project.py

import streamlit as st
import pandas as pd
import numpy as np
# import matplotlib.pyplot as plt
# import seaborn as sns
import pydeck as pdk
import plotly.express as px

# from geopy.geocoders import Nominatim

st.set_page_config(layout="wide")

# filename = '/Users/ksheerajaraghavan/CMU/2nd Sem/Interactive Data Science/HW2/HW2_Data_Science/cleaned.csv'

url='https://drive.google.com/file/d/1d71oTtuquCXbSqeSLzhnUhBSbOzpBllP/view?usp=sharing'
path = 'https://drive.google.com/uc?export=download&id='+url.split('/')[-2]
df = pd.read_csv(path)

# convert states
states = {'Alabama': 'AL', 'Alaska': 'AK', 'Arizona': 'AZ', 'Arkansas': 'AR', 'California': 'CA',
          'Colorado': 'CO', 'Connecticut': 'CT', 'Delaware': 'DE', 'Florida': 'FL', 'Georgia': 'GA',
          'Hawaii': 'HI', 'Idaho': 'ID', 'Illinois': 'IL', 'Indiana': 'IN', 'Iowa': 'IA', 'Kansas': 'KS',
          'Kentucky': 'KY', 'Louisiana': 'LA', 'Maine': 'ME', 'Maryland': 'MD', 'Massachusetts': 'MA',
          'Michigan': 'MI', 'Minnesota': 'MN', 'Mississippi': 'MS', 'Missouri': 'MO', 'Montana': 'MT',
          'Nebraska': 'NE', 'Nevada': 'NV', 'New Hampshire': 'NH', 'New Jersey': 'NJ', 'New Mexico': 'NM',
          'New York': 'NY', 'North Carolina': 'NC', 'North Dakota': 'ND', 'Ohio': 'OH', 'Oklahoma': 'OK',
          'Oregon': 'OR', 'Pennsylvania': 'PA', 'Rhode Island': 'RI', 'South Carolina': 'SC',
          'South Dakota': 'SD', 'Tennessee': 'TN', 'Texas': 'TX', 'Utah': 'UT', 'Vermont': 'VT',
          'Virginia': 'VA', 'Washington': 'WA', 'West Virginia': 'WV', 'Wisconsin': 'WI', 'Wyoming': 'WY'}


# geolocator = Nominatim(user_agent='application')

@st.cache
def load_data(f):
    df = pd.read_csv(f)
    return df

def year_line(data):
    df = data[data['State'] != 'Country Of Mexico']  # deleting Mexico
    df = df[df['State'] != 'District Of Columbia']  # deleting Columbia
    state_list = data.State.unique()
    stat = st.selectbox(label='Select a state', options=state_list)
    df = df[df['State'] == stat]
    # df['sr'] = df.State.apply(lambda x: states[x])
    # st.write(df.head())
    b = df.drop(['NO2 Mean', 'SO2 Mean', 'O3 Mean', 'CO Mean', 'NO2 1st Max Value', 'SO2 1st Max Value', 'O3 1st Max Value', 'CO 1st Max Value','NO2 1st Max Hour', 'SO2 1st Max Hour', 'O3 1st Max Hour', 'CO 1st Max Hour'], axis=1)
    # st.write(b.head())
    #b = b.set_index('Date Local')
    b['Date Local'] = pd.to_datetime(b['Date Local'])
    # b = b.set_index('Date Local')
    # st.write(b.head())
    a = b.resample('Y',on='Date Local').mean()
    a.reset_index(inplace=True)
    # st.write('A is ')
    # st.write(a)
    a['Year'] = a['Date Local'].dt.year
    # Sorting values by Date Local (for animated choropleth presented below)
    a.sort_values(by = 'Date Local', inplace = True)
    # st.write(a)
    a = a.drop(['Date Local'], axis=1)
    a = a.set_index('Year')
    # st.write(a)
    m = a.max()
    # m[0] = m[0].astype(int)
    # st.write(m)
    st.text('The max metrics are :')
    col1, col2 = st.columns(2)
    col1.metric('NO2 AQI', m['NO2 AQI'])
    col2.metric('O3 AQI', m['O3 AQI'])
    col3, col4 = st.columns(2)
    col3.metric('SO2 AQI', m['SO2 AQI'])
    col4.metric('CO AQI', m['CO AQI'])
    st.text('Over Time...........')
    st.line_chart(a)
    st.text('From what I see, the trends fluctuate with different states')



