pyshp 3.0.3
Pure Python read/write support for ESRI Shapefile format
Requires Python: >=3.9
Keywords: geographic, geospatial, gis, shapefile, shapefiles

# PyShp

The Python Shapefile Library (PyShp) reads and writes ESRI Shapefiles in pure Python.

![pyshp logo](http://4.bp.blogspot.com/_SBi37QEsCvg/TPQuOhlHQxI/AAAAAAAAAE0/QjFlWfMx0tQ/S350/GSP_Logo.png "PyShp")

![build status](https://github.com/GeospatialPython/pyshp/actions/workflows/build.yml/badge.svg)

- **Author**: [Joel Lawhead](https://github.com/GeospatialPython)
- **Maintainers**: [Karim Bahgat](https://github.com/karimbahgat)
- **Version**: 3.0.3.dev0
- **Date**: 10th October, 2025
- **License**: [MIT](https://github.com/GeospatialPython/pyshp/blob/master/LICENSE.TXT)

## Contents

- [Overview](#overview)
- [Version Changes](#version-changes)
- [The Basics](#the-basics)
	- [Reading Shapefiles](#reading-shapefiles)
		- [The Reader Class](#the-reader-class)
			- [Reading Shapefiles from Local Files](#reading-shapefiles-from-local-files)
			- [Reading Shapefiles from Zip Files](#reading-shapefiles-from-zip-files)
			- [Reading Shapefiles from URLs](#reading-shapefiles-from-urls)
			- [Reading Shapefiles from File-Like Objects](#reading-shapefiles-from-file-like-objects)
			- [Reading Shapefiles Using the Context Manager](#reading-shapefiles-using-the-context-manager)
			- [Reading Shapefile Meta-Data](#reading-shapefile-meta-data)
		- [Reading Geometry](#reading-geometry)
		- [Reading Records](#reading-records)
		- [Reading Geometry and Records Simultaneously](#reading-geometry-and-records-simultaneously)
	- [Writing Shapefiles](#writing-shapefiles)
		- [The Writer Class](#the-writer-class)
			- [Writing Shapefiles to Local Files](#writing-shapefiles-to-local-files)
			- [Writing Shapefiles to File-Like Objects](#writing-shapefiles-to-file-like-objects)
			- [Writing Shapefiles Using the Context Manager](#writing-shapefiles-using-the-context-manager)
			- [Setting the Shape Type](#setting-the-shape-type)
		- [Adding Records](#adding-records)
		- [Adding Geometry](#adding-geometry)
		- [Geometry and Record Balancing](#geometry-and-record-balancing)
- [Advanced Use](#advanced-use)
    - [Common Errors and Fixes](#common-errors-and-fixes)
        - [Warnings and Logging](#warnings-and-logging)
        - [Shapefile Encoding Errors](#shapefile-encoding-errors)
	- [Reading Large Shapefiles](#reading-large-shapefiles)
		- [Iterating through a shapefile](#iterating-through-a-shapefile)
		- [Limiting which fields to read](#limiting-which-fields-to-read)
		- [Attribute filtering](#attribute-filtering)
		- [Spatial filtering](#spatial-filtering)
	- [Writing large shapefiles](#writing-large-shapefiles)
		- [Merging multiple shapefiles](#merging-multiple-shapefiles)
		- [Editing shapefiles](#editing-shapefiles)
	- [3D and Other Geometry Types](#3d-and-other-geometry-types)
    	- [Shapefiles with measurement (M) values](#shapefiles-with-measurement-m-values)
		- [Shapefiles with elevation (Z) values](#shapefiles-with-elevation-z-values)
		- [3D MultiPatch Shapefiles](#3d-multipatch-shapefiles)
- [Testing](#testing)
- [Contributors](#contributors)


# Overview

The Python Shapefile Library (PyShp) provides read and write support for the
Esri Shapefile format. The Shapefile format is a popular Geographic
Information System vector data format created by Esri. For more information
about this format please read the well-written "ESRI Shapefile Technical
Description - July 1998" located at [http://www.esri.com/library/whitepapers/p
dfs/shapefile.pdf](http://www.esri.com/library/whitepapers/pdfs/shapefile.pdf)
. The Esri document describes the shp and shx file formats. However a third
file format called dbf is also required. This format is documented on the web
as the "XBase File Format Description" and is a simple file-based database
format created in the 1960's. For more on this specification see: [http://www.clicketyclick.dk/databases/xbase/format/index.html](http://www.clicketyclick.dk/databases/xbase/format/index.html)

Both the Esri and XBase file-formats are very simple in design and memory
efficient which is part of the reason the shapefile format remains popular
despite the numerous ways to store and exchange GIS data available today.

This document provides examples for using PyShp to read and write shapefiles. However
many more examples are continually added to the blog [http://GeospatialPython.com](http://GeospatialPython.com),
and by searching for PyShp on [https://gis.stackexchange.com](https://gis.stackexchange.com).

Currently the sample census blockgroup shapefile referenced in the examples is available on the GitHub project site at
[https://github.com/GeospatialPython/pyshp](https://github.com/GeospatialPython/pyshp). These
examples are straight-forward and you can also easily run them against your
own shapefiles with minimal modification.

