MIT

Python bindings for the primesieve C++ library.

Generates primes orders of magnitude faster than any pure Python code!

**Features:**

- Get an array of primes
- Iterate over primes using little memory
- Find the nth prime
- Count/print primes and prime k-tuplets
- Multi-threaded for counting primes and finding the nth prime
- NumPy support

We provide primesieve wheels (distribution packages) for Windows, macOS and Linux for x86 and x64 CPUs. For other operating systems and/or CPUs you need to have installed a C++ compiler.

```
# Ubuntu/Debian
sudo apt install g++ python-dev
# Fedora
sudo dnf install gcc-c++ python-devel
# macOS
xcode-select --install
```

```
# Python 3.5 or later
pip install primesieve
# For Python 2.7 use:
pip install "primesieve<=1.4.4"
```

You don't need to install a C++ compiler when installing python-primesieve using Conda.

```
conda install -c conda-forge python-primesieve
```

```
>>> from primesieve import *
# Get an array of the primes <= 40
>>> primes(40)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]
# Get an array of the primes between 100 and 120
>>> primes(100, 120)
[101, 103, 107, 109, 113]
# Get an array of the first 10 primes
>>> n_primes(10)
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
# Get an array of the first 10 primes >= 1000
>>> n_primes(10, 1000)
[1009, 1013, 1019, 1021, 1031, 1033, 1039, 1049, 1051, 1061]
# Get the 10th prime
>>> nth_prime(10)
29
# Count the primes below 10**9
>>> count_primes(10**9)
50847534
```

Here is a list of all available functions.

Instead of generating a large array of primes and then do something with the primes it is also possible to simply iterate over the primes which uses less memory.

```
>>> import primesieve
it = primesieve.Iterator()
prime = it.next_prime()
# Iterate over the primes below 10000
while prime < 10000:
print prime
prime = it.next_prime()
# Set iterator start number to 100
it.skipto(100)
prime = it.prev_prime()
# Iterate backwards over the primes below 100
while prime > 0:
print prime
prime = it.prev_prime()
```

Using the `primesieve.numpy`

module you can generate an array of
primes using native C++ performance!

In comparison the `primesieve`

module generates an array of primes
about 3 times slower mostly because the conversion of the C primes
array into a python array is quite slow.

```
>>> from primesieve.numpy import *
# Generate a numpy array with the primes below 100
>>> primes(100)
array([ 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59,
61, 67, 71, 73, 79, 83, 89, 97])
# Generate a numpy array with the first 100 primes
>>> n_primes(100)
array([ 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41,
43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101,
103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167,
173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239,
241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313,
317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397,
401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467,
479, 487, 491, 499, 503, 509, 521, 523, 541])
```

You need to have installed a C++ compiler, see Prerequisites.

```
# Install prerequisites
pip install cython pytest numpy
# Clone repository
git clone --recursive https://github.com/kimwalisch/primesieve-python
cd primesieve-python
# Build and install primesieve-python
pip install . --upgrade
# Run tests
pytest
```

- You need to be a maintainer of the primesieve-python repo.
- You need to be a maintainer of the primesieve pypi project.
- Compare
`.travis.yml`

with cibuildwheel#example-setup and update`.travis.yml`

if needed. - Update the supported Python versions in
`setup.py`

(we support the same versions as cibuildwheel). - Increment the primesieve-python version in
`setup.py`

. Ideally this should be the last commit before the release as this uploads the new primesieve wheels to https://test.pypi.org. - Check if all primesieve wheels (Windows, macOS, Linux) have been uploaded to https://test.pypi.org.
- If not, read the Travis CI logs and fix the bugs.
- Finally, do a new release on GitHub.

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