Advanced Examples

Optimize Transformations

Here are a few tricks to try out if you want to optimize your transformations.

Repeated transformations

New in version 2.1.0.

If you use the same transform, using the pyproj.transformer.Transformer can help optimize your transformations.

import numpy as np
from pyproj import Transformer, transform

transformer = Transformer.from_crs(2263, 4326)
x_coords = np.random.randint(80000, 120000)
y_coords = np.random.randint(200000, 250000)

Example with pyproj.transformer.transform():

transform(2263, 4326, x_coords, y_coords)

Results: 160 ms ± 3.68 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Example with pyproj.transformer.Transformer:

transformer.transform(x_coords, y_coords)

Results: 6.32 µs ± 49.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

Tranforming with the same projections

pyproj will skip transformations if they are exacly the same by default. However, if you sometimes throw in the projections that are about the same and the results being close enough is what you want, the skip_equivalent option can help.


From PROJ code: The objects are equivalent for the purpose of coordinate operations. They can differ by the name of their objects, identifiers, other metadata. Parameters may be expressed in different units, provided that the value is (with some tolerance) the same once expressed in a common unit.

Transformation Group

New in version 2.3.0.

The pyproj.transformer.TransformerGroup provides both available transformations as well as missing transformations.

  1. Helpful if you want to use an alternate transformation and have a good reason for it.

>>> from pyproj.transformer import TransformerGroup
>>> trans_group = TransformerGroup("epsg:4326","epsg:2964")
>>> trans_group
<TransformerGroup: best_available=True>
- transformers: 8
- unavailable_operations: 1
>>> trans_group.best_available
>>> trans_group.transformers[0].transform(66, -153)
(149661.2825058747, 5849322.174897663)
>>> trans_group.transformers[1].transform(66, -153)
(149672.928811047, 5849311.372139239)
>>> trans_group.transformers[2].transform(66, -153)
(149748.32734832275, 5849274.621409136)
  1. Helpful if want to check that the best possible transformation exists. And if not, how to get the missing grid.

>>> from pyproj.transformer import TransformerGroup
>>> tg = TransformerGroup("epsg:4326", "+proj=aea +lat_0=50 +lon_0=-154 +lat_1=55 +lat_2=65 +x_0=0 +y_0=0 +datum=NAD27 +no_defs +type=crs +units=m", always_xy=True)
UserWarning: Best transformation is not available due to missing Grid(short_name=ntv2_0.gsb, full_name=, package_name=proj-datumgrid-north-america, url=, direct_download=True, open_license=True, available=False)
>>> tg
<TransformerGroup: best_available=False>
- transformers: 37
- unavailable_operations: 41
>>> tg.transformers[0].description
'axis order change (2D) + Inverse of NAD27 to WGS 84 (3) + axis order change (2D) + unknown'
>>> tg.unavailable_operations[0].name
'Inverse of NAD27 to WGS 84 (33) + axis order change (2D) + unknown'
>>> tg.unavailable_operations[0].grids[0].url

Area of Interest

New in version 2.3.0.

Depending on the location of your transformation, using the area of interest may impact which transformation operation is selected in the transformation.

>>> from pyproj.transformer import Transformer, AreaOfInterest
>>> transformer = Transformer.from_crs("epsg:4326", "epsg:2694")
>>> transformer
<Concatenated Operation Transformer: pipeline>
Description: Inverse of Pulkovo 1995 to WGS 84 (2) + 3-degree Gauss-Kruger zone 60
Area of Use:
- name: Russia
- bounds: (18.92, 39.87, -168.97, 85.2)
>>> transformer = Transformer.from_crs(
...     "epsg:4326",
...     "epsg:2694",
...     area_of_interest=AreaOfInterest(-136.46, 49.0, -60.72, 83.17),
... )
>>> transformer
<Concatenated Operation Transformer: pipeline>
Description: Inverse of NAD27 to WGS 84 (13) + Alaska Albers
Area of Use:
- name: Canada - NWT; Nunavut; Saskatchewan
- bounds: (-136.46, 49.0, -60.72, 83.17)


The pyproj.transformer.Transformer and classes each have their own PROJ context. However, contexts cannot be shared across threads. As such, it is recommended to create the object within the thread that uses it.

Here is a simple demonstration:

import concurrent.futures

from pyproj import Transformer

def transform_point(point):
    transformer = Transformer.from_crs(4326, 3857)
    return transformer.transform(point, point * 2)

with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
    for result in, range(5)):