feat(data): ajoute l'import historique des donnees
This commit is contained in:
Meryemel-gham
+2
-1
@@ -52,7 +52,8 @@ standalone_admin_password.txt
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secrets/
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# Donnees locales
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data/
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data/raw/*
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!data/raw/.gitkeep
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*.sqlite3
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monitoring/grafana/data/
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monitoring/prometheus/data/
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@@ -0,0 +1,783 @@
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from __future__ import annotations
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import argparse
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import asyncio
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import hashlib
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import json
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from pathlib import Path
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from typing import Any
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import pandas as pd
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import AsyncConnection, create_async_engine
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from app.core.config import get_settings
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REQUIRED_COLUMNS = {
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"timestamp",
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"site_id",
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"site_type",
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"site_name",
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"consumption_kwh",
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"consumption_euros",
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"temperature_celsius",
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"humidity_percent",
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"solar_irradiance_wm2",
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"hour",
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"day_of_week",
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"day_name",
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"month",
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"is_weekend",
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"is_working_hours",
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}
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MEASURE_COLUMNS = [
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"consumption_kwh",
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"consumption_euros",
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"temperature_celsius",
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"humidity_percent",
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"solar_irradiance_wm2",
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]
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SOURCE_NAME = "historical_csv"
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def compute_sha256(path: Path) -> str:
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"""Calcule l'empreinte SHA-256 du fichier source."""
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sha256 = hashlib.sha256()
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with path.open("rb") as source:
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for block in iter(lambda: source.read(1024 * 1024), b""):
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sha256.update(block)
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return sha256.hexdigest()
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def load_metadata(path: Path) -> dict[str, Any]:
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"""Charge les métadonnées fournies avec le dataset."""
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with path.open("r", encoding="utf-8") as source:
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return json.load(source)
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def classify_quality(
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row: dict[str, Any],
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) -> tuple[str, list[str]]:
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"""
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Déduit une qualité technique à partir des champs manquants.
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Les valeurs NULL sont conservées. On ne cherche pas ici à
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déterminer la cause physique exacte de leur absence.
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"""
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missing = [
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column
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for column in MEASURE_COLUMNS
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if pd.isna(row.get(column))
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]
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if not missing:
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quality = "good"
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elif len(missing) == len(MEASURE_COLUMNS):
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quality = "critical"
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elif "consumption_kwh" in missing:
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quality = "degraded"
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else:
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quality = "partial"
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reasons = [
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f"missing:{column}"
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for column in missing
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]
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return quality, reasons
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def validate_source(
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frame: pd.DataFrame,
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metadata: dict[str, Any],
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) -> None:
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"""Valide le dataset avant tout chargement en base."""
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missing_columns = REQUIRED_COLUMNS.difference(
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frame.columns
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)
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if missing_columns:
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raise ValueError(
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"Colonnes obligatoires absentes : "
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f"{sorted(missing_columns)}"
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)
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expected_records = int(metadata["total_records"])
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if len(frame) != expected_records:
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raise ValueError(
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"Nombre de lignes inattendu : "
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f"{len(frame)} au lieu de "
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f"{expected_records}"
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)
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expected_sites = set(metadata["sites"].keys())
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actual_sites = set(frame["site_id"].unique())
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if actual_sites != expected_sites:
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raise ValueError(
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"Sites incohérents. "
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f"Attendus={sorted(expected_sites)}, "
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f"trouvés={sorted(actual_sites)}"
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)
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duplicated = frame.duplicated(
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subset=["site_id", "timestamp"]
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).sum()
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if duplicated:
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raise ValueError(
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f"{duplicated} doublons "
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"(site_id, timestamp) détectés"
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)
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static_variants = (
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frame.groupby("site_id")[
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["site_type", "site_name"]
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]
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.nunique()
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)
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if (static_variants > 1).any().any():
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raise ValueError(
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"Un site possède plusieurs valeurs "
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"de site_type ou site_name."
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)
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# Vérifie également que tous les timestamps
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# peuvent être interprétés correctement.
