df.columns = df.columns.str.replace(r"\(.*\)","") #Parathesis contain no useful data; delete all values with parathesis
df.columns = df.columns.str.replace(".","") #Periods waste space
df.columns = df.columns.str.replace(" ","") #Spaces waste space and complicate code
df.columns = df.columns.str.replace("/","") #forwardslashes waste space and complicate code
df.columns = df.columns.str.replace("Province","") #there are no providences in our data we just call them states anyways
df.columns = df.columns.str.replace("PostalCode","") #we are calling it zip for space
df.columns = df.columns.str.replace("PotentialCustomer", "customer") #shortening to save space
df.columns = df.columns.str.replace("LineOfBusiness", "industry") #shorten to one word
df.columns = df.columns.str.replace("TotalDealRevenue", "estrev") #replace long bad description with short good one
df.columns = df.columns.str.replace("ActualRevenue", "actrev") #shorten to 6 char

df.columns = map(str.lower, df.columns) #all char lowercase for simplicity

df.drop(columns=['opportunity', 'rowchecksum', 'modifiedon', 'dealname',
         'probability', 'createdon', 'owner', 'email', 'payterms', 'project', 
         'certificateofinsurance', 'commissionform', 'equipmentrep', 'rentalrep', 'accountsterms'])

So the output has all the columns except for the ones that I have dropped. However when I enter

df.head(5)

I am getting output of all columns including the ones that were supposedly dropped.