Fyrefly

A quant-based library to compute mathematical operations, run SQL queries, get AI-powered insights, and visualize your data.

Installation

pip install fyrefly

Usage — Math functions

from fyrefly import add, mul, div, mod, diff

add(5, 4, 6, 10)     # 25
mul(5, 4, 6, 10)     # 1200
div(100, 5, 2)       # 10.0
mod(17, 5)           # 2
diff(10, 3, 2)       # 5

Each function accepts any number of arguments:

add(1)                # 1
add(1, 2, 3, 4, 5)    # 15
mul()                  # 1 (identity)
diff(10, 3)            # 7

Usage — Data functions

from fyrefly import load, loadh, loadc, loads, sql, xtract, clean

c = load("data.csv")                 # loads a CSV/Excel/Google Sheet, previews it
loadh(c)                             # preview column headers
loadc(c)                             # preview record count
loads(c)                             # preview schema (columns + datatypes)

c = clean(c)                         # dedupe, strip whitespace, standardize column names

d = sql("select * from c where age > 30")   # run ANY SQL query on loaded data
xtract(d)                                    # export the result to CSV
xtract(sql("select * from c"), filename="all.csv")  # chained form also works

Note: for Google Sheets, the sheet must be shared as "Anyone with the link – Viewer" for load() to access it.

Usage — AI functions (require an API key)

from fyrefly import ask, insights

c = load("sales.csv")

# Ask questions in plain English — converted to SQL and run automatically
ask(c, "what were total sales by region last quarter?", api_key="AIza...")

# Query across multiple datasets (e.g. a join) — scales to any count
ask((c, d), "who are our top customers by total sales?", api_key="AIza...")
ask((c, d, e), "...", api_key="AIza...")

# Get a plain-English summary of a dataset
insights(c, api_key="AIza...")

The provider (Anthropic, OpenAI, Gemini, or any OpenAI-compatible API like Groq/Mistral) is auto-detected from your API key's format — you usually don't need to specify it. API keys can also be set via environment variables instead of passing them directly: ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY.

⚠️ Never commit an API key into a script you push to GitHub. Use an environment variable instead:

export GEMINI_API_KEY="AIza..."

ask(c, "what were total sales by region?")   # picked up automatically from the env var

Usage — Visualization (no AI, no API key needed)

from fyrefly import viz

viz(c)                                          # auto -> correlation heatmap
viz(c, x="department")                          # auto -> bar chart of counts
viz(c, x="age")                                 # auto -> histogram
viz(c, kind="scatter", x="age", y="salary")
viz(c, kind="box", x="department", y="salary")
viz(c, kind="pie", x="department")
viz(c, kind="line", x="date", y="revenue")
viz(c, kind="pairplot", hue="department")
viz(c, kind="bar", x="department", y="salary", save="chart.png")   # also saves a file

Supported chart types: line, bar, barh, scatter, hist, box, violin, kde, pie, heatmap, pairplot, area, plus "auto" which picks a sensible chart based on your data.

Functions

Development

pip install -e ".[dev]"
pytest

License

MIT