def maps_AQI(data):
    st.text('Change the pollutants and Play the video')
    df = data[data['State'] != 'Country Of Mexico']  # deleting Mexico
    df = df[df['State'] != 'District Of Columbia']  # deleting Columbia
    df['sr'] = df.State.apply(lambda x: states[x])
    # st.write(df.head())
    b = df.drop(['NO2 Mean', 'SO2 Mean', 'O3 Mean', 'CO Mean', 'NO2 1st Max Value', 'SO2 1st Max Value', 'O3 1st Max Value', 'CO 1st Max Value','NO2 1st Max Hour', 'SO2 1st Max Hour', 'O3 1st Max Hour', 'CO 1st Max Hour'], axis=1)
    # st.write(b.head())
    #b = b.set_index('Date Local')
    b['Date Local'] = pd.to_datetime(b['Date Local'])
    pollutant_list = ['NO2 AQI', 'SO2 AQI', 'O3 AQI', 'CO AQI']
    pollutant1 = st.selectbox('What pollutant do you want to see', pollutant_list)
    # b = b.set_index('Date Local')
    a = b.groupby('sr').resample('Y',on='Date Local').mean()
    a.reset_index(inplace=True)
    # st.write('A is ')
    # st.write(a)
    a['Year'] = a['Date Local'].dt.year
    # Sorting values by Date Local (for animated choropleth presented below)
    a.sort_values(by = 'Date Local', inplace = True)
    # st.write(a)
    # Plotly choropleth showing AQI for Nitrogen Dioxide changes from 2000 to 2016
    fig_NO2 = px.choropleth(a,
                  locations = 'sr',
                  animation_frame="Year", # showing changes through the years
                  color=pollutant1,
                  # Creating fixed scale (the same as defined by EPA)
                  color_continuous_scale = [(0.00, "green"),   (0.1, "green"),
                                            (0.1, "yellow"), (0.2, "yellow"),
                                            (0.2, "orange"),  (0.3, "orange"),
                                            (0.3, "red"),  (0.6, "red"),
                                            (0.6, "maroon"),  (1.00, "maroon"),
                                            ],
                  range_color = (0, 500),
                  locationmode='USA-states',
                  scope="usa",
                  title='Mean values of Air Quality Index (AQI) per year for ' + pollutant1,
                  height=600,
                 )

    # Modifying legend 
    fig_NO2.update_layout(coloraxis_colorbar=dict(
        title="Air Quality Index (AQI)",
        ticks="outside", 
        dtick=50
    ))
    st.plotly_chart(fig_NO2)
    # resample('Y').mean(4)


def onestate(data):
    st.subheader('Let us look at everything for one state')
    # st.subheader('Choose all the pollutants for one state')
    state_list = data.State.unique()
    values = st.selectbox(label='Choose one state', options=state_list)
    pollutant_list = ['NO2 Mean', 'SO2 Mean', 'O3 Mean', 'CO Mean',  'NO2 1st Max Value', 'SO2 1st Max Value', 'O3 1st Max Value', 'CO 1st Max Value','NO2 1st Max Hour', 'SO2 1st Max Hour', 'O3 1st Max Hour', 'CO 1st Max Hour']
    pollutant1 = st.multiselect('What pollutant(s) do you want to see ? (You can choose multiple)', pollutant_list,['NO2 Mean'])
    # st.write(data.head(10000))
    st.text('Resampling the data based on year and month can change the graph... ')
    st.text('May be we can find patterns')
    time_list = ['Year', 'Month']
    time1 = st.selectbox('Choose Resampling time: ', time_list)
    b = data[data['State'] == values]
    pollutant1 = pollutant1 + ['Date Local', 'State']
    b = b.filter(pollutant1)
    b = b.set_index('Date Local')
    b.index = pd.to_datetime(b.index)
    b = b.resample(time1[0]).mean()
    st.line_chart(b)


