infer-ts

Infer timestamp formats from string columns using CSP constraint elimination, built in Rust via PyO3 for use with Python and Polars.

Why this library?

Polars can parse string columns to Datetime via str.to_datetime(format=...), but requires you to supply the format string. When no format is given (format=None), Polars infers it — but with two significant limitations:

Only ISO 8601 variants are reliably inferred. Common real-world formats fail entirely with format=None:

pl.Series(["01/15/2024"]).str.to_datetime()
# ComputeError: could not find an appropriate format to parse dates,
# please define a format

pl.Series(["1705312200"]).str.to_datetime()   # Unix timestamps
pl.Series(["Jan 15, 2024"]).str.to_datetime() # Month-name dates
pl.Series(["20240115T103000"]).str.to_datetime() # Compact dates
# All raise the same error

Format is inferred from the first non-null value only. For slash dates, Polars always assumes day-first (%d/%m/%Y). If your data is US-formatted (%m/%d/%Y), you either get silently wrong dates or a parse error on the first value where day > 12:

# Polars locks on %d/%m/%Y from the first row, then fails on month=15
pl.Series(["03/04/2024", "01/15/2024"]).str.to_datetime()
# ComputeError: … failed for 1 out of 2 values: ["01/15/2024"]

The alternative — wrapping str.to_datetime in a try/except loop over candidate formats — is verbose, slow, and still only tells you the first format that works on the first value.

infer-ts solves this by scanning the entire column once with a CSP algorithm that tracks all compatible formats simultaneously. It returns every format consistent with the full column, handles ambiguity explicitly (e.g. reporting both %d/%m/%Y and %m/%d/%Y when the data doesn't distinguish them), and supports dozens of formats that Polars cannot auto-infer.

How it works

The inference engine treats each candidate timestamp format as a variable in a Constraint Satisfaction Problem (CSP). Each cell value in the column acts as a constraint that narrows the candidate set:

1. Initialise – start with all format combinations as candidates.
2. Propagate – for each non-null cell, eliminate every format that cannot parse that value.
3. Early exit (default) – as soon as a single format remains, return it immediately. Set exhaustive=True to disable this and check all values.
4. Return – return all formats that survived constraint propagation.

This approach is both efficient (resolves in the first few rows for most real data) and flexible (use exhaustive=True to validate the entire column).

Supported formats

See FORMATS.md for the full reference tables (date patterns, time patterns, timezone suffixes, and Unix epoch formats with their Polars format strings). That file is auto-generated from the Rust source — to regenerate after changing any format definitions:

cargo test -- --ignored dump_formats

Architecture: Compositional format design

Internally, formats are represented using a compositional structure rather than a flat enum:

Format
├── Date { date: DateFmt }
├── DateTime { date: DateFmt, sep: Separator, time: TimeFmt, tz: Option<Timezone>, spaced_tz: bool }
└── Unix { precision: UnixPrecision }

All combinations of the above components are tried automatically. Structurally invalid ones (e.g. a value whose first character isn't T or space at the separator position) are eliminated by the parser on the first value, so the performance cost is negligible.

Adding a new format variant (e.g. named timezones) requires a single new enum variant and a validator — no changes to the combinatorial logic.

Installation

Install the Python package with pip:

pip install infer-ts

Then import it as infer_ts:

import infer_ts

If you are installing from a local checkout instead of PyPI, use:

pip install .

Development

Development requires Rust, maturin, and uv for reproducible installs.

uv sync --all-extras
uv run maturin develop --release
bash build-and-test.sh

Usage

Quick start

import polars as pl
import infer_ts

df = pl.DataFrame({"ts": ["2024-01-15T10:30:00", "2024-06-20T08:00:00"]})

# Series → Series
series = infer_ts.to_datetime(df["ts"])

# Column name → Expr  (works inside lazy frames too)
df = df.with_columns(infer_ts.to_datetime("ts"))

# Expr → Expr
df = df.with_columns(infer_ts.to_datetime(pl.col("ts")))

# Namespace style (equivalent to the Expr form above)
df = df.with_columns(pl.col("ts").infer_ts.to_datetime())

# Control the output time unit (default: "us")
df = df.with_columns(infer_ts.to_datetime("ts", time_unit="ns"))

# Infer the format strings only (returns a list of all matching formats)
fmts = infer_ts.infer_format(df["ts"])

Basic inference

import infer_ts

# Returns a list of compatible formats
fmts = infer_ts.infer_format([
    "2024-01-15T10:30:00",
    "2024-06-20T08:00:00",
    None,                       # nulls are skipped
])
print(fmts)       # ["%Y-%m-%dT%H:%M:%S"]
print(fmts[0])    # "%Y-%m-%dT%H:%M:%S"

# Use exhaustive=True to process all values
fmts = infer_ts.infer_format(["01/02/2024", "03/04/2024"], exhaustive=True)
print(fmts)  # ["%d/%m/%Y", "%m/%d/%Y"] - both US and EU formats match

Polars – using the inferred format directly

If you need the format string itself (e.g. to pass to other tools), use infer_format and call str.to_datetime manually:

import polars as pl
import infer_ts

df = pl.DataFrame({"ts": ["2024-01-15T10:30:00", "2024-06-20T08:00:00"]})

fmts = infer_ts.infer_format(df["ts"])
# Use first format (or handle multiple if ambiguous)
fmt = fmts[0]
df = df.with_columns(pl.col("ts").str.to_datetime(format=fmt))

Polars – Unix epoch formats

infer_format returns a @-prefixed marker for epoch columns (e.g. @unix_seconds, @unix_ms, @unix_us, @unix_ns). to_datetime handles these automatically with correct integer scaling — no manual casting needed:

import polars as pl
import infer_ts

df = pl.DataFrame({"ts": ["1705312200", "1705398600"]})

df = df.with_columns(infer_ts.to_datetime("ts"))       # Expr form
result = infer_ts.to_datetime(df["ts"])                # Series form

Handling ambiguity

US and EU slash dates are inherently ambiguous when every day value is ≤ 12. Instead of raising an error, the library returns all compatible formats:

import infer_ts

# Ambiguous – both mm/dd and dd/mm interpretations are valid for every row
fmts = infer_ts.infer_format(["01/02/2024", "03/04/2024"])
print(fmts)       # ["%d/%m/%Y", "%m/%d/%Y"]
print(len(fmts))  # 2 - caller can choose or prompt user

# Resolved – day 15 > 12 eliminates the US interpretation
fmts = infer_ts.infer_format(["01/02/2024", "15/03/2024"])
print(fmts)  # ["%d/%m/%Y"] - uniquely EU

# No match returns empty list
fmts = infer_ts.infer_format(["not a timestamp"])
print(fmts)  # []

Contributing

This project was developed with heavy use of LLM agents (primarily Claude) for both the initial implementation and subsequent refinement. If you spot a bug, an edge case the parser mishandles, or a timestamp format that should be supported, issues and pull requests are very welcome.