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pd.to_datetime(
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frame["timestamp"],
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errors="raise",
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)
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def normalize_timestamps(
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frame: pd.DataFrame,
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source_timezone: str,
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) -> pd.DataFrame:
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"""
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Normalise les timestamps et leur associe une timezone.
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Les timestamps originaux sont conservés dans une colonne
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temporaire afin de pouvoir les stocker dans raw_data.
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"""
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normalized = frame.copy()
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normalized["_source_timestamp"] = (
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normalized["timestamp"]
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)
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timestamps = pd.to_datetime(
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normalized["timestamp"],
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errors="raise",
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)
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if timestamps.dt.tz is None:
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timestamps = timestamps.dt.tz_localize(
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source_timezone
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)
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else:
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timestamps = timestamps.dt.tz_convert(
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source_timezone
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)
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normalized["timestamp"] = timestamps
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return normalized
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def to_json_value(value: Any) -> Any:
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"""
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Convertit une valeur Pandas/Numpy en valeur
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compatible JSON.
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"""
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if value is None:
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return None
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try:
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if pd.isna(value):
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return None
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except (TypeError, ValueError):
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pass
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if isinstance(value, pd.Timestamp):
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return value.isoformat()
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if hasattr(value, "item"):
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return value.item()
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return value
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async def ensure_dataset(
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connection: AsyncConnection,
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metadata: dict[str, Any],
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sha256: str,
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source_timezone: str,
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storage_uri: str,
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) -> int:
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"""
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Crée l'entrée dataset si elle n'existe pas.
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Le SHA-256 permet de reconnaître un fichier déjà importé
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et participe à l'idempotence et à la traçabilité.
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"""
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result = await connection.execute(
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text(
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"""
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SELECT dataset_id
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FROM dataset
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WHERE archive_sha256 = :sha256
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LIMIT 1
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"""
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),
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{
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"sha256": sha256,
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},
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)
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existing = result.scalar_one_or_none()
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if existing is not None:
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return int(existing)
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metadata_summary = {
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"generator_version": metadata.get(
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"generator_version"
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),
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"total_sites": metadata.get(
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"total_sites"
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),
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"total_records": metadata.get(
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"total_records"
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),
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"date_range": metadata.get(
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"date_range"
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),
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"frequency": metadata.get(
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"frequency"
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),
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"null_injection_enabled": metadata.get(
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"null_injection_enabled"
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),
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"null_strategies": metadata.get(
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"null_strategies"
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),
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"importer": "historical_import_v1",
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}
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result = await connection.execute(
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text(
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"""
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INSERT INTO dataset (
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dataset_name,
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archive_sha256,
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storage_uri,
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source_timezone,
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"metadata"
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)
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VALUES (
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:dataset_name,
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:archive_sha256,
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:storage_uri,
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:source_timezone,
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CAST(:metadata AS jsonb)
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)
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RETURNING dataset_id
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"""
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),
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{
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"dataset_name": (
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"EnerVision historical dataset "
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"2023-2024"
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),
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"archive_sha256": sha256,
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"storage_uri": storage_uri,
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"source_timezone": source_timezone,
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"metadata": json.dumps(
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metadata_summary,
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ensure_ascii=False,
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),
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},
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)
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return int(result.scalar_one())
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async def upsert_sites(
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connection: AsyncConnection,
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frame: pd.DataFrame,
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) -> None:
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"""Insère ou met à jour les sites du dataset."""
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sites = (
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frame[
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[
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"site_id",
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"site_type",
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"site_name",
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]
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]
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.drop_duplicates(
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subset=["site_id"]
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)
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.to_dict(
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orient="records"
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)
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)
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await connection.execute(
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text(
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"""
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INSERT INTO site (
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site_id,
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site_type,
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site_name
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)
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VALUES (
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:site_id,
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:site_type,
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:site_name
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)
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ON CONFLICT (site_id)
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DO UPDATE SET
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site_type = EXCLUDED.site_type,
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site_name = EXCLUDED.site_name
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"""
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),
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sites,
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)
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def build_reading_batch(
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chunk: pd.DataFrame,
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dataset_id: int,
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) -> list[dict[str, Any]]:
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"""
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Transforme un chunk Pandas en lignes prêtes
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à être chargées dans la table reading.
