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Python Data Driven web application using Streamlit
# --- BEGIN
#1. Install Streamlit from command prompt
# pip install streamlit
# go to https://www.streamlit.io for more information and API documentation
#2. importing libraries
import streamlit as st
import pandas as pd
import numpy as np
import itertools as IT
import altair as alt
from PIL import Image
#3. Title of the web application
st.title("Interactive Data Driven Dashboard")
#4. Load Data function
@st.cache
def load_data(nrows):
data = pd.read_csv('C:/Users/Bia Ch/Desktop/amazon-final.csv')
data['date first available'] = pd.to_datetime(data['date first available'])
data = data.sort_values(by=['date first available'])
data = data.set_index(data['date first available'])
data['discount percentage'] = (data['discount percentage'].str.replace("%",'')). astype(int)
lowercase = lambda x: str(x).lower()
data.rename(lowercase, axis='columns', inplace=True)
return data
# Loading data text...
data_load_state = st.text('Loading data...')
# Load 1000 rows of data
data = load_data(1000)
# Successfull data loaded
data_load_state.text("Done! (using st.cache)")
#5. show whole dataset
if st.checkbox('Show raw data'):
st.subheader('Raw data')
st.write(data)
#6. show seller and products based on the select filter
sellers = data[['seller name', 'product name']]
options = st.multiselect("Select seller name to show corresponding sold products: ",sellers['seller name'].unique())
st.write(options)
show = sellers['seller name'].isin(options)
data_seller = sellers[show]
st.write(data_seller)
#7. Show mrp, discount % and sale price based on the selected year filter in the bar graph
discounts = data[['mrp','discount percentage', 'sale price']]
year_to_filter = st.slider('date', 2014, 2020, 2017) # min: 2015, max: 2020, default: 2017
filtered_data = discounts[data['date first available'].dt.year == year_to_filter]
st.bar_chart(filtered_data)
# END ----
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