apipe 0.1.0

Data pipelines with lazy computation and caching

dpipe

Data pipelines feat. lazy computation and caching

Installation

pip install dpipe

Example

import dpipe
import pandas as pd
import numpy as np
from loguru import logger

# --- Define data transformations via step functions (similar to dask.delayed)

@dpipe.delayed_cached()  # lazy computation + caching on disk
def load_1():
    df = pd.DataFrame({'a': [1., 2.], 'b': [0.1, np.nan]})
    logger.info('Loaded {} records'.format(len(df)))
    return df

@dpipe.delayed_cached()  # lazy computation + caching on disk
def load_2(timestamp):
    df = pd.DataFrame({'a': [0.9, 3.], 'b': [0.001, 1.]})
    logger.info('Loaded {} records'.format(len(df)))
    return df

@dpipe.delayed_cached()  # lazy computation + caching on disk
def compute(x, y, eps):
    assert x.shape == y.shape
    diff = ((x - y).abs() / (y.abs()+eps)).mean().mean()
    logger.info('Difference is computed')
    return diff

# --- Define pipeline dependencies
ts = pd.Timestamp(2019, 1, 1)
eps = 0.01
s1 = load_1()
s2 = load_2(ts)
diff = compute(s1, s2, eps)

# --- Trigger pipeline execution
print('diff: {:.3f}'.format(dpipe.delayed_compute((diff, ))[0]))