streamlit_dashboard.py

import streamlit as st
from PIL import Image
import altair as alt
#import plotly.express as px
import pandas as pd
from pandas.api.types import (
    is_categorical_dtype,
    is_datetime64_any_dtype,
    is_numeric_dtype,
    is_object_dtype,
)




def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame:
    """
    Adds a UI on top of a dataframe to let viewers filter columns

    Args:
        df (pd.DataFrame): Original dataframe

    Returns:
        pd.DataFrame: Filtered dataframe
    """
    modify = st.checkbox("Add filters")

    if not modify:
        return df

    df = df.copy()

    # Try to convert datetimes into a standard format (datetime, no timezone)
    for col in df.columns:
        if is_object_dtype(df[col]):
            try:
                df[col] = pd.to_datetime(df[col])
            except Exception:
                pass

        if is_datetime64_any_dtype(df[col]):
            df[col] = df[col].dt.tz_localize(None)

    modification_container = st.container()

    with modification_container:
        to_filter_columns = st.multiselect("Filter dataframe on", df.columns)
        for column in to_filter_columns:
            left, right = st.columns((1, 20))
            # Treat columns with < 10 unique values as categorical
            if is_categorical_dtype(df[column]) or df[column].nunique() < 10:
                user_cat_input = right.multiselect(
                    f"Values for {column}",
                    df[column].unique(),
                    default=list(df[column].unique()),
                )
                df = df[df[column].isin(user_cat_input)]
            elif is_numeric_dtype(df[column]):
                _min = float(df[column].min())
                _max = float(df[column].max())
                step = (_max - _min) / 100
                user_num_input = right.slider(
                    f"Values for {column}",
                    min_value=_min,
                    max_value=_max,
                    value=(_min, _max),
                    step=step,
                )
                df = df[df[column].between(*user_num_input)]
            elif is_datetime64_any_dtype(df[column]):
                user_date_input = right.date_input(
                    f"Values for {column}",
                    value=(
                        df[column].min(),
                        df[column].max(),
                    ),
                )
                if len(user_date_input) == 2:
                    user_date_input = tuple(map(pd.to_datetime, user_date_input))
                    start_date, end_date = user_date_input
                    df = df.loc[df[column].between(start_date, end_date)]
            else:
                user_text_input = right.text_input(
                    f"Substring or regex in {column}",
                )
                if user_text_input:
                    df = df[df[column].astype(str).str.contains(user_text_input, case=False)]

    return df


st.set_page_config(
    page_title="Product Data for Armstrong Ceilings",
    page_icon="🏂",
    layout="wide",
    initial_sidebar_state="expanded")

alt.themes.enable("dark")

df = pd.read_csv('ArmstrongProductList.csv')

description = """Product data scraped from [Armstrong Ceilings](https://www.armstrongceilings.com/) utilizing Selenium Webdriver + BeautifulSoup"""

image = Image.open('ceiling_design.png')
st.sidebar.write(description)
st.sidebar.image(image, use_column_width='auto')
st.sidebar.write('\n\n\n\n'+'[Github Repository](https://github.com/pkhiev/WebScraping-ArmstrongCeilingProducts)')


st.header("Armstrong Product Data")
st.dataframe(filter_dataframe(df))