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"""
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rows: list[dict[str, Any]] = []
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for record in chunk.to_dict(
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orient="records"
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):
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quality, reasons = classify_quality(
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record
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)
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raw_data = {
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column: to_json_value(value)
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for column, value in record.items()
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if column != "_source_timestamp"
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}
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# Dans raw_data, on conserve le timestamp
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# exactement tel qu'il était dans le CSV.
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raw_data["timestamp"] = to_json_value(
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record["_source_timestamp"]
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)
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rows.append(
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{
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"site_id": record["site_id"],
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"timestamp": record["timestamp"],
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"source": SOURCE_NAME,
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"dataset_id": dataset_id,
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# Non fourni par le dataset historique.
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"consumption_kw": None,
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"consumption_kwh": to_json_value(
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record["consumption_kwh"]
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),
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"consumption_euros": to_json_value(
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record["consumption_euros"]
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),
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# Non fournis par le CSV historique.
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"voltage_v": None,
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"current_a": None,
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"power_factor": None,
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"temperature_celsius": (
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to_json_value(
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record[
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"temperature_celsius"
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]
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)
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),
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"humidity_percent": (
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to_json_value(
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record[
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"humidity_percent"
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]
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)
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),
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"solar_irradiance_wm2": (
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to_json_value(
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record[
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"solar_irradiance_wm2"
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]
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)
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),
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"is_working_hours": bool(
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record[
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"is_working_hours"
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]
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),
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"data_quality": quality,
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"null_reasons": reasons,
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# Aucune imputation pendant
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# l'ingestion RAW.
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"imputed_values": json.dumps(
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{}
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),
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"imputation_method": None,
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# Conservation de la donnée source
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# pour la traçabilité.
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"raw_data": json.dumps(
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raw_data,
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ensure_ascii=False,
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),
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}
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)
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return rows
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READING_INSERT = text(
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"""
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INSERT INTO reading (
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site_id,
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timestamp,
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source,
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dataset_id,
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consumption_kw,
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consumption_kwh,
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consumption_euros,
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voltage_v,
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current_a,
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power_factor,
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temperature_celsius,
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humidity_percent,
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solar_irradiance_wm2,
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is_working_hours,
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data_quality,
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null_reasons,
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imputed_values,
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imputation_method,
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raw_data
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)
|
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VALUES (
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:site_id,
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:timestamp,
|
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:source,
|
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:dataset_id,
|
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:consumption_kw,
|
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:consumption_kwh,
|
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:consumption_euros,
|
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:voltage_v,
|
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:current_a,
|
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:power_factor,
|
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:temperature_celsius,
|
||||
:humidity_percent,
|
||||
:solar_irradiance_wm2,
|
||||
:is_working_hours,
|
||||
:data_quality,
|
||||
:null_reasons,
|
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CAST(:imputed_values AS jsonb),
|
||||
:imputation_method,
|
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CAST(:raw_data AS jsonb)
|
||||
)
|
||||
ON CONFLICT DO NOTHING
|
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"""
|
||||
)
|
||||
|
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|
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async def import_historical(
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csv_path: Path,
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metadata_path: Path,
|
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source_timezone: str,
|
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batch_size: int,
|
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dry_run: bool,
|
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storage_uri: str,
|
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) -> None:
|
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"""
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Exécute le pipeline ETL historique EnerVision.
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|
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Étapes :
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1. Extract
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2. Validate
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3. Transform
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4. Load
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"""
|
||||
metadata = load_metadata(
|
||||
metadata_path
|
||||
)
|
||||
|
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frame = pd.read_csv(
|
||||
csv_path
|
||||
)
|
||||
|
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validate_source(
|
||||
frame,
|
||||
metadata,
|
||||
)
|
||||
|
||||
print(
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||||
f"Lignes : {len(frame)}"
|
||||
)
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print(
|
||||
"Sites : "
|
||||
f"{frame['site_id'].nunique()}"
|
||||
)
|
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print(
|
||||
"Période : "
|
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f"{frame['timestamp'].min()} -> "
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||||
f"{frame['timestamp'].max()}"
|
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)
|
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print(
|
||||
"Doublons : "
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f"{frame.duplicated(['site_id', 'timestamp']).sum()}"
|
||||
)
|
||||
|
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print("\nValeurs NULL :")
|
||||
print(
|
||||
frame[
|
||||
MEASURE_COLUMNS
|
||||
].isna().sum()
|
||||
)
|
||||
|
||||
sha256 = compute_sha256(
|
||||
csv_path
|
||||
)
|
||||
|
||||
print(
|
||||
f"\nSHA-256 : {sha256}"
|
||||
)
|
||||
|
||||
if dry_run:
|
||||
print(
|
||||
"\nDry-run terminé : "
|
||||
"aucune donnée écrite."
