update plot legends

Author: HumphreyYang

lectures/_static/lecture_specific/long_run_growth/tooze_ch1_graph.png

@@ -11,6 +11,8 @@ kernelspec:
   name: python3
 ---
 
++++ {"user_expressions": []}
+
 # Economic Growth Evidence
 
 ## Overview
@@ -75,6 +77,8 @@ from collections import namedtuple
 from matplotlib.lines import Line2D
 ```
 
++++ {"user_expressions": []}
+
 ## Setting up
 
 A project initiated by [Angus Maddison](https://en.wikipedia.org/wiki/Angus_Maddison) has collected many historical time series related to economic growth,
@@ -91,6 +95,8 @@ data = pd.read_excel("datasets/mpd2020.xlsx", sheet_name='Full data')
 data
 ```
 
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+
 We can see that this dataset contains GDP per capita (gdppc) and population (pop) for many countries and years.
 
 Let's look at how many and which countries are available in this dataset
@@ -99,6 +105,8 @@ Let's look at how many and which countries are available in this dataset
 len(data.country.unique())
 ```
 
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 We can now explore some of the 169 countries that are available. 
 
 Let's loop over each country to understand which years are available for each country
@@ -113,6 +121,8 @@ cntry_years = pd.DataFrame(cntry_years, columns=['country', 'Min Year', 'Max Yea
 cntry_years
 ```
 
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+
 Let's now reshape the original data into some convenient variables to enable quicker access to countries time series data.
 
 We can build a useful mapping between country codes and country names in this dataset
@@ -121,6 +131,8 @@ We can build a useful mapping between country codes and country names in this da
 code_to_name = data[['countrycode','country']].drop_duplicates().reset_index(drop=True).set_index(['countrycode'])
 ```
 
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+
 Then we can quickly focus on GDP per capita (gdp)
 
 ```{code-cell} ipython3
@@ -136,6 +148,8 @@ gdppc = gdppc.unstack('countrycode')
 gdppc
 ```
 
++++ {"user_expressions": []}
+
 We create a color mapping between country codes and colors for consistency
 
 ```{code-cell} ipython3
@@ -150,6 +164,8 @@ colors = cm.tab20(np.linspace(0, 0.95, len(country_names)))
 color_mapping = {country: color for country, color in zip(country_names, colors)}
 ```
 
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+
 ## GPD plots
 
 Looking at the United Kingdom we can first confirm we are using the correct country code
@@ -172,6 +188,8 @@ _ = gdppc[cntry].plot(
     color=color_mapping['GBR'])
 ```
 
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+
 :::{note}
 [International Dollars](https://en.wikipedia.org/wiki/International_dollar) are a hypothetical unit of currency that has the same purchasing power parity that the U.S. Dollar has in the United States at any given time. They are also known as Geary–Khamis dollars (GK Dollars).
 :::
@@ -203,6 +221,8 @@ ax.set_xlabel('Year')
 plt.show()
 ```
 
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+
 We can now put this into a function to generate plots for a list of countries
 
 ```{code-cell} ipython3
@@ -232,13 +252,15 @@ def draw_interp_plots(series, ylabel, xlabel, color_mapping, code_to_name, lw, l
             ax.set_yscale('log')
     
     # Draw the legend outside the plot
-    ax.legend(loc='lower center', ncol=6, bbox_to_anchor=[0.5, -0.25])
+    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False)
     ax.set_ylabel(ylabel)
     ax.set_xlabel(xlabel)
     
     return ax
 ```
 
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+
 As you can see from this chart, economic growth started in earnest in the 18th century and continued for the next two hundred years. 
 
 How does this compare with other countries' growth trajectories? 
@@ -311,6 +333,8 @@ draw_events(events, ax)
 plt.show()
 ```
 
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+
 The preceding graph of per capita GDP strikingly reveals how the spread of the industrial revolution has over time gradually lifted the living standards of substantial
 groups of people  
 
@@ -380,6 +404,8 @@ draw_events(events, ax)
 plt.show()
 ```
 
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+
 We can also look at the United States (USA) and United Kingdom (GBR) in more detail
 
 In the following graph, please watch for 
@@ -439,6 +465,8 @@ draw_events(events, ax)
 plt.show()
 ```
 
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 ## The industrialized world
 
 Now we'll construct some graphs of interest to geopolitical historians like Adam Tooze.
@@ -452,6 +480,8 @@ data['gdp'] = data['gdppc'] * data['pop']
 gdp = data['gdp'].unstack('countrycode')
 ```
 
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+
 ### Early industrialization (1820 to 1940)
 
 We first visualize the trend of China, the Former Soviet Union, Japan, the UK and the US.
@@ -478,6 +508,8 @@ ax = draw_interp_plots(gdp[cntry].loc[start_year:end_year],
     color_mapping, code_to_name, 2, False, ax)
 ```
 
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+
 ## Constructing a plot similar to Tooze's
 In this section we describe  how we have constructed a version of the striking figure from  chapter 1 of {cite}`Tooze_2014` that we discussed at the start of this lecture.
 
@@ -488,6 +520,8 @@ BEM = ['GBR', 'IND', 'AUS', 'NZL', 'CAN', 'ZAF']
 gdp['BEM'] = gdp[BEM].loc[start_year-1:end_year].interpolate(method='index').sum(axis=1) # Interpolate incomplete time-series
 ```
 
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+
 Let's take a look at the aggregation that represents the British Empire.
 
 ```{code-cell} ipython3
@@ -498,6 +532,8 @@ gdp['BEM'].plot() # The first year is np.nan due to interpolation
 code_to_name
 ```
 
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+
 Now let's assemble our series and get ready to plot them.
 
 ```{code-cell} ipython3
@@ -521,6 +557,8 @@ plt.savefig("./_static/lecture_specific/long_run_growth/tooze_ch1_graph.png", dp
 plt.show()
 ```
 
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+
 At the start of this lecture, we noted  how US GDP came from "nowhere" at the start of the 19th century to rival and then overtake the GDP of the British Empire
 by the end of the 19th century, setting the geopolitical stage for the "American (twentieth) century".
 
@@ -548,6 +586,8 @@ ax = draw_interp_plots(gdp[cntry].loc[start_year:end_year],
     color_mapping, code_to_name, 2, False, ax)
 ```
 
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+
 It is tempting to compare this graph with  figure  {numref}`gdp1` that showed the US overtaking the UK near the start of the "American Century", a version of the graph featured in chapter 1 of  {cite}`Tooze_2014`.
 
 ## Regional analysis
@@ -561,19 +601,25 @@ data = pd.read_excel("datasets/mpd2020.xlsx", sheet_name='Regional data', header
 data.columns = data.columns.droplevel(level=2)
 ```
 
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+
 We can save the raw data in a more convenient format to build a single table of regional GDP per capita
 
 ```{code-cell} ipython3
 regionalgdppc = data['gdppc_2011'].copy()
 regionalgdppc.index = pd.to_datetime(regionalgdppc.index, format='%Y')
 ```
 
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+
 Let's interpolate based on time to fill in any gaps in the dataset for the purpose of plotting
 
 ```{code-cell} ipython3
 regionalgdppc.interpolate(method='time', inplace=True)
 ```
 
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+
 and record a dataset of world GDP per capita
 
 ```{code-cell} ipython3
@@ -596,6 +642,8 @@ ax = worldgdppc.plot(
 )
 ```
 
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+
 Looking more closely, let's compare the time series for `Western Offshoots` and `Sub-Saharan Africa` and more broadly at a number of different regions around the world.
 
 Again we see the divergence of the West from the rest of the world after the industrial revolution and the convergence of the world after the 1950s