Data format
mobts expects mobility count data as a single pandas DataFrame containing all counters together, and not one DataFrame per counter. Each row represents one observation: one c0ounter, at one timestamp, with one count.
mobts supports both hourly and daily granularity, but a given dataset should be consistently one or the other.
Typical input data
A typical dataset may look like this:
counter_id |
date |
N_veh |
|---|---|---|
A |
2025-01-01 |
120 |
A |
2025-01-02 |
135 |
A |
2025-01-03 |
NaN |
B |
2025-01-01 |
90 |
B |
2025-01-02 |
95 |
Your column names do not need to match this example. You will tell mobts which column is which via the counter_col, timestamp_col, and count_col arguments (see Quickstart).
You also do not need to pre-populate every expected timestamp as its own row. mobts accepts sparse input and handles gaps in the time index internally. Provide whatever observations you actually have. An explicit NaN row for a missing period is accepted, but not obligated.
What mobts does with your columns
Internally, mobts renames your three input columns to a fixed set of canonical names (counter, timestamp, count), and derives several temporal columns automatically. date, weekday, and week_num for daily data, plus hour and hour-of-week (how) for hourly data. You do not need to provide these yourself. The canonical names, and every other default used across the package, can be overridden. See Configuration.
Optional metadata columns
If your dataset has extra columns you want preserved through preprocessing and imputation (for example a station name or spatial identifier) without them being treated as count data, pass their names via metadata_cols when calling run().
Assumptions and checks
Before using the package, make sure that:
the timestamp column can be parsed as a pandas datetime
the count column is numeric
missing values are represented as
NaN, and not0
Before running preprocessing or imputation, it is usually a good idea to check:
df.head()
df.info()
df.isna().sum()
This helps confirm the dataset has the expected columns and types before it reaches the pipeline.