Important: If you are new to GIS you should read about map projections.
Please visit: [https://github.com/GeospatialPython/pyshp/wiki/Map-Projections](https://github.com/GeospatialPython/pyshp/wiki/Map-Projections)

I sincerely hope this library eliminates the mundane distraction of simply
reading and writing data, and allows you to focus on the challenging and FUN
part of your geospatial project.


# Version Changes


## 3.0.3

### Type checking
- Add optional dependency, "stubs", containing the package "pyshp-stubs" generated with stubgen (to avoid
including py.typed, and no longer being a single file project).

### Bug fix
- Prevented UnboundLocalError when reading non-single point M and Z type Shapefiles (@ekawas-vrify).

### Testing.
- Test PyShp on the Python 3.14 official release (officially released this week, no longer in beta).

## 3.0.2

### .post1
- Update version at the top of this Readme file (to make what PyPi users see consistent with the changelog and __version__).

### .post0
- Re-release to trigger the deploy job to publish PyShp to Pypi

### Bug fix
- Deleted py.typed (probably temporarily).  Including the py.typed marker file with a single file package caused type checkers to type check all other libraries installed in the same directory (whether they're typed or not, enforcing type checking for all the adjacent dirs libraries, regardless of the user's intentions for the type checker) (pointed out by @dl1jbe - thanks Thomas).  Discussions will be started about a longer term fix, possibly e.g. possibly refactoring to a package, and restoring py.typed.

### Code quality:
- Ruff check's UP rule added (mimicks PyUpgrade) (@mwtoews).


## 3.0.1

### Improvements
- Reader(shp=, dbf=, shx=) now support pathlib.Paths, and any pathlike object (@mwtoews).
### Bug fixes
- PyShp 3 no longer modifies the global doctest module (@JamesParrott).
### Code quality
- isort replaced by Ruff check's I rule (@mwtoews).
- mypy --strict used in CI (@JamesParrott).
- LICENSE.TXT re-encoded in UTF-8 (@musicinmybrain).

## 3.0.0

### Breaking Changes:
- Python 2 and Python 3.8 support dropped.
- Field info tuple is now a namedtuple (Field) instead of a list.
- Field type codes are now FieldType 'enum' members.
- bbox, mbox and zbox attributes are all new Namedtuples.
- Writer does not mutate Shapes.
- New custom subclasses for each shape type: Null, Multipatch, Point, Polyline,
  Multipoint, and Polygon, plus the latter 4's M and Z variants (Reader and
  Writer are still compatible with their base class, Shape, as before).
- Shape sub classes are creatable from, and serializable to bytes streams,
  as per the shapefile spec.

### Improvements:
- Speeded up writing shapefiles by up to another ~27% (on top of the recent ~39% improvement in 2.4.1).

### Code quality
- Statically typed, and checked with Mypy
- Checked with Ruff.
- f-strings
- Remove Python 2 specific functions.
- Run doctests against wheels.
- Testing of wheels before publishing them
- pyproject.toml src layout
- Slow test marked.

## 2.4.1

###	Improvements:
- Speed up writing shapefiles by up to ~39%.  Combined for loops of calls to f.write(pack(...)), into single calls.

### Breaking Change.  Support for Python 2 and Pythons <= 3.8 to be dropped.
- PyShp 2.4.1 is the latest (and likely last) version of PyShp to support Python 2.7 and Pythons <= 3.8.
These CPython versions have reached [end of life](https://devguide.python.org/versions/#versions).
- Future development will focus on PyShp v3.0.0 onwards (currently intended to supporting Pythons >= 3.9).
- This will not break any projects, as pip and other package managers should not install PyShp 3.0.0
(after its release) in unsupported Pythons.  But we no longer promise such projects will get PyShp's latest
bug fixes and features.
- If this negatively impacts your project, all feedback about this decision is welcome
on our [the discussion page](https://github.com/GeospatialPython/pyshp/discussions/290).

## 2.4.0

### New Features:
- Reader.iterRecords now allows start and stop to be specified, to lookup smaller ranges of records.
- Equality comparisons between Records now also require the fields to be the same (and in the same order).

### Development:
- Code quality tools (Ruff format) run on PyShp
- Network, non-network, or all doctests selectable via command line args
- Network tests made runnable on localhost.

## 2.3.1

### Bug fixes:

- Fix recently introduced issue where Reader/Writer closes file-like objects provided by user (#244)

## 2.3.0

### New Features:

- Added support for pathlib and path-like shapefile filepaths (@mwtoews).
- Allow reading individual file extensions via filepaths.

### Improvements:

- Simplified setup and deployment (@mwtoews)
- Faster shape access when missing shx file
- Switch to named logger (see #240)

### Bug fixes:

- More robust handling of corrupt shapefiles (fixes #235)
- Fix errors when writing to individual file-handles (fixes #237)
- Revert previous decision to enforce geojson output ring orientation (detailed explanation at https://github.com/SciTools/cartopy/issues/2012)
- Fix test issues in environments without network access (@sebastic, @musicinmybrain).

