Geopandas is an awesome project that brings the power of pandas to geospatial data. I am not sure if we can load GPX data directly, so for this notebook I will use a GeoJSON that I previously converted from a GPX. (In a future post I will try to write a GPX reader for geopandas.)

Let's load the data!

In [2]:

import geopandas as gpd

fname = "./data/2013-04-29-Running.geojson"

df = gpd.read_file(fname)

print(type(df))

df

<class 'geopandas.geodataframe.GeoDataFrame'>

Out[2]:

	geometry
0	(LINESTRING (-46.7350076045841 -23.55963248759...

That is a GeoDataFrame with only one column. Let's see if slicing it we get GeoSeries.

In [3]:

df = df.ix[0]

print('{!r}'.format(type(df)))

<class 'pandas.core.series.Series'>

Apparently not. As you can see the "Geo" type was not preserved. To fix that we can just call the GeoSeries method on the pandas Series object.

In [4]:

series = gpd.GeoSeries(df)
type(series)

Out[4]:

geopandas.geoseries.GeoSeries

Now let's explore what is in this GeoSeries object.

In [5]:

series.total_bounds  # Nice tuple that we can use for axis lims/bbox.

Out[5]:

(-46.737991729751229,
 -23.564499272033572,
 -46.713051293045282,
 -23.551835222169757)

In [6]:

series.bounds  # Same as above, but as a DataFrame.

Out[6]:

	minx	miny	maxx	maxy
geometry	-46.737992	-23.564499	-46.713051	-23.551835

In [7]:

deg2km = 111

series.length * deg2km

Out[7]:

geometry    7.278114
dtype: float64

In [8]:

series.centroid

Out[8]:

geometry    POINT (-46.72445652998366 -23.55928803359001)
dtype: object

The GeoSeries and GeoDataFrame have plenty more useful method and properties that I did not show here. I will address some of them in another post.

Let's finish this with my favorite feature, plotting!

In [9]:

ax = series.plot()

Better yet, let's plot it as UTM instead of WGS84.

In [10]:

original = dict(ellps='WGS84', datum='WGS84', proj='longlat')

series.crs = original

target = dict(ellps='WGS84', datum='WGS84', proj='utm', zone=23, units='km', no_defs=True)

ax = series.to_crs(crs=target).plot()

PS: Here is an "eastern egg" that I found out while writing this post. IPython notebook rich display renders Shapely objects.

In [11]:

print('{!r}'.format(type(series.values[0])))

series.values[0]

<class 'shapely.geometry.multilinestring.MultiLineString'>

Out[11]:

In [12]:

HTML(html)

Out[12]:

This post was written as an IPython notebook. It is available for download or as a static html.

python4oceanographers by Filipe Fernandes is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://ocefpaf.github.io/.

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