|
||||
)
|
||||
return
|
||||
|
||||
normalized = normalize_timestamps(
|
||||
frame,
|
||||
source_timezone,
|
||||
)
|
||||
|
||||
settings = get_settings()
|
||||
|
||||
engine = create_async_engine(
|
||||
str(settings.database_url),
|
||||
pool_pre_ping=True,
|
||||
)
|
||||
|
||||
try:
|
||||
async with engine.begin() as connection:
|
||||
dataset_id = await ensure_dataset(
|
||||
connection=connection,
|
||||
metadata=metadata,
|
||||
sha256=sha256,
|
||||
source_timezone=source_timezone,
|
||||
storage_uri=storage_uri,
|
||||
)
|
||||
|
||||
await upsert_sites(
|
||||
connection,
|
||||
normalized,
|
||||
)
|
||||
|
||||
result = await connection.execute(
|
||||
text(
|
||||
"""
|
||||
SELECT COUNT(*)
|
||||
FROM reading
|
||||
WHERE dataset_id = :dataset_id
|
||||
AND source = :source
|
||||
"""
|
||||
),
|
||||
{
|
||||
"dataset_id": dataset_id,
|
||||
"source": SOURCE_NAME,
|
||||
},
|
||||
)
|
||||
|
||||
before = int(
|
||||
result.scalar_one()
|
||||
)
|
||||
|
||||
for start in range(
|
||||
0,
|
||||
len(normalized),
|
||||
batch_size,
|
||||
):
|
||||
chunk = normalized.iloc[
|
||||
start : start + batch_size
|
||||
]
|
||||
|
||||
rows = build_reading_batch(
|
||||
chunk,
|
||||
dataset_id,
|
||||
)
|
||||
|
||||
await connection.execute(
|
||||
READING_INSERT,
|
||||
rows,
|
||||
)
|
||||
|
||||
loaded = min(
|
||||
start + batch_size,
|
||||
len(normalized),
|
||||
)
|
||||
|
||||
print(
|
||||
"Chargement : "
|
||||
f"{loaded}/"
|
||||
f"{len(normalized)}"
|
||||
)
|
||||
|
||||
result = await connection.execute(
|
||||
text(
|
||||
"""
|
||||
SELECT COUNT(*)
|
||||
FROM reading
|
||||
WHERE dataset_id = :dataset_id
|
||||
AND source = :source
|
||||
"""
|
||||
),
|
||||
{
|
||||
"dataset_id": dataset_id,
|
||||
"source": SOURCE_NAME,
|
||||
},
|
||||
)
|
||||
|
||||
after = int(
|
||||
result.scalar_one()
|
||||
)
|
||||
|
||||
print(
|
||||
"\nImport terminé."
|
||||
)
|
||||
print(
|
||||
"dataset_id : "
|
||||
f"{dataset_id}"
|
||||
)
|
||||
print(
|
||||
"lectures avant : "
|
||||
f"{before}"
|
||||
)
|
||||
print(
|
||||
"lectures après : "
|
||||
f"{after}"
|
||||
)
|
||||
print(
|
||||
"nouvelles lectures : "
|
||||
f"{after - before}"
|
||||
)
|
||||
|
||||
finally:
|
||||
await engine.dispose()
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Définit les arguments CLI de l'import."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description=(
|
||||
"Import historique EnerVision"
|
||||
)
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--csv",
|
||||
type=Path,
|
||||
required=True,
|
||||
help="Chemin vers le CSV historique.",
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--metadata",
|
||||
type=Path,
|
||||
required=True,
|
||||
help=(
|
||||
"Chemin vers le fichier "
|
||||
"dataset_metadata.json."
