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import streamlit as st |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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import plotly.express as px |
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def main(): |
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st.set_page_config(page_title="Advance Data Visualization Tool", layout="wide") |
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st.title("📊 Advance Data Visualization Tool") |
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with st.sidebar: |
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st.header("Upload Data") |
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uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"]) |
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if uploaded_file: |
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df = pd.read_csv(uploaded_file) |
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display_data_preview(df) |
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selected_features = select_features(df) |
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if selected_features: |
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if validate_features(df, selected_features): |
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chart_type = select_chart_type() |
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display_chart(df, selected_features, chart_type) |
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def display_data_preview(df): |
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with st.expander("Preview of the uploaded data", expanded=False): |
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st.write(df.head()) |
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def select_features(df): |
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selected_features = st.multiselect("Select features for comparison", df.columns) |
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if not selected_features: |
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st.warning("Please select at least one feature for comparison.") |
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return selected_features |
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def validate_features(df, selected_features): |
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if not all(df[feature].dtype in (int, float) for feature in selected_features): |
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st.error("Selected features must be numeric for visualization.") |
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return False |
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return True |
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def select_chart_type(): |
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with st.sidebar: |
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st.header("Chart Selection") |
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return st.selectbox( |
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"Select the type of chart", |
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["Line Chart", "Bar Chart", "Scatter Plot", "Pie Chart", "Histogram", "Heatmap"], |
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) |
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def display_chart(df, selected_features, chart_type): |
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st.subheader(f"{chart_type}") |
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if chart_type == "Line Chart": |
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generate_line_chart(df, selected_features) |
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elif chart_type == "Bar Chart": |
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generate_bar_chart(df, selected_features) |
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elif chart_type == "Scatter Plot": |
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generate_scatter_plot(df, selected_features) |
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elif chart_type == "Pie Chart": |
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generate_pie_chart(df, selected_features) |
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elif chart_type == "Histogram": |
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generate_histogram(df, selected_features[0]) |
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elif chart_type == "Heatmap": |
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generate_heatmap(df, selected_features) |
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def generate_line_chart(df, selected_features): |
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fig, ax = plt.subplots(figsize=(15, 8)) |
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for feature in selected_features: |
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ax.plot(df.index, df[feature], label=feature) |
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ax.set_xlabel("Index") |
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ax.set_ylabel("Values") |
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ax.set_title("Line Chart") |
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ax.legend() |
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st.pyplot(fig) |
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def generate_bar_chart(df, selected_features): |
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fig = px.bar(df, x=df.index, y=selected_features, barmode="group") |
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fig.update_layout(title="Bar Chart", xaxis_title="Index", yaxis_title="Values", height=600, width=1000) |
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st.plotly_chart(fig) |
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def generate_scatter_plot(df, selected_features): |
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fig = px.scatter( |
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df, |
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x=selected_features[0], |
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y=selected_features[1], |
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title="Scatter Plot", |
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labels={ |
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selected_features[0]: selected_features[0], |
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selected_features[1]: selected_features[1], |
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}, |
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height=600, |
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width=1000 |
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) |
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st.plotly_chart(fig) |
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def generate_pie_chart(df, selected_features): |
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fig = px.pie(df, names=selected_features, title="Pie Chart", hole=0.3, height=600, width=1000) |
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st.plotly_chart(fig) |
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def generate_histogram(df, selected_feature): |
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fig, ax = plt.subplots(figsize=(15, 8)) |
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ax.hist(df[selected_feature], bins=20, edgecolor="black") |
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ax.set_xlabel(selected_feature) |
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ax.set_ylabel("Frequency") |
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ax.set_title("Histogram") |
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st.pyplot(fig) |
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def generate_heatmap(df, selected_features): |
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fig = px.imshow(df[selected_features].corr(), title="Heatmap", height=600, width=1000) |
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st.plotly_chart(fig) |
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if __name__ == "__main__": |
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main() |
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