## 2.2.0

### New Features:

- Read shapefiles directly from zipfiles.
- Read shapefiles directly from urls.
- Allow fast extraction of only a subset of dbf fields through a `fields` arg.
- Allow fast filtering which shapes to read from the file through a `bbox` arg.

### Improvements:

- More examples and restructuring of README.
- More informative Shape to geojson warnings (see #219).
- Add shapefile.VERBOSE flag to control warnings verbosity (default True).
- Shape object information when calling repr().
- Faster ring orientation checks, enforce geojson output ring orientation.

### Bug fixes:

- Remove null-padding at end of some record character fields.
- Fix dbf writing error when the number of record list or dict entries didn't match the number of fields.
- Handle rare garbage collection issue after deepcopy (https://github.com/mattijn/topojson/issues/120)
- Fix bug where records and shapes would be assigned incorrect record number (@karanrn)
- Fix typos in docs (@timgates)

## 2.1.3

### Bug fixes:

- Fix recent bug in geojson hole-in-polygon checking (see #205)
- Misc fixes to allow geo interface dump to json (eg dates as strings)
- Handle additional dbf date null values, and return faulty dates as unicode (see #187)
- Add writer target typecheck
- Fix bugs to allow reading shp/shx/dbf separately
- Allow delayed shapefile loading by passing no args
- Fix error with writing empty z/m shapefile (@mcuprjak)
- Fix signed_area() so ignores z/m coords
- Enforce writing the 11th field name character as null-terminator (only first 10 are used)
- Minor README fixes
- Added more tests

## 2.1.2

### Bug fixes:

- Fix issue where warnings.simplefilter('always') changes global warning behavior [see #203]

## 2.1.1

### Improvements:

- Handle shapes with no coords and represent as geojson with no coords (GeoJSON null-equivalent)
- Expand testing to Python 3.6, 3.7, 3.8 and PyPy; drop 3.3 and 3.4 [@mwtoews]
- Added pytest testing [@jmoujaes]

### Bug fixes:

- Fix incorrect geo interface handling of multipolygons with complex exterior-hole relations [see #202]
- Enforce shapefile requirement of at least one field, to avoid writing invalid shapefiles [@Jonty]
- Fix Reader geo interface including DeletionFlag field in feature properties [@nnseva]
- Fix polygons not being auto closed, which was accidentally dropped
- Fix error for null geometries in feature geojson
- Misc docstring cleanup [@fiveham]

## 2.1.0

### New Features:

- Added back read/write support for unicode field names.
- Improved Record representation
- More support for geojson on Reader, ShapeRecord, ShapeRecords, and shapes()

### Bug fixes:

- Fixed error when reading optional m-values
- Fixed Record attribute autocomplete in Python 3
- Misc readme cleanup

## 2.0.0

The newest version of PyShp, version 2.0 introduced some major new improvements.
A great thanks to all who have contributed code and raised issues, and for everyone's
patience and understanding during the transition period.
Some of the new changes are incompatible with previous versions.
Users of the previous version 1.x should therefore take note of the following changes
(Note: Some contributor attributions may be missing):

### Major Changes:

- Full support for unicode text, with custom encoding, and exception handling.
  - Means that the Reader returns unicode, and the Writer accepts unicode.
- PyShp has been simplified to a pure input-output library using the Reader and Writer classes, dropping the Editor class.
- Switched to a new streaming approach when writing files, keeping memory-usage at a minimum:
  - Specify filepath/destination and text encoding when creating the Writer.
  - The file is written incrementally with each call to shape/record.
  - Adding shapes is now done using dedicated methods for each shapetype.
- Reading shapefiles is now more convenient:
  - Shapefiles can be opened using the context manager, and files are properly closed.
  - Shapefiles can be iterated, have a length, and supports the geo interface.
  - New ways of inspecting shapefile metadata by printing. [@megies]
  - More convenient accessing of Record values as attributes. [@philippkraft]
  - More convenient shape type name checking. [@megies]
- Add more support and documentation for MultiPatch 3D shapes.
- The Reader "elevation" and "measure" attributes now renamed "zbox" and "mbox", to make it clear they refer to the min/max values.
- Better documentation of previously unclear aspects, such as field types.

### Important Fixes:

- More reliable/robust:
  - Fixed shapefile bbox error for empty or point type shapefiles. [@mcuprjak]
  - Reading and writing Z and M type shapes is now more robust, fixing many errors, and has been added to the documentation. [@ShinNoNoir]
  - Improved parsing of field value types, fixed errors and made more flexible.
  - Fixed bug when writing shapefiles with datefield and date values earlier than 1900 [@megies]
- Fix some geo interface errors, including checking polygon directions.
- Bug fixes for reading from case sensitive file names, individual files separately, and from file-like objects. [@gastoneb, @kb003308, @erickskb]
- Enforce maximum field limit. [@mwtoews]


# The Basics

Before doing anything you must import the library.


	>>> import shapefile

The examples below will use a shapefile created from the U.S. Census Bureau
Blockgroups data set near San Francisco, CA and available in the git
repository of the PyShp GitHub site.