|
||||
),
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--source-timezone",
|
||||
default="UTC",
|
||||
help=(
|
||||
"Timezone associée aux timestamps "
|
||||
"du dataset. Défaut : UTC."
|
||||
),
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--batch-size",
|
||||
type=int,
|
||||
default=1000,
|
||||
help=(
|
||||
"Nombre de lignes insérées "
|
||||
"par batch. Défaut : 1000."
|
||||
),
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"--dry-run",
|
||||
action="store_true",
|
||||
help=(
|
||||
"Valide les données sans "
|
||||
"écrire en base."
|
||||
),
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Point d'entrée CLI du pipeline."""
|
||||
args = parse_args()
|
||||
|
||||
if args.batch_size <= 0:
|
||||
raise ValueError(
|
||||
"--batch-size doit être "
|
||||
"strictement supérieur à 0."
|
||||
)
|
||||
|
||||
# resolve() est volontairement exécuté ici,
|
||||
# dans la partie synchrone du programme.
|
||||
# Cela évite une opération filesystem bloquante
|
||||
# à l'intérieur d'une fonction async.
|
||||
storage_uri = (
|
||||
args.csv.resolve().as_uri()
|
||||
)
|
||||
|
||||
asyncio.run(
|
||||
import_historical(
|
||||
csv_path=args.csv,
|
||||
metadata_path=args.metadata,
|
||||
source_timezone=(
|
||||
args.source_timezone
|
||||
),
|
||||
batch_size=args.batch_size,
|
||||
dry_run=args.dry_run,
|
||||
storage_uri=storage_uri,
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -16,6 +16,7 @@ dependencies = [
|
||||
"pyjwt>=2.10",
|
||||
"argon2-cffi>=23.1",
|
||||
"anyio>=4.0",
|
||||
"pandas>=3.0.5",
|
||||
]
|
||||
|
||||
[dependency-groups]
|
||||
|
||||
Generated
+95
@@ -1,6 +1,11 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = "==3.14.*"
|
||||
resolution-markers = [
|
||||
"sys_platform == 'win32'",
|
||||
"sys_platform == 'emscripten'",
|
||||
"sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "alembic"
|
||||
@@ -311,6 +316,7 @@ dependencies = [
|
||||
{ name = "argon2-cffi" },
|
||||
{ name = "asyncpg" },
|
||||
{ name = "fastapi" },
|
||||
{ name = "pandas" },
|
||||
{ name = "prometheus-fastapi-instrumentator" },
|
||||
{ name = "pydantic", extra = ["email"] },
|
||||
{ name = "pydantic-settings" },
|
||||
@@ -337,6 +343,7 @@ requires-dist = [
|
||||
{ name = "argon2-cffi", specifier = ">=23.1" },
|
||||
{ name = "asyncpg", specifier = ">=0.31.0" },
|
||||
{ name = "fastapi", specifier = ">=0.141.1" },
|
||||
{ name = "pandas", specifier = ">=3.0.5" },
|
||||
{ name = "prometheus-fastapi-instrumentator", specifier = ">=8.1.0" },
|
||||
{ name = "pydantic", extras = ["email"], specifier = ">=2.13.5" },
|
||||
{ name = "pydantic-settings", specifier = ">=2.15.0" },
|
||||
@@ -595,6 +602,35 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/79/7b/2c79738432f5c924bef5071f933bcc9efd0473bac3b4aa584a6f7c1c8df8/mypy_extensions-1.1.0-py3-none-any.whl", hash = "sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505", size = 4963, upload-time = "2025-04-22T14:54:22.983Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "2.5.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/13/01/11703282db468b85f6f7b8c7f22d058de5970d5c7e60a3a8aaa313c3de36/numpy-2.5.3.tar.gz", hash = "sha256:df2d5874ff183595a4ba404edd04f6bd9b5505c1d7708573f6a6c17489a67563", size = 20791231, upload-time = "2026-09-06T16:27:47.073Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/70/78/cf416f15dc29375a229d9dfebf8db6e313f291580b39fa1a568b6052bb07/numpy-2.5.3-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:350ba9783ce969cf9f7ce6e6a9a58e1a6e2a19ca025b7ee448c4db727706212a", size = 16998686, upload-time = "2026-09-06T16:25:33.171Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/9e/59/abcc2d8def4fd60eec7d87f92d27c13448ffd9ab14339bcc63a0d7a2fdea/numpy-2.5.