Lab 01: Dealing with Spatial Data in Python#

In this tutorial, we will work on dealing with general non-spatial and spatial data using the Python library pandas and geopandas. Many concepts/techniques will be echoed to the ones your learned from Lecture 02: Spatial Database I.

To follow this tutorial, you should have installed Jupyter Notebook (or Jupyter Lab) on your own computer (note: computers in the lab should have already installed it).

Requred packages include:

Mostly, you can use the commond like pip install pandas to install the package

Basics in Pandas (for non-spatial data)#

Pandas is currently the mostly important tool for data scientists working in Python. It is the backbone of many state-of-the-art techniques like machine learning and visualization. Here we cover the basics of using Pandas. For more comprehensive tutorial, follow this video and this post

First of all, the two key components in Pandas are Series and DataFrame. A Series is essentially a column, and a DataFrame is a multi-dimensional table made up of a collection of Series. These two components are quite similar in that many operations that you can do with one you can do with the other, such as filling in null values and calculating the mean.

Series and DataFrame

Create dataframe from scratch#

There are many ways to create a DataFrame from scratch, but a great option is to just use a simple dict (this is a common data structure called dictionary, which is composed by a key:value pair). Each key:value item in data corresponds to a column in the resulting DataFrame.

Let’s say we have a fruit stand that sells apples and oranges. We want to have a column for each fruit and a row for each customer purchase. To organize this as a dictionary for pandas we could do something like:

import pandas as pd
data = {
    'apples': [3, 2, 0, 1], 
    'oranges': [0, 3, 7, 2]
purchases = pd.DataFrame(data)
apples oranges
0 3 0
1 2 3
2 0 7
3 1 2

The Index of this DataFrame was given to us as the numbers 0-3. We could also create our own when we initialize the DataFrame.

purchases = pd.DataFrame(data, index=['June', 'Robert', 'Lily', 'David'])
apples oranges
June 3 0
Robert 2 3
Lily 0 7
David 1 2

So now we could locate a customer’s order by using their name:

apples     3
oranges    0
Name: June, dtype: int64

Loading data#

We can also load in data from data formats like csv, json, txt, and so on. For example, if you downloaded purchase.csv to your local directory, you should be able to load the data by running:

purchases_loaded = pd.read_csv('purchase.csv')
Unnamed:0 apples oranges
0 June 3 0
1 Robert 2 3
2 Lily 0 7
3 David 1 2

Note here that CSVs don’t have indexes like DataFrames, so we need to designate the index_col when reading:

purchases_loaded = pd.read_csv('purchase.csv', index_col=0)
apples oranges
June 3 0
Robert 2 3
Lily 0 7
David 1 2

Viewing your data#

There are many operations to view/describe your data. For example, you can use .head() to check the first several rows of your dataframe, .tail() to see the last severl rows, .info() to have a list of information about your dataframe (you should always run it first after your data is loaded), .shape to see the dimension of your dataframe (i.e., how many rows and columns are there?), etc. Let’s try .info() here and you should also try the others yourself.
<class 'pandas.core.frame.DataFrame'>
Index: 4 entries, June to David
Data columns (total 2 columns):
 #   Column   Non-Null Count  Dtype
---  ------   --------------  -----
 0   apples   4 non-null      int64
 1   oranges  4 non-null      int64
dtypes: int64(2)
memory usage: 96.0+ bytes

From the output, you can see our loaded purchases_loaded dataframe has 4 entries (rows), and there are two columnes, each has 4 non-null values and their data types are both int64 (i.e., integer with 64 digits). Data type here is an important concept (we have covered it in our lecture as well). Different data types might have various operations/analysis. See below table for a full list of data types in Pandas, and Python and NumPy (another important package in Python).


Querying (selecting, slicing, extracting) Dataframe#

Similar to the complex DBMS, Pandas also support selecting, slicing or extracting data from the Dataframe.

