How to iterate over a list of csv files and compile files with common filenames into a single csv as multiple columns - pandas

Question

I am currently iterating through a list of csv files and want to combine csv files with common filename strings into a single csv file merging the data from the new csv file as a set of two new columns. I am having trouble with the final part of this in that the append command adds the data as rows at the base of the csv. I have tried with pd.concat, but must be going wrong somewhere. Any help would be much appreciated.
**Note the code is using Python 2 - just for compatibility with the software I am using - Python 3 solution welcome if it translates.
Here is the code I'm currently working with:
rb_headers = ["OID_RB", "Id_RB", "ORIG_FID_RB", "POINT_X_RB", "POINT_Y_RB"]
for i in coords:
if fnmatch.fnmatch(i, '*RB_bank_xycoords.csv'):
df = pd.read_csv(i, header=0, names=rb_headers)
df2 = df[::-1]
#Export the inverted RB csv file as a new csv to the original folder overwriting the original
df2.to_csv(bankcoords+i, index=False)
#Iterate through csvs to combine those with similar key strings in their filenames and merge them into a single csv
files_of_interest = {}
forconc = []
for filename in coords:
if filename[-4:] == '.csv':
key = filename[:39]
files_of_interest.setdefault(key, [])
files_of_interest[key].append(filename)
for key in files_of_interest:
buff_df = pd.DataFrame()
for filename in files_of_interest[key]:
buff_df = buff_df.append(pd.read_csv(filename))
files_of_interest[key]=buff_df
redundant_headers = ["OID", "Id", "ORIG_FID", "OID_RB", "Id_RB", "ORIG_FID_RB"]
outdf = buff_df.drop(redundant_headers, axis=1)

Answer 1

If you want only to merge in one file:
paths_list=['path1', 'path2',...]
dfs = [pd.read_csv(f, header=None, sep=";") for f in paths_list]
dfs=pd.concat(dfs,ignore_index=True)
dfs.to