{
"cells": [
{
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"+ title: Interactive maps with Python made easy: Introducing Geoviews \n",
"+ date: 2019-07-28\n",
"+ modified: 2019-08-13\n",
"+ tags: python, geoviews, holoviews, bokeh, maps, pyviz, holoviz\n",
"+ Slug: interactive-maps-made-easy-geoviews\n",
"+ Category: Python\n",
"+ Authors: MC\n",
"+ Summary: Do you want to build map visualizations in Python? Look no further than GeoViews. It is not only super simple to use but also offers several interactive features that make your visualization stand out. Using geo spatial data from our bike rental data set we explore some of the possibilities. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get this post as an interactive Jupyter Notebook and execute the code via Binder:\n",
""
]
},
{
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"metadata": {},
"source": [
"## Motivation \n",
"\n",
"Map visualizations are an effective way to gain insights from geo-spatial data. In a previous [post]({filename}/kvb_part2.md) we looked at rental bike data in Cologne. For that, we used the `Basemap` extension for `Matplotlib`. However, using Matplotlib often feels cumbersome and the output is static. Moreover, the charts look kind of outdated. In a follow up [post]({filename}/kvb_bokeh1.md) we dealt with these issues by introducing `bokeh` as an alternative. The result was a good looking visualization with lots of interactivity.
\n",
"However, when thinking about visualization libraries in Python the whole landscape is way wider: "
]
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"
\n", " | lat | \n", "lon | \n", "scrape_weekday | \n", "
---|---|---|---|
bike_id | \n", "\n", " | \n", " | \n", " |
21536 | \n", "50.952545 | \n", "6.947140 | \n", "Tue | \n", "
21083 | \n", "50.943495 | \n", "6.933420 | \n", "Tue | \n", "
21648 | \n", "50.950995 | \n", "6.926986 | \n", "Tue | \n", "
22171 | \n", "50.927228 | \n", "6.907721 | \n", "Wed | \n", "
21150 | \n", "50.940424 | \n", "7.010536 | \n", "Wed | \n", "