Read and write shapefiles

Learn how to read from, manage, and write to shapefiles. A shapefile data source behaves like other file formats within Spark (parquet, ORC, etc.). You can use shapefiles to read data from, or to write data to.

In this tutorial you will read from shapefiles, write results to new shapefiles, and partition data logically.

Read shapefiles

Prepare your input shapefile

1. Download the sample shapefile from ArcGIS Online.

Set up the workspace

1. Import the required modules.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   # Import the required modules
   import geoanalytics_fabric
   from geoanalytics_fabric.sql import functions as ST

2. Set the output directory to write your formatted data to.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   # Set the workspace
   output_dir = os.path.normpath(r"Files/shapefile_demo")

Read from your shapefile and display columns of interest

1. Read the shapefile into a DataFrame. Note that the folder containing the shapefile is specified, and not the full path to the .shp file. A folder can contain multiple shapefiles with the same schema and be read as a single DataFrame.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   # Read the shapefile into a DataFrame
   shapefileDF=spark.read.format("shapefile").load(r"Files/shapefile_demo/Mineplants")

2. Visualize a subset of the columns, including the geometry column, by showing a sample of five rows from the input.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   # Sample your DataFrame
   shapefileDF.select("commodity","COMPANY_NA","geometry").show(5, truncate=False)
   Result

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   +---------+-------------------------------+------------------------+
   |commodity|COMPANY_NA                     |geometry                |
   +---------+-------------------------------+------------------------+
   |Aluminum |Alcoa Inc                      |{"x":-87.336,"y":37.915}|
   |Aluminum |Century Aluminum Co            |{"x":-86.786,"y":37.942}|
   |Aluminum |Alcan Inc                      |{"x":-87.5,"y":37.65}   |
   |Aluminum |Ormet Corp                     |{"x":-90.923,"y":30.138}|
   |Aluminum |Kaiser Aluminum & Chemical Corp|{"x":-90.755,"y":30.049}|
   +---------+-------------------------------+------------------------+
   only showing top 5 rows

Write shapefiles

Write a DataFrame to a shapefile

Use a defined dataset to create a DataFrame and write it to a shapefile.

1. Define your own dataset.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   # Define a point dataset
   myPoints = [(0, -4655711.2806, 222503.076),
   	(1, -4570473.292, 322503.076),
   	(2, -4830838.089, 146545.398),
   	(3, -4570771.608, 116617.112),
   	(4, -4682228.671, 173377.654)]
   
   fields = ["id", "latitude", "longitude"]

2. Create a DataFrame from your dataset definition.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   # Create a DataFrame
   df = spark.createDataFrame(myPoints, fields)
   
   # Enable geometry
   df = df.withColumn("geometry",
   ST.srid(ST.point("longitude", "latitude"), 6329)) \
   .st.set_geometry_field("geometry")

3. Write your DataFrame to a shapefile.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   # Write to a single shapefile - update the path to a location accessible to you
   myshp = df.coalesce(1).write.format("shapefile").mode("overwrite").save(r"Files/output_shapefile")

Merge shapefiles with different schemas

Use schema merging when a collection of datasets contains varying schemas. For example, data have been collected over time. Each month a new dataset was created and a new column name for that month was introduced. Use schema merging to resolve the schema differences.

1. If you haven't already downloaded the sample shapefile, follow the steps to prepare your input shapefile.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   # Read the shapefile into a DataFrame
   shapefileDF=spark.read.format("shapefile").load(r"Files/shapefile_demo/Mineplants")

2. Set the output location for the shapefiles. These are the shapefiles that will have their schemas merged to form a single DataFrame.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   # Set the output path to store your shapefiles
   output_shapefiles = os.path.join(output_dir, "merged_shapefile")

3. Create three subset shapefiles. Specify a value of 1 for .coalesce() to write each query result to a single (1) shapefile. A coalesce value enables the number of partitions to be reduced, resulting in fewer output shapefiles. By default, a shapefile will be written for each partition. Each shapefile will have three columns with names in common (geometry, id and commodity), and one column with a unique name.

   • Rows with id values between 1 and 5 will have a column named site_name.
   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   # Create the first subset shapefile
   shapefileDF.where("id <= 5").select("id", "commodity","site_name","geometry") \
       .coalesce(1).write.format("shapefile").mode("overwrite").save(output_shapefiles)
   • Rows with id values between 6 and 10 will have a column named company_na.
   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   # Create the second subset shapefile
   shapefileDF.where("id between 6 and 10").select("id","commodity", "company_na",
       "geometry").coalesce(1).write.format("shapefile").mode("append").save(output_shapefiles)
   • Rows with id values between 11 and 15 will have a column named state_loca.
   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   # Create the third subset shapefile
   shapefileDF.where("id between 11 and 15").select("id", "commodity", "state_loca",
       "geometry").coalesce(1).write.format("shapefile").mode("append").save(output_shapefiles)

4. Use schema merging to create a DataFrame with a single, combined schema.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   # Merge schemas for the three subset shapefiles
   spark.read.format("shapefile").option("mergeSchemas","true").load(output_shapefiles) \
       .orderBy("id").show()
   Result

