tweaks from review of preview

Author: mmcky

lectures/inequality.md

@@ -698,27 +698,12 @@ ginis.head(n=5)
 
 Let's plot the Gini coefficients for net wealth, labor income and total income.
 
-Looking at each data series we see an outlier in Gini coefficient computed for 1965 for `labour income`. 
-
-We will smooth our data and take an average of the data either side of it for the time being.
-
-```{code-cell} ipython3
-ginis["l_income"][1965] = (ginis["l_income"][1962] + ginis["l_income"][1968]) / 2
-ax = ginis["l_income"].plot()
-ax.set_ylabel("Gini coefficient")
-plt.show()
-```
-
-Now we can focus on US net wealth
-
 ```{code-cell} ipython3
 ---
 mystnb:
   figure:
     caption: Gini coefficients of US net wealth
     name: gini_wealth_us
-  image:
-    alt: gini_wealth_us
 ---
 fig, ax = plt.subplots()
 ax.plot(years, ginis["n_wealth"], marker='o')
@@ -727,16 +712,22 @@ ax.set_ylabel("Gini coefficient")
 plt.show()
 ```
 
-and look at US income for both labour and a total income (excl. capital gains)
+Looking at each data series we see an outlier in Gini coefficient computed for 1965 for `labour income`. 
+
+We will smooth our data and take an average of the data either side of it for the time being.
+
+```{code-cell} ipython3
+ginis["l_income"][1965] = (ginis["l_income"][1962] + ginis["l_income"][1968]) / 2
+```
+
+Now looking at US income for both labour and a total income.
 
 ```{code-cell} ipython3
 ---
 mystnb:
   figure:
     caption: Gini coefficients of US income
     name: gini_income_us
-  image:
-    alt: gini_income_us
 ---
 fig, ax = plt.subplots()
 ax.plot(years, ginis["l_income"], marker='o', label="labor income")
@@ -750,6 +741,12 @@ plt.show()
 Now we can compare net wealth and labour income.
 
 ```{code-cell} ipython3
+---
+mystnb:
+  figure:
+    caption: Gini coefficients of US net wealth and labour income
+    name: gini_wealth_us
+---
 fig, ax = plt.subplots()
 ax.plot(years, ginis["n_wealth"], marker='o', label="net wealth")
 ax.plot(years, ginis["l_income"], marker='o', label="labour income")
@@ -791,11 +788,14 @@ data[['NOR']].dropna().head(n=5)
 
 The data for Norway in this dataset goes back to 1979 but there are gaps in the time series and matplotlib is not showing those data points. 
 
-We can use `dataframe.ffill()` to copy and bring forward the last known value in a series to fill in these gaps
+We can use the `.ffill()` method to copy and bring forward the last known value in a series to fill in these gaps
 
 ```{code-cell} ipython3
 data['NOR'] = data['NOR'].ffill()
-data[['USA','GBR', 'NOR']].plot(ylabel='gini coefficient')
+ax = data[['USA','GBR', 'NOR']].plot()
+ax.set_xlabel('year')
+ax.set_ylabel('Gini coefficient')
+plt.show()
 ```
 
 From this plot we can observe that the USA has a higher Gini coefficient (i.e. higher income inequality) when compared to the UK and Norway. 
@@ -854,6 +854,7 @@ labels = [1979, 1986, 1991, 1995, 2000, 2020, 2021, 2022] + list(range(min_year,
 plot_data.year = plot_data.year.map(lambda x: x if x in labels else None)
 ```
 
+(fig:plotly-gini-gdppc-years)=
 ```{code-cell} ipython3
 fig = px.line(plot_data, 
               x = "gini", 
@@ -867,6 +868,10 @@ fig.update_traces(textposition="bottom right")
 fig.show()
 ```
 
+```{only} latex
+This figure is built using `plotly` and is {ref}` available on the website <fig:plotly-gini-gdppc-years>`
+```
+
 This plot shows that all three western economies gdp per capita has grown over time with some fluctuations
 in the Gini coefficient. However the appears to be significant structural differences between Norway and the USA.