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')

df1 = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master/assets/dat.csv") #df1 = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master/assets/dat.csv", parse_dates =['year'])

df1

df1.head()

df1.head(10)

df1.tail()

df1.tail(10)

df1.columns

df1.dtypes

df1['country'].unique()

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

df1.describe().round(2)

df1.s

df2 = df1[['country', 'year', 'GDPpc', 'h', 'ky', 'TFP']] df2

df_2014 = df1[['country', 'region', 'hi1990', 'year', 'GDPpc', 'h', 'ky', 'TFP']].query("year == 2014") df_2014

df_china = df1[['country', 'year','s', 'GDPpc', 'h', 'ky', 'TFP']].query("country == 'China'") df_china

df1[['country', 'year', 'h','s','Y','pop','GDPpc']].query("country==['China', 'South Korea','Japan','Singapore'] and year == 2014")

# Define the logical condictions is_Americas = df1['region'] == 'Americas' is_HighIncome = df1['hi1990'] == 'yes' is_2014 = df1['year'] == 2014 # Apply the logical conditions df1[is_Americas & is_HighIncome & is_2014]

is_Americas = df1['region'] == 'Americas' is_HighIncome = df1['hi1990'] == 'no' is_2014 = df1['year'] == 2014 df1[is_Americas & is_HighIncome & is_2014]

is_Europe = df1['region'] == 'Europe' is_HighIncome = df1['hi1990'] == 'no' is_2014 = df1['year'] == 2014 # Apply the logical conditions df1[is_Europe & is_HighIncome & is_2014]

df_2014.groupby('region').mean().round(2)

df_2014.groupby('region').std().round(2)

#df_2014.groupby('region')['h'].agg(['mean', 'std', 'min', 'max']).round(2) df_2014.groupby('region').h.agg(['mean', 'std', 'min', 'max']).round(2)

df1.groupby('region').s.agg(['mean', 'std', 'min', 'max']).round(2)

# Pivot panel data from long form to wide form df1_s_wide = df1.pivot_table( index = ['country', 'region', 'hi1990'], columns = 'year', values = 's').reset_index(drop=False) # Make sure the column names are strings df1_s_wide.columns = df1_s_wide.columns.astype(str)

df1_s_wide

df1_s_wide.describe().round(2)

df1_s_wide.std().round(2).plot();

df1_s_wide.loc[:,'1990':'2014']

df1_s_wide.loc[:,'1990':'2014'].std().round(2).plot();

df1[df1.country.isin(['China', 'Japan'])]

px.strip(df_2014, x = 'GDPpc', hover_name= 'country')

#fig1 = px.strip(df_2014, x = 'GDPpc', hover_name= 'country') #save2cs.plot(fig1, filename = 'figureName', auto_open=True)

px.strip(df1, x = 's', y = 'region', hover_name= 'country', hover_data= ['GDPpc', 'h'], color= 'region', animation_frame= 'year')

px.line(df1, x='year', y='GDPpc', color='country')

px.line(df1, x='year', y='GDPpc', color='country', facet_col= 'region', facet_col_wrap= 2, height= 800)

px.line(df1.query("country=='China'"), x="year", y="GDPpc")

fig1 = px.line(df1.query("country=='China'"), x="year", y="GDPpc") fig1.write_html("fig1.html")

px.line( df1.query("country==['China', 'Japan','South Korea','Singapore']"), x="year", y="GDPpc", color="country" )

px.line( df1.query("region=='Europe'"), x="year", y="GDPpc", color="country" )

px.histogram(df1, x = 's', hover_name= 'country', color= 'hi1990', marginal='rug')

px.histogram( df1, x="GDPpc", color="hi1990", hover_name= 'country', marginal='box', animation_frame = 'year' )

px.box( df1, x="GDPpc", color="hi1990", hover_name= 'country', animation_frame = 'year' )

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

px.ecdf( df1, x="s", range_x= [0, 16], color="hi1990", hover_name= 'country', animation_frame = 'year' )

px.treemap(df1.query("year == 2014"), color = "GDPpc", values = "pop", path = ["region", "country"], hover_name = "country")

px.sunburst(df1.query("year == 2014"), color = "GDPpc", values = "pop", path = ["region", "country"], hover_name = "country")

px.scatter( df1, x="lp", y="GDPpc", color="region", hover_name="country", animation_frame = 'year' )

px.scatter( df1.query("year == 2014"), x="lp", y="GDPpc", color="hi1990", hover_name="country", hover_data= ['region'], trendline="ols", trendline_scope="overall" )

px.scatter( df1, x="lp", y="GDPpc", color="hi1990", hover_name="country", animation_frame = 'year', trendline="ols")

px.scatter( df1, x="log_h", y="log_lp", range_x= [0.2, 1.62], range_y= [7, 12.2], color="hi1990", hover_name="country", animation_frame = 'year', trendline="lowess")

px.scatter( df1.query("year == 2014"), x="lp", y="GDPpc", hover_name="country", color="hi1990", trendline="ols", marginal_x="box", marginal_y="box")

px.scatter( df1.query("year == 2014"), x="lp", y="GDPpc", color="region", size="pop", size_max=60, hover_name="country", labels=dict(lp="Labor productivity in 2014", GDPpc="GDP per capita in 2014", region="Continent", pop= "Population") )

fig = px.scatter(df1.query("year == 2014"), y="lp", x="GDPpc", log_x = False, color = "region", size ="pop", size_max=60, hover_name = "country", height =500, width=800, template = "simple_white", color_discrete_sequence=px.colors.qualitative.G10, #title = "Year 2014", labels=dict(region = "Continent", pop = "Population", lp = "Labor productivity in 2014", GDPpc = "GDP per capita in 2014") ) fig.update_layout(font_family = "Rockwell", legend=dict(orientation = "h", title="", y=1.1, x=1, xanchor="right", yanchor="bottom")) fig.add_hline(df1.query("year == 2014")['lp'].mean(), line_width=1, line_dash="dot") fig.add_vline(df1.query("year == 2014")['GDPpc'].mean(), line_width=1, line_dash="dot") fig.show()

px.scatter(df1, animation_frame="year", x="log_h", y="log_lp", range_x= [0.2, 1.62], range_y= [7, 12.2], color="region", size="pop", size_max=60, hover_name="country", hover_data = ['log_ky', 'log_tfp'], labels=dict(log_h="Human capital index (in logs)", log_lp="Labor productivity (in logs)", region="Continent", pop= "Population") )

px.scatter( df1, x="s", y="h", color="region", size="pop", size_max=60, trendline="ols", hover_name="country", facet_col="hi1990", animation_frame="year" )

px.density_contour( df1, x="s", y="h", hover_name="country", marginal_x="histogram", marginal_y="histogram", animation_frame="year" )

px.density_contour( df1, x="s", y="h", marginal_x="box", marginal_y="box", animation_frame="year" )

px.density_contour( df1, x="s", y="h", hover_name="country", marginal_x="rug", marginal_y="rug", animation_frame="year" )

den = px.density_contour( df1.query("year == 2014"), x="s", y="h", ) den.update_traces(contours_coloring="fill", contours_showlabels = True) den.show()

den = px.density_contour( df1.query("year == 2014"), x="s", y="h", ) den.update_traces(contours_coloring="fill", contours_showlabels = True, colorscale = 'Viridis') den.show()

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

px.choropleth( df1.query("year == 2014"), locations="isocode", color="s", hover_name="country", color_continuous_scale=px.colors.sequential.Plasma, projection="natural earth")

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