## Reading Shapefiles

### The Reader Class

#### Reading Shapefiles from Local Files

To read a shapefile create a new "Reader" object and pass it the name of an
existing shapefile. The shapefile format is actually a collection of three
files. You specify the base filename of the shapefile or the complete filename
of any of the shapefile component files.


	>>> sf = shapefile.Reader("shapefiles/blockgroups")

OR


	>>> sf = shapefile.Reader("shapefiles/blockgroups.shp")

OR


	>>> sf = shapefile.Reader("shapefiles/blockgroups.dbf")

OR any of the other 5+ formats which are potentially part of a shapefile. The
library does not care about file extensions. You can also specify that you only
want to read some of the file extensions through the use of keyword arguments:


	>>> sf = shapefile.Reader(dbf="shapefiles/blockgroups.dbf")

#### Reading Shapefiles from Zip Files

If your shapefile is wrapped inside a zip file, the library is able to handle that too, meaning you don't have to worry about unzipping the contents:


	>>> sf = shapefile.Reader("shapefiles/blockgroups.zip")

If the zip file contains multiple shapefiles, just specify which shapefile to read by additionally specifying the relative path after the ".zip" part:


	>>> sf = shapefile.Reader("shapefiles/blockgroups_multishapefile.zip/blockgroups2.shp")

#### Reading Shapefiles from URLs

Finally, you can use all of the above methods to read shapefiles directly from the internet, by giving a url instead of a local path, e.g.:


	>>> # from a zipped shapefile on website
	>>> sf = shapefile.Reader("https://github.com/JamesParrott/PyShp_test_shapefile/raw/main/gis_osm_natural_a_free_1.zip")

	>>> # from a shapefile collection of files in a github repository
	>>> sf = shapefile.Reader("https://github.com/nvkelso/natural-earth-vector/blob/master/110m_cultural/ne_110m_admin_0_tiny_countries.shp?raw=true")

This will automatically download the file(s) to a temporary location before reading, saving you a lot of time and repetitive boilerplate code when you just want quick access to some external data.

#### Reading Shapefiles from File-Like Objects

You can also load shapefiles from any Python file-like object using keyword
arguments to specify any of the three files. This feature is very powerful and
allows you to custom load shapefiles from arbitrary storage formats, such as a protected url or zip file, a serialized object, or in some cases a database.


	>>> myshp = open("shapefiles/blockgroups.shp", "rb")
	>>> mydbf = open("shapefiles/blockgroups.dbf", "rb")
	>>> r = shapefile.Reader(shp=myshp, dbf=mydbf)

Notice in the examples above the shx file is never used. The shx file is a
very simple fixed-record index for the variable-length records in the shp
file. This file is optional for reading. If it's available PyShp will use the
shx file to access shape records a little faster but will do just fine without
it.

#### Reading Shapefiles Using the Context Manager

The "Reader" class can be used as a context manager, to ensure open file
objects are properly closed when done reading the data:

    >>> with shapefile.Reader("shapefiles/blockgroups.shp") as shp:
    ...     print(shp)
    shapefile Reader
        663 shapes (type 'POLYGON')
        663 records (44 fields)

#### Reading Shapefile Meta-Data

Shapefiles have a number of attributes for inspecting the file contents.
A shapefile is a container for a specific type of geometry, and this can be checked using the
shapeType attribute.


	>>> sf = shapefile.Reader("shapefiles/blockgroups.dbf")
	>>> sf.shapeType
	5

Shape types are represented by numbers between 0 and 31 as defined by the
shapefile specification and listed below. It is important to note that the numbering system has
several reserved numbers that have not been used yet, therefore the numbers of
the existing shape types are not sequential:

- NULL = 0
- POINT = 1
- POLYLINE = 3
- POLYGON = 5
- MULTIPOINT = 8
- POINTZ = 11
- POLYLINEZ = 13
- POLYGONZ = 15
- MULTIPOINTZ = 18
- POINTM = 21
- POLYLINEM = 23
- POLYGONM = 25
- MULTIPOINTM = 28
- MULTIPATCH = 31

Based on this we can see that our blockgroups shapefile contains
Polygon type shapes. The shape types are also defined as constants in
the shapefile module, so that we can compare types more intuitively:


	>>> sf.shapeType == shapefile.POLYGON
	True

For convenience, you can also get the name of the shape type as a string:


	>>> sf.shapeTypeName == 'POLYGON'
	True

Other pieces of meta-data that we can check include the number of features
and the bounding box area the shapefile covers:


	>>> len(sf)
	663
	>>> sf.bbox
	(-122.515048, 37.652916, -122.327622, 37.863433)

Finally, if you would prefer to work with the entire shapefile in a different
format, you can convert all of it to a GeoJSON dictionary, although you may lose
some information in the process, such as z- and m-values:


	>>> sf.__geo_interface__['type']
	'FeatureCollection'

### Reading Geometry

A shapefile's geometry is the collection of points or shapes made from
vertices and implied arcs representing physical locations. All types of
shapefiles just store points. The metadata about the points determine how they
are handled by software.

You can get a list of the shapefile's geometry by calling the shapes()
method.


	>>> shapes = sf.shapes()

The shapes method returns a list of Shape objects describing the geometry of
each shape record.


