python - Pandas: query string where column name contains special characters

Question

I am working with a data frame that has a structure something like the following:

In[75]: df.head(2)
Out[75]: 
  statusdata             participant_id association  latency response  
0   complete  CLIENT-TEST-1476362617727       seeya      715  dislike   
1   complete  CLIENT-TEST-1476362617727      welome      800     like   

   stimuli elementdata statusmetadata demo$gender  demo$question2  
0  Sample B    semi_imp       complete        male              23   
1  Sample C    semi_imp       complete      female              23   

I want to be able to run a query string against the column demo$gender.

I.e,

df.query("demo$gender=='male'")

But this has a problem with the $ sign. If I replace the $ sign with another delimited (like -) then the problem persists. Can I fix up my query string to avoid this problem. I would prefer not to rename the columns as these correspond tightly with other parts of my application.

I really want to stick with a query string as it is supplied by another component of our tech stack and creating a parser would be a heavy lift for what seems like a simple problem.

Thanks in advance.

Answer 1

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