mobts

Python package for preprocessing and imputing urban mobility time series data.

Designed for transport datasets such as bike counts, traffic loops, and station-based observations.

Documentation

Full documentation, including a user guide, configuration reference, and API reference, is available at:

https://mobidec.github.io/mobts/index.html

What this package does

• Clean time series data:
  • Detects measurement errors
  • Flags and removes them
• Imputes missing/invalid data based on a multi-tier method

Installation

pip install mobts

Example of running the code

from mobts import preprocess, impute

# Step 1: clean raw data
pp = preprocess()
df_clean = pp.run(
    df_raw=df,
    counter_col=counter_col,
    timestamp_col=timestamp_col,
    count_col=count_col,
)

# Step 2: impute missing values
imp = impute()
df_imputed = imp.run(
    df=df_clean,
    counter_col=counter_col,
    timestamp_col=timestamp_col,
    count_col=count_col,
)

See the Quickstart page for a complete walkthrough.

Functional examples

A full, illustrated example is available in the documentation:

• mobts_basic_example

License

This project is licensed under the MIT License, which means it is freely usable for personal and commercial purposes. The MIT License is one of the most permissive open source licenses. It allows you to do almost anything with the source code, as long as you retain the original license notice and copyright information when redistributing the software or substantial portions of it. This license comes without any warranties, so the software is provided "as is." For more details, please refer to the included LICENSE file.