pages/Yearly Co2 Emissions data Analysis.py

import pandas as pd
import numpy as np
import plotly.express as px
import streamlit as st


st.set_page_config(
    page_title='Yearly Co2 Emissions data Analysis',
    page_icon='📈',
    layout='wide'
)

Years=['1895','1896','1897','1898','1899','1900','1901','1902','1903','1904','1905','1906','1907','1908','1909',
'1910','1911','1912','1913','1914','1915','1916','1917','1918','1919','1920','1921','1922','1923','1924',
'1925','1926','1927','1928','1929','1930','1931','1932','1933','1934','1935','1936','1937','1938','1939',
'1940','1941','1942','1943','1944','1945','1946','1947','1948','1949','1950','1951','1952','1953','1954',
'1955','1956','1957','1958','1959','1960','1961','1962','1963','1964','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']

@st.cache_data 
def load_data():
    df=pd.read_csv('data/yearly_co2_emissions_1000_tonnes.csv')
    df.rename(columns={'geo':'Country'},inplace=True)
    df.set_index('Country',inplace=True)
    df.drop(['1751','1752','1753','1754','1755','1756','1757','1758','1759','1760','1761','1762','1763','1764',
    '1765','1766','1767','1768','1769','1770','1771','1772','1773','1774','1775','1776','1777','1778','1779',
    '1780','1781','1782','1783','1784','1785','1786','1787','1788','1789','1790','1791','1792','1793','1794',
    '1795','1796','1797','1798','1799','1800','1801','1802', '1803', '1804', '1805', '1806', '1807', '1808', 
    '1809', '1810', '1811', '1812', '1813','1814','1815', '1816', '1817', '1818', '1819', '1820', '1821',
    '1822', '1823', '1824', '1825', '1826', '1827', '1828','1829', '1830', '1831','1832', '1833', '1834','1835',
    '1836', '1837', '1838', '1839', '1840', '1841', '1842', '1843','1844', '1845', '1846', '1847', '1848', 
    '1849', '1850', '1851', '1852', '1853', '1854', '1855', '1856', '1857', '1858', '1859', '1860', '1861', '1862',
    '1863', '1864', '1865', '1866', '1867','1868', '1869', '1870','1871', '1872', '1873', '1874','1875', '1876', '1877', 
    '1878', '1879', '1880', '1881','1882','1883', '1884', '1885', '1886', '1887','1888', '1889','1890', '1891',
    '1892', '1893', '1894'],axis=1,inplace=True)
    df['Total'] = df[Years].sum(axis=1)
    df['Avgrage']=df.mean(axis=1)
    df['Maximum']=df.max(axis=1)
    df['Minimum']=df.min(axis=1)
    df.sort_index(inplace=True)
    return df

st.title('Yearly Co2 Emissions 1000 Tonnes')
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} Yearly Co2 Emissions 1000 Tonnes')
if Graph == Graphs[1]:
    fig = px.pie(cdf, 'Years',country, title=f'{country} Yearly Co2 Emissions 1000 Tonnes')
if Graph == Graphs[2]:
    fig = px.line(cdf, 'Years',country, title=f'{country} Yearly Co2 Emissions 1000 Tonnes')
if Graph == Graphs[3]:
    fig = px.area(cdf, 'Years',country, title=f'{country} Yearly Co2 Emissions 1000 Tonnes')
if Graph == Graphs[4]:
    fig = px.funnel(cdf, 'Years',country, title=f'{country} Yearly Co2 Emissions 1000 Tonnes')
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 Yearly Co2 Emissions 1000 Tonnes')
st.plotly_chart(fig1, use_container_width=True)

dfavg = df.sort_values(by='Avgrage').reset_index()
dfavg.rename({'index':'Country'},axis=1,inplace=True)
fig2=px.bar(dfavg, 'Country', 'Avgrage', title="Avgrage Yearly Co2 Emissions by Country")
st.plotly_chart(fig2, use_container_width=True)

dfmax=df.sort_values(by='Maximum').reset_index()
dfmax.rename({'index':'Country'},axis=1,inplace=True)
fig3=px.bar(dfmax,'Country','Maximum',title='Maximum Yearly Co2 Emissions by the Country')
st.plotly_chart(fig3, use_container_width=True)

dfmin=df.sort_values(by='Minimum').reset_index()
dfmin.rename({'index':'Country'},axis=1,inplace=True)
fig4=px.bar(dfmin,'Country','Minimum',title='Minimum Yearly Co2 Emissions by the 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 Yearly Co2 Emissions comparisons')
fig5.add_scatter(x=df.index, y=df['Minimum'], mode='lines',)
st.plotly_chart(fig5, use_container_width=True)