Pandas: query string where column name contains special characters

For the interested here is a simple proceedure I used to accomplish the task:

# Identify invalid column names
invalid_column_names = [x for x in list(df.columns.values) if not x.isidentifier() ]

# Make replacements in the query and keep track
# NOTE: This method fails if the frame has columns called REPL_0 etc.
replacements = dict()
for cn in invalid_column_names:
    r = 'REPL_'+ str(invalid_column_names.index(cn))
    query = query.replace(cn, r)
    replacements[cn] = r

inv_replacements = {replacements[k] : k for k in replacements.keys()}

df = df.rename(columns=replacements) # Rename the columns
df  = df.query(query) # Carry out query

df = df.rename(columns=inv_replacements)

Which amounts to identifying the invalid column names, transforming the query and renaming the columns. Finally we perform the query and then translate the column names back.

Credit to @chrisb for their answer that pointed me in the right direction

The current implementation of query requires the string to be a valid python expression, so column names must be valid python identifiers. Your two options are renaming the column, or using a plain boolean filter, like this:

df[df['demo$gender'] =='male']