Select by column#

We can extract a column using square brackets like this:

purchases_apple = purchases_loaded['apples']
June       3
Robert     2
Lily       0
David      1
Name: apples, dtype: int64

Notice that the returned purchases_apple is a Series. To extract a column as a DataFrame, we need to pass a list of column names. In our case that’s just a single column:

purchases_apple = purchases_loaded[['apples']]

Select by row#

For rows, we can use two ways to extract data:

  • loc: locates by name

  • iloc: locates by numerical index

For example, we can select the row of June (how many apples and oranges June has got?) from our purchase_loaded dataframe.

purchases_June = purchases_loaded.loc["June"]
apples     3
oranges    0
Name: June, dtype: int64

Conditional selection#

So far, We’ve gone over how to select columns and rows, but what if we want to make a conditional selection?

For example, what if we want to filter our purchases_loaded DataFrame to show only people who bought apples less than 2?

To do that, we take a column from the DataFrame and apply a Boolean condition to it. Here’s an example of a Boolean condition:

condition = (purchases_loaded['apples'] < 2)
June       False
Robert     False
Lily        True
David       True
Name: apples, dtype: bool

A little bit more complex, how about showing people who bought apples less than 2 but oranges larger than 2? Can you try it? Hint, you need to use the logic operator & to connect two conditions.

GeoPandas for Spatial Data#

Geopandas is designed to process spatial data in Python. Geopandas combines the capabilities of the data analysis library pandas with other packages like shapely and fiona for managing spatial data. The main data structures in geopandas are GeoSeries and GeoDataFrame which extend the capabilities of Series and DataFrame from pandas.

The key difference between GeoDataFrame and pandas DataFrame is that a GeoDataFrame should contain at least one column for geometries. By default, the name of this column is 'geometry'. The geometry column is a GeoSeries which contains the geometries (points, lines, polygons, multipolygons etc.) as shapely objects.

Loading spatial data#

Spatial data that are in the format of geojson, shp, etc. can all be loaded as GeoPandas Dataframe by using the function read_file(). Let’s use a shapefile (shp) downloaded from OpenStreetMap as an example here. You can also find the specific data (building in Bristol) from Blackboard.

import geopandas as gpd

# Filepath
bristol_building_file = "./bristol-buildings.shp/gis_osm_buildings_a_free_1.shp" # make sure the directory is correct in your case

# Read the file
bristol_building = gpd.read_file(bristol_building_file)

# How does it look?
osm_id code fclass name type geometry
0 4309554 1500 building Bristol City Hall None POLYGON ((-2.60242 51.45242, -2.60241 51.45244...
1 4315318 1500 building Clifton Cathedral church POLYGON ((-2.61673 51.45965, -2.61673 51.45965...
2 4315809 1500 building Clifton Down Shopping Centre retail POLYGON ((-2.61133 51.46431, -2.61070 51.46441...
3 4317900 1500 building Za Za Bazaar None POLYGON ((-2.59869 51.45029, -2.59865 51.45042...
4 4317901 1500 building Mackenzies Café Bar commercial POLYGON ((-2.59851 51.45107, -2.59850 51.45110...

As we can see, the GeoDataFrame bristol_building contains various attributes in separate columns. The geometry column contains the spatial information (it is WKT format, which is implemented by shapely). We can next take a look of some of the basic information of bristol_building using the command:
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 149805 entries, 0 to 149804
Data columns (total 6 columns):
 #   Column    Non-Null Count   Dtype   
---  ------    --------------   -----   
 0   osm_id    149805 non-null  object  
 1   code      149805 non-null  int64   
 2   fclass    149805 non-null  object  
 3   name      7587 non-null    object  
 4   type      98915 non-null   object  
 5   geometry  149805 non-null  geometry
dtypes: geometry(1), int64(1), object(4)
memory usage: 6.9+ MB

What kind of information can you get from this output?

