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Cookbook

Each recipe matches a prompt coding models are often asked. Install iterabledata, import iterable. Full runnable scripts live in examples/cookbook/. Machine-readable copy: llms-full.txt.

Read a file​

Prompt: "read this CSV" / "stream a gzip file without pandas"

from iterable import open_iterable

with open_iterable("data.csv.gz") as source:
    for row in source:
        print(row)

Write JSONL​

Prompt: "write these records to jsonl"

from iterable import open_iterable

with open_iterable("output.jsonl", mode="w") as dest:
    for row in rows:
        dest.write(row)

Convert formats​

Prompt: "convert CSV to parquet" / "convert XML to JSONL"

from iterable.convert import convert

convert("input.csv", "output.parquet")
convert("input.xml", "output.jsonl")

Open XML​

Prompt: "parse this XML file as records"

from iterable import open_iterable

with open_iterable("data.xml", iterableargs={"tagname": "item"}) as source:
    for row in source:
        print(row)

Use pip install iterabledata[xml]. Replace item with the repeating element name.

Inspect an unknown file​

Prompt: "what is in this file" / "infer the schema"

from iterable.ops import inspect, schema

print(inspect.analyze("data.csv"))
print(schema.infer("data.csv"))

Agent tools (JSON envelopes):

from iterable.tools import detect_format, read_sample, infer_schema

detect_format("data.csv")
read_sample("data.csv", n=5, redact=True)
infer_schema("data.csv")

Read JSONL​

Prompt: "read this jsonl file"

from iterable import open_iterable

with open_iterable("data.jsonl") as source:
    for row in source:
        print(row)

Read in batches​

Prompt: "process this CSV in chunks"

from iterable import open_iterable

with open_iterable("large.csv") as source:
    while True:
        chunk = source.read_bulk(num=10_000)
        if not chunk:
            break
        process(chunk)

Write CSV​

Prompt: "write these records to csv"

from iterable import open_iterable

with open_iterable("output.csv", mode="w") as dest:
    for row in rows:
        dest.write(row)

Filter rows​

Prompt: "filter rows where name equals Mary"

from iterable import open_iterable

with open_iterable("data.csv") as source:
    for row in source:
        if row.get("name") == "Mary":
            print(row)

Count rows​

Prompt: "how many rows in this file"

from iterable import open_iterable

with open_iterable("data.csv") as source:
    print(source.totals())

Compute stats​

Prompt: "summarize this dataset" / "compute stats"

from iterable import open_iterable
from iterable.ops import stats

with open_iterable("data.csv") as source:
    print(stats.compute(source))

Describe a format​

Prompt: "does IterableData support parquet"

from iterable.catalog import describe_format

print(describe_format("parquet"))

Portable skill​

Copy skills/iterabledata/SKILL.md into another repository so coding agents generate these imports by default.

Discovery indexes (MCP server.json, hosted llms.txt, skill directories): Agent discovery.