Treat thresh as a minimum evidence rule for incomplete rows: it tells pandas how many non-missing values a row must contain before dropna keeps it. section 2.20
Keep mostly complete parking records
Suppose four columns matter: Plate ID, Registration State, Vehicle Make, and Street Name. Requiring all four would be strict; allowing any three keeps rows with one missing value: semi_good_df = df.dropna(subset=['Plate ID', 'Registration State', 'Vehicle Make', 'Street Name'], thresh=3). The trade-off is deliberate: more records survive, but some missing values remain. section 5.2
semi_good_df = df.dropna(subset=['Plate ID',
'Registration State',
'Vehicle Make',
'Street Name'],
thresh=3)
Inspect the complete pandas operation for applying thresh to a selected subset of columns. section 5.2
Use thresh when deciding how much incomplete data a workflow can tolerate:
- Combine subset with thresh when only selected columns determine whether a row is usable. section 5.2
- Compare row counts and downstream costs after changing the threshold; stricter filtering can discard valuable records. section 5.2
- Copy a result before modifying it when necessary, because dropna returns a new object and later assignment can trigger a warning. section 2.20