Since our data is intrinsically spatial (it has a geometry column), we can visualize it to understand the spatial distribution of the data better. plot() is the function for it:


Saving spatial data#

Once you are done with your process/analysis, you can also save your GeoDataFrame into files (e.g., .shp, .geojson, etc). Here, since we loaded data from .shp, let’s now try to save our data to .geojson:

bristol_building.to_file('osm_bristol_buildings.geojson', driver='GeoJSON') 
## this will save your data to the current directory same to this notebook. 
## you can check the current directory by ruing cwd = os.getcwd()

Retrieving data directly from OSM#

We have so far seen how to read spatial data from disk (i.e., the data is downloaded and saved on your local directory). Next, let’s see how we can retrieve data from OSM directlt using a library called pyrosm. With pyrosm, you can easily retrieve data from anywhere in the world based on OSM.PBF (a specific data format for OSM) files that are distributed by Geofabrik (this is where the Bristol buildings data were downloaded). The package aims to be an efficient way to parse OSM data covering large geographical areas (such as countries and cities)

Note that if you would like to be flexible about your download, e.g., selecting a bounding box by yourself rather than by administrative regions, you can consider using OSMnx library.

from pyrosm import OSM, get_data

# Download data for Bristol
bristol = get_data("bristol")

# Initialize the reader object for Bristol
osm = OSM(bristol)
Downloaded Protobuf data 'Bristol.osm.pbf' (22.96 MB) to:

In the first command, we downloaded the data for “Bristol” using the get_data function. This function in fact automates the data downloading process and stores the data locally in a temporary folder. The next step was to initialize a reader object called osm. The OSM() function takes the filepath to a given osm.pbf file as an input. Notice that at this point we actually didn’t yet read any data into GeoDataFrame.

OSM contains a lot of information about the world, which is contributed by citizens like you and me. In principle, we can retrieve information under various themes from OSM using the following functions.

  • road networks –> osm.get_network()

  • buildings –> osm.get_buildings()

  • Points of Interest (POI) –> osm.get_pois()

  • landuse –> osm.get_landuse()

  • natural elements –> osm.get_natural()

  • boundaries –> osm.get_boundaries()

Here, let’s extract the road network at Bristol from OSM:

bristol_roadnetwork = osm.get_network()
access bicycle bridge busway cycleway est_width foot footway highway junction ... tunnel turn width id timestamp version tags osm_type geometry length
0 None None None None None None None None secondary None ... None None None 190 0 -1 {"bus_name":"Chew Valley Explorer","route":"bus"} way MULTILINESTRING ((-2.60972 51.36571, -2.60955 ... 184.0
1 None None None None None None None None unclassified None ... None None None 193 0 -1 None way MULTILINESTRING ((-2.61912 51.36762, -2.61940 ... 55.0
2 None yes None None None None None None unclassified None ... None None None 196 0 -1 None way MULTILINESTRING ((-2.60464 51.37130, -2.60449 ... 1957.0
3 None None None None None None None None primary None ... None None None 209 0 -1 {"maxweight":"7.5","maxweight:conditional":"no... way MULTILINESTRING ((-2.36077 51.38521, -2.36083 ... 91.0
4 None None None None None None None None primary None ... None None None 210 0 -1 {"maxweight":"7.5","maxweight:conditional":"no... way MULTILINESTRING ((-2.36347 51.38456, -2.36342 ... 85.0

5 rows × 39 columns

We can get the lenth of this DataFrame (how many road network do we have in Bristol?) and some basic descritions of it by running:

bristol_roadnetwork.describe() # note that it only provide a statistical summary for columns whoes data type is numeric
id timestamp version length
count 9.684400e+04 96844.0 96844.0 96844.000000
mean 4.124801e+08 0.0 -1.0 111.237836
std 3.486543e+08 0.0 0.0 198.167947
min 1.900000e+02 0.0 -1.0 0.000000
25% 7.782050e+07 0.0 -1.0 21.000000
50% 3.113436e+08 0.0 -1.0 50.000000
75% 7.220495e+08 0.0 -1.0 114.000000
max 1.102395e+09 0.0 -1.0 7571.000000

Likewise, we can also plot it. Please try it yourself.