def statewise(data):
    st.subheader('One Pollutant for One state')
    state_list = data.State.unique()
    state = st.selectbox('Choose state: ', state_list)
    st.write('You have selected: ' + state)
    pollutant_list = ['NO2 Mean', 'SO2 Mean', 'O3 Mean', 'CO Mean', 'NO2 AQI', 'SO2 AQI', 'O3 AQI', 'CO AQI']
    pollutant = st.selectbox('Choose pollutant: ', pollutant_list)
    st.write('You have selected: ' + pollutant)
    time_list = ['Month', 'Year']
    time2 = st.selectbox('Choose time: ', time_list)
    st.write('You have selected: ' + time2)
    column = data.columns
    # filter for histogram
    hist_data = data[data['State'] == state]
    a = hist_data.copy()
    a = a.set_index('Date Local')

    a.index = pd.to_datetime(a.index)
    b = a.resample(time2[0]).mean()
    b = b.filter([pollutant])
    st.subheader('Graph of ' + state + ' with pollutant ' + pollutant + ' for ' + time2 + 'ly view')
    st.line_chart(b)


def year(data):
    import time
    state_list = data.State.unique()
    st.text('Let us be more specific. Think of your home state...')
    values = st.selectbox(label='Select State', options=state_list)
    year_list = np.arange(2000, 2017)
    # st.write(year_list)
    year = st.selectbox('What year do you want to see?', year_list)
    # st.write(data.head(10000))

    b = data[data['State'] == values]
    # st.write(b)
    b['Date Local'] = pd.to_datetime(b['Date Local'])
    mask = b['Date Local'].dt.year == int(year)
    b = b[mask]
    b = b.drop(['NO2 1st Max Value', 'NO2 1st Max Hour', 'O3 1st Max Value', 'O3 1st Max Hour','SO2 1st Max Value', 'SO2 1st Max Hour', 'CO 1st Max Value', 'CO 1st Max Hour' ],axis=1)
    # st.write(b)
    # st.write(len(b.columns))
    po = ['NO2', 'O3', 'SO2', 'CO']
    j = 0
    for i in range(4, len(b.columns)-1 ,2):
        st.subheader(po[j])
        col1, col2 = st.columns(2)
        with col1:
            fig = px.scatter(b, x='Date Local', y=b.columns[i])
            st.plotly_chart(fig)
        with col2:
            fig = px.scatter(b, x='Date Local', y=b.columns[i+1])
            st.plotly_chart(fig)
        j += 1

    
    st.subheader('As we change the year... We can notice the magnitudes of the pollutants change... ')
    st.text('NO2 tends to decrease in most states')
    st.text('CO also tends to decrese')
    st.text('SO2 also tends to increse')


    # st.line_chart(b)


if __name__ == '__main__':
    data = load_data(path)
    st.title('Have we destroyed our planet Earth?')
    st.header('Let us look at US Air Quality.....')
    st.text('Let us look at the raw data...')
    st.write(data.head(100))
    st.text('Hmm... I think I need to be an Environmental Engineer to understand this...')

    st.subheader('Lets make it simpler.')
    st.text('We can look at trends over the years')
    year_line(data)
    # st.subheader('Select Check Box for State Wise Plots')
    # if st.checkbox('State Plots'):
    #     statewise(data)

  
    st.subheader('For a single state let us visualise the trends of the pollutants')
    onestate(data)
    # statewise(data)
    # st.title('Year')
    st.subheader('Can we be more specific? Let us look at one year for one state')
    year(data)
    st.header('Let us summarise ... ')
    st.subheader('US map overall')
    maps_AQI(data)
    st.subheader('Observations')
    st.text('Depending on the sate we notice that certain pollutants decrease like NO2 and CO')
    st.text('The states with large urban population, has higher amounts of pollutants in the atmosphere')
    st.text('Depending on the sate we notice that certain pollutants decrease like ozone increase')
    st.text('NO2 mean has a similar U-shaped graph every year for most states, showing annual trends.')
    st.text(' This is visualised by looking at the different scatter plots for different states.')