	>>> len(shapes)
	663

To read a single shape by calling its index use the shape() method. The index
is the shape's count from 0. So to read the 8th shape record you would use its
index which is 7.


	>>> s = sf.shape(7)
	>>> s
	Polygon #7


	>>> # Read the bbox of the 8th shape to verify
	>>> # Round coordinates to 3 decimal places
	>>> ['%.3f' % coord for coord in s.bbox]
	['-122.450', '37.801', '-122.442', '37.808']

Each shape record (except Points) contains the following attributes. Records of
shapeType Point do not have a bounding box 'bbox'.
# TODO!!  Fix attributes

	>>> for name in dir(shapes[3]):
	...     if not name.startswith('_'):
	...         name
	'bbox'
	'from_byte_stream'
	'oid'
	'parts'
	'points'
	'shapeType'
	'shapeTypeName'
	'write_to_byte_stream'

  * `oid`: The shape's index position in the original shapefile.


		>>> shapes[3].oid
		3

  * `shapeType`: an integer representing the type of shape as defined by the
	  shapefile specification.


		>>> shapes[3].shapeType
		5

  * `shapeTypeName`: a string representation of the type of shape as defined by shapeType. Read-only.


		>>> shapes[3].shapeTypeName
		'POLYGON'

  * `bbox`: If the shape type contains multiple points this tuple describes the
	  lower left (x,y) coordinate and upper right corner coordinate creating a
	  complete box around the points. If the shapeType is a
	  Null (shapeType == 0) then an AttributeError is raised.


		>>> # Get the bounding box of the 4th shape.
		>>> # Round coordinates to 3 decimal places
		>>> bbox = shapes[3].bbox
		>>> ['%.3f' % coord for coord in bbox]
		['-122.486', '37.787', '-122.446', '37.811']

  * `parts`: Parts simply group collections of points into shapes. If the shape
	  record has multiple parts this attribute contains the index of the first
	  point of each part. If there is only one part then a list containing 0 is
	  returned.


		>>> shapes[3].parts
		[0]

  * `points`: The points attribute contains a list of tuples containing an
	  (x,y) coordinate for each point in the shape.


		>>> len(shapes[3].points)
		173
		>>> # Get the 8th point of the fourth shape
		>>> # Truncate coordinates to 3 decimal places
		>>> shape = shapes[3].points[7]
		>>> ['%.3f' % coord for coord in shape]
		['-122.471', '37.787']

In most cases, however, if you need to do more than just type or bounds checking, you may want
to convert the geometry to the more human-readable [GeoJSON format](http://geojson.org),
where lines and polygons are grouped for you:


	>>> s = sf.shape(0)
	>>> geoj = s.__geo_interface__
	>>> geoj["type"]
	'MultiPolygon'

The results from the shapes() method similarly supports converting to GeoJSON:


	>>> shapes.__geo_interface__['type']
	'GeometryCollection'

Note: In some cases, if the conversion from shapefile geometry to GeoJSON encountered any problems
or potential issues, a warning message will be displayed with information about the affected
geometry. To ignore or suppress these warnings, you can disable this behavior by setting the
module constant VERBOSE to False:


	>>> shapefile.VERBOSE = False


### Reading Records

A record in a shapefile contains the attributes for each shape in the
collection of geometries. Records are stored in the dbf file. The link between
geometry and attributes is the foundation of all geographic information systems.
This critical link is implied by the order of shapes and corresponding records
in the shp geometry file and the dbf attribute file.

The field names of a shapefile are available as soon as you read a shapefile.
You can call the "fields" attribute of the shapefile as a Python list. Each
field is a Python namedtuple (Field) with the following information:

  * name: the name describing the data at this column index (a string).
  * field_type: a FieldType enum member determining the type of data at this column index. Names can be:
       * "C": Characters, text.
	   * "N": Numbers, with or without decimals.
	   * "F": Floats (same as "N").
	   * "L": Logical, for boolean True/False values.
	   * "D": Dates.
	   * "M": Memo, has no meaning within a GIS and is part of the xbase spec instead.
  * size: Field length: the length of the data found at this column index. Older GIS
	   software may truncate this length to 8 or 11 characters for "Character"
	   fields.
  * deci: Decimal length. The number of decimal places found in "Number" fields.

A new field can be created directly from the type enum member etc., or as follows:

	>>> shapefile.Field.from_unchecked("Population", "N", 10,0)
	Field(name="Population", field_type=FieldType.N, size=10, decimal=0)

Using this method the conversion from string to enum is done automatically.

