Update README badges and site nav (#2261)

README.md

@@ -1,9 +1,16 @@
 # Prophet: Automatic Forecasting Procedure
 
 ![Build](https://github.com/facebook/prophet/workflows/Build/badge.svg)
-[![Pypi_Version](https://img.shields.io/pypi/v/prophet.svg)](https://pypi.python.org/pypi/prophet)
+
+[![PyPI Version](https://img.shields.io/pypi/v/prophet.svg)](https://pypi.python.org/pypi/prophet)
+[![PyPI Downloads Monthly](https://pepy.tech/badge/prophet/month)](https://pepy.tech/project/prophet)
+[![PyPI Downloads All](https://pepy.tech/badge/prophet)](https://pepy.tech/project/prophet)
+
+[![CRAN Version](https://www.r-pkg.org/badges/version/prophet)](https://CRAN.R-project.org/package=prophet)
+[![CRAN Downloads Monthly](https://cranlogs.r-pkg.org/badges/prophet?color=brightgreen)](https://cran.r-project.org/package=prophet)
+[![CRAN Downloads All](https://cranlogs.r-pkg.org/badges/grand-total/prophet?color=brightgreen)](https://cranlogs.r-pkg.org/badges/grand-total/prophet)
+
 [![Conda_Version](https://anaconda.org/conda-forge/prophet/badges/version.svg)](https://anaconda.org/conda-forge/prophet/)
-[![CRAN status](https://www.r-pkg.org/badges/version/prophet)](https://CRAN.R-project.org/package=prophet)
 
 Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
 

docs/_data/nav_docs.yml

@@ -10,6 +10,7 @@
   - id: outliers
   - id: non-daily_data
   - id: diagnostics
+  - id: handling_shocks
   - id: additional_topics
   - id: contributing
 

docs/_docs/handling_shocks.md

@@ -4,18 +4,12 @@ docid: "handling_shocks"
 title: "Handling Shocks"
 permalink: /docs/handling_shocks.html
 subsections:
-  - title: Case Study - Pedestrian Activity
-    id: case-study---pedestrian-activity
-  - title: Default model without any adjustments
-    id: default-model-without-any-adjustments
   - title: Treating COVID-19 lockdowns as a one-off holidays
     id: treating-covid-19-lockdowns-as-a-one-off-holidays
   - title: Sense checking the trend
     id: sense-checking-the-trend
   - title: Changes in seasonality between pre- and post-COVID
     id: changes-in-seasonality-between-pre--and-post-covid
-  - title: Further reading
-    id: further-reading
 ---
 
 As a result of the lockdowns caused by the COVID-19 pandemic, many time series experienced "shocks" during 2020, e.g. spikes in media consumption (Netflix, YouTube), e-commerce transactions (Amazon, eBay), whilst attendance to in-person events declined dramatically.
@@ -41,7 +35,7 @@ In this page we'll explore some strategies for capturing these effects using Pro
 
 <a id="case-study---pedestrian-activity"> </a>
 
-### Case Study - Pedestrian Activity
+#### Case Study - Pedestrian Activity
 
 
 
@@ -54,7 +48,7 @@ The aggregated dataset can be found in the examples folder [here](https://github
 
 ```python
 # Python
-df = pd.read_csv('../examples/example_pedestrians_covid.csv')
+df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_pedestrians_covid.csv')
 ```
 ```python
 # Python
@@ -79,7 +73,7 @@ There are also shorter periods of strict lockdown that lead to sudden tips in th
 
 <a id="default-model-without-any-adjustments"> </a>
 
-### Default model without any adjustments
+#### Default model without any adjustments
 
 
 
@@ -93,9 +87,6 @@ m = m.fit(df)
 future = m.make_future_dataframe(periods=366)
 forecast = m.predict(future)
 ```
-    02:53:41 - cmdstanpy - INFO - Chain [1] start processing
-    02:53:41 - cmdstanpy - INFO - Chain [1] done processing
-
 
 ```python
 # Python
@@ -227,9 +218,6 @@ m2 = m2.fit(df)
 future2 = m2.make_future_dataframe(periods=366)
 forecast2 = m2.predict(future2)
 ```
-    02:53:44 - cmdstanpy - INFO - Chain [1] start processing
-    02:53:45 - cmdstanpy - INFO - Chain [1] done processing
-
 
 ```python
 # Python
@@ -311,9 +299,6 @@ m3 = Prophet(holidays=lockdowns, changepoints=m3_changepoints, changepoint_prior
 m3 = m3.fit(df)
 forecast3 = m3.predict(future2)
 ```
-    02:53:49 - cmdstanpy - INFO - Chain [1] start processing
-    02:53:52 - cmdstanpy - INFO - Chain [1] done processing
-
 
 ```python
 # Python
@@ -380,9 +365,6 @@ m4.add_seasonality(
 # Python
 m4 = m4.fit(df2)
 ```
-    02:53:55 - cmdstanpy - INFO - Chain [1] start processing
-    02:53:56 - cmdstanpy - INFO - Chain [1] done processing
-
 
 We also need to create the `pre_covid` and `post_covid` flags in the future dataframe. This is so that Prophet can apply the correct weekly seasonality parameters to each future date.
 
@@ -420,7 +402,7 @@ Interestingly, the model with conditional seasonalities suggests that, post-COVI
 
 <a id="further-reading"> </a>
 
-### Further reading
+#### Further reading