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:012e66aca395d795496446e52aeeb5866312a5d4d3f27da270e5a0b43f70dc5c", size = 12013862, upload-time = "2026-09-06T16:25:36.748Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/94/75/4640d2d6e4b64a049e48425a82728a41ef4adb61332d2cba68055774878b/numpy-2.5.3-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:adc1ada2662f8a5f960b8a10d9986897e7499ef07e06d4cfe7197f8cce923c07", size = 5449793, upload-time = "2026-09-06T16:25:39.476Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/96/cd/625b57ae33d4ca560f32cc0b47b4a5922146d9beb998ddf773900d440a73/numpy-2.5.3-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:54a115e5a73b8fc44f0cebef486365a1894b5c9760685d4558b72b7c3eb846e0", size = 6785176, upload-time = "2026-09-06T16:25:42.069Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/9c/72/12918652e7912ef9751e8694c88820fcd1908e0618cb23f5f3caa6004b7b/numpy-2.5.3-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:be5a8381859b6da607c84f4f7d6847725f1cf1853ef8a2c9e115b7d58bef47dc", size = 15703377, upload-time = "2026-09-06T16:25:45.135Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/45/8f/9beacf79ca7c650688ad0baa80931adb988fe6e6e5d5903c23cc3dbd70eb/numpy-2.5.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b0521d0f4aebb6e06189451025fa17a913287b13c03d5fe05c017333b654ea5b", size = 16711928, upload-time = "2026-09-06T16:25:48.461Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/09/8d/41d0a56e1ac4c87495c897a211b1368691b7237aadabec8b3b8f3a74d48f/numpy-2.5.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:9deb49575e5b0b94ed72c8a64ec4d033381adc27e9060ae842971f697ba96104", size = 17059507, upload-time = "2026-09-06T16:25:51.873Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/08/1e/0dfbc5cc251d54e2af790f254d24ec38637fa97ec7d5d11de7ffed787098/numpy-2.5.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:b00eefbcf0f292945c4b4dec2ae845389ef5bcdcd596e6e4328051db5b5ba694", size = 18471002, upload-time = "2026-09-06T16:25:55.233Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b5/2c/dfa40f6991f8185c8c30ffd023dfcbb11888e823cfab9557b920f3bb7bed/numpy-2.5.3-cp314-cp314-win32.whl", hash = "sha256:c2381f82999704f818e2c987a865050e285ec3621262c66d40f5a96c8f899f8e", size = 6180485, upload-time = "2026-09-06T16:25:58.157Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a4/73/d2c08231e4fde7e415501fd02c715d96e98599b2d8384445933944152984/numpy-2.5.3-cp314-cp314-win_amd64.whl", hash = "sha256:2c25dfa72943e4336ddb6b0ee4277b47a0c85bede0807530ec68103bf58e2c10", size = 12698179, upload-time = "2026-09-06T16:26:00.789Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/5c/e9/dcdcc9b95cf5f49815055573aee1b11cfbf5299f38a180e437ded050810f/numpy-2.5.3-cp314-cp314-win_arm64.whl", hash = "sha256:15aa985ac73a8db02db7663381aa109510449d3819d37206caed27b33a65a8a6", size = 10769383, upload-time = "2026-09-06T16:26:04.011Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/49/c4/af8bc08a7ef4e1529a7c0cf24969accce316b783999802089a581ec99272/numpy-2.5.