Constant column check with nunique

[sbrugman]

Try to respond to as many of the following as possible

Generally describe the pandas behavior that the linter should check for and why that is a problem. Links to resources, recommendations, docs appreciated

The linter should check for nunique being compared to 1. The detected pattern is less performant because it does not leverage short-circuiting when multiple unique values are found, and simply continues counting..

def setup(n):
    return pd.Series(list(range(n)))

def setup(n):
    return pd.Series([1] * (n - 1) + [2])

• In the wild
• SO Thread, SO Thread 2

Suggest specific syntax or pattern(s) that should trigger the linter (e.g., .iat)

• df.column.nunique() == 1
• df.column.nunique() != 1
• df.column.nunique(dropna=True) == 1
• df.column.nunique(dropna=True) != 1
• df.column.nunique(dropna=False) == 1
• df.column.nunique(dropna=False) != 1

Suggest specific syntax or pattern(s) that the linter should allow (e.g., .iloc)

Note that the solution is simple when there are no NaN values:

(series.values[0] == series.values).all()

And needs some additional logic when NaN/NA values are present.

For dropna=True

v = series.values
v = remove_na_arraylike(v)
if v.shape[0] == 0:
    return False
(v[0] == v).all()

For dropna=False

v = s.values
if v.shape[0] == 0:
    return False
(v[0] == v).all() or not pd.notna(v).any()

if included in pandas:

series.is_constant()

Suggest a specific error message that the linter should display (e.g., "Use '.iloc' instead of '.iat'. If speed is important, use numpy indexing")

Consider checking equality to first element instead of .nunique() == 1 for checking for a constant column.

Are you willing to try to implement this check?

• Yes
• No
• Maybe, with some guidance

[sbrugman]

Note that pandas-dev/pandas#54064 was merged. This adds documentation to the cookbook on how users can check for constant columns.

[deppen8]

Thanks for flagging this, @sbrugman. This would be nice to add, indeed. We can match the PD101 code from ruff if it gets done.

Side note: I had no idea ruff wrapped in the pandas-vet checks!

This repo isn't very active (pandas doesn't change much these days) but it is still alive. I just made a number of long-overdue improvements to docs and CI/CD that should hopefully keep things current for a while.