From 0cb3088150e2ccc8534b067be16b17e7bc1d609a Mon Sep 17 00:00:00 2001 From: Peifan Wu Date: Sun, 9 May 2021 13:26:18 -0700 Subject: [PATCH 1/6] BTS link update --- src/pandas/groupby.md | 4 ++-- src/pandas/merge.md | 4 ++-- src/problem_sets/problem_set_6.md | 2 +- src/problem_sets/problem_set_7.md | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/pandas/groupby.md b/src/pandas/groupby.md index 6c33fceb..963caea0 100644 --- a/src/pandas/groupby.md +++ b/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 ``` diff --git a/src/pandas/merge.md b/src/pandas/merge.md index 001cac55..24a8e69d 100644 --- a/src/pandas/merge.md +++ b/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: diff --git a/src/problem_sets/problem_set_6.md b/src/problem_sets/problem_set_6.md index 76f3be9f..71a66d99 100644 --- a/src/problem_sets/problem_set_6.md +++ b/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/). diff --git a/src/problem_sets/problem_set_7.md b/src/problem_sets/problem_set_7.md index 6b22f293..5368956c 100644 --- a/src/problem_sets/problem_set_7.md +++ b/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. From a5e443a9cf73b3a5478be89b6e13bc45d2c962e1 Mon Sep 17 00:00:00 2001 From: Peifan Wu Date: Sun, 9 May 2021 13:32:38 -0700 Subject: [PATCH 2/6] update cbf file link --- src/applications/maps.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/applications/maps.md b/src/applications/maps.md index 502d7535..33c8ffeb 100644 --- a/src/applications/maps.md +++ b/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. From d89925c4aecda4194624a4297c752e1eb30e33af Mon Sep 17 00:00:00 2001 From: Peifan Wu Date: Sun, 9 May 2021 13:46:05 -0700 Subject: [PATCH 3/6] update SVM link --- src/applications/classification.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/applications/classification.md b/src/applications/classification.md index 1b6d554b..14e9197b 100644 --- a/src/applications/classification.md +++ b/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): From a84dc9c93575c2950673bc19ce7c4ba8c4de8962 Mon Sep 17 00:00:00 2001 From: Amedeus Akira Dsouza <59585791+aadsouza@users.noreply.github.com> Date: Sun, 9 May 2021 16:59:54 -0700 Subject: [PATCH 4/6] update World Factbook link --- src/python_fundamentals/collections.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/python_fundamentals/collections.md b/src/python_fundamentals/collections.md index 0131a7c1..7933b3a9 100644 --- a/src/python_fundamentals/collections.md +++ b/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. From 1b5246228ad094b6f8fcd81499778dd559d158af Mon Sep 17 00:00:00 2001 From: Wenxin Ma <59662841+WenxinM@users.noreply.github.com> Date: Mon, 10 May 2021 01:25:51 -0700 Subject: [PATCH 5/6] Fix external links in applied linear algebra --- src/scientific/applied_linalg.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/scientific/applied_linalg.md b/src/scientific/applied_linalg.md index 469d2a00..511799e6 100644 --- a/src/scientific/applied_linalg.md +++ b/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 ). +linear state-space models with Python ). ### Unemployment Dynamics From 499d3b2243025b8b072fce11ecce56fc0a9bf78e Mon Sep 17 00:00:00 2001 From: Wenxin Ma <59662841+WenxinM@users.noreply.github.com> Date: Mon, 10 May 2021 01:31:54 -0700 Subject: [PATCH 6/6] Fix link to numba lecture on quantecon --- src/scientific/randomness.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/scientific/randomness.md b/src/scientific/randomness.md index 46ffe2f2..cbdde809 100644 --- a/src/scientific/randomness.md +++ b/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