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| from turtle import color | |
| from pyparsing import col | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| import base64 | |
| # Function to load data | |
| def load_data(uploaded_file): | |
| try: | |
| if uploaded_file.name.endswith('.csv'): | |
| df = pd.read_csv(uploaded_file) | |
| elif uploaded_file.name.endswith('.xlsx'): | |
| df = pd.read_excel(uploaded_file) | |
| else: | |
| st.error("Unsupported file type.") | |
| return None | |
| return df | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| return None | |
| # Function to show dataframe | |
| def show_dataframe(df): | |
| st.write(df) | |
| # Function to create surface data | |
| def create_surface_data(df): | |
| # This function would need to process 'df' to produce 'x', 'y', and 'z' for the surface plot | |
| # Here we just create a simple example with numpy | |
| x = np.outer(np.linspace(-10, 10, 30), np.ones(30)) | |
| y = x.copy().T # transpose | |
| z = np.cos(x ** 2 + y ** 2) | |
| return x, y, z | |
| def get_image_as_base64(path): | |
| with open(path, "rb") as f: | |
| return base64.b64encode(f.read()).decode() | |
| # Main function where the app runs | |
| def main(): | |
| st.set_page_config(page_title="Data Analysis Application", page_icon="📊", layout="wide") | |
| st.title("Data Analysis Application") | |
| logo_base64 = get_image_as_base64("C:/Users/gmi/OneDrive - Hewlett Packard Enterprise/Pictures/HPE LOGO SUITE/hpe_logos/hpe logos PNG/hpe logos PNG/primary logo small png_files/hpesm_pri_grn_rev_rgb.png") | |
| st.sidebar.markdown( | |
| f'<img src="data:image/png;base64,{logo_base64}" alt="logo" width="200"><br><br>', unsafe_allow_html=True | |
| ) | |
| # File uploader | |
| uploaded_file = st.sidebar.file_uploader("Upload your CSV or Excel file.", type=["csv", "xlsx"]) | |
| if uploaded_file is not None: | |
| df = load_data(uploaded_file) | |
| if df is not None: | |
| # Home Page Options | |
| st.sidebar.title("What would you like to do?") | |
| options = st.sidebar.radio("", ('EDA', 'Data Visualization'), label_visibility="collapsed") | |
| if options == 'EDA': | |
| # Display EDA options | |
| eda_option = st.sidebar.selectbox("Choose an EDA option:", | |
| ("Show dtypes", "Show columns", "Show summary", "Show missing values", | |
| "Show percentage of missing values", "Show number of unique values", | |
| "Show skewness and kurtosis", "Check for outliers"), label_visibility="collapsed") | |
| if eda_option == "Show dtypes": | |
| st.write(df.dtypes) | |
| elif eda_option == "Show columns": | |
| st.write(df.columns.tolist()) | |
| elif eda_option == "Show summary": | |
| st.write(df.describe()) | |
| elif eda_option == "Show missing values": | |
| st.write(df.isnull().sum()) | |
| elif eda_option == "Show percentage of missing values": | |
| st.write(df.isnull().mean() * 100) | |
| elif eda_option == "Show number of unique values": | |
| st.write(df.nunique()) | |
| elif eda_option == "Show skewness and kurtosis": | |
| try: | |
| st.write("Skewness:") | |
| st.write(df.skew()) | |
| st.write("Kurtosis:") | |
| st.write(df.kurtosis()) | |
| except Exception as e: | |
| st.error(f"An error occurred when calculating skewness and kurtosis: {e}") | |
| elif eda_option == "Check for outliers": | |
| # Select numeric columns, specify the data types explicitly | |
| numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns.tolist() | |
| selected_column = st.sidebar.selectbox("Select Column", numeric_cols, label_visibility="visible") | |
| if st.button("Show Outliers for Selected Column"): | |
| fig = px.box(df, y=selected_column) | |
| st.plotly_chart(fig) | |
| # Calculate Z-score and display outliers | |
| z_scores = (df[selected_column] - df[selected_column].mean()) / df[selected_column].std() | |
| st.write(df[abs(z_scores) > 3]) | |
| elif options == 'Data Visualization': | |
| # Display Data Visualization options | |
