Quickstart¶

Run a complete quality check on your time series data in 5 lines.

Step 1: Import and Load Data¶

import tsqc
import pandas as pd

df = pd.read_csv("sensor_data.csv")

Your data must contain at least a timestamp column and a value column. An optional tag_name column lets you run checks on multiple sensors at once.

Step 2: Run the Check¶

result = tsqc.check(df, assume_tz="UTC")

The assume_tz parameter tells the library what timezone your timestamps are in. If your CSV already contains UTC-aware timestamps (ISO 8601 with +00:00), you can omit it.

All output — chart x-axis, result.df, summaries — automatically displays timestamps in this same timezone. No extra parameter is needed.

Step 3: View Results¶

result.plot().show()
print(result.summary())

The timeline chart shows a color-coded horizontal bar for each tag. The summary table shows the percentage of good, suspect, and bad data per tag.

Complete Example¶

import tsqc
import pandas as pd

df = pd.read_csv("sensor_data.csv")
result = tsqc.check(df, assume_tz="UTC")
result.plot().show()
print(result.summary())
result.export_report("report.html")

Output Schema¶

result.df adds two columns:

Next Steps¶

• User Guide — detailed walkthrough
• YAML Configuration — create rules without Python
• API Reference — full method documentation