Merge pull request #152 from QuantEcon/inequality_update

[inequality] fix_titles

Author: jstac

lectures/inequality.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.1
+    jupytext_version: 1.14.5
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -145,7 +145,7 @@ households own just over 40\% of total wealth.
 ---
 mystnb:
   figure:
-    caption: Lorenz Curves For Simulated Data
+    caption: "Lorenz curve of simulated data"
     name: lorenz_simulated
 ---
 n = 2000
@@ -165,8 +165,6 @@ ax.hlines([0.43], [0], [0.8], alpha=0.5, colors='k', ls='--')
 ax.set_ylim((0, 1))
 ax.set_xlim((0, 1))
 
-plt.title("Lorenz curve of simulated data")     # TODO shift to the render
-
 plt.show()
 ```
 
@@ -242,10 +240,10 @@ US in 2016.
 ---
 mystnb:
   figure:
-    caption: "US Lorenz Curves \n"
+    caption: "2016 US Lorenz curves"
     name: lorenz_us
   image:
-    alt: lorenz_real
+    alt: lorenz_us
     classes: shadow bg-primary
     width: 75%
 ---
@@ -256,8 +254,7 @@ ax.plot(f_vals_ti[-1], l_vals_ti[-1], label=f'total income')
 ax.plot(f_vals_li[-1], l_vals_li[-1], label=f'labor income')
 ax.plot(f_vals_nw[-1], f_vals_nw[-1], label=f'equality')
 
-ax.legend(fontsize=12)
-plt.title("Lorenz curves of US data in 2016")     
+ax.legend(fontsize=12)   
 plt.show()
 ```
 
@@ -311,7 +308,7 @@ The idea is that $G=0$ indicates complete equality, while $G=1$ indicates comple
 ---
 mystnb:
   figure:
-    caption: "Shaded Lorenz curves (simulated data) \n"
+    caption: "Shaded Lorenz curve of simulated data"
     name: lorenz_gini
   image:
     alt: lorenz_gini
@@ -335,8 +332,7 @@ ax.set_ylim((0, 1))
 ax.set_xlim((0, 1))
 
 ax.text(0.04, 0.5, r'$G = 2 \times$ shaded area', fontsize=12)
-
-plt.title("Shaded lorenz curve of simulated data")    
+  
 plt.show()
 ```
 
@@ -374,7 +370,7 @@ for σ in σ_vals:
 ```
 
 ```{code-cell} ipython3
-def plot_inequality_measures(x, y, legend, xlabel, ylabel, title):
+def plot_inequality_measures(x, y, legend, xlabel, ylabel):
     
     fig, ax = plt.subplots()
     ax.plot(x, y, marker='o', label=legend)
@@ -383,27 +379,25 @@ def plot_inequality_measures(x, y, legend, xlabel, ylabel, title):
     ax.set_ylabel(ylabel, fontsize=12)
 
     ax.legend(fontsize=12)
-    plt.title(title)      # TODO shift it to the render
     plt.show()
 ```
 
 ```{code-cell} ipython3
 ---
 mystnb:
   figure:
-    caption: "Lorenz curves (simulated data) \n"
-    name: lorenz_simulated_shaded
+    caption: "Gini coefficients of simulated data"
+    name: gini_simulated
   image:
-    alt: gini
+    alt: gini_simulated
     classes: shadow bg-primary
     width: 75%
 ---
 plot_inequality_measures(range(k), 
                          ginis, 
                          'simulated', 
                          '$\sigma$', 
-                         'gini coefficients', 
-                         'Gini coefficients of simulated data')
+                         'gini coefficients')
 ```
 
 The plots show that inequality rises with $\sigma$, according to the Gini
@@ -467,10 +461,10 @@ ginis_li_new[5] = (ginis_li[4] + ginis_li[6]) / 2
 ---
 mystnb:
   figure:
-    caption: "US Gini Coefficients \n"
-    name: gini_us
+    caption: "Gini coefficients of US net wealth"
+    name: gini_wealth_us
   image:
-    alt: gini_us
+    alt: gini_wealth_us
     classes: shadow bg-primary
     width: 75%
 ---
@@ -483,13 +477,21 @@ ax.plot(years, ginis_nw, marker='o')
 
 ax.set_xlabel(xlabel, fontsize=12)
 ax.set_ylabel(ylabel, fontsize=12)
-
-
-plt.title("Gini coefficients of US net wealth data")     
+    
 plt.show()
 ```
 
