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apps/labs/posts/ibis-python-data-analysis-productivity-framework.md

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+---
+title: "Ibis: Python data analysis productivity framework"
+author: ivan-ogasawara
+published: July 9, 2019
+description: ''
+category: [PyData ecosystem]
+featuredImage:
+  src: /posts/hello-world-post/featured.png
+  alt: 'Excellent alt-text describing the featured image'
+hero:
+  imageSrc: /posts/hello-world-post/hero.jpeg
+  imageAlt: 'Excellent alt-text describing the hero image'
+---
+
+Ibis is a library pretty useful on data analysis tasks that provides a
+pandas-like API that allows operations like create filter, add columns,
+apply math operations etc in a `lazy` mode so all the operations are
+just registered in memory but not executed and when you want to get the
+result of the expression you created, Ibis compiles that and makes a
+request to the remote server (remote storage and execution systems like
+Hadoop components or SQL databases). Its goal is to simplify analytical
+workflows and make you more productive.
+
+Ibis was created by [Wes McKinney](https://github.com/wesm) and is
+mainly maintained by [Phillip Cloud](https://github.com/cpcloud) and
+[Krisztián Szűcs](https://github.com/kszucs). Also, recently, I was
+invited to become a maintainer of the Ibis repository!
+
+Maybe you are thinking: \"why should I use Ibis?\". Well, if you have
+any of the following issues, probably you should consider using Ibis in
+your analytical workflow!
+
+-   if you need to get data from a SQL database but you don't know much
+    about SQL \...
+-   if you create SQL statements manually using string and have a lot of
+    `IF`'s in your code that compose specific parts of your SQL code
+    (it could be pretty hard to maintain and it will makes your code
+    pretty ugly) \...
+-   if you need to handle data with a big volume \...
+
+If you want to learn more about ibis consider taking a look at these
+tutorials:
+
+-   <https://docs.ibis-project.org/tutorial.html>
+
+Do you want to watch some interesting video about Ibis? Check this out:
+
+-   <https://www.youtube.com/embed/8Tzh42mQjrw?start=1625>
+
+**Now, let's check out some work developed here at Quansight in the
+last months!**
+
+During the last months **OmniSci** and **Quansight** were working
+together to add a backend to Ibis for **OmniSciDB** (formerly MapD
+Core)! In a few words, OmniSciDB is an in-memory, column store, SQL
+relational database designed from the ground up to run on GPUs. If you
+don't know yet this amazing database, I invite you to [check it
+out](https://omnisci.com).
+
+The implementation of this new backend also resulted in the creation of
+new expressions/operators on Ibis core, such as:
+
+-   GeoSpatial data types and operations
+-   Trigonometric operations
+-   Some statistcal operations
+
+First, let's connect to a *OmniSciDB* and play with this new features!
+
+``` python
+# install the dependencies if you need!
+# !conda install -y ibis-framework=1.1.0 pyarrow pymapd vega geopandas geoalchemy2 shapely matplotlib --force-reinstall
+```
+
+``` python
+import ibis
+from matplotlib import pyplot as plt
+
+print('ibis:', ibis.__version__)
+```
+
+    ibis: 1.2.0+7.g3afa8b0d
+
+``` python
+# metis.mapd.com is used in some OmniSci docs
+# but maybe you want to install your own OmniSciDB instance
+# you can take a look into installation section at 
+# https://www.omnisci.com/docs/latest/
+# also you maybe want to check the omniscidb-cpu conda package
+# conda install -c conda-forge omniscidb-cpu
+# if you need any help, feel free to open an issue at
+# https://github.com/conda-forge/omniscidb-cpu-feedstock/
+omniscidb_cli = ibis.mapd.connect(
+    host='metis.mapd.com', 
+    user='mapd', 
+    password='HyperInteractive',
+    port=443, 
+    database='mapd',
+    protocol='https'
+)
+```
+
+### GeoSpatial features
+
+You need to handle geospatial data in a esier way?
+
+Let's take a look inside `zipcodes_2017` table!
+
+Well, currently `omniscidb` backend doesn't support `geopandas` output,
+so let's use a workaround for that! It should be implemented into
+`omniscidb` backend soon! (see:
+[gist-code](https://gist.githubusercontent.com/xmnlab/587dd1bde44850f3117a1087ed3f0f28/raw/0750400db90cf97319a91aa514648c31ad4ace45/omniscidb_geopandas_output.py))
+
+``` python
+gist_url = 'https://gist.githubusercontent.com/xmnlab/587dd1bde44850f3117a1087ed3f0f28/raw/0750400db90cf97319a91aa514648c31ad4ace45/omniscidb_geopandas_output.py'
+!wget {gist_url} -O omniscidb_geopandas_output.py
+```
+
+    --2019-07-05 11:31:57--  https://gist.githubusercontent.com/xmnlab/587dd1bde44850f3117a1087ed3f0f28/raw/0750400db90cf97319a91aa514648c31ad4ace45/omniscidb_geopandas_output.py
+    Resolviendo gist.githubusercontent.com (gist.githubusercontent.com)... 151.101.48.133
+    Conectando con gist.githubusercontent.com (gist.githubusercontent.com)[151.101.48.133]:443... conectado.
+    Petición HTTP enviada, esperando respuesta... 200 OK
+    Longitud: 1874 (1,8K) [text/plain]
+    Guardando como: “omniscidb_geopandas_output.py”
+
+    omniscidb_geopandas 100%[===================>]   1,83K  --.-KB/s    en 0s      
+
+    2019-07-05 11:31:57 (70,1 MB/s) - “omniscidb_geopandas_output.py” guardado [1874/1874]
+
+``` python
+# workaround to use geopandas output
+from omniscidb_geopandas_output import enable_geopandas_output 
+enable_geopandas_output(omniscidb_cli)
+```
+
+``` python
+t = omniscidb_cli.table('zipcodes_2017')
+display(t)
+```
+
+![A DatabaseTable with its data types](a0a51ad71e1a32140f3e47e71145e6a67d061750.png)
+
+``` python
+print('# rows:', t.count().execute())
+```
+
+    # rows: 33144
+
+This table has \~33k rows. For this example, let's use just the first
+1k rows.
+
+``` python
+expr = t[t.omnisci_geo].head(1000)
+df = expr.execute()
+```
+
+Instead of getting all rows from the database and get from that the
+first 1000 rows, Ibis will prepare a SQL statement to get just the first
+1000 rows! So it reduces the memory consuming to just the data you need!
+
+This is what Ibis will request to the database:
+
+``` python
+print(expr.compile())
+```
+
+    SELECT "omnisci_geo"
+    FROM zipcodes_2017
+    LIMIT 1000
+
+Of course geospatial data reading as text wouldn't be useful, so let's
+plot the result!
+
+**Remember: we are using geopandas here!**
+
+``` python
+# let's add some custom style :)
+style_kwds = {
+    'linewidth': 2,
+    'markersize': 2,
+    'facecolor': 'red',
+    'edgecolor': 'red'
+}
+
+df['omnisci_geo'].iloc[::3].plot(**style_kwds)
+plt.show()
+```
+
+![A scatterplot graph clustered in the upper right corner. It is clusetered -120 to 80 on the X axis and 40-60 in the Y axis.](e62b7c1311b137ea2d1bfd6e7715369df26b2570.png)
+
+### Trigonometric operations
+
+Currently the OmniSciDB backend supports the follow trigonometric
+operations: `acos`, `asin`, `atan`, `atan2`, `cos`, `cot`, `sin`, `tan`.
+
+Let's check an example using a `sin` operation over `rowid` from
+`zipcodes_2017`.
+
+``` python
+# if you want to use a SQL statement try`sql` method!
+t = omniscidb_cli.sql('select rowid from zipcodes_2017')
+
+expr = t[t.rowid, t.rowid.sin().name('rowid_sin')].sort_by('rowid').head(100)
+expr.execute().rowid_sin.plot()
+plt.show()
+```
+
+![A sine wave plot that repeats many times, the high points at 1,-- and -1.00. The X axis is 0 to 80.](70f0a567ee713d1392bab6d8fef07bbe9777c033.png)
+
+### Some statistical operations
+
+The OmniSciDB Ibis backend also implements some statistical operations,
+such as: `Correlation (corr)`, `Standard Deviation (stddev)`,
+`Variance (var)` and `Covariance (cov)`.
+
+Let's check a pretty simple example: if there is any correlation in
+this dataset between `per capita income` and `education`.
+
+``` python
+t = omniscidb_cli.table('demo_vote_clean')
+# remove some conflictives fields: 'TYPE', 'NAME', 'COUNTY' field
+fields = [name for name in t.schema().names if name not in ('TYPE', 'NAME', 'COUNTY')]
+t = t[fields].distinct()
+t.PerCapitaIncome.corr(t.Education).execute()
+```
+
+    0.7212061029308654
+
+The result `~0.72` means that `Per Capita Income` and `Education` has a
+positive correlation in this dataset.
+
+### Conclusions
+
+Ibis is a cool library that can help you in your data analysis tasks. If
+you already use pandas, it will be pretty easy to add Ibis in your
+workflow!
+
+So \...
+
+-   Are you excited to use Ibis? [Try it out
+    now](https://docs.ibis-project.org/getting-started.html)!
+-   Have you already used Ibis? Reach out to me,
+    <ivan.ogasawara@quansight.com>, and share your experience!
+-   Are you interested in contributing to Ibis? Check the [good first
+    issues](https://github.com/ibis-project/ibis/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)
+    label on GitHub!
+-   Do you want to add new features and want to fund Ibis? Contact us at
+    <info@quansight.com>