Coordinate Reference System for GeoDataFrame#

Another difference between GeoDataFrames and DataFrames is that the former has intrinsic coordinate reference system (CRS) as it has the geometry column. To check this information, we can call its attribute crs:
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

It shows that coordinates in geometry column are using the WGS 84 with a EPSG code 4326. In fact, it is the mostly used coordinate reference system (CRS) in spatial data science as it is a global coordinate system and has been used for GPS as well. However, as we covered in the lecture, those global CRSs are not that accurate for local regions. For the UK, or Bristol, a more commonly used CRS is EPSG:27700 (National Grid for Great Britain), and this CRS is also projected. Let’s then transfer bristol_roadnetwork from EPSG:4326 to EPSG:27700:

bristol_roadnetwork_reprojected = bristol_roadnetwork.to_crs(epsg=27700)
<Derived Projected CRS: EPSG:27700>
Name: OSGB36 / British National Grid
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: United Kingdom (UK) - offshore to boundary of UKCS within 49°45'N to 61°N and 9°W to 2°E; onshore Great Britain (England, Wales and Scotland). Isle of Man onshore.
- bounds: (-9.0, 49.75, 2.01, 61.01)
Coordinate Operation:
- name: British National Grid
- method: Transverse Mercator
Datum: Ordnance Survey of Great Britain 1936
- Ellipsoid: Airy 1830
- Prime Meridian: Greenwich

Now we have an projected CRS for the road network data in Bristol. To confirm the difference, let’s take a look at the geometry of the first row in our original road network bristol_roadnetwork and the projected bristol_roadnetwork_reprojected.

orig_geom = bristol_roadnetwork.loc[0, "geometry"]
projected_geom = bristol_roadnetwork_reprojected.loc[0, "geometry"]

print("Orig:\n", orig_geom, "\n")
print("Proj:\n", projected_geom)
 MULTILINESTRING ((-2.6097212 51.3657117, -2.6095465 51.3656477), (-2.6095465 51.3656477, -2.6092646 51.3654812), (-2.6092646 51.3654812, -2.6088734 51.3653167), (-2.6088734 51.3653167, -2.6085088 51.3650842), (-2.6085088 51.3650842, -2.6083427 51.3649802), (-2.6083427 51.3649802, -2.6081261 51.3648105), (-2.6081261 51.3648105, -2.6078208 51.3645815)) 

 MULTILINESTRING ((357648.823472627 163137.45959180896, 357660.9265108949 163130.24084904365), (357660.9265108949 163130.24084904365, 357680.3978917962 163111.560813186), (357680.3978917962 163111.560813186, 357707.480545406 163093.04007779417), (357707.480545406 163093.04007779417, 357732.64877296326 163066.97239782498), (357732.64877296326 163066.97239782498, 357744.11646257003 163055.31031830708), (357744.11646257003 163055.31031830708, 357759.0393570516 163036.31243119104), (357759.0393570516 163036.31243119104, 357780.08292435773 163010.6684798217))

As we be seen, the coordinates that form our road segments (MULTILINESTRING) has changed from decimal degrees to meters. Next, let’s visualize it:


As you can see, the shape of the two road segments are quite different (e.g., the lenth, where the curve occures, etc.). This is exactly due to the difference between the two CRSs.

It is also worth noting here, the data type, MultiLineString, of the variables orig_geom and projected_geom are defined by shapely. It enables us to conduct these kind of spatial operations and visializations.


Computation on GeoDataFrame#

There are many operations embeded in GeoDataFrame that can be directly called to do some spatial computations. For example, we can get the area of buildings for our bristol_building dataframe:

bristol_building["building_area"] = bristol_building.area
/var/folders/xg/5n3zc4sn5hlcg8zzz6ysx21m0000gq/T/ipykernel_8067/ UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  bristol_building["building_area"] = bristol_building.area
count    1.498050e+05
mean     1.341308e-08
std      6.657677e-08
min      5.667000e-11
25%      6.084315e-09
50%      7.575225e-09
75%      1.033238e-08
max      1.038017e-05
Name: building_area, dtype: float64

Here, you can see a warning that WGS is a geographic CRS, hence the results of area mighht not be accurate. Can you project the dataframe to a projected coordicate reference system (e.g.,EPSG:27700 in our case)? After your projection, do the area computation again. What do the results look like? What is the unit of the area?

Spatial join#

As we discussed in the lecture, joining tables using keys is a core operation for DBMS. Regarding spatial data, spatial join is somewhat similar to table join but with the operation being based on geometries.