To see the fields for the Reader object above (sf) call the "fields"
attribute:


	>>> sf.fields
	[Field(name="DeletionFlag", field_type=FieldType.C, size=1, decimal=0), Field(name="AREA", field_type=FieldType.N, size=18, decimal=5), Field(name="BKG_KEY", field_type=FieldType.C, size=12, decimal=0), Field(name="POP1990", field_type=FieldType.N, size=9, decimal=0), Field(name="POP90_SQMI", field_type=FieldType.N, size=10, decimal=1), Field(name="HOUSEHOLDS", field_type=FieldType.N, size=9, decimal=0), Field(name="MALES", field_type=FieldType.N, size=9, decimal=0), Field(name="FEMALES", field_type=FieldType.N, size=9, decimal=0), Field(name="WHITE", field_type=FieldType.N, size=9, decimal=0), Field(name="BLACK", field_type=FieldType.N, size=8, decimal=0), Field(name="AMERI_ES", field_type=FieldType.N, size=7, decimal=0), Field(name="ASIAN_PI", field_type=FieldType.N, size=8, decimal=0), Field(name="OTHER", field_type=FieldType.N, size=8, decimal=0), Field(name="HISPANIC", field_type=FieldType.N, size=8, decimal=0), Field(name="AGE_UNDER5", field_type=FieldType.N, size=8, decimal=0), Field(name="AGE_5_17", field_type=FieldType.N, size=8, decimal=0), Field(name="AGE_18_29", field_type=FieldType.N, size=8, decimal=0), Field(name="AGE_30_49", field_type=FieldType.N, size=8, decimal=0), Field(name="AGE_50_64", field_type=FieldType.N, size=8, decimal=0), Field(name="AGE_65_UP", field_type=FieldType.N, size=8, decimal=0), Field(name="NEVERMARRY", field_type=FieldType.N, size=8, decimal=0), Field(name="MARRIED", field_type=FieldType.N, size=9, decimal=0), Field(name="SEPARATED", field_type=FieldType.N, size=7, decimal=0), Field(name="WIDOWED", field_type=FieldType.N, size=8, decimal=0), Field(name="DIVORCED", field_type=FieldType.N, size=8, decimal=0), Field(name="HSEHLD_1_M", field_type=FieldType.N, size=8, decimal=0), Field(name="HSEHLD_1_F", field_type=FieldType.N, size=8, decimal=0), Field(name="MARHH_CHD", field_type=FieldType.N, size=8, decimal=0), Field(name="MARHH_NO_C", field_type=FieldType.N, size=8, decimal=0), Field(name="MHH_CHILD", field_type=FieldType.N, size=7, decimal=0), Field(name="FHH_CHILD", field_type=FieldType.N, size=7, decimal=0), Field(name="HSE_UNITS", field_type=FieldType.N, size=9, decimal=0), Field(name="VACANT", field_type=FieldType.N, size=7, decimal=0), Field(name="OWNER_OCC", field_type=FieldType.N, size=8, decimal=0), Field(name="RENTER_OCC", field_type=FieldType.N, size=8, decimal=0), Field(name="MEDIAN_VAL", field_type=FieldType.N, size=7, decimal=0), Field(name="MEDIANRENT", field_type=FieldType.N, size=4, decimal=0), Field(name="UNITS_1DET", field_type=FieldType.N, size=8, decimal=0), Field(name="UNITS_1ATT", field_type=FieldType.N, size=7, decimal=0), Field(name="UNITS2", field_type=FieldType.N, size=7, decimal=0), Field(name="UNITS3_9", field_type=FieldType.N, size=8, decimal=0), Field(name="UNITS10_49", field_type=FieldType.N, size=8, decimal=0), Field(name="UNITS50_UP", field_type=FieldType.N, size=8, decimal=0), Field(name="MOBILEHOME", field_type=FieldType.N, size=7, decimal=0)]

The first field of a dbf file is always a 1-byte field called "DeletionFlag",
which indicates records that have been deleted but not removed. However,
since this flag is very rarely used, PyShp currently will return all records
regardless of their deletion flag, and the flag is also not included in the list of
record values. In other words, the DeletionFlag field has no real purpose, and
should in most cases be ignored. For instance, to get a list of all fieldnames:


	>>> fieldnames = [f[0] for f in sf.fields[1:]]

You can get a list of the shapefile's records by calling the records() method:


	>>> records = sf.records()

	>>> len(records)
	663

To read a single record call the record() method with the record's index:


	>>> rec = sf.record(3)

Each record is a list-like Record object containing the values corresponding to each field in
the field list (except the DeletionFlag). A record's values can be accessed by positional indexing or slicing.
For example in the blockgroups shapefile the 2nd and 3rd fields are the blockgroup id
and the 1990 population count of that San Francisco blockgroup:


	>>> rec[1:3]
	['060750601001', 4715]

For simpler access, the fields of a record can also accessed via the name of the field,
either as a key or as an attribute name. The blockgroup id (BKG_KEY) of the blockgroups shapefile
can also be retrieved as:


    >>> rec['BKG_KEY']
    '060750601001'

    >>> rec.BKG_KEY
    '060750601001'

The record values can be easily integrated with other programs by converting it to a field-value dictionary:


