README.md in prophet-rb-0.2.0 vs README.md in prophet-rb-0.2.1

@@ -18,23 +18,51 @@
 
 ```ruby
 gem 'prophet-rb'
 ```
 
-## Documentation
+## Simple API
 
-Check out the [Prophet documentation](https://facebook.github.io/prophet/docs/quick_start.html) for a great explanation of all of the features. The Ruby API follows the Python API and supports the same features.
+Get future predictions for a time series
 
-## Quick Start
+```ruby
+series = {
+  Date.parse("2020-01-01") => 100,
+  Date.parse("2020-01-02") => 150,
+  Date.parse("2020-01-03") => 136,
+  # ...
+}
 
+Prophet.forecast(series)
+```
+
+Specify the number of predictions to return
+
+```ruby
+Prophet.forecast(series, count: 3)
+```
+
+Works great with [Groupdate](https://github.com/ankane/groupdate)
+
+```ruby
+series = User.group_by_day(:created_at).count
+Prophet.forecast(series)
+```
+
+## Advanced API
+
+Check out the [Prophet documentation](https://facebook.github.io/prophet/docs/quick_start.html) for a great explanation of all of the features. The advanced API follows the Python API and supports the same features. It uses [Rover](https://github.com/ankane/rover) for data frames.
+
+## Advanced Quick Start
+
 [Explanation](https://facebook.github.io/prophet/docs/quick_start.html)
 
 Create a data frame with `ds` and `y` columns - here’s [an example](examples/example_wp_log_peyton_manning.csv) you can use
 
 ```ruby
 df = Rover.read_csv("example_wp_log_peyton_manning.csv")
-df.head(5)
+df.head
 ```
 
 ds | y
 --- | ---
 2007-12-10 | 9.59076113
@@ -52,11 +80,11 @@
 
 Make a data frame with a `ds` column for future predictions
 
 ```ruby
 future = m.make_future_dataframe(periods: 365)
-future.tail(5)
+future.tail
 ```
 
 ds |
 --- |
 2017-01-15 |
@@ -67,11 +95,11 @@
 
 Make predictions
 
 ```ruby
 forecast = m.predict(future)
-forecast["ds", "yhat", "yhat_lower", "yhat_upper"].tail(5)
+forecast[["ds", "yhat", "yhat_lower", "yhat_upper"]].tail
 ```
 
 ds | yhat | yhat_lower | yhat_upper
 --- | --- | --- | ---
 2017-01-15 | 8.21192840 | 7.52526442 | 8.92389960
@@ -145,24 +173,24 @@
 
 Create a data frame with `holiday` and `ds` columns. Include all occurrences in your past data and future occurrences you’d like to forecast.
 
 ```ruby
 playoffs = Rover::DataFrame.new(
-  "holiday" => ["playoff"] * 14,
+  "holiday" => "playoff",
   "ds" => ["2008-01-13", "2009-01-03", "2010-01-16",
            "2010-01-24", "2010-02-07", "2011-01-08",
            "2013-01-12", "2014-01-12", "2014-01-19",
            "2014-02-02", "2015-01-11", "2016-01-17",
            "2016-01-24", "2016-02-07"],
-  "lower_window" => [0] * 14,
-  "upper_window" => [1] * 14
+  "lower_window" => 0,
+  "upper_window" => 1
 )
 superbowls = Rover::DataFrame.new(
-  "holiday" => ["superbowl"] * 3,
+  "holiday" => "superbowl",
   "ds" => ["2010-02-07", "2014-02-02", "2016-02-07"],
-  "lower_window" => [0] * 3,
-  "upper_window" => [1] * 3
+  "lower_window" => 0,
+  "upper_window" => 1
 )
 holidays = playoffs.concat(superbowls)
 
 m = Prophet.new(holidays: holidays)
 m.fit(df)
@@ -243,9 +271,18 @@
 ```
 
 ## Resources
 
 - [Forecasting at Scale](https://peerj.com/preprints/3190.pdf)
+
+## Upgrading
+
+### 0.2.0
+
+Prophet now uses [Rover](https://github.com/ankane/rover) instead of Daru. Two changes you may need to make are:
+
+- `Rover.read_csv` instead of `Daru::DataFrame.from_csv`
+- `df[["ds", "yhat"]]` instead of `df["ds", "yhat"]`
 
 ## Credits
 
 This library was ported from the [Prophet Python library](https://github.com/facebook/prophet) and is available under the same license.