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| import streamlit as st | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| from scipy.stats import zscore | |
| # Set up the Streamlit page | |
| st.title("Data Visualization") | |
| st.sidebar.header("Upload and Navigation") | |
| # File upload | |
| uploaded_file = st.sidebar.file_uploader("Upload your Excel file", type=["xlsx"]) | |
| if uploaded_file: | |
| # Load the dataset | |
| data = pd.read_excel(uploaded_file) | |
| st.sidebar.success("File uploaded successfully!") | |
| st.write("### Dataset Overview") | |
| st.dataframe(data.head()) | |
| # Navigation options | |
| options = st.sidebar.selectbox("Select Visualization", [ | |
| "Distribution of a Column", | |
| "Top-N Categories by Aggregated Value", | |
| "Heatmap of Numerical Data", | |
| "Outlier Detection", | |
| "Box Plot Comparison", | |
| "Time Series Analysis", | |
| "Stacked Bar Chart" | |
| ]) | |
| # Task 1: Distribution of a Column | |
| if options == "Distribution of a Column": | |
| st.header("Distribution of a Column") | |
| column = st.selectbox("Select Column for Distribution", data.columns) | |
| if data[column].dtype in ['int64', 'float64', 'object']: | |
| fig, ax = plt.subplots(figsize=(8, 6)) | |
| if data[column].dtype == 'object': | |
| sns.countplot(data=data, x=column, palette='coolwarm', ax=ax) | |
| else: | |
| sns.histplot(data=data, x=column, kde=True, color='blue', ax=ax) | |
| ax.set_title(f'Distribution of {column}') | |
| st.pyplot(fig) | |
| else: | |
| st.error("Selected column is not suitable for distribution visualization.") | |
| # Task 2: Top-N Categories by Aggregated Value | |
| elif options == "Top-N Categories by Aggregated Value": | |
| st.header("Top-N Categories by Aggregated Value") | |
| category_column = st.selectbox("Select Category Column", data.columns) | |
| numeric_column = st.selectbox("Select Numeric Column", data.columns) | |
| n = st.slider("Select Top-N Categories", 1, 20, 5) | |
| if data[category_column].dtype == 'object' and data[numeric_column].dtype in ['int64', 'float64']: | |
| grouped_data = data.groupby(category_column)[numeric_column].sum().reset_index() | |
| top_n = grouped_data.sort_values(by=numeric_column, ascending=False).head(n) | |
| fig, ax = plt.subplots(figsize=(10, 6)) | |
| sns.barplot(data=top_n, x=numeric_column, y=category_column, palette='viridis', ax=ax) | |
| ax.set_title(f'Top-{n} {category_column} by {numeric_column}') | |
| st.pyplot(fig) | |
| else: | |
| st.error("Ensure you select a categorical column and a numeric column.") | |
| # Task 3: Heatmap of Numerical Data | |
| elif options == "Heatmap of Numerical Data": | |
| st.header("Heatmap of Numerical Data") | |
| numeric_columns = data.select_dtypes(include=['int64', 'float64']).columns | |
| if len(numeric_columns) > 1: | |
| heatmap_data = data[numeric_columns].corr() | |
| fig, ax = plt.subplots(figsize=(10, 6)) | |
| sns.heatmap(heatmap_data, annot=True, cmap='YlGnBu', ax=ax) | |
| ax.set_title("Correlation Heatmap") | |
| st.pyplot(fig) | |
| else: | |
| st.error("Dataset does not have enough numerical columns for a heatmap.") | |
| # Task 4: Outlier Detection | |
| elif options == "Outlier Detection": | |
| st.header("Outlier Detection") | |
| numeric_column = st.selectbox("Select Numeric Column for Outlier Detection", data.select_dtypes(include=['int64', 'float64']).columns) | |
| threshold = st.slider("Select Z-Score Threshold", 1.0, 5.0, 3.0) | |
| data['Z_Score'] = zscore(data[numeric_column]) | |
| outliers = data[data['Z_Score'].abs() > threshold] | |
| fig, ax = plt.subplots(figsize=(10, 6)) | |
| sns.scatterplot(data=data, x=data.index, y=numeric_column, label='Data', color='blue', ax=ax) | |
| sns.scatterplot(data=outliers, x=outliers.index, y=numeric_column, label='Outliers', color='red', ax=ax) | |
| ax.set_title(f'Outlier Detection in {numeric_column}') | |
| ax.legend() | |
| st.pyplot(fig) | |
| # Task 5: Box Plot Comparison | |
| elif options == "Box Plot Comparison": | |
| st.header("Box Plot Comparison") | |
| category_column = st.selectbox("Select Categorical Column", data.columns) | |
| numeric_column = st.selectbox("Select Numeric Column", data.columns) | |
| if data[category_column].dtype == 'object' and data[numeric_column].dtype in ['int64', 'float64']: | |
| fig, ax = plt.subplots(figsize=(12, 8)) | |
| sns.boxplot(data=data, x=category_column, y=numeric_column, palette='Set2', ax=ax) | |
| ax.set_title(f'Box Plot of {numeric_column} by {category_column}') | |
| st.pyplot(fig) | |
| else: | |
| st.error("Ensure you select a categorical column and a numeric column.") | |
| # Task 6: Time Series Analysis | |
| elif options == "Time Series Analysis": | |
| st.header("Time Series Analysis") | |
| if 'Time' in data.columns: | |
| data['Time'] = pd.to_datetime(data['Time']) | |
| time_column = st.selectbox("Select Time Column", ['Time']) | |
| value_column = st.selectbox("Select Value Column", data.select_dtypes(include=['int64', 'float64']).columns) | |
| time_data = data.groupby(data[time_column].dt.to_period('M'))[value_column].sum().reset_index() | |
| time_data[time_column] = time_data[time_column].dt.to_timestamp() | |
| fig, ax = plt.subplots(figsize=(12, 8)) | |
| sns.lineplot(data=time_data, x=time_column, y=value_column, ax=ax, marker='o') | |
| ax.set_title(f'Time Series Analysis of {value_column}') | |
| st.pyplot(fig) | |
| else: | |
| st.error("The dataset does not have a time column.") | |
| # Task 7: Stacked Bar Chart | |
| elif options == "Stacked Bar Chart": | |
| st.header("Stacked Bar Chart") | |
| category_column = st.selectbox("Select Categorical Column", data.columns) | |
| numeric_columns = st.multiselect("Select Numeric Columns", data.select_dtypes(include=['int64', 'float64']).columns) | |
| if len(numeric_columns) > 1 and category_column: | |
| grouped_data = data.groupby(category_column)[numeric_columns].sum() | |
| fig, ax = plt.subplots(figsize=(12, 8)) | |
| grouped_data.plot(kind='bar', stacked=True, ax=ax, colormap='coolwarm') | |
| ax.set_title(f'Stacked Bar Chart of {", ".join(numeric_columns)} by {category_column}') | |
| st.pyplot(fig) | |
| else: | |
| st.error("Ensure you select one categorical column and multiple numeric columns.") | |
| else: | |
| st.warning("Please upload an Excel file to begin.") | |