	>>> dct = rec.as_dict()
	>>> sorted(dct.items())
	[('AGE_18_29', 1467), ('AGE_30_49', 1681), ('AGE_50_64', 92), ('AGE_5_17', 848), ('AGE_65_UP', 30), ('AGE_UNDER5', 597), ('AMERI_ES', 6), ('AREA', 2.34385), ('ASIAN_PI', 452), ('BKG_KEY', '060750601001'), ('BLACK', 1007), ('DIVORCED', 149), ('FEMALES', 2095), ('FHH_CHILD', 16), ('HISPANIC', 416), ('HOUSEHOLDS', 1195), ('HSEHLD_1_F', 40), ('HSEHLD_1_M', 22), ('HSE_UNITS', 1258), ('MALES', 2620), ('MARHH_CHD', 79), ('MARHH_NO_C', 958), ('MARRIED', 2021), ('MEDIANRENT', 739), ('MEDIAN_VAL', 337500), ('MHH_CHILD', 0), ('MOBILEHOME', 0), ('NEVERMARRY', 703), ('OTHER', 288), ('OWNER_OCC', 66), ('POP1990', 4715), ('POP90_SQMI', 2011.6), ('RENTER_OCC', 3733), ('SEPARATED', 49), ('UNITS10_49', 49), ('UNITS2', 160), ('UNITS3_9', 672), ('UNITS50_UP', 0), ('UNITS_1ATT', 302), ('UNITS_1DET', 43), ('VACANT', 93), ('WHITE', 2962), ('WIDOWED', 37)]

If at a later point you need to check the record's index position in the original
shapefile, you can do this through the "oid" attribute:


	>>> rec.oid
	3

### Reading Geometry and Records Simultaneously

You may want to examine both the geometry and the attributes for a record at
the same time. The shapeRecord() and shapeRecords() method let you do just
that.

Calling the shapeRecords() method will return the geometry and attributes for
all shapes as a list of ShapeRecord objects. Each ShapeRecord instance has a
"shape" and "record" attribute. The shape attribute is a Shape object as
discussed in the first section "Reading Geometry". The record attribute is a
list-like object containing field values as demonstrated in the "Reading Records" section.


	>>> shapeRecs = sf.shapeRecords()

Let's read the blockgroup key and the population for the 4th blockgroup:


	>>> shapeRecs[3].record[1:3]
	['060750601001', 4715]

The results from the shapeRecords() method is a list-like object that can be easily converted
to GeoJSON through the _\_geo_interface\_\_:


	>>> shapeRecs.__geo_interface__['type']
	'FeatureCollection'

The shapeRecord() method reads a single shape/record pair at the specified index.
To get the 4th shape record from the blockgroups shapefile use the third index:


	>>> shapeRec = sf.shapeRecord(3)
	>>> shapeRec.record[1:3]
	['060750601001', 4715]

Each individual shape record also supports the _\_geo_interface\_\_ to convert it to a GeoJSON feature:


	>>> shapeRec.__geo_interface__['type']
	'Feature'


## Writing Shapefiles

### The Writer Class

PyShp tries to be as flexible as possible when writing shapefiles while
maintaining some degree of automatic validation to make sure you don't
accidentally write an invalid file.

PyShp can write just one of the component files such as the shp or dbf file
without writing the others. So in addition to being a complete shapefile
library, it can also be used as a basic dbf (xbase) library. Dbf files are a
common database format which are often useful as a standalone simple database
format. And even shp files occasionally have uses as a standalone format. Some
web-based GIS systems use an user-uploaded shp file to specify an area of
interest. Many precision agriculture chemical field sprayers also use the shp
format as a control file for the sprayer system (usually in combination with
custom database file formats).

#### Writing Shapefiles to Local Files

To create a shapefile you begin by initiating a new Writer instance, passing it
the file path and name to save to:


	>>> w = shapefile.Writer('shapefiles/test/testfile')
	>>> w.field('field1', 'C')

File extensions are optional when reading or writing shapefiles. If you specify
them PyShp ignores them anyway. When you save files you can specify a base
file name that is used for all three file types. Or you can specify a name for
one or more file types:


	>>> w = shapefile.Writer(dbf='shapefiles/test/onlydbf.dbf')
	>>> w.field('field1', 'C')

In that case, any file types not assigned will not
save and only file types with file names will be saved.

#### Writing Shapefiles to File-Like Objects

Just as you can read shapefiles from python file-like objects you can also
write to them:


	>>> try:
	...     from StringIO import StringIO
	... except ImportError:
	...     from io import BytesIO as StringIO
	>>> shp = StringIO()
	>>> shx = StringIO()
	>>> dbf = StringIO()
	>>> w = shapefile.Writer(shp=shp, shx=shx, dbf=dbf)
	>>> w.field('field1', 'C')
	>>> w.record()
	>>> w.null()
	>>> w.close()

	>>> # To read back the files you could call the "StringIO.getvalue()" method later.
	>>> assert shp.getvalue()
	>>> assert shx.getvalue()
	>>> assert dbf.getvalue()

	>>> # In fact, you can read directly from them using the Reader
	>>> r = shapefile.Reader(shp=shp, shx=shx, dbf=dbf)
	>>> len(r)
	1



#### Writing Shapefiles Using the Context Manager

The "Writer" class automatically closes the open files and writes the final headers once it is garbage collected.
In case of a crash and to make the code more readable, it is nevertheless recommended
you do this manually by calling the "close()" method:


	>>> w.close()

Alternatively, you can also use the "Writer" class as a context manager, to ensure open file
objects are properly closed and final headers written once you exit the with-clause:


	>>> with shapefile.Writer("shapefiles/test/contextwriter") as w:
	... 	w.field('field1', 'C')
	... 	pass

#### Setting the Shape Type

The shape type defines the type of geometry contained in the shapefile. All of
the shapes must match the shape type setting.

