semvec/pypi-packages huggingface dataset:
pyipums 0.0.4
A Python package to explore IPUMS data.
License: MIT
Requires Python: >=3.8,<4.0

# PyIPUMS

![pypums (1)](https://github.com/franciscojavierarceo/pyipums/assets/4163062/398bb2b5-8974-4eb9-84a0-92938da184c1)

PyIPUMS is a library for working with data from [IPUMS](https://www.ipums.org/).

# Example

Example that provides the IPUMS metadata in a dictionary.
```
import json
import pandas as pd
from src.pyipums.parse_xml import read_ipums_ddi
from ipumspy import readers, ddi


def read_ipums_micro(ddi, data_file_path, n_max=None):
    # Read the fixed-width data file using the extracted column information
    df = pd.read_fwf(
        data_file_path,
        dtypes=ddi["column_dtypes"],
        colspecs=ddi["column_specs"],
        header=None,
        names=ddi["columns"],
        nrows=n_max,
        compression="gzip",
    )

    return df


def main():
    ddi_file_path = "./usa_00003.xml"
    data_file_path = "./usa_00003.dat.gz"
    cps_ddi = read_ipums_ddi(ddi_file_path)
    print(json.dumps(cps_ddi["file_metadata"], indent=2))
    cps_data = read_ipums_micro(cps_ddi, data_file_path, n_max=100)
    print(cps_data.head())
```


# Modifying 

If you are looking to make changes to the library I recommend using [poetry](https://python-poetry.org/docs/).
```
poetry env use 3.8
pyenv shell 3.8
poetry shell
```

# License
MIT
mambabyte 0.0.2
MambaByte - Pytorch
License: MIT
Requires Python: >=3.6,<4.0
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)


# MambaByte
Implementation of MambaByte in "MambaByte: Token-free Selective State Space Model" in Pytorch and Zeta. Note this will be a higher performance implementation of Mamba with parallel scan 


## Installation

```bash
pip install mambabyte
```

# Usage
```python
import torch 
from mambabyte import MambaConfig, Mamba

x = torch.randn(2, 3, 4)
config = MambaConfig(
    dim = 4,
    depth = 3,
    dt_rank = 2,
    d_state = 2,
    expand_factor = 2,
    d_conv = 3,
    dt_min = 0.001,
    dt_max = 0.1,
    dt_init = "random",
    dt_scale = 1.0,
    bias = False,
    conv_bias = True,
    pscan = True
)

model = Mamba(config)

out = model(x)

print(out)

```


# License
MIT
defipy 0.0.2
DeFi for Python
License: MIT

# defipy
Python package for DeFi modelling
* Currently in Beta (version 0.0.2) until fully tested and analyzed

## Install
To install package:
```
> git clone https://github.com/icmoore/defipy
> pip install .
```
or
```
> pip install DeFiPy
```

## DeFi Overview
	* Python for DeFi analytics