vecly 0.1.1

Lightweight Vector/Column/DataFrame utilities

vecly

Lightweight, NumPy-backed building blocks for quick finance/research workflows.

vecly provides three small primitives:

• Vector: a thin wrapper around numpy.ndarray with operator overloading and basic stats.
• Column: a named 1D series-like object built on Vector.
• DataFrame: a minimal column container for quick prototyping and feature workflows.

The goal is fast iteration: get just enough structure for experiments (ML/finance/research) without pulling in a full dataframe framework.

Install

From PyPI:

python -m pip install vecly

For local development:

python -m pip install -e .

Quickstart

import numpy as np
from vecly import Vector, Column, DataFrame

# Vector: NumPy-friendly wrapper
v = Vector([1, 2, 3])
print(v + 2)          # Vector([3 4 5])
print(v.mean())       # 2.0
print(np.log(v))      # works via NumPy interop

# Column: named vector
price = Column("price", [100, 101, 103, 102])

# Simple feature workflow (returns -> log-returns)
ret = price / price.shift(1) - 1
log_ret = ret.log()

df = DataFrame([price])
df.append(Column("ret", ret))
df.append(Column("log_ret", log_ret))

print(df)             # pretty preview
print(df["price"])    # -> 1D np.ndarray
print(df[["ret", "log_ret"]])  # -> 2D np.ndarray (n_rows, 2)

Core API

Vector

• Construction: Vector(data)
• Underlying array: v.data or v.to_numpy()
• Math: + - * / ** (elementwise; supports scalars, array-likes, other Vector)
• Stats: v.sum(), v.mean(), v.var(), v.std()
• Transforms:
  • v.log() → returns a new Vector
  • v.shift(n=1, fill_value=np.nan) → returns a new Vector
• NumPy interop:
  • np.asarray(v), np.log(v), np.column_stack([v1, v2]), etc.

Column

• Construction: Column(name, x) where x can be list/ndarray/Vector
• Name: col.name
• Data: col.vec (Vector) or col.to_numpy() (np.ndarray)
• Transforms:
  • col.shift(n=1) → Vector
  • col.log() → Vector
• Elementwise math:
  • col / other (where other can be Column, Vector, scalar, array-like) → Vector

DataFrame

DataFrame is intentionally minimal: it stores a list of Column objects.

• Construction: DataFrame([Column(...), ...])
• Append/replace column: df.append(col) (replaces if same col.name)
• Add from raw data: df.add_col(name, data)
• Select:
  • df["x"] → 1D np.ndarray
  • df[["x", "y"]] → 2D np.ndarray shaped (n_rows, 2)

License

MIT. See LICENSE.