There are three ways to set the shape type:
  * Set it when creating the class instance.
  * Set it by assigning a value to an existing class instance.
  * Set it automatically to the type of the first non-null shape by saving the shapefile.

To manually set the shape type for a Writer object when creating the Writer:


	>>> w = shapefile.Writer('shapefiles/test/shapetype', shapeType=3)
	>>> w.field('field1', 'C')

	>>> w.shapeType
	3

OR you can set it after the Writer is created:


	>>> w.shapeType = 1

	>>> w.shapeType
	1


### Adding Records

Before you can add records you must first create the fields that define what types of
values will go into each attribute.

There are several different field types, all of which support storing None values as NULL.

Text fields are created using the 'C' type, and the third 'size' argument can be customized to the expected
length of text values to save space:


	>>> w = shapefile.Writer('shapefiles/test/dtype')
	>>> w.field('TEXT', 'C')
	>>> w.field('SHORT_TEXT', 'C', size=5)
	>>> w.field('LONG_TEXT', 'C', size=250)
	>>> w.null()
	>>> w.record('Hello', 'World', 'World'*50)
	>>> w.close()

	>>> r = shapefile.Reader('shapefiles/test/dtype')
	>>> assert r.record(0) == ['Hello', 'World', 'World'*50]

Date fields are created using the 'D' type, and can be created using either
date objects, lists, or a YYYYMMDD formatted string.
Field length or decimal have no impact on this type:


	>>> from datetime import date
	>>> w = shapefile.Writer('shapefiles/test/dtype')
	>>> w.field('DATE', 'D')
	>>> w.null()
	>>> w.null()
	>>> w.null()
	>>> w.null()
	>>> w.record(date(1898,1,30))
	>>> w.record([1998,1,30])
	>>> w.record('19980130')
	>>> w.record(None)
	>>> w.close()

	>>> r = shapefile.Reader('shapefiles/test/dtype')
	>>> assert r.record(0) == [date(1898,1,30)]
	>>> assert r.record(1) == [date(1998,1,30)]
	>>> assert r.record(2) == [date(1998,1,30)]
	>>> assert r.record(3) == [None]

Numeric fields are created using the 'N' type (or the 'F' type, which is exactly the same).
By default the fourth decimal argument is set to zero, essentially creating an integer field.
To store floats you must set the decimal argument to the precision of your choice.
To store very large numbers you must increase the field length size to the total number of digits
(including comma and minus).


	>>> w = shapefile.Writer('shapefiles/test/dtype')
	>>> w.field('INT', 'N')
	>>> w.field('LOWPREC', 'N', decimal=2)
	>>> w.field('MEDPREC', 'N', decimal=10)
	>>> w.field('HIGHPREC', 'N', decimal=30)
	>>> w.field('FTYPE', 'F', decimal=10)
	>>> w.field('LARGENR', 'N', 101)
	>>> nr = 1.3217328
	>>> w.null()
	>>> w.null()
	>>> w.record(INT=nr, LOWPREC=nr, MEDPREC=nr, HIGHPREC=-3.2302e-25, FTYPE=nr, LARGENR=int(nr)*10**100)
	>>> w.record(None, None, None, None, None, None)
	>>> w.close()

	>>> r = shapefile.Reader('shapefiles/test/dtype')
	>>> assert r.record(0) == [1, 1.32, 1.3217328, -3.2302e-25, 1.3217328, 10000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000]
	>>> assert r.record(1) == [None, None, None, None, None, None]


Finally, we can create boolean fields by setting the type to 'L'.
This field can take True or False values, or 1 (True) or 0 (False).
None is interpreted as missing.


	>>> w = shapefile.Writer('shapefiles/test/dtype')
	>>> w.field('BOOLEAN', 'L')
	>>> w.null()
	>>> w.null()
	>>> w.null()
	>>> w.null()
	>>> w.null()
	>>> w.null()
	>>> w.record(True)
	>>> w.record(1)
	>>> w.record(False)
	>>> w.record(0)
	>>> w.record(None)
	>>> w.record("Nonsense")
	>>> w.close()

	>>> r = shapefile.Reader('shapefiles/test/dtype')
	>>> r.record(0)
	Record #0: [True]
	>>> r.record(1)
	Record #1: [True]
	>>> r.record(2)
	Record #2: [False]
	>>> r.record(3)
	Record #3: [False]
	>>> r.record(4)
	Record #4: [None]
	>>> r.record(5)
	Record #5: [None]

You can also add attributes using keyword arguments where the keys are field names.


	>>> w = shapefile.Writer('shapefiles/test/dtype')
	>>> w.field('FIRST_FLD','C', 40)
	>>> w.field('SECOND_FLD','C', 40)
	>>> w.null()
	>>> w.null()
	>>> w.record('First', 'Line')
	>>> w.record(FIRST_FLD='First', SECOND_FLD='Line')
	>>> w.close()

### Adding Geometry

Geometry is added using one of several convenience methods. The "null" method is u