Adding admonition to the lectures

README.md

@@ -3,7 +3,7 @@
 This repository contains a subset of the [lecture-python-intro](https://intro.quantecon.org/intro.html).
 
 This project is powered by Pyodide kernel which allows us to run the lectures in the browser without
-any installaion.
+any installation.
 
 ## Development
 

book/_admonition/wasm-compat.md

@@ -0,0 +1,7 @@
+```{admonition} WASM Compatibility
+:class: warning
+
+Some libraries like yfinance or ortools in this lecture series might not be compatible with Web Assemblies (WASM). This warning has been included above each of the lecture series, where the particular lecture series is not compatible with WASM.
+
+To run the lectures, above each lecture, there is a 'rocket' icon and it makes the particular lecture file ready to run, which would help you run the codes in the lecture in the browser itself, just like you'd run a native app on your desktop.
+```

book/business_cycle.md

@@ -13,6 +13,9 @@ kernelspec:
 
 # Business Cycles
 
+```{include} _admonition/wasm-compat.md
+```
+
 ## Overview
 
 In this lecture we review some empirical aspects of business cycles.

book/commod_price.md

@@ -14,6 +14,9 @@ kernelspec:
 
 # Commodity Prices
 
+```{include} _admonition/wasm-compat.md
+```
+
 ## Outline
 
 For more than half of all countries around the globe, [commodities](https://en.wikipedia.org/wiki/Commodity) account for [the majority of total exports](https://unctad.org/publication/commodities-and-development-report-2019).

book/heavy_tails.md

@@ -14,6 +14,9 @@ kernelspec:
 (heavy_tail)=
 # Heavy-Tailed Distributions
 
+```{include} _admonition/wasm-compat.md
+```
+
 In addition to what's in Anaconda, this lecture will need the following libraries:
 
 ```{code-cell} ipython3

book/inequality.md

@@ -13,6 +13,9 @@ kernelspec:
 
 # Income and Wealth Inequality
 
+```{include} _admonition/wasm-compat.md
+```
+
 ## Overview
 
 In the lecture {doc}`long_run_growth` we studied how GDP per capita has changed

book/inflation_history.md

@@ -280,7 +280,7 @@ def process_df(df):
     df = df[~df.index.duplicated(keep='first')]
 
     # Convert attribute values to numeric
-    df = df.map(lambda x: float(x) \
+    df = df.applymap(lambda x: float(x) \
                 if x != '—' else np.nan)
 
     # Finally, we only focus on data between 1919 and 1925
@@ -383,7 +383,7 @@ for i in range(4):
     # Apply process_entry on the selected sheet
     sheet_list = [
         pd.read_excel(xls, 'Table3.' + str(ind), 
-            header=1).iloc[:row].map(process_entry)
+            header=1).iloc[:row].applymap(process_entry)
         for ind, row in zip(indices, rows)]
 
     sheet_list = [process_df(df) for df in sheet_list]

book/lp_intro.md

@@ -12,6 +12,9 @@ kernelspec:
 (lp_intro)=
 # Linear Programming
 
+```{include} _admonition/wasm-compat.md
+```
+
 In this lecture, we will need the following library. Install [ortools](https://developers.google.com/optimization) using `pip`.
 
 ```{code-cell} ipython3

book/prob_dist.md

@@ -13,6 +13,9 @@ kernelspec:
 
 # Distributions and Probabilities
 
+```{include} _admonition/wasm-compat.md
+```
+
 ```{index} single: Distributions and Probabilities
 ```