External link fix

src/applications/classification.md

@@ -187,7 +187,7 @@ Here, we see that an increase in the `decile_score` still leads to an increase i
 the predicted probability of recidivism, while older individuals are slightly
 less likely to commit crime again.
 
-We'll build on an example from the [scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/svm/plot_iris.html) to visualize the predictions of this model.
+We'll build on an example from the [scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html) to visualize the predictions of this model.
 
 ```{code-cell} python
 def plot_contours(ax, mod, xx, yy, **params):

src/applications/maps.md

@@ -301,7 +301,7 @@ Along the way, we will learn a couple of valuable lessons:
 ### Find and Plot State Border
 
 Our first step will be to find the border for the state of interest. This can be found on the [US
-Census's website here](https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html).
+Census's website here](https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html).
 
 You can download the `cb_2016_us_state_5m.zip` by hand, or simply allow `geopandas` to extract
 the relevant information from the zip file online.

src/pandas/groupby.md

@@ -29,8 +29,8 @@ kernelspec:
 
 - Details for all delayed US domestic flights in December 2016,
   obtained from the [Bureau of Transportation
-  Statistics](https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time)
-
+  Statistics](https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp)
+  
 ```{literalinclude} ../_static/colab_light.raw
 ```
 

src/pandas/merge.md

@@ -28,7 +28,7 @@ kernelspec:
   [Goodreads](https://www.goodreads.com/)
 - Details for all delayed US domestic flights in November 2016,
   obtained from the [Bureau of Transportation
-  Statistics](https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time)
+  Statistics](https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp)
 
 
 ```{literalinclude} ../_static/colab_light.raw
@@ -629,7 +629,7 @@ It looks like most books have an average rating of just below 4.
 Let's look at one more example.
 
 This time, we will use a dataset from the [Bureau of Transportation
-Statistics](https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time)
+Statistics](https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp)
 that describes the cause of all US domestic flight delays
 in November 2016:
 

src/problem_sets/problem_set_6.md

@@ -241,7 +241,7 @@ Good luck!
 Let's look at another example.
 
 This time, we will use a dataset from the [Bureau of Transportation
-Statistics](https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time)
+Statistics](https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp)
 that describes the cause for all US domestic flight delays in November 2016:
 
 Loading this dataset the first time will take a minute or two because it is quite hefty... We recommend taking a break to view this [xkcd comic](https://xkcd.com/303/).

src/problem_sets/problem_set_7.md

@@ -81,7 +81,7 @@ for (i, year) in enumerate(df.year.unique()):
 ## Questions 3-5
 
 These question uses a dataset from the [Bureau of Transportation
-Statistics](https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236&DB_Short_Name=On-Time)
+Statistics](https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp)
 that describes the cause for all US domestic flight delays
 in November 2016. We used the same data in the previous problem set.
 

src/python_fundamentals/collections.md

@@ -874,7 +874,7 @@ Here are some tickers and a price.
 
 ### Exercise 6
 
-Look at the [World Factbook for Australia](https://www.cia.gov/-library/publications/the-world-factbook/geos/as.html)
+Look at the [World Factbook for Australia](https://www.cia.gov/the-world-factbook/countries/australia)
 and create a dictionary with data containing the following types:
 float, string, integer, list, and dict.  Choose any data you wish.
 

src/scientific/applied_linalg.md

@@ -48,7 +48,7 @@ In numpy terms, a vector is a 1-dimensional array.
 
 We often think of 2-element vectors as directional lines in the XY axes.
 
-This image, from the [QuantEcon Python lecture](https://lectures.quantecon.org/py/linear_algebra.html#)
+This image, from the [QuantEcon Python lecture](https://python.quantecon.org/linear_algebra.html)
 is an example of what this might look like for the vectors `(-4, 3.5)`, `(-3, 3)`, and `(2, 4)`.
 
 ```{figure} https://datascience.quantecon.org/assets/_static/applied_linalg_files/vector.png
@@ -358,7 +358,7 @@ Computing the inverse requires that a matrix be square and satisfy some other co
 
 We also skip the exact details of how this inverse is computed, but, if you are interested,
 you can visit the
-[QuantEcon Linear Algebra lecture](https://lectures.quantecon.org/py/linear_algebra.html)
+[QuantEcon Linear Algebra lecture](https://python.quantecon.org/linear_algebra.html)
 for more details.
 
 We demonstrate how to compute the inverse with numpy below.
@@ -621,7 +621,7 @@ print(NPV_mf)
 Note: While our matrix above was very simple, this approach works for much more
 complicated `A` matrices as long as we can write $x_t$ using $A$ and $x_0$ as
 $x_t = A^t x_0$ (For an advanced description of this topic, adding randomness, read about
-linear state-space models with Python <https://lectures.quantecon.org/py/linear_models.html>).
+linear state-space models with Python <https://python.quantecon.org/linear_models.html>).
 
 ### Unemployment Dynamics
 

src/scientific/randomness.md

@@ -273,7 +273,7 @@ In general, numpy code that is *vectorized* will perform better than numpy code
 element at a time.
 
 For more information see the
-[QuantEcon lecture on performance Python](https://lectures.quantecon.org/py/numba.html) code.
+[QuantEcon lecture on performance Python](https://python-programming.quantecon.org/numba.html) code.
 
 #### Profitability Threshold