time-series-profiler 1.0.2

A clean adapter-pattern library for profiling time-series data

Time Series Profiler

Profile time-series data to understand structure, gaps, and statistical properties.

Features

• Basic statistics (mean, std, min, max, percentiles)
• Gap detection and sampling analysis
• Categorical column profiling
• Multi-entity/user data support
• JSON and HTML output

Installation

From PyPI (Recommended)

pip install time-series-profiler

From Source

git clone https://github.com/adilsaid/time-series-profiler.git
cd time-series-profiler
pip install -e .

Usage

import pandas as pd
from tsp import ProfileReport, Config

# With DatetimeIndex
df = pd.DataFrame({
    'value': [1.0, 2.5, 3.2, 4.1],
    'category': ['A', 'B', 'A', 'C']
}, index=pd.date_range('2023-01-01', periods=4, freq='1H'))

report = ProfileReport(df, Config())
print(report.to_json())

# With time column and multiple entities
df = pd.read_csv("data.csv", parse_dates=["time"])
config = Config(time_col="time", entity_cols=("user_id",))
report = ProfileReport(df, config)

print(report.to_json())
html_output = report.to_html()

Example

cd examples
python quickstart.py

License

Apache-2.0 - see LICENSE file for details.