euspinolia

A small, educational CSV/table library with its engine in Zig and a thin ctypes layer in Python. It parses a CSV into typed columns, reduces, filters and groups them, and writes them back out — faster than pandas for most of that, with no dependencies and a shared library that links nothing, not even libc.

The name comes from Euspinolia, the genus of the velvet ant known as the "panda ant".

>>> import euspinolia
>>> df = euspinolia.read_csv("people.csv")
>>> df
        name  age  score      city
0        ada   36   91.5    London
1      grace   45   88.0  New York
2  Doe, John   29  73.25     Paris

[3 rows x 4 columns]
>>> df["age"].to_list()
[36, 45, 29]
>>> df[df["age"] > 30]
    name  age  score      city
0    ada   36   91.5    London
1  grace   45   88.0  New York

[2 rows x 4 columns]
>>> df.groupby("city").agg({"score": "mean"})
       city  score
0    London   91.5
1  New York   88.0
2     Paris  73.25

[3 rows x 2 columns]

Install

pip install euspinolia

Wheels are published for Linux (x86_64, aarch64), macOS (x86_64, arm64) and Windows (x64, arm64), for any Python 3.9 or newer. On anything else, pip builds from source; that needs no Zig install either, since the build pulls it from the ziglang package.

To work from a checkout instead, see docs/development.md.

What it does

import euspinolia

df = euspinolia.read_csv("people.csv")   # parse a file
df = euspinolia.parse_csv(csv_text)      # parse text already in memory

df.shape          # (3, 4) — (rows, columns)
df.columns        # ('name', 'age', 'score', 'city')
df.dtypes         # (string, int, float, string)
df.head(2)        # the first two rows as tuples

column = df["age"]   # by name; df[1] works too
column[0]            # 36 — read straight out of the Zig buffer
column.to_list()     # [36, 45, 29]

column.sum()      # 110 — exact, reduced in Zig
column.mean()     # 36.666666666666664
column.min()      # 29
column.max()      # 45

df[df["age"] > 30]                          # a new DataFrame with the matching rows
df[(df["age"] > 30) & (df["score"] < 90)]   # conditions chain with &
df.filter("city", "==", "Paris")            # the same thing, as a call

df.groupby("city").agg({"score": "mean", "age": "max"})   # one row per city
df.groupby("city").sum()                                  # every numeric column
df.groupby("city").count()                                # rows per group

df.to_csv("out.csv")   # write it back out; reads back as the same frame
df.to_csv()            # or as a string

with euspinolia.read_csv("people.csv") as df:
    ...   # Zig-side memory freed on the way out; df.close() does the same

Every column has one type — int, float or string — inferred from its values. Reductions keep that type, so an integer column sums exactly. A filtered or grouped frame owns its own memory and outlives its source. Errors are ordinary Python exceptions: FileNotFoundError, ParseError, TypeError for text where a number was needed, OverflowError for a sum that leaves 64 bits.

The full reference is in docs/api.md.

Performance

500,000 rows, 5 columns, 18 MB; best of five on one laptop, against pandas 3.0 and the standard csv module:

Parsing, writing and grouping are ahead of pandas; reductions are a wash, as native code against native code should be; filtering is the one loss, because the result copies its strings rather than sharing them. What each number means, and how to run the benchmark yourself, is in docs/benchmarks.md.

How it works

[Python]  df = euspinolia.read_csv("data.csv")
              │  ctypes call
              ▼
[Zig]     CSV scanner → type inference → one typed array per column
              │
              ├─▶ sum / mean / min / max   → one value
              ├─▶ filter, groupby          → a new frame
              └─▶ to_csv                   → bytes
              │  opaque handle + borrowed column pointers
              ▼
[Python]  DataFrame / Column wrap the handle; df["age"][0] reads the
          Zig buffer through ctypes, no copy

Columns are struct-of-arrays: int and float are flat []i64 / []f64, strings are one packed byte buffer plus offsets. A filter or an aggregate walks one contiguous array; a groupby hashes each key into a dense group id and folds into a flat accumulator; Python reads numeric columns in place. Module by module: docs/internals.md.

Scope

This is a teaching project, and the scope was fixed at the start: one delimiter, one header row, three column types, and the operations above. Multi-index, date/time types, NaN semantics, join/merge and pivot tables are out, on purpose. The goal is not a real table engine but a subset that genuinely works, is fast for real reasons, and can be read end to end in an afternoon — about 3,000 lines of Zig, tests included, and 900 of Python.

Documentation

• API reference — every function, method, argument and exception
• Internals — the Zig side, module by module, and the ABI
• Benchmarks — the numbers above, what they measure and why they come out that way
• Development — building, testing, cross-compiling wheels, releasing
• Changelog

License

MIT.