import pandas as pd
import plotly.graph_objects as go
#################################################################
#CASES OVERALL
df = pd.read_csv('cases-overall.csv')
fig1 = go.Figure(go.Scatter(x = df['districtid'], y = df['cases'],
                  name='overall cases'))
fig1.update_layout(title='OVERALL CASES',
                   plot_bgcolor='rgb(230, 230,230)',
                   showlegend=True)
fig1.show()
##################################################################

#NEIGHBOR MEAN
df = pd.read_csv('neighbor-overall.csv')
fig2 = go.Figure(go.Scatter(x = df['districtid'], y = df['neighbormean'],
                  name='NEIGHBOR MEAN'))
fig2.update_layout(title='NEIGHBOR OVERALL OBSERVATION',
                   plot_bgcolor='rgb(230, 230,230)',
                   showlegend=True)
fig2.show()


##################################################################

#STATE MEAN
df = pd.read_csv('state-overall.csv')
fig3 = go.Figure(go.Scatter(x = df['districtid'], y = df['statemean'],
                  name='STATE MEAN'))
fig3.update_layout(title='STATE OVERALL OBSERVATION',
                   plot_bgcolor='rgb(230, 230,230)',
                   showlegend=True)
fig3.show()

#################################################################

#NEIGHBORHOOD ZSCORE OVERALL
df = pd.read_csv('zscore-overall.csv')
fig4 = go.Figure(go.Scatter(x = df['districtid'], y = df['neighborhoodzscore'],
                  name='NEIGHBORHOOD ZSCORE'))
fig4.update_layout(title='NEIGHBORHOOD ZSCORE',
                   plot_bgcolor='rgb(230, 230,230)',
                   showlegend=True)
fig4.show()

###################################################################
#
##STATE ZSCORE OVERALL
df = pd.read_csv('zscore-overall.csv')
fig5 = go.Figure(go.Scatter(x = df['districtid'], y = df['statezscore'],
                  name='STATE ZSCORE'))
fig5.update_layout(title='STATE ZSCORE',
                   plot_bgcolor='rgb(230, 230,230)',
                   showlegend=True)
fig5.show()
###################################################################