chore(docs): add contains to docs

docarray · Jan 10, 2022 · 9112c5a · 9112c5a
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diff --git a/docs/fundamentals/documentarray/access-attributes.md b/docs/fundamentals/documentarray/access-attributes.md
@@ -159,7 +159,7 @@ for d in da:
 
 ## Dunder syntax for nested attributes
 
-Some attributes are nested by nature, e.g. `.tags` and `.scores`. Accessing the deep nested value is easy thanks to the dunder syntax. You can access `.tags['key1']` via `d[:, 'tags__key1']`. 
+Some attributes are nested by nature, e.g. `.tags` and `.scores`. Accessing the deep nested value is easy thanks to the dunder (double under) expression. You can access `.tags['key1']` via `d[:, 'tags__key1']`. 
 
 Let's see an example,
 

diff --git a/docs/fundamentals/documentarray/access-elements.md b/docs/fundamentals/documentarray/access-elements.md
@@ -84,6 +84,23 @@ print(da['7e27fa246e6611ec9a441e008a366d49', '7e27fb826e6611ec9a441e008a366d49']
 
 No need to worry about efficiency here, it is `O(1)`.
 
+Based on the same technique, one can check if a Document is inside a DocumentArray via Python `in` syntax:
+
+```python
+from docarray import DocumentArray, Document
+
+da = DocumentArray.empty(10)
+
+da[0] in da
+Document() in da
+```
+
+```text
+True
+False
+```
+
+
 ## Index by boolean mask
 
 You can use a boolean mask to select Documents. This becomes useful when you want to update or filter our certain Documents:

diff --git a/docs/get-started/what-is.md b/docs/get-started/what-is.md
@@ -1,8 +1,8 @@
 # What is DocArray?
 
-- It is like JSON, but for intensive computing.
+- It is like JSON, but for intensive computation.
 - It is like `numpy.ndarray`, but for unstructured data. 
-- It is like `pandas.DataFrame`, but for nested and mixed media data.
+- It is like `pandas.DataFrame`, but for nested and mixed media data with embeddings.
 - It is like Protobuf, but for data scientists and deep learning engineers. 
 
 If you are a **data scientist** who works with image, text, video, audio data in Python all day, you should use DocArray: it can greatly accelerate the work on representing, embedding, matching, visualizing, evaluating, sharing data; while stay close with your favorite toolkits, e.g. Torch, Tensorflow, ONNX, PaddlePaddle, JupyterLab, Google Colab.