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ac7bb1c52d445bd4f8f7f97fefe6abc3a084dc4d63df50d79b17fa2b78e89297", size = 12132668, upload-time = "2026-09-06T16:26:07.138Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c5/ae/0f15eb56d4ec5e13c1f7ff04ff407f997d1acbadb45d3e1f2e2645a8f43c/numpy-2.5.3-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:e6ab667ba76450084eb64013762c438ea76d9d29cc676dcd6c2e9892ba37f841", size = 5568580, upload-time = "2026-09-06T16:26:09.828Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/23/fb/c72a8f25d4b6e96c354e7ab45ace3b27dc11e5d6a13b6c7d0cd6b08bf112/numpy-2.5.3-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:f7fabeb6cea87d65f3b926de33d03fb016cfdc29314c90974383b5582ae72891", size = 6882634, upload-time = "2026-09-06T16:26:12.524Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/07/a9/968c90ed2ab15060c338e8137f1215b5a60756ae07328e0a60d1c6734df4/numpy-2.5.3-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1fb6f8fb9ff0b3a69f52c66ce397b0246583e9f28616231b0e32ca49259a5fa6", size = 15748923, upload-time = "2026-09-06T16:26:15.092Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/59/08/9df04103947b95e3b6b1f2ed1a70521f325647a31b82da6a2aae3a485508/numpy-2.5.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:93e1f5447e2b1e479d7bd74701e84746b86450cff1fc368b132d195e2b8f8211", size = 16746748, upload-time = "2026-09-06T16:26:18.43Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/41/a0/14c8d5fe5b53a334aabb653deb391c0fef49558f491880ea300ed6785224/numpy-2.5.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c00abe94c1a69d75d827dcf1c025b25c8a45d230b3bcd77a9020883a1b047653", size = 17111561, upload-time = "2026-09-06T16:26:22.113Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c4/a6/d7e96e42f01522e154c32489640f16dfc4f6181d165d05fc3bec8c2c4999/numpy-2.5.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:536f963710a4e63934d80ac0dc4f478804a83e9a84b6828018f25d09953ada33", size = 18513945, upload-time = "2026-09-06T16:26:25.401Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/25/39/3453afb7119d0449ef11c886874120ff180e2c337760e0e2d88f70f1a945/numpy-2.5.3-cp314-cp314t-win32.whl", hash = "sha256:4c8a6d2ebce6305fd82fbefca827775437147052a976ee7c94b36a0c1b52ac6c", size = 6335421, upload-time = "2026-09-06T16:26:28.175Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/99/01/22815d2b19a1a746b1d45205cffebb3fe511a18acb75fba6c88491fc9894/numpy-2.5.3-cp314-cp314t-win_amd64.whl", hash = "sha256:9a37475425b431b4d060f23b4f52cd2f3aef6bc7c654bd760adf0040eec9d435", size = 12896420, upload-time = "2026-09-06T16:26:31.265Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/fa/ee/a7cbba67eeaff038dc29ca8b98a88396c8b0cc9c89d4924f4a27a5c9150b/numpy-2.5.3-cp314-cp314t-win_arm64.whl", hash = "sha256:2d8240cb4c16fd831074aa2b2cf9fc54664d826341d61c372245b96a74a49a9a", size = 10857177, upload-time = "2026-09-06T16:26:34.167Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "packaging"
|
||||
version = "26.3"
|
||||
@@ -604,6 +640,35 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/63/34/ba1c580383c9eada3711951fef0795c80b829a078d72188184bcab9dd527/packaging-26.3-py3-none-any.whl", hash = "sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c", size = 129956, upload-time = "2026-08-04T18:15:27.159Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pandas"
|
||||
version = "3.0.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
{ name = "python-dateutil" },
|
||||
{ name = "tzdata", marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/be/4f/5f3422a2afec5ffc46308b79e53291365a93748b498ac2e58bead0197916/pandas-3.0.5.tar.gz", hash = "sha256:dca3734d6ab7c906e6730f0788b0a1dbb9f2467731f9711f77995c8e9d62d712", size = 4658219, upload-time = "2026-07-22T22:19:28.819Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/51/2f/cf6aae281264f4463f0875bcbb15fd2bb6d291cc535187dad1732475e4a9/pandas-3.0.5-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:2f264fc46911cc8131a7322a16199bbf8e353d27c10bb211f5bd0c814324dc36", size = 10390034, upload-time = "2026-07-22T22:18:49.818Z" },
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Reference in New Issue
Block a user