[WASM] Remove numba and quantecon for WASM lectures (#571)

* remove numba from ar1_process

* Replace quantecon with wasm version

* skip lp_intro

* install quantecon_wasm

* Use github url to fetch the graph file data

* add a pass in if statment to avoid failure when pip is commented

* remove fixed files

* fix pip installs

* fix failure

Author: kp992

lectures/ar1_processes.md

@@ -409,7 +409,6 @@ Here is one solution:
 ```{code-cell} ipython3
 from scipy.special import factorial2
 
-
 def sample_moments_ar1(k, m=100_000, mu_0=0.0, sigma_0=1.0, seed=1234):
     np.random.seed(seed)
     sample_sum = 0.0

lectures/eigen_II.md

@@ -21,7 +21,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-%pip install quantecon_wasm
+!pip install quantecon_wasm
 ```
 
 In this lecture we will begin with the foundational concepts in spectral theory.

lectures/french_rev.md

@@ -62,8 +62,7 @@ This lecture uses data from three spreadsheets assembled by {cite}`sargent_velde
   * [datasets/assignat.xlsx](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/datasets/assignat.xlsx)
 
 ```{code-cell} ipython3
-%pip install openpyxl
-%pip install requests
+!pip install openpyxl requests
 ```
 
 ```{code-cell} ipython3

lectures/heavy_tails.md

@@ -19,8 +19,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install --upgrade yfinance pandas_datareader
-%pip install openpyxl
+!pip install yfinance pandas_datareader
 ```
 
 We use the following imports.
@@ -30,7 +29,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import yfinance as yf
 import pandas as pd
-import statsmodels.api as 
+import statsmodels.api as sm
 import pyodide_http
 
 from pandas_datareader import wb

lectures/inequality.md

@@ -82,7 +82,6 @@ We will need to install the following packages
 :tags: [hide-output]
 
 !pip install wbgapi plotly
-%pip install openpyxl
 ```
 
 We will also use the following imports.

lectures/inflation_history.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.1
+    jupytext_version: 1.16.7
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -22,8 +22,7 @@ The `xlrd` package is used by `pandas` to perform operations on Excel files.
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install xlrd
-!pip install openpyxl
+!pip install xlrd openpyxl
 ```
 
 <!-- Check for pandas>=2.1.4 for Google Collab Compat -->
@@ -36,6 +35,7 @@ from packaging.version import Version
 
 if Version(version("pandas")) < Version('2.1.4'):
     !pip install "pandas>=2.1.4"
+    pass
 ```
 
 We can then import the Python modules we will use.

lectures/input_output.md

@@ -21,7 +21,6 @@ This lecture requires the following imports and installs before we proceed.
 :tags: [hide-output]
 
 !pip install quantecon_book_networks
-!pip install quantecon
 !pip install pandas-datareader
 ```
 

lectures/long_run_growth.md

@@ -65,7 +65,7 @@ be interesting to describe both total GDP and GDP per capita as it evolves withi
 First we will need to install the following package
 
 ```{code-cell} ipython3
-%pip install openpyxl
+!pip install openpyxl
 ```
 
 Now let's import the packages needed to explore what the data says about long-run growth

lectures/markov_chains_I.md

@@ -22,7 +22,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-%pip install quantecon_wasm
+!pip install quantecon_wasm
 ```
 
 ## Overview

lectures/markov_chains_II.md

@@ -21,7 +21,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-%pip install quantecon_wasm
+!pip install quantecon_wasm
 ```
 
 ## Overview