TimeSeries

Time series data with complete processing history and metadata.

This class represents a single time series with its associated pandas Series
data, complete processing history, and index metadata. It maintains a full
audit trail of all transformations applied to the data from its raw state
to the current processed form.

The class handles serialization of pandas objects and preserves critical
index information to ensure proper reconstruction. It's the fundamental
building block for environmental time series analysis workflows.

Field Definitions

Field	Type	Required	Default	Description
series	Series	✗	Series([], dtype: object)	The pandas Series containing the actual time series data
processing_steps	list	✗	Empty list ([])	Complete history of processing operations applied to this time series
index_metadata	None	✗	None	Metadata about the time series index for proper reconstruction
values_dtype	str	✗	str	Data type of the time series values
created_on	datetime	✗	Factory: now()	Timestamp when this TimeSeries object was created

Detailed Field Descriptions

series

Type: Series Required: No Default: Series([], dtype: object)

The pandas Series containing the actual time series data

processing_steps

Type: list Required: No Default: Empty list ([])

Complete history of processing operations applied to this time series

index_metadata

Type: None Required: No Default: None

Metadata about the time series index for proper reconstruction

values_dtype

Type: str Required: No Default: str

Data type of the time series values

created_on

Type: datetime Required: No Default: Factory: now()

Timestamp when this TimeSeries object was created

Usage Example

from meteaudata.types import TimeSeries

# Create a TimeSeries instance
import pandas as pd

# Create with pandas Series
data = pd.Series([20, 21, 22, 23], name='temperature')
ts = TimeSeries(series=data)

# Or load from files
ts = TimeSeries.load(
    data_file_path="data.csv",
    metadata_file_path="metadata.yaml"
)