pages/Income per Person GDP 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='Income per Person GDP data Analysis',
    page_icon='📈',
    layout='wide'
)

Years=['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','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','2015','2016','2017','2018']

@st.cache_data 
def load_data():
    df=pd.read_csv('data/income_per_person_gdppercapita_ppp_inflation_adjusted.csv')
    df.rename(columns={'geo':'Country'},inplace=True)
    df.set_index('Country',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('Income per Person GDP per Capital ppp Inflation Adjusted')
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} Income per Person GDP per Capital ppp Inflation Adjusted')
if Graph == Graphs[1]:
    fig = px.pie(cdf, 'Years',country, title=f'{country} Income per Person GDP per Capital ppp Inflation Adjusted')
if Graph == Graphs[2]:
    fig = px.line(cdf, 'Years',country, title=f'{country} Income per Person GDP per Capital ppp Inflation Adjusted')
if Graph == Graphs[3]:
    fig = px.area(cdf, 'Years',country, title=f'{country} Income per Person GDP per Capital ppp Inflation Adjusted')
if Graph == Graphs[4]:
    fig = px.funnel(cdf, 'Years',country, title=f'{country} Income per Person GDP per Capital ppp Inflation Adjusted')
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 Income per Person GDP per Capital ppp Inflation Adjusted')
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 Income per Person GDP per Capital ppp Inflation Adjusted 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 Income per Person GDP per Capital ppp Inflation Adjusted 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 Income per Person GDP per Capital ppp Inflation Adjusted 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 Income per Person GDP per Capital ppp Inflation Adjusted comparisons')
fig5.add_scatter(x=df.index, y=df['Minimum'], mode='lines',)
st.plotly_chart(fig5, use_container_width=True)