# Load computational modules 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 !pip install statsmodels !pip install --upgrade pip import statsmodels.api as sm import statsmodels.formula.api as smf

# Alternative way to load the data df = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master/assets/dat.csv") df

px.bar(df, x = 'region', y ='GDPpc', animation_frame = 'year', color = 'region', hover_name='country', title = 'GDP PerCapita, Regional distribution', labels=dict(region = "Region"))

px.choropleth(df, color='GDPpc', locations='isocode', animation_frame = 'year', hover_name='country', projection="natural earth", labels=dict(log_GDPpc_raw = 'GDP PerCapita'))

px.treemap(df1, color = 'GDPpc', values = 'pop', path = ["region", "country"], hover_name = 'country', height = 500, labels=dict(GDPpc = 'GDP Percap'))

df1 = df.query("region == 'Americas'") df2 = df1.drop(labels=range(375, 400), axis=0) df3 = df2.drop(labels=range(2550, 2575), axis=0) df3

df3.describe()

px.line(df1.query("country != ['United States', 'Canada']"), y = "GDPpc", x = "year", color = "country", labels =dict(country = "Country"), hover_name = "country", title = "GDP Percapita Latin America")

df4.describe()

px.line(df1.query("country == ['United States', 'Canada']"), x = "year", y = "GDPpc", color = "country", labels = dict(country = "Country"), title = "GDP Percapita North America")

df1.groupby('hi1990').std()

px.line(df1, x = "year", y = "GDPpc", color = "hi1990", hover_name="country", labels = dict(hi1990 = "Northern countries"), title = "GDP Percapita Latin America vs North America")

px.scatter(df1, x="s",y="GDPpc", color="hi1990", hover_name="country", trendline="ols", labels =dict(s = "Years of Schooling", hi1990 = "Northern countries"), title = "Regression Education-GDPpc, Latin America vs North America")

px.histogram(df1, x = "s", y = "country", color = "hi1990", labels = dict(hi1990 = "Northern countries", s = "Years of schooling", country = "Countries"), title = "Years of Schooling, Latin America vs North America")

px.histogram(df1, x = "h", y = "country", color = "hi1990", labels = dict(hi1990 = "Northern countries", h = "Human Capital", country = "Countries"), title = "Human capital, Latin America vs North America")

px.scatter(df1.query("year == 2014"), x="log_GDPpc", color="hi1990", hover_name="country", trendline="ols", labels =dict(log_GDPpc = "Trend Log GDPpc", hi1990 = "Northern countries"), title = "Trend of Log GDPpc, Latin America vs North America")

px.choropleth(df1, color='lp', locations='isocode', animation_frame = 'year', hover_name='country', projection="natural earth", labels=dict(log_GDPpc_raw = 'GDP PerCapita'))