chore: update README (#761)

* chore: update README

Co-authored-by: Jovan Veljanoski <jovan.veljanoski@gmail.com>

README.md

@@ -0,0 +1,70 @@
+
+[![Documentation](https://readthedocs.org/projects/vaex/badge/?version=latest)](https://docs.vaex.io)
+
+# What is Vaex?
+
+Vaex is a high performance Python library for lazy **Out-of-Core DataFrames**
+(similar to Pandas), to visualize and explore big tabular datasets. It
+calculates *statistics* such as mean, sum, count, standard deviation etc, on an
+*N-dimensional grid* for more than **a billion** (`10^9`) samples/rows **per
+second**. Visualization is done using **histograms**, **density plots** and **3d
+volume rendering**, allowing interactive exploration of big data. Vaex uses
+memory mapping, zero memory copy policy and lazy computations for best
+performance (no memory wasted).
+
+# Key features
+## Instant opening of Huge data files (memory mapping)
+[HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) and [Apache Arrow](https://arrow.apache.org/) supported.
+
+![opening1a](https://user-images.githubusercontent.com/1765949/82818563-31c1e200-9e9f-11ea-9ee0-0a8c1994cdc9.png)
+
+
+![opening1b](https://user-images.githubusercontent.com/1765949/82820352-49e73080-9ea2-11ea-9153-d73aa399d329.png)
+
+[Read the documentation on how to efficiently convert your data](https://docs.vaex.io/en/latest/example_io.html) from CSV files, Pandas DataFrames, or other sources.
+
+
+Lazy streaming from S3 supported in combination with memory mapping.
+
+![opening1c](https://user-images.githubusercontent.com/1765949/82820516-a21e3280-9ea2-11ea-948b-07df26c4b5d3.png)
+
+
+## Expression system
+Don't waste memory or time with feature engineering, we (lazily) transform your data when needed.
+
+
+![expression](https://user-images.githubusercontent.com/1765949/82818733-70f03300-9e9f-11ea-80b0-ab28e7950b5c.png)
+
+
+
+## Out-of-core DataFrame
+Filtering and evaluating expressions will not waste memory by making copies; the data is kept untouched on disk, and will be streamed only when needed. Delay the time before you need a cluster.
+
+
+![occ-animated](https://user-images.githubusercontent.com/1765949/82821111-c6c6da00-9ea3-11ea-9f9e-498de8133cc2.gif)
+
+## Fast groupby / aggregations
+Vaex implements parallelized, highly performant `groupby` operations, especially when using categories (>1 billion/second).
+
+
+![groupby](https://user-images.githubusercontent.com/1765949/82818807-97ae6980-9e9f-11ea-8820-41dd4441057a.png)
+
+
+## Fast and efficient join
+Vaex doesn't copy/materialize the 'right' table when joining, saving gigabytes of memory. With subsecond joining on a billion rows, it's pretty fast!
+
+![join](https://user-images.githubusercontent.com/1765949/82818840-a268fe80-9e9f-11ea-8ba2-6a6d52c4af88.png)
+
+## More features
+
+ * Remote DataFrames (documentation coming soon)
+ * Integration into [Jupyter and Voila for interactive notebooks and dashboards](https://vaex.readthedocs.io/en/latest/tutorial_jupyter.html)
+ * [Machine Learning without (explicit) pipelines](https://vaex.readthedocs.io/en/latest/tutorial_ml.html)
+
+
+# Learn how to use Vaex efficiently
+ * [Follow our tutorials](https://docs.vaex.io/en/latest/tutorials.html)
+ * Watch our more recent talks:
+   * [PyData London 2019](https://www.youtube.com/watch?v=2Tt0i823-ec)
+   * [SciPy 2019](https://www.youtube.com/watch?v=ELtjRdPT8is)
+ * Contact us for training or enterprise support at https://vaex.io/

dodo.py

@@ -4,6 +4,7 @@
 import pkg_resources
 import vaex.meta._version
 import re
+import shutil
 
 
 def task_mybinder():
@@ -24,3 +25,16 @@ def action(targets):
         'targets': ["binder/requirements.txt"],
         'file_dep': ['packages/vaex-meta/vaex/meta/_version.py']
         }
+
+
+def task_sync_readme():
+    """Make the README for veax-meta up to date"""
+
+    def action(targets):
+        shutil.copy('README.md', targets[0])
+
+    return {
+        'actions': [action],
+        'targets': ["packages/vaex-meta/README.md"],
+        'file_dep': ['README.md']
+        }