| vis_option = st.sidebar.selectbox("Choose a plot type:", | |
| ("Univariate Plots", "Bivariate Plots", "Multivariate Plots"), label_visibility="visible") | |
| selected_color = st.sidebar.color_picker("Pick a color", "#01A982") | |
| # Universal plot settings | |
| if vis_option == "Univariate Plots": | |
| column_to_plot = st.sidebar.selectbox("Choose a column to plot:", df.columns, label_visibility="visible") | |
| plot_type = st.sidebar.selectbox("Choose plot type:", ("Bar", "Box", "Box Plot (enhanced)", "Histogram", | |
| "Pie Chart", "Violin Plot", "Density Plot (KDE)", | |
| "Area Chart", "Rug Plot", "Cumulative Distribution Function", | |
| "Funnel Chart"), label_visibility="visible") | |
| hue_column = None | |
| if plot_type in ["Bar", "Box", "Box Plot (enhanced)", "Violin Plot", "Histogram", "Density Plot (KDE)", "Rug Plot", "Cumulative Distribution Function"]: | |
| hue_options = [None] + list(df.select_dtypes(include=['object']).columns) | |
| hue_column = st.sidebar.selectbox("Choose a categorical column for color coding hue:", hue_options, format_func=lambda x:'None' if x is None else x, label_visibility="visible") | |
| if plot_type == "Bar": | |
| fig = px.bar(df, y=column_to_plot, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Box": | |
| fig = px.box(df, y=column_to_plot, color=hue_column, color_discrete_sequence=[selected_color]) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Box Plot (enhanced)": | |
| fig = px.box(df, y=column_to_plot, points="all", color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Histogram": | |
| fig = px.histogram(df, x=column_to_plot, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Pie Chart": | |
| fig = px.pie(df, names=column_to_plot, color_discrete_sequence=[selected_color]) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Violin Plot": | |
| fig = px.violin(df, y=column_to_plot, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Density Plot (KDE)": # KDE = Kernel Density Estimation | |
| fig = px.density_contour(df, x=column_to_plot, marginal_x="histogram", color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Area Chart": | |
| fig = px.area(df, y=column_to_plot, color_discrete_sequence=[selected_color]) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Rug Plot": | |
| fig = px.density_contour(df, x=column_to_plot, marginal_x="rug", color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Cumulative Distribution Function": | |
| fig = px.histogram(df, x=column_to_plot, cumulative=True, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif plot_type == "Funnel Chart": | |
| fig = px.funnel(df, y=column_to_plot, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif vis_option == "Bivariate Plots": | |
| # Select plot type | |
| bivariate_plot_type = st.sidebar.selectbox( | |
| "Choose bivariate plot type:", | |
| ("Scatter Plot", "Line Plot", "Bubble Chart", "Area Chart", | |
| "Joint Plot", "Stacked Bar Chart", "Grouped Bar Chart", | |
| "Contour Plot", "Box Plot", "Error Bars Plot", "Violin Plot", | |
| ), | |
| label_visibility="visible" | |
| ) | |
| # User selects two columns for Bivariate plots | |
| col1 = st.sidebar.selectbox("Choose the first column:", df.columns, label_visibility="visible") | |
| col2 = st.sidebar.selectbox("Choose the second column:", df.columns, label_visibility="visible") | |
| hue_column = None | |
| if bivariate_plot_type in ["Scatter Plot", "Line Plot", "Bubble Chart", "Area Chart", "Joint Plot", "Stacked Bar Chart", "Grouped Bar Chart", "Contour Plot", "Box Plot", "Error Bars Plot", "Violin Plot"]: | |
| hue_options = [None] + list(df.select_dtypes(include=['object']).columns) | |
| hue_column = st.sidebar.selectbox( | |
| "Choose a categorical column for color coding (hue):", | |
| hue_options, | |
| format_func=lambda x:'None' if x is None else x, | |
| label_visibility="visible" | |
| ) | |
