What timestamp format should I use in CSV files?

Use ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ) with UTC timezone. This format sorts lexicographically, is unambiguous, and is parsed correctly by virtually all tools.

How do I resample time-series data to a different interval?

In Python, pandas provides resample() for aggregating time-series to different intervals. In SQL, use DATE_TRUNC with GROUP BY to bucket rows into fixed time windows.

How do I detect gaps in time-series data?

Sort by timestamp and check that the difference between consecutive rows equals your expected interval. Any larger gap indicates missing data that may need interpolation or flagging.

Format a CSV Time-Series Dataset

Time-series CSV files are the standard output format for monitoring systems, IoT sensors, and analytics exports. Consistent timestamp formatting is critical for correct sorting and charting. This example shows a server metrics time series with CPU, memory, and request rate columns. The CSV viewer lets you verify the timestamp format and spot gaps or anomalies before feeding the data to a visualization tool.

Example

timestamp,cpu_percent,memory_percent,requests_per_sec,error_rate
2024-01-15T10:00:00Z,23.4,61.2,142,0.001
2024-01-15T10:05:00Z,45.1,63.5,287,0.002
2024-01-15T10:10:00Z,82.3,71.8,521,0.012
2024-01-15T10:15:00Z,91.7,79.2,634,0.041
2024-01-15T10:20:00Z,54.2,68.1,312,0.008
2024-01-15T10:25:00Z,28.9,62.4,167,0.001

[ open in CSV File Viewer → ]

FAQ

What timestamp format should I use in CSV files?: Use ISO 8601 format (YYYY-MM-DDTHH:MM:SSZ) with UTC timezone. This format sorts lexicographically, is unambiguous, and is parsed correctly by virtually all tools.
How do I resample time-series data to a different interval?: In Python, pandas provides resample() for aggregating time-series to different intervals. In SQL, use DATE_TRUNC with GROUP BY to bucket rows into fixed time windows.
How do I detect gaps in time-series data?: Sort by timestamp and check that the difference between consecutive rows equals your expected interval. Any larger gap indicates missing data that may need interpolation or flagging.

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