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| import pandas as pd | |
| import numpy as np | |
| import plotly.express as px | |
| import streamlit as st | |
| st.set_page_config( | |
| page_title='Coal Consumption data Analysis', | |
| page_icon='📈', | |
| layout='wide' | |
| ) | |
| Years=['1965','1966','1967','1968','1969','1970','1971','1972','1973','1974','1975','1976','1977','1978','1979', | |
| '1980','1981','1982','1983','1984','1985','1986','1987','1988','1989','1990','1991','1992','1993','1994','1995', | |
| '1996','1997','1998','1999','2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010','2011', | |
| '2012','2013','2014','2015','2016'] | |
| def load_data(): | |
| df=pd.read_csv('data/coal_consumption_per_cap.csv') | |
| df.rename({'geo':'Country'},axis=1,inplace=True) | |
| df.set_index('Country',inplace=True) | |
| df.sort_values('Country',inplace=True) | |
| df['Total']=df[Years].sum(axis=1) | |
| df['Average']=df.mean(axis=1) | |
| df['Minimum']=df.min(axis=1) | |
| df['Maximum']=df.max(axis=1) | |
| return df | |
| st.title('Coal Consumption Per Capital') | |
| df=load_data() | |
| st.dataframe(df,use_container_width=True) | |
| countries=df.index.unique().tolist() | |
| Graphs = ['bar','pie','line','area','funnel'] | |
| c1,c2=st.columns(2) | |
| country = c1.selectbox("Select a Country",countries) | |
| Graph = c2.selectbox("Select a Graph type",Graphs) | |
| st.header("Country wise visualization") | |
| cdf = df.loc[country,Years].reset_index() | |
| cdf.rename({'index':'Years'},axis=1, inplace=True) | |
| if Graph == Graphs[0]: | |
| fig = px.bar(cdf, 'Years',country, title=f'{country} coal consumption per cap') | |
| if Graph == Graphs[1]: | |
| fig = px.pie(cdf, 'Years',country, title=f'{country} coal consumption per cap') | |
| if Graph == Graphs[2]: | |
| fig = px.line(cdf, 'Years',country, title=f'{country} coal consumption per cap') | |
| if Graph == Graphs[3]: | |
| fig = px.area(cdf, 'Years',country, title=f'{country} coal consumption per cap') | |
| if Graph == Graphs[4]: | |
| fig = px.funnel(cdf, 'Years',country, title=f'{country} co2 emissions tonnes by per person') | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.header("Comparison of Countries") | |
| clist = st.multiselect("Select countries to compare", countries, default='India') | |
| cdf = df.loc[clist, Years].T # T to rotate the data in 90deg | |
| st.write(cdf) | |
| figc = px.line(cdf,cdf.index, clist, title=f'Comparing {", ".join(clist)}') | |
| st.plotly_chart(figc, use_container_width=True) | |
| df.sort_values(by='Total', ascending=False, inplace=True) | |
| fig1=px.bar(df, x=df.index, y='Total',title='Total coal consumption per cap by Country') | |
| st.plotly_chart(fig1, use_container_width=True) | |
| dfavg = df.sort_values(by='Average').reset_index() | |
| dfavg.rename({'index':'Country'},axis=1,inplace=True) | |
| fig2=px.bar(dfavg, 'Country', 'Average', title="Average coal consumption per cap by Country") | |
| st.plotly_chart(fig2, use_container_width=True) | |
| dfmin=df.sort_values(by='Minimum').reset_index() | |
| dfmin.rename({'index':'Country'},axis=1,inplace=True) | |
| fig3=px.bar(dfmin,'Country','Minimum',title='Minimum coal consumption by Country' ) | |
| st.plotly_chart(fig3, use_container_width=True) | |
| dfmax=df.sort_values(by='Maximum').reset_index() | |
| dfmax.rename({'index':'Country'},axis=1,inplace=True) | |
| fig4=px.bar(dfmax,'Country','Maximum',title='Maximum coal consumption by Country' ) | |
| st.plotly_chart(fig4, use_container_width=True) | |
| dfcomp=df.sort_values(by='Country',ascending=False,inplace=True) | |
| fig5 = px.line(df, x=df.index, y='Maximum',title='Maximum and Minimum coal consumption comparisons') | |
| fig5.add_scatter(x=df.index, y=df['Minimum'], mode='lines',) | |
| st.plotly_chart(fig5, use_container_width=True) |