| if bivariate_plot_type == "Scatter Plot": | |
| fig = px.scatter(df, x=col1, y=col2, color=hue_column, trendline="ols", trendline_color_override="yellow",color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Line Plot": | |
| fig = px.line(df, x=col1, y=col2, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Bubble Chart": | |
| size_column = st.sidebar.selectbox("Choose a column for bubble size:", df.columns, label_visibility="visible") | |
| fig = px.scatter(df, x=col1, y=col2, size=size_column, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Area Chart": | |
| fig = px.area(df, x=col1, y=col2, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Joint Plot": | |
| fig = px.scatter(df, x=col1, y=col2, color=hue_column, marginal_x="histogram", marginal_y="histogram", trendline="ols", trendline_color_override="yellow",color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Stacked Bar Chart": | |
| fig = px.bar(df, x=col1, y=col2, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Grouped Bar Chart": | |
| fig = px.bar(df, x=col1, y=col2, color=hue_column, barmode="group", color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Contour Plot": | |
| fig = px.density_contour(df, x=col1, y=col2, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Box Plot": | |
| fig = px.box(df, x=col1, y=col2, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Error Bars Plot": | |
| fig = px.scatter(df, x=col1, y=col2, color=hue_column, error_x=col1, error_y=col2, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif bivariate_plot_type == "Violin Plot": | |
| fig = px.violin(df, x=col1, y=col2, color=hue_column, color_discrete_sequence=[selected_color] if hue_column is None else None) | |
| st.plotly_chart(fig) | |
| elif vis_option == "Multivariate Plots": | |
| # Ensure `selected_columns` is a list, not a Pandas Index or Series. | |
| selected_columns = st.sidebar.multiselect("Choose columns for multivariate plot:", | |
| options=df.columns.tolist(), | |
| default=df.columns[:3].tolist(), | |
| label_visibility="visible") | |
| # Check if the selection is not empty. | |
| if selected_columns: # This checks if the list is not empty | |
| # Now we can proceed to select the plot type. | |
| multivariate_plot_type = st.sidebar.selectbox( | |
| "Choose multivariate plot type:", | |
| ("3D Scatter Plot", "Parallel Coordinates", "Ternary Plot", "3D Surface Plot"), | |
| label_visibility="visible" | |
| ) | |
| if multivariate_plot_type == "3D Scatter Plot": | |
| # Ensure that three distinct columns have been chosen. | |
| if len(selected_columns) >= 3: | |
| col1, col2, col3 = selected_columns[:3] # Take the first three selections | |
| fig = px.scatter_3d(df, x=col1, y=col2, z=col3, color=col1) | |
| st.plotly_chart(fig) | |
| else: | |
| st.error("Please select at least three columns for the 3D Scatter Plot.") | |
| elif multivariate_plot_type == "Parallel Coordinates": | |
| fig = px.parallel_coordinates(df, color=selected_columns[0]) | |
| st.plotly_chart(fig) | |
| elif multivariate_plot_type == "Ternary Plot": | |
| # Ensure that three distinct columns have been chosen. | |
| if len(selected_columns) >= 3: | |
| col1, col2, col3 = selected_columns[:3] | |
| fig = px.scatter_ternary(df, a=col1, b=col2, c=col3, color=col1) | |
| st.plotly_chart(fig) | |
| else: | |
| st.error("Please select at least three columns for the Ternary Plot.") | |
| elif multivariate_plot_type == "3D Surface Plot": | |
| # Ensure that three distinct columns have been chosen. | |
| if len(selected_columns) >= 3: | |
| col1, col2, col3 = selected_columns[:3] | |
| x, y, z = create_surface_data(df) | |
| fig = go.Figure(data=[go.Surface(x=x, y=y, z=z)]) | |
| st.plotly_chart(fig) | |
| else: | |
| st.error("Please select at least three columns for the 3D Surface Plot.") | |
| else: | |
| st.warning("Please select at least one column to create a plot.") | |
| if __name__ == "__main__": | |
| main() |