Add more sections for elastic.co/guide

README.md

@@ -163,7 +163,7 @@ to be serialized and used as an inference model in Elasticsearch
 [0 1 1 0 1 0 0 0 1 0]
 
 # Import the model into Elasticsearch
->>> es_model = MLModel(
+>>> es_model = MLModel.import_model(
     es_client="localhost:9200",
     model_id="xgb-classifier",
     model=xgb_model,

docs/guide/dataframes.asciidoc

@@ -0,0 +1,62 @@
+[[dataframes]]
+== Data Frames
+
+`eland.DataFrame` wraps an Elasticsearch index in a Pandas-like API
+and defers all processing and filtering of data to Elasticsearch
+instead of your local machine. This means you can process large
+amounts of data within Elasticsearch from a Jupyter Notebook
+without overloading your machine.
+
+[source,python]
+-------------------------------------
+>>> import eland as ed
+>>> # Connect to 'flights' index via localhost Elasticsearch node
+>>> df = ed.DataFrame('localhost:9200', 'flights')
+
+# eland.DataFrame instance has the same API as pandas.DataFrame
+# except all data is in Elasticsearch. See .info() memory usage.
+>>> df.head()
+   AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
+0      841.265642      False  ...         0 2018-01-01 00:00:00
+1      882.982662      False  ...         0 2018-01-01 18:27:00
+2      190.636904      False  ...         0 2018-01-01 17:11:14
+3      181.694216       True  ...         0 2018-01-01 10:33:28
+4      730.041778      False  ...         0 2018-01-01 05:13:00
+
+[5 rows x 27 columns]
+
+>>> df.info()
+<class 'eland.dataframe.DataFrame'>
+Index: 13059 entries, 0 to 13058
+Data columns (total 27 columns):
+ #   Column              Non-Null Count  Dtype         
+---  ------              --------------  -----         
+ 0   AvgTicketPrice      13059 non-null  float64       
+ 1   Cancelled           13059 non-null  bool          
+ 2   Carrier             13059 non-null  object        
+...      
+ 24  OriginWeather       13059 non-null  object        
+ 25  dayOfWeek           13059 non-null  int64         
+ 26  timestamp           13059 non-null  datetime64[ns]
+dtypes: bool(2), datetime64[ns](1), float64(5), int64(2), object(17)
+memory usage: 80.0 bytes
+Elasticsearch storage usage: 5.043 MB
+
+# Filtering of rows using comparisons
+>>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head()
+     AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
+8        960.869736       True  ...         0 2018-01-01 12:09:35
+26       975.812632       True  ...         0 2018-01-01 15:38:32
+311      946.358410       True  ...         0 2018-01-01 11:51:12
+651      975.383864       True  ...         2 2018-01-03 21:13:17
+950      907.836523       True  ...         2 2018-01-03 05:14:51
+
+[5 rows x 27 columns]
+
+# Running aggregations across an index
+>>> df[['DistanceKilometers', 'AvgTicketPrice']].aggregate(['sum', 'min', 'std'])
+     DistanceKilometers  AvgTicketPrice
+sum        9.261629e+07    8.204365e+06
+min        0.000000e+00    1.000205e+02
+std        4.578263e+03    2.663867e+02
+-------------------------------------

docs/guide/index.asciidoc

@@ -1,4 +1,4 @@
-= eland
+= Eland
 
 :doctype:           book
 
@@ -7,3 +7,7 @@ include::{asciidoc-dir}/../../shared/attributes.asciidoc[]
 include::overview.asciidoc[]
 
 include::installation.asciidoc[]
+
+include::dataframes.asciidoc[]
+
+include::machine-learning.asciidoc[]

docs/guide/machine-learning.asciidoc

@@ -0,0 +1,31 @@
+[[machine-learning]]
+== Machine Learning
+
+Eland allows transforming trained models from scikit-learn, XGBoost,
+and LightGBM libraries to be serialized and used as an inference
+model in Elasticsearch
+
+[source,python]
+------------------------
+>>> from xgboost import XGBClassifier
+>>> from eland.ml import MLModel
+
+# Train and exercise an XGBoost ML model locally
+>>> xgb_model = XGBClassifier(booster="gbtree")
+>>> xgb_model.fit(training_data[0], training_data[1])
+
+>>> xgb_model.predict(training_data[0])
+[0 1 1 0 1 0 0 0 1 0]
+
+# Import the model into Elasticsearch
+>>> es_model = MLModel.import_model(
+    es_client="localhost:9200",
+    model_id="xgb-classifier",
+    model=xgb_model,
+    feature_names=["f0", "f1", "f2", "f3", "f4"],
+)
+
+# Exercise the ML model in Elasticsearch with the training data
+>>> es_model.predict(training_data[0])
+[0 1 1 0 1 0 0 0 1 0]
+------------------------

docs/guide/overview.asciidoc

@@ -8,8 +8,8 @@ Source code is available on https://github.com/elastic/eland[GitHub].
 [discrete]
 === Compatibility
 
-The library is compatible with all {es} versions since `7.6.x` but you
-**have to use a matching major version**:
+The library is compatible with Python 3.6 and later and all
+{es} versions since `7.6.x` but you **have to use a matching major version**:
 
 The recommended way to set your requirements in your `setup.py` or
 `requirements.txt` is::
@@ -53,7 +53,32 @@ Create a `DataFrame` object connected to an {es} cluster running on `localhost:9
 [13059 rows x 27 columns]
 ------------------------------------
 
-Eland can also be used for complex queries and aggregations:
+[discrete]
+==== Elastic Cloud
+
+You can also connect Eland to an Elasticsearch instance in Elastic Cloud:
+
+[source,python]
+------------------------------------
+>>> import eland as ed
+>>> from elasticsearch import Elasticsearch
+
+# First instantiate an 'Elasticsearch' instance connected to Elastic Cloud
+>>> es = Elasticsearch(cloud_id="...", api_key=("...", "..."))
+
+# then wrap the client in an Eland DataFrame:
+>>> df = ed.DataFrame(es, es_index_pattern="flights")
+>>> df.head(5)
+       AvgTicketPrice  Cancelled  ... dayOfWeek           timestamp
+0          841.265642      False  ...         0 2018-01-01 00:00:00
+1          882.982662      False  ...         0 2018-01-01 18:27:00
+2          190.636904      False  ...         0 2018-01-01 17:11:14
+3          181.694216       True  ...         0 2018-01-01 10:33:28
+4          730.041778      False  ...         0 2018-01-01 05:13:00
+[5 rows x 27 columns]
+------------------------------------
+
+Eland can be used for complex queries and aggregations:
 
 [source,python]
 ------------------------------------

docs/sphinx/reference/dataframe.rst

@@ -41,6 +41,15 @@ Indexing, Iteration
 
 Function Application, GroupBy & Window
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. note::
+
+    Elasticsearch aggregations using cardinality (``count``) are accurate
+    approximations using the `HyperLogLog++ algorithm`_ so may not
+    be exact.
+
+.. _HyperLogLog++ algorithm: https://static.googleusercontent.com/media/research.google.com/fr//pubs/archive/40671.pdf
+
 .. autosummary::
    :toctree: api/