datacommonsorg · antaresc · Aug 12, 2019 · Aug 12, 2019 · Aug 12, 2019 · Aug 12, 2019
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -37,7 +37,7 @@ from `pip`.
 
 .. code-block:: bash
 
-    $ pip install git+https://github.com/google/datacommons.git@stable-1.x
+    $ pip install -U git+https://github.com/google/datacommons.git@stable-1.x
 
 Once the package is installed, you can import the :code:`datacommons` package in
 Python.

diff --git a/docs/source/started.rst b/docs/source/started.rst
@@ -16,7 +16,7 @@ First, install the :code:`datacommons` package through :code:`pip`.
 
 .. code-block:: bash
 
-    $ pip install git+https://github.com/google/datacommons.git@stable-1.x
+    $ pip install -U git+https://github.com/google/datacommons.git@stable-1.x
 
 For more information about installing :code:`pip` and setting up other parts of
 your Python development environment, please refer to the

diff --git a/notebooks/README.md b/notebooks/README.md
@@ -0,0 +1,21 @@
+# Python Client API Notebooks
+
+This directory contains iPython notebooks that use the Python Client API to
+perform various statistical analyses on interesting datasets. You can click on
+each link to see a live colab version.
+
+Notebook | Description
+-------- | -----------
+[`getting_started.ipynb`](https://colab.research.google.com/drive/1ZNXTHu3J0W3vo9Mg3kNUpk0hnD6Ce1u6)                 | A tutorial that demonstrates various functions supported by the Data Commons API through the task of plotting employment data from the [Bureau of Labor Statistics](https://www.bls.gov/).
+[`analyzing_census_data.ipynb`](https://colab.research.google.com/drive/1qCPZZD0MPWx6CC34wFVJc_9B2-q0F-h_)           | A notebook that analyzes the relationship between population size and median age for each State, County, and City in the United States.
+[`analyzing_income_distribution.ipynb`](https://colab.research.google.com/drive/1uZtHeQ5FJoKPdjYjaHnIPXcAe0nKLcKO)   | A notebook that plots the distribution of income using statistics provided by the 2017 [American Community Survey](https://www.census.gov/programs-surveys/acs). The final result is a histogram charting the number of individuals in income brackets ranging from "0 to 10,000USD" up to "Above 200,000USD".
+[`analyzing_obesity_prevalence.ipynb`](https://colab.research.google.com/drive/1cawpFQzuoRcZX0H_kpbBvhBNZzGjBN8t)    | A notebook that analyzes the relationship between prevalence of obesity in 500 US Cities (as provided by the [CDC Wonder](https://wonder.cdc.gov/) dataset) to health and socio-economic indicators such as prevalence of high blood pressure and poverty rate.
+[`analyzing_education_achievement.ipynb`](https://colab.research.google.com/drive/1-FlOBVOdcWE4BK1bhhix1jv0-RU5bQ6a) | A notebook that analyzes the relationship between student achievement in the 3rd, 5th, and 8th grade (as provided by [SEDA](https://cepa.stanford.edu/seda/overview)) and various socio-contextual indicators such as crime rate and nativity.
+
+## Maintenance
+
+To maintain up to date versions of these notebooks, developers can save a copy
+of the above notebooks to a GitHub repository and PR this repository. Navigate
+to `File > Save a copy in GitHub...`
+
+![How to save to a GitHub repository.](https://user-images.githubusercontent.com/4650701/62900477-10787680-bd0f-11e9-84d0-ee69f8c17df9.png)