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   +---+---------+--------------------+--------------------+--------------------+--------------+
   | id|commodity|          company_na|            geometry|           site_name|    state_loca|
   +---+---------+--------------------+--------------------+--------------------+--------------+
   |  1| Aluminum|                null|{"x":-87.336,"y":...|Evansville (Warri...|          null|
   |  2| Aluminum|                null|{"x":-86.786,"y":...|  Hawesville Smelter|          null|
   |  3| Aluminum|                null|{"x":-87.5,"y":37...|      Sebree Smelter|          null|
   |  4| Aluminum|                null|{"x":-90.923,"y":...|   Burnside Refinery|          null|
   |  5| Aluminum|                null|{"x":-90.755,"y":...|   Gramercy Refinery|          null|
   |  6| Aluminum|           Alcoa Inc|{"x":-77.469,"y":...|                null|          null|
   |  7| Aluminum|Noranda Aluminum Inc|{"x":-89.564,"y":...|                null|          null|
   |  8| Aluminum|Columbia Falls Al...|{"x":-114.139,"y"...|                null|          null|
   |  9| Aluminum|           Alcoa Inc|{"x":-74.75,"y":4...|                null|          null|
   | 10| Aluminum|           Alcoa Inc|{"x":-74.881,"y":...|                null|          null|
   | 11| Aluminum|                null|{"x":-80.873,"y":...|                null|          Ohio|
   | 12| Aluminum|                null|{"x":-80.05,"y":3...|                null|South Carolina|
   | 13| Aluminum|                null|{"x":-83.968,"y":...|                null|     Tennessee|
   | 14| Aluminum|                null|{"x":-96.554,"y":...|                null|         Texas|
   | 15| Aluminum|                null|{"x":-97.076,"y":...|                null|         Texas|
   +---+---------+--------------------+--------------------+--------------------+--------------+

Partition your shapefile into logical groups

Datasets can be partitioned by values within one or more columns. Each unique value in a column becomes a directory with the name <column_name>=<value>. In this example, you will logically separate the data based on column values for spatial regions.

Without partitioning and coalescing when writing data, you will end up with a shapefile for each record by default. Partitioning your data logically enables you to read, write, and store data in meaningful storage structures.

1. Specify the location to output your newly partitioned data.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   # Set the output path to store your partitioned datasets
   partitioned_output = os.path.join(output_dir, "partitioned")

2. Partition your data based on the values for the columns "state_loca" and "commodity".

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   # Partition your data by state and resource type
   shapefileDF.write.format("shapefile").partitionBy("state_loca",
       "commodity").mode("overwrite").save(partitioned_output)

3. The result will be a new folder for each state. To preview the results of the partition, list the first thirty newly created datasets.

   Python

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   # Use the File API path
   file_api_partitioned_output = "/lakehouse/default/Files/shapefile_demo/partitioned"
   # Print out the first 30 partitions to visualize results
   for index, (path, names, filenames) in enumerate(os.walk(file_api_partitioned_output)):
       print(os.path.relpath(path, file_api_partitioned_output))
       if index == 30:
           break;
   Result

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   20
   21
   22
   23
   24
   25
   26
   27
   28
   29
   30
   31
   partitioned
   partitioned\state_loca=Alabama
   partitioned\state_loca=Alabama\commodity=Bentonite
   partitioned\state_loca=Alabama\commodity=Cement
   partitioned\state_loca=Alabama\commodity=Common%20Clay%20and%20Shale
   partitioned\state_loca=Alabama\commodity=Crushed%20Stone
   partitioned\state_loca=Alabama\commodity=Dimension%20Stone
   partitioned\state_loca=Alabama\commodity=Gypsum
   partitioned\state_loca=Alabama\commodity=Iron%20Oxide%20Pigments
   partitioned\state_loca=Alabama\commodity=Kaolin
   partitioned\state_loca=Alabama\commodity=Lime
   partitioned\state_loca=Alabama\commodity=Perlite
   partitioned\state_loca=Alabama\commodity=Salt
   partitioned\state_loca=Alabama\commodity=Sand%20and%20Gravel
   partitioned\state_loca=Alabama\commodity=Silicon
   partitioned\state_loca=Alabama\commodity=Sulfur
   partitioned\state_loca=Alaska
   partitioned\state_loca=Alaska\commodity=Crushed%20Stone
   partitioned\state_loca=Alaska\commodity=Germanium
   partitioned\state_loca=Alaska\commodity=Gold
   partitioned\state_loca=Alaska\commodity=Lead
   partitioned\state_loca=Alaska\commodity=Sand%20and%20Gravel
   partitioned\state_loca=Alaska\commodity=Silver
   partitioned\state_loca=Alaska\commodity=Zinc
   partitioned\state_loca=Arizona
   partitioned\state_loca=Arizona\commodity=Bentonite
   partitioned\state_loca=Arizona\commodity=Cement
   partitioned\state_loca=Arizona\commodity=Common%20Clay%20and%20Shale
   partitioned\state_loca=Arizona\commodity=Copper
   partitioned\state_loca=Arizona\commodity=Crushed%20Stone
   partitioned\state_loca=Arizona\commodity=Gemstones

What's next?

Learn about how to read in other data types or analyze your data through SQL functions and analysis tools:

• Read and write to databases
• Read from feature services
• Using DataFrames
• Tools
• SQL Functions