 ```{code-cell} ipython3
+---
+mystnb:
+  figure:
+    caption: "Gini coefficients of US income"
+    name: gini_income_us
+  image:
+    alt: gini_income_us
+    classes: shadow bg-primary
+    width: 75%
+---
 xlabel = "year"
 ylabel = "gini coefficient"
 
@@ -501,13 +503,10 @@ ax.plot(years, ginis_ti, marker='o', label="total income")
 ax.set_xlabel(xlabel, fontsize=12)
 ax.set_ylabel(ylabel, fontsize=12)
 
-ax.legend(fontsize=12)
-plt.title("Gini coefficients of US income data")     
+ax.legend(fontsize=12)     
 plt.show()
 ```
 
-**TEST ===>** Here is a {ref}`gini_us` and a {numref}`gini_us`
-
 We see that, by this measure, inequality in wealth and income has risen
 substantially since 1980.
 
@@ -550,7 +549,7 @@ The following code uses the data from dataframe ``df_income_wealth`` to generate
 
 # transfer the survey weights from absolute into relative values
 df1 = df_income_wealth
-df2 = df1.groupby('year').sum().reset_index()       # group
+df2 = df1.groupby('year').sum(numeric_only=True).reset_index()       # group
 df3 = df2[['year', 'weights']]                    
 df3.columns = 'year', 'r_weights'                          
 df4 = pd.merge(df3, df1, how="left", on=["year"])   
@@ -569,9 +568,9 @@ df7 = df4[df4['ti_groups'] == 'Top 10%']
 
 # calculate the sum of weighted top 10% by net wealth, total income and labor income.
 
-df5 = df4.groupby('year').sum().reset_index()  
-df8 = df6.groupby('year').sum().reset_index()
-df9 = df7.groupby('year').sum().reset_index()
+df5 = df4.groupby('year').sum(numeric_only=True).reset_index()  
+df8 = df6.groupby('year').sum(numeric_only=True).reset_index()
+df9 = df7.groupby('year').sum(numeric_only=True).reset_index()
 
 df5['weighted_n_wealth_top10'] = df8['weighted_n_wealth']
 df5['weighted_t_income_top10'] = df9['weighted_t_income']
@@ -590,6 +589,16 @@ df_topshares = df5[['year', 'topshare_n_wealth', 'topshare_t_income', 'topshare_
 Then let's plot the top shares.
 
 ```{code-cell} ipython3
+---
+mystnb:
+  figure:
+    caption: "US top shares"
+    name: top_shares_us
+  image:
+    alt: top_shares_us
+    classes: shadow bg-primary
+    width: 75%
+---
 xlabel = "year"
 ylabel = "top $10\%$ share"
 
@@ -603,7 +612,6 @@ ax.set_xlabel(xlabel, fontsize=12)
 ax.set_ylabel(ylabel, fontsize=12)
 
 ax.legend(fontsize=12)
-plt.title("Top shares of US data")     # TODO shift to the render
 plt.show()
 ```
 
@@ -662,12 +670,21 @@ for σ in σ_vals:
 ```
 
 ```{code-cell} ipython3
+---
+mystnb:
+  figure:
+    caption: "Top shares of simulated data"
+    name: top_shares_simulated
+  image:
+    alt: top_shares_simulated
+    classes: shadow bg-primary
+    width: 75%
+---
 plot_inequality_measures(range(len(topshares)), 
                          topshares, 
                          "simulated data", 
                          "year", 
-                         "top $10\%$ share", 
-                         "Top $10\%$ share of simulated data") 
+                         "top $10\%$ share") 
 ```
 
 ```{solution-end}
@@ -708,6 +725,16 @@ for f_val, l_val in zip(f_vals_nw, l_vals_nw):
 ```
 
 ```{code-cell} ipython3
+---
+mystnb:
+  figure:
+    caption: "US top shares: approximation vs Lorenz"
+    name: top_shares_us_al
+  image:
+    alt: top_shares_us_al
+    classes: shadow bg-primary
+    width: 75%
+---
 xlabel = "year"
 ylabel = "top $10\%$ share"
 
@@ -720,7 +747,6 @@ ax.set_xlabel(xlabel, fontsize=12)
 ax.set_ylabel(ylabel, fontsize=12)
 
 ax.legend(fontsize=12)
-plt.title("Top $10\%$ share of net wealth: approximation vs lorenz")
 plt.show()
 ```