[PYTHON] Factors related to Human well-being by plotly express

Source:This notebook is based on Carlos sensei's notebook (https://deepnote.com/@carlos-mendez/PYTHON-Plotly-Express-3e900d54-8143-405b-8be2-266157d4ec75).

Setup

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px import plotly.graph_objects as go #chart_studio.tools.set_credentials_file(username='econdata777', api_key='ADDhere') import statsmodels.api as sm import statsmodels.formula.api as smf import warnings #warnings.filterwarnings('ignore')

Import data

Import data from "https://github.com/quarcs-lab/mendez2020-convergence-clubs-code-data"

df1 = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master/assets/dat.csv")

df1

dataset definitions

df1_def = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master//assets/dat-definitions.csv") df1_def

select

df_well = df1[['country','region', 'year', 'Y', 'pop', 'L', 'lp', 'GDPpc', 's', 'h','hi1990']] df_well

descriptive statistics

df_well.describe().round(2)

query

Let's query the data for the year 2014.

df_2014 = df_well[['country', 'region', 'hi1990', 'year', 'GDPpc', 'L','s']].query("year == 2014") df_2014

Let's query the data for the country "Singapore"

df_singapore = df_well[['country', 'region', 'hi1990', 'year', 'Y','GDPpc', 'L', 'lp','s']].query("country == 'Singapore'") df_singapore

isin

df_well[df_well.country.isin(['China', 'Japan' 'Singapore'])]

Visualize data

Human well being is not only about economic development, but also related to many factors like the degree of education and people' longevity.

Strip plot

Try to create an animated figure to see the development of GDP pc by region

px.strip(df_well, x = 'GDPpc', y = 'region', hover_name= 'country', hover_data= ['pop', 'Y'], color= 'region', animation_frame= 'year')

Line plots

Plot the development of GDP pc of countries from different region

px.line(df_well, x='year', y='GDPpc', color='country', facet_col= 'region', facet_col_wrap= 3, height= 500)

Then we focus on the evolution of GDP per capital in China

px.line(df_well.query("country=='China'"), x="year", y="GDPpc")

Compare the evolution of GDP pc of China with some Asian countries.

px.line( df_well.query("country==['China', 'Japan','Singapore','South Korea']"), x="year", y="GDPpc", color="country" )

Now let's study the distribution of year of schooling(level of education) from 1990 to 2014

Box plot

px.box( df_well, x="s", color="hi1990", hover_name= 'country', animation_frame = 'year' )

Violin plot

px.violin( df1, y="hi1990", x="s", range_x= [0, 16], color="hi1990", box=True, hover_name= 'country', animation_frame = 'year', points="all")

Scatter plots

Regression

Does the change of GDP pc affect the year of schooling?

What is the relation between GDP pc and year of schooling?

px.scatter( df_well.query("year == 1990"), x="GDPpc", y="s", color="hi1990", hover_name="country", hover_data= ['region'], trendline="ols", trendline_scope="overall" )

Regression by groups

px.scatter( df_well, x="GDPpc", y="s", color="hi1990", hover_name="country", animation_frame = 'year', trendline="ols")

Multivariate

Is there a definite link between population and human well being(evaluated by economy, education and life expectancy)?

px.scatter( df1.query("year == 2014"), x="GDPpc", y="s", color="region", size="pop", size_max=60, hover_name="country", labels=dict(GDPpc="GDP per capital", s="year of schooling", region="Continent", pop= "Population") )

Animated multivariate plot

px.scatter(df1, animation_frame="year", x="GDPpc", y="s", range_x= [313.98,81879.52], range_y= [0.89,15], color="region", size="pop", size_max=60, hover_name="country", hover_data = ['GDPpc', 's'], labels=dict(GDPpc="GDP per capital", s="year of schooling", region="Continent", pop= "Population") )

3D

px.scatter_3d( df1.query("year == 1990"), x="GDPpc", y="s", z= "pop", color="region", hover_name="country" )

Animated map

px.choropleth( df1, locations="isocode", color="s", hover_name="country", animation_frame="year", color_continuous_scale=px.colors.sequential.Plasma, projection="natural earth")

.css-15w88e5{color:var(--chakra-colors-fg-neutral-primary);font-weight:inherit;letter-spacing:-0.09px;}[PYTHON] Factors related to Human well-being by plotly express