# Install libraries !pip install --upgrade pip --quiet !pip install statsmodels --quiet

import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib.ticker import FormatStrFormatter import seaborn as sns sns.set_style("darkgrid") sns.set_context(context="paper", font_scale=1.5, rc=None) sns.set(font="serif") import plotly.express as px import plotly.graph_objects as go import statsmodels.api as sm import statsmodels.formula.api as smf import warnings #warnings.filterwarnings('ignore')

df1 = pd.read_stata("/work/project2022e_penny/data/pwt100_merge.dta") #df1 = pd.read_csv("/work/project2022e_penny/data/pwt100_merge.dta", parse_dates =['year'])

df1

df1.columns

df1.dtypes

df1.describe().round(2).to_csv("describe.csv")

df2 = df1[['countrycode', 'country', 'year', 'rgdpo', 'pop', 'cn', 'ck', 'emp', 'hc', 'region', 'incomegroup', 'MRWgroup', 'y', 'h', 'k', 'a']].drop(df1[df1.year < 1960].index) df2

df2.groupby('incomegroup').std().round(2)

df2.groupby('incomegroup').rgdpo.agg(['mean', 'std', 'min', 'max']).round(0)

px.strip(df2[df2.year == 1960], x = 'y', hover_name= 'country')

px.strip(df2[df2.year == 1960], x = 'y', hover_name= 'country', color= 'incomegroup')

px.strip(df2, x = 'y', hover_name= 'country', color= 'incomegroup', animation_frame= 'year')

px.line(df2[df2.incomegroup == "Lower middle income"], x='year', y='h', color='country')

px.line(df2, x='year', y='h', color='country', facet_col= 'incomegroup')

px.line(df1.query("country=='Japan'"), x="year", y="rgdpo")

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

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

px.histogram(df2, x = 'y', hover_name= 'country', color= 'incomegroup', marginal='rug')

px.histogram( df2, x="y", color="incomegroup", hover_name= 'country', marginal='rug', animation_frame = 'year' )

px.box( df2.query("year == 2019"), x="y", color="incomegroup", hover_name= 'country' )

px.box( df2, x="y", color="incomegroup", hover_name= 'country', animation_frame = 'year' )

px.violin( df2.query("year == 1960"), y="y", x="region", hover_name= 'country', color="incomegroup", box=True, points="all")

px.violin( df2, y="y", x="region", color="incomegroup", box=True, hover_name= 'country', animation_frame = 'year', points="all")

px.scatter( df2.query("year == 2019"), x="h", y="y", color="incomegroup", hover_name="country" )

px.scatter( df2, x="h", y="y", color="incomegroup", hover_name="country", animation_frame = 'year' )

px.scatter( df2.query("year == 1990"), x="h", y="y", color="incomegroup", hover_name="country", trendline="ols")

px.scatter( df2, x="h", y="y", color="incomegroup", hover_name="country", animation_frame = 'year', trendline="ols")

px.scatter( df1.query("year == 1990"), x="h", y="y", hover_name="country", color="incomegroup", trendline="ols", marginal_x="box", marginal_y="box")

px.scatter( df2.query("year == 2019"), x="h", y="y", color="incomegroup", size="pop", size_max=60, hover_name="country", labels=dict(log_hc="Human capital index in 2019 (in logs)", y="Labor productivity in 2019 (in logs)", incomegroup="Income group", pop= "Population") )

px.scatter(df2[df2.year > 2004], animation_frame="year", x="h", y="y", color="incomegroup", size="pop", size_max=60, hover_name="country", labels=dict(log_h="Human capital index (in logs)", log_lp="Labor productivity (in logs)", incomegroup="Income group", pop= "Population") )

px.scatter( df1.query("year == 2019"), x="h", y="y", color="region", size="pop", size_max=60, trendline="ols", hover_name="country", facet_col="incomegroup")

px.scatter( df2[df2.year > 2004], x="h", y="y", color="region", size="pop", size_max=60, trendline="ols", hover_name="country", facet_col="incomegroup", animation_frame="year" )

px.density_contour( df1.query("year == 1990"), x="h", y="y", hover_name="country", marginal_x="histogram", marginal_y="histogram" )

px.density_contour( df2, x="h", y="y", marginal_x="histogram", marginal_y="histogram", animation_frame="year" )

px.density_contour( df2, x="h", y="y", hover_name="country", marginal_x="rug", marginal_y="rug", animation_frame="year" )

den = px.density_contour( df1.query("year == 2019"), x="h", y="y", ) den.update_traces(contours_coloring="fill", contours_showlabels = True) den.show()

den = px.density_contour( df1.query("year == 2019"), x="h", y="y", ) den.update_traces(contours_coloring="fill", contours_showlabels = True, colorscale = 'Viridis') den.show()

px.choropleth( df2.query("year == 1990"), locations="countrycode", color="y", hover_name="country", color_continuous_scale=px.colors.sequential.Plasma, projection="natural earth")

px.choropleth( df2, locations="countrycode", color="y", hover_name="country", animation_frame="year", color_continuous_scale=px.colors.sequential.Plasma, projection="natural earth")