In this tutorial, we will try to conduct a spatial join and merge information between two GeoDataFrames. First, let’s read all restaurants (a type of Point of Interests (POI)) at Bristol from the OSM. Then, we combine information from restaurants to the underlying building (restaurants typically are within buildings). We will again use pyrosm for reading the data, but this time we will use the get_pois() function:

# Read Points of Interest (POI) using the same OSM reader object that was initialized earlier
# The custom_filter={"amenity": ["restaurant"]} indicates that we want only "restaurant", a type of POI
bristol_restaurants = osm.get_pois(custom_filter={"amenity": ["restaurant"]})
/Users/gy22808/opt/anaconda3/lib/python3.9/site-packages/pyrosm/ ShapelyDeprecationWarning: The array interface is deprecated and will no longer work in Shapely 2.0. Convert the '.coords' to a numpy array instead.
  gdf = get_poi_data(
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 645 entries, 0 to 644
Data columns (total 30 columns):
 #   Column            Non-Null Count  Dtype   
---  ------            --------------  -----   
 0   tags              613 non-null    object  
 1   changeset         140 non-null    float64 
 2   timestamp         645 non-null    int64   
 3   lon               139 non-null    float32 
 4   lat               139 non-null    float32 
 5   version           645 non-null    int8    
 6   id                645 non-null    int64   
 7   addr:city         484 non-null    object  
 8   addr:country      52 non-null     object  
 9   addr:housenumber  459 non-null    object  
 10  addr:housename    100 non-null    object  
 11  addr:postcode     592 non-null    object  
 12  addr:place        29 non-null     object  
 13  addr:street       552 non-null    object  
 14  email             25 non-null     object  
 15  name              643 non-null    object  
 16  opening_hours     85 non-null     object  
 17  operator          10 non-null     object  
 18  phone             95 non-null     object  
 19  website           228 non-null    object  
 20  amenity           645 non-null    object  
 21  internet_access   5 non-null      object  
 22  source            21 non-null     object  
 23  start_date        4 non-null      object  
 24  geometry          645 non-null    geometry
 25  osm_type          645 non-null    object  
 26  bar               2 non-null      object  
 27  building          496 non-null    object  
 28  building:levels   72 non-null     object  
 29  wikipedia         5 non-null      object  
dtypes: float32(2), float64(1), geometry(1), int64(2), int8(1), object(23)
memory usage: 141.8+ KB

From the info(), we can see that there are 642 restaurants in Bristol according to OSM. Note. that OSM is a valunteered geographic information platform. So the quality, accuracy, and completness of the data might be low. Next, let’s join data from bristol_buildings to the bristol_restaurants using sjoin() function from geopandas:

# Join information from buildings to restaurants
bristol_join = gpd.sjoin(bristol_restaurants, bristol_building)

# Print column names

# Show rows
Index(['tags', 'changeset', 'timestamp', 'lon', 'lat', 'version', 'id',
       'addr:city', 'addr:country', 'addr:housenumber', 'addr:housename',
       'addr:postcode', 'addr:place', 'addr:street', 'email', 'name_left',
       'opening_hours', 'operator', 'phone', 'website', 'amenity',
       'internet_access', 'source', 'start_date', 'geometry', 'osm_type',
       'bar', 'building', 'building:levels', 'wikipedia', 'index_right',
       'osm_id', 'code', 'fclass', 'name_right', 'type', 'building_area'],
tags changeset timestamp lon lat version id addr:city addr:country addr:housenumber ... building building:levels wikipedia index_right osm_id code fclass name_right type building_area
9 None 0.0 0 -2.611020 51.458733 0 853556896 None None None ... NaN NaN NaN 24609 451622999 1500 building The Clifton None 9.942395e-09
74 {"addr:suburb":"Clifton","contact:facebook":"h... 0.0 0 -2.610929 51.458771 0 4375465482 Bristol None None ... NaN NaN NaN 24609 451622999 1500 building The Clifton None 9.942395e-09
12 None 0.0 0 -2.625203 51.453415 0 1207448023 None None None ... NaN NaN NaN 1164 104679655 1500 building Avon Gorge Hotel hotel 8.777610e-08
14 None 0.0 0 -2.619858 51.455570 0 1386051923 Bristol None 4 ... NaN NaN NaN 23228 444980827 1500 building Rodney Hotel None 2.753519e-08
18 {"cuisine":"regional","outdoor_seating":"yes",... 0.0 0 -2.585233 51.451546 0 1881624837 Bristol None None ... NaN NaN NaN 2138 125956114 1500 building Hilton Garden Inn Bristol City Centre None 1.725836e-07
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
643 {"house":"terraced"} NaN 0 NaN NaN -1 1069613264 Bristol None 15 ... house 2 None 145380 1069613264 1500 building None house 6.423220e-09
643 {"house":"terraced"} NaN 0 NaN NaN -1 1069613264 Bristol None 15 ... house 2 None 145379 1069613263 1500 building None house 9.538465e-09
644 {"addr:suburb":"Clifton","cuisine":"indian","f... 0.0 0 NaN NaN -1 13721513418 Bristol NaN 12 ... yes NaN NaN 10989 279457775 1500 building None None 4.035725e-09
644 {"addr:suburb":"Clifton","cuisine":"indian","f... 0.0 0 NaN NaN -1 13721513418 Bristol NaN 12 ... yes NaN NaN 138593 13450709 1500 building The Mint Room None 1.037321e-08
644 {"addr:suburb":"Clifton","cuisine":"indian","f... 0.0 0 NaN NaN -1 13721513418 Bristol NaN 12 ... yes NaN NaN 138594 1003617567 1500 building Clifton Community Bookshop None 5.714835e-09

