:
+                scatter = ax.scatter(df[x_column], df[y_column], 
+                                   c=np.arange(len(df)), cmap=color_scheme, 
+                                   alpha=0.6, s=100)
+                plt.colorbar(scatter, ax=ax, label='Index')
+                ax.set_title(f"Scatter Plot of {y_column} vs {x_column}", pad=20, fontsize=14)
+                ax.set_xlabel(x_column, fontsize=12)
+                ax.set_ylabel(y_column, fontsize=12)
+
+        elif plot_type == "Histogram":
+            column = st.selectbox("Select column:", df.columns)
+            bins = st.slider("Number of bins:", min_value=5, max_value=50, value=20)
+
+            if not pd.api.types.is_numeric_dtype(df[column]):
+                st.warning("Column must be numeric for histogram.")
+            else:
+                n, bins, patches = ax.hist(df[column], bins=bins, edgecolor='white', linewidth=1)
+                for i, patch in enumerate(patches):
+                    patch.set_facecolor(plt.cm.get_cmap(color_scheme)(i/len(patches)))
+                
+                ax.set_title(f"Histogram of {column}", pad=20, fontsize=14)
+                ax.set_xlabel(column, fontsize=12)
+                ax.set_ylabel("Frequency", fontsize=12)
+
+        elif plot_type == "Box Plot":
+            x_column = st.selectbox("Select grouping column:", df.columns)
+            y_column = st.selectbox("Select value column:", df.columns)
+
+            if not pd.api.types.is_numeric_dtype(df[y_column]):
+                st.warning("Value column must be numeric for box plot.")
+            else:
+                box_plot = ax.boxplot([group[1][y_column].values for group in df.groupby(x_column)],
+                                    labels=df[x_column].unique(),
+                                    patch_artist=True)
+                
+                # Color the boxes
+                colors = [plt.cm.get_cmap(color_scheme)(i/len(box_plot['boxes'])) 
+                         for i in range(len(box_plot['boxes']))]
+                for patch, color in zip(box_plot['boxes'], colors):
+                    patch.set_facecolor(color)
+                    patch.set_alpha(0.7)
+                
+                ax.set_title(f"Box Plot of {y_column} grouped by {x_column}", pad=20, fontsize=14)
+                ax.set_xlabel(x_column, fontsize=12)
+                ax.set_ylabel(y_column, fontsize=12)
+                plt.xticks(rotation=45 if len(df[x_column].unique()) > 10 else 0)
+
+        elif plot_type == "Correlation Matrix":
+            numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns
+            numeric_df = df[numeric_columns]
+            
+            if len(numeric_columns) == 0:
+                st.warning("No numeric columns found in the dataset for correlation matrix.")
+            else:
+                corr = numeric_df.corr()
+                im = ax.imshow(corr, cmap=color_scheme)
+                plt.colorbar(im, ax=ax)
+                
+                # Add correlation values
+                for i in range(len(corr)):
+                    for j in range(len(corr)):
+                        text = ax.text(j, i, f'{corr.iloc[i, j]:.2f}',
+                                     ha='center', va='center',
+                                     color='white' if abs(corr.iloc[i, j]) > 0.5 else 'black')
+                
+                ax.set_xticks(range(len(corr.columns)))
+                ax.set_yticks(range(len(corr.columns)))
+                ax.set_xticklabels(corr.columns, rotation=45, ha='right')
+                ax.set_yticklabels(corr.columns)
+                ax.set_title("Correlation Matrix", pad=20, fontsize=14)
+
+        # Adjust layout and display plot
+        plt.tight_layout()
+        st.pyplot(fig)
+        
+        # Download button
+        buffer = BytesIO()
+        plt.savefig(buffer, format="png", dpi=300, bbox_inches='tight')
+        buffer.seek(0)
+        st.download_button(
+            label="Download Plot as PNG",
+            data=buffer,
+            file_name="plot.png",
+            mime="image/png"
+        )
+
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
+        st.info("Please make sure your dataset is properly formatted and contains appropriate data types for the selected plot type.")