This lesson is in the early stages of development (Alpha version)

Explore and Plot by Shapefile Attributes


Teaching: 40 min
Exercises: 20 min
  • How can I compute on the attributes of a spatial object?

  • Query attributes of a spatial object.

  • Subset spatial objects using specific attribute values.

  • Plot a shapefile, colored by unique attribute values.

# learners will have this data loaded from previous episodes
point_HARV = gpd.read_file("data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp")
lines_HARV = gpd.read_file("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp")
aoi_boundary_HARV <- gpd.read_file(

Things You’ll Need To Complete This Episode

See the lesson homepage for detailed information about the software, data, and other prerequisites you will need to work through the examples in this episode.

This episode continues our discussion of shapefile attributes and covers how to work with shapefile attributes in Python. It covers how to identify and query shapefile attributes, as well as how to subset shapefiles by specific attribute values. Finally, we will learn how to plot a shapefile according to a set of attribute values.

Load the Data

We will continue using the geopandas, and rioxarray and matplotlib.pyplot packages in this episode. Make sure that you have these packages loaded. We will continue to work with the three shapefiles that we loaded in the Open and Plot Shapefiles in R episode.

Query Shapefile Metadata

As we discussed in the Open and Plot Shapefiles in R episode, we can view metadata associated with a GeoDataFrame using:

We started to explore our point_HARV object in the previous episode. We can view the object with point_HARV or print a summary of the object itself to the console.


We view the columns in lines_HARV with .columns to count the number of attributes associated with a spatial object too. Note that the geometry is just another column and counts towards the total.


Challenge: Attributes for Different Spatial Classes

Explore the attributes associated with the point_HARV and aoi_boundary_HARV spatial objects.

  1. How many attributes does each have?
  2. Who owns the site in the point_HARV data object?
  3. Which of the following is NOT an attribute of the point_HARV data object?

    A) Latitude B) County C) Country


1) To find the number of attributes, we use the len() and .columns attribute:


2) Ownership information is in a column named Ownership:


3) To see a list of all of the attributes, we can use the .columns method:


“Country” is not an attribute of this object.

Explore Values within One Attribute

We can explore individual values stored within a particular attribute. Comparing attributes to a spreadsheet or a data frame, this is similar to exploring values in a column. We did this with the gapminder dataframe in an earlier lesson. For GeoDataFrames, we can use the same syntax: GeoDataFrame.attributeName or GeoDataFrame["attributeName"].

We can see the contents of the TYPE field of our lines shapefile:


To see only unique values within the TYPE field, we can use the np.unique() function for extracting the possible values of a categorical (or numerical) variable.


Subset Shapefiles

We can use the filter() function from dplyr that we worked with in an earlier lesson to select a subset of features from a spatial object in Python, just like with data frames.

For example, we might be interested only in features that are of TYPE “footpath”. Once we subset out this data, we can use it as input to other code so that code only operates on the footpath lines.

footpath_HARV = lines_HARV[lines_HARV.TYPE == "footpath"]

Our subsetting operation reduces the features count to 2. This means that only two feature lines in our spatial object have the attribute TYPE == footpath. We can plot only the footpath lines:


There are two features in our footpaths subset. Why does the plot look like there is only one feature? Let’s adjust the colors used in our plot. If we have 2 features in our vector object, we can plot each using a unique color by assigning a color map, or cmap to each geometry/row in our GeoDataFrame. We can also alter the default line thickness by using the size = parameter, as the default value can be hard to see.

footpath_HARV.plot(cmap="viridis", linewidth=4)

Now, we see that there are in fact two features in our plot!

Challenge: Subset Spatial Line Objects Part 1

Subset out all woods road from the lines layer and plot it. There are many more color maps to use, so if you’d like, do a web search to find a matplotlib cmap that works better for this plot than viridis.


First we will save an object with only the boardwalk lines:

woods_road_HARV = lines_HARV[lines_HARV.TYPE == "woods_road_HARV"]

Let’s check how many features there are in this subset:


Now let’s plot that data:

woods_road_HARV.plot(cmap="viridis", linewidth=3)

Adjust Line Width

We adjusted line color by applying an arbitrary color map earlier. If we want a unique line color for each attribute category in our GeoDataFrame, we can use the following argument, column, as well as some style arguments to improv ethe visuals.

We already know that we have four different TYPE levels in the lines_HARV object, so we will set four different line colors.

import matplotlib.pyplot as plt"ggplot")
lines_HARV.plot(column="TYPE", linewidth=3, legend=True, figsize=(16,10))

Our map is starting together, in the next lesson we will add our Canopy Height Model that we calculated in an earlier episode.

Challenge: Plot Polygon by Attribute

  1. Create a map of the state boundaries in the United States using the data located in your downloaded data folder: NEON-DS-Site-Layout-Files/US-Boundary-Layers\US-State-Boundaries-Census-2014. Apply a fill color to each state using its region value. Add a legend.


First we read in the data and check how many levels there are in the region column:

state_boundary_US =


Now we can create our plot:

state_boundary_US.plot(column = "region", linewidth = 2, legend = True, figsize=(20,5))

Key Points

  • A GeoDataFrame in geopandas is similar to standard pandas data frames and can be manipulated using the same functions.

  • Almost any feature of a plot can be customized using the various functions and options in the matplotlib package.