Pandas

SigilYX can read and write YXDB files as Pandas DataFrames. Data flows through PyArrow under the hood, so you need the [pandas] extra installed.

Installation

pip install sigilyx[pandas]

Reading

import sigilyx as yx

df = yx.read_yxdb_pandas("data.yxdb")
print(type(df))  # <class 'pandas.core.frame.DataFrame'>

The read path is: Rust reader -> Arrow arrays -> PyArrow Table -> Pandas DataFrame. Despite the conversion steps, this is still orders of magnitude faster than pure-Python YXDB readers.

Writing

import sigilyx as yx
import pandas as pd

df = pd.read_csv("data.csv")
yx.write_yxdb_pandas("output.yxdb", df)

Pandas DataFrames are converted to PyArrow Tables before being passed to the Rust writer.

Type Mapping

Pandas dtype	YXDB Type	Notes
bool	Boolean
int16	Int16
int32	Int32
int64	Int64
float32	Float
float64	Double
object (strings)	V_WString	Variable-length UTF-16
datetime64[ns]	DateTime	Converted to microseconds
object (date)	Date
object (time)	Time	datetime.time values
object (Decimal)	FixedDecimal	decimal.Decimal values
bytes	Blob	Raw binary data

Prefer Polars for performance

The Polars path (read_yxdb()) is the fastest because it avoids the Pandas conversion overhead. If you're starting a new project and don't have an existing Pandas dependency, consider using Polars directly. See the Polars guide.

Common Patterns

Read YXDB, process with Pandas, write back

import sigilyx as yx

df = yx.read_yxdb_pandas("input.yxdb")

# Standard Pandas operations
df["total"] = df["quantity"] * df["price"]
df = df[df["total"] > 100]
df = df.sort_values("total", ascending=False)

yx.write_yxdb_pandas("output.yxdb", df)

Convert YXDB to CSV

import sigilyx as yx

df = yx.read_yxdb_pandas("data.yxdb")
df.to_csv("data.csv", index=False)

Convert Excel to YXDB

import sigilyx as yx
import pandas as pd

df = pd.read_excel("data.xlsx")
yx.write_yxdb_pandas("data.yxdb", df)

Interop with scikit-learn

import sigilyx as yx
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

df = yx.read_yxdb_pandas("training_data.yxdb")

X = df[["feature_1", "feature_2", "feature_3"]]
y = df["target"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = LinearRegression().fit(X_train, y_train)
print(f"R² = {model.score(X_test, y_test):.3f}")