1037 rows × 37 columns

Now with this joined table, you can check which building each restaurant is locatd in. Note that after have joining information from the buildings to restaurants. The geometries of the left GeoDataFrame, i.e. restaurants, were kept by default as the geometries. So if we plot bristol_join, you will only see restaurants, rather than buildings + restaurant. Please try!

By default, sjoin() use the topological relation intersects. You can also specify this parameter (i.e., contains and within) in the function. More details can be found at:


So far, we simply used the plot() function to visualize GeoDataFrame. These maps are less appealing compared to the ones generated via GIS softwares. In fact, the package: matplotlib is very powerful in providing us beautiful visualization in Python. Let’s try it.

First, let’s add some legends to the bristol_building data using its building type:

ax = bristol_building.plot(column="type", cmap="RdYlBu", figsize=(12,12), legend=True)

Here, we used the parameter column to specify the attribute that is used to specify the color for each building (can be categorical or continuous). We used cmap to specify the colormap for the categories and we added the legend by specifying legend=True. Note that since the type of buildings for Bristol is very diverse, we see a long list of legend. There are ways to make it into two columns, for example. Can you explore it? Feel free to use Google search!

Another issue is that the map is in a very large map scale. Next, we would like to zoom in a little bit. To do so, we can use set_xlim() and set_ylim() functions:

# Zoom into city center by specifying X and Y coordinate extent
# These values should be given in the units that our data is presented (here decimal degrees)
xmin, xmax = -2.65, -2.55
ymin, ymax = 51.44, 51.48

# Plot the map again
ax = bristol_building.plot(column="type", cmap="RdYlBu", figsize=(12,12), legend=True)

# Control and set the x and y limits for the axis
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
(51.44, 51.48)

As you can see, we now zoomed in to the city center quite a lot. You can adjust the parameters yourself and test more!

Meanwhile, you may wonder whether we can overlay multiple dataframes into the map? The answer is yes. Here is a sample code:

# Zoom into city center by specifying X and Y coordinate extent
# These values should be given in the units that our data is presented (here decimal degrees)
xmin, xmax = -2.65, -2.55
ymin, ymax = 51.44, 51.48

# Plot the map again
ax = bristol_building.plot(column="type", cmap="RdYlBu", figsize=(12,12), legend=True)

# Plot the roads into the same axis
ax = bristol_roadnetwork.plot(ax=ax, edgecolor="gray", linewidth=0.75)

# Control and set the x and y limits for the axis
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
KeyboardInterrupt                         Traceback (most recent call last)
Input In [32], in <cell line: 10>()
      7 ax = bristol_building.plot(column="type", cmap="RdYlBu", figsize=(12,12), legend=True)
      9 # Plot the roads into the same axis
---> 10 ax = bristol_roadnetwork.plot(ax=ax, edgecolor="gray", linewidth=0.75)
     12 # Control and set the x and y limits for the axis
     13 ax.set_xlim([xmin, xmax])

File ~/opt/anaconda3/lib/python3.9/site-packages/geopandas/, in GeoplotAccessor.__call__(self, *args, **kwargs)
    961 kind = kwargs.pop("kind", "geo")
    962 if kind == "geo":
--> 963     return plot_dataframe(data, *args, **kwargs)
    964 if kind in self._pandas_kinds:
    965     # Access pandas plots
    966     return PlotAccessor(data)(kind=kind, **kwargs)

File ~/opt/anaconda3/lib/python3.9/site-packages/geopandas/, in plot_dataframe(df, column, cmap, color, ax, cax, categorical, legend, scheme, k, vmin, vmax, markersize, figsize, legend_kwds, categories, classification_kwds, missing_kwds, aspect, **style_kwds)
    701     markersize = df[markersize].values
    703 if column is None:
--> 704     return plot_series(
    705         df.geometry,
    706         cmap=cmap,
    707         color=color,
    708         ax=ax,
    709         figsize=figsize,
    710         markersize=markersize,
    711         aspect=aspect,
    712         **style_kwds,
    713     )
    715 # To accept pd.Series and np.arrays as column
    716 if isinstance(column, (np.ndarray, pd.Series)):

File ~/opt/anaconda3/lib/python3.9/site-packages/geopandas/, in plot_series(s, cmap, color, ax, figsize, aspect, **style_kwds)
    465     values_ = values[line_idx] if cmap else None
    466     color_ = expl_color[line_idx] if color_given else color
--> 468     _plot_linestring_collection(
    469         ax, lines, values_, color=color_, cmap=cmap, **style_kwds
    470     )
    472 # plot all Points in the same collection
    473 points = expl_series[point_idx]

File ~/opt/anaconda3/lib/python3.9/site-packages/geopandas/, in _plot_linestring_collection(ax, geoms, values, color, cmap, vmin, vmax, **kwargs)
    231 _expand_kwargs(kwargs, multiindex)
    233 segments = [np.array(linestring.coords)[:, :2] for linestring in geoms]
--> 234 collection = LineCollection(segments, **kwargs)
    236 if values is not None:
    237     collection.set_array(np.asarray(values))

File ~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/, in LineCollection.__init__(self, segments, zorder, *args, **kwargs)
   1442 kwargs.setdefault('facecolors', 'none')
   1443 super().__init__(
   1444     zorder=zorder,
   1445     **kwargs)
-> 1446 self.set_segments(segments)

File ~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/, in LineCollection.set_segments(self, segments)
   1455         seg = np.asarray(seg, float)
   1456     _segments.append(seg)
-> 1458 self._paths = [mpath.Path(_seg) for _seg in _segments]
   1459 self.stale = True

File ~/opt/anaconda3/lib/python3.9/site-packages/matplotlib/, in <listcomp>(.0)
   1455         seg = np.asarray(seg, float)
   1456     _segments.append(seg)
-> 1458 self._paths = [mpath.Path(_seg) for _seg in _segments]
   1459 self.stale = True


Congrats! You have now finished the very first lab of using Python to process spatial data. I hope you enjoyed it and have seen the power of GeoPandas, and Python in general, for processing and studying spatial data. It is also worth highlighting that the functions introduced in this tutorial are selective. There are way more interesting and useful functions provided by these aforementioned packages. I highly recommended you to explore them by yourself. Learning never stops!

Finally, let’s go back to the task of asking you to figure out how to better organize the long legend box. Below is the solution I found. There might be other ways of doing it. How is yours?

Basically, I used the parameter ‘legend_kwds’ to set up the number of columns (‘ncol’) to be 4. For more details, check its official documentation. Note that knowing how to read these kinds of documentations would be very helpful, so it is an important skill you should develop.

from mpl_toolkits.axes_grid1 import make_axes_locatable

xmin, xmax = -2.65, -2.55
ymin, ymax = 51.44, 51.48

fig, ax = plt.subplots(figsize=(12, 12))

                legend_kwds={'loc': 'lower left',
                             'ncol': 4,
                             'bbox_to_anchor': (0, 0, 0.5,0.5)})
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])