# Quickstart

This is the minimal path from a raw dataset to a completed time series. For details on what your input data needs to look like, see [Data format](data_format.md).

```python
import pandas as pd
from mobts import preprocess
from mobts import impute

df = pd.read_csv('your_data.csv')

counter_col = 'counter_id'  # column identifying the counter/station
timestamp_col = 'timestamp'  # column with the observation timestamp
count_col = 'count_number'  # column with the observed count

# Step 1 - preprocess: detect and remove suspicious observations
pp = preprocess()
df_preprocessed = pp.run(
    df_raw=df,
    counter_col=counter_col,
    timestamp_col=timestamp_col,
    count_col=count_col,
)

# Step 2 - impute: fill missing values
imp = impute()
df_imputed = imp.run(
    df=df_preprocessed,
    counter_col=counter_col,
    timestamp_col=timestamp_col,
    count_col=count_col,
)
```

The completed time-series is in the `count_imputed/clean` column of `df_imputed`.

By default, both steps assume hourly data. If your data is daily, pass `data_is_hourly=False` to `pp.run()`.

```{note}
The default preprocessing threshold was tuned on a specific dataset and may not suit yours as-is. Before trusting the preprocessed output, inspect the detected outliers and adjust the threshold if needed. See [the example notebook](../notebooks/mobts_basic_example.ipynb) for how.
```


## Next steps

- [Preprocessing](preprocessing.md) and [Imputation](imputation.md) explain what each stage does and how methods are chosen.
- [Pipeline notes](pipeline_notes.md) covers some notes on the functions and their usages in more detail.
- [Configuration](configuration.md) covers every threshold and configuration you can override.