{"nbformat": 4, "nbformat_minor": 2, "metadata": {"kernelspec": {"language": "python", "name": "python3", "display_name": "Python 3"}, "language_info": {"version": "3.6.6", "codemirror_mode": {"version": 3, "name": "ipython"}, "name": "python", "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py", "mimetype": "text/x-python"}, "nikola": {"author": "Ian Rose, Tony Fast, Saul Shanabrook", "type": "text", "description": "# Using Ibis and Altair for OmniSci\n\nIn this notebook, we will use Ibis to build OmniSci query and then use Altair to compose a visualization on top of it.\n\nWe will be plotting a map of a bunch of tweets.\n\n## Assembling the Query\nFirst, let's connect to the omnisci backend and assemble the query:", "date": "2018-12-07 16:41:35 UTC", "slug": "Ibis + Altair.ipynb", "title": "Ibis + Altair.ipynb"}}, "cells": [{"source": ["# Using Ibis and Altair for OmniSci\n", "\n", "In this notebook, we will use Ibis to build OmniSci query and then use Altair to compose a visualization on top of it.\n", "\n", "We will be plotting a map of a bunch of tweets.\n", "\n", "## Assembling the Query\n", "First, let's connect to the omnisci backend and assemble the query:"], "cell_type": "markdown", "metadata": {}}, {"execution_count": 1, "outputs": [{"output_type": "stream", "text": ["C:\\Anaconda3\\lib\\site-packages\\ibis\\sql\\postgres\\compiler.py:223: UserWarning: locale specific date formats (%c, %x, %X) are not yet implemented for Windows\n", " 'for %s' % platform.system()\n"], "name": "stderr"}], "cell_type": "code", "source": ["import omnisci_renderer\n", "import ibis\n", "import altair as alt"], "metadata": {}}, {"execution_count": 2, "outputs": [], "cell_type": "code", "source": ["omnisci_cli = ibis.mapd.connect(\n", " host='metis.mapd.com', user='mapd', password='HyperInteractive',\n", " port=443, database='mapd', protocol= 'https'\n", ")"], "metadata": {}}, {"execution_count": 3, "outputs": [], "cell_type": "code", "source": ["t = omnisci_cli.table('tweets_nov_feb')"], "metadata": {}}, {"execution_count": 4, "outputs": [{"output_type": "stream", "text": ["SELECT \"goog_x\" AS x, \"goog_y\" AS y, \"tweet_id\" AS rowid\n", "FROM tweets_nov_feb\n"], "name": "stdout"}], "cell_type": "code", "source": ["expr = t[t.goog_x.name('x'), t.goog_y.name('y'), t.tweet_id.name('rowid')]\n", "print(expr.compile())"], "metadata": {}}, {"source": ["## Browser rendering\n", "Now, let's first try rendering this by getting a Pandas DataFrame \n", "and rendering this on the browser"], "cell_type": "markdown", "metadata": {}}, {"execution_count": 5, "outputs": [{"execution_count": 5, "data": {"text/plain": [" x y rowid\n", "0 14901896.0 4116482.50 528340935361318912\n", "1 16176363.0 -4547984.00 528340935378096128\n", "2 -9273029.0 5176628.00 528340939178536960\n", "3 3618257.5 4582324.00 528340939178524672\n", "4 -5483653.0 -2930803.75 528340943553171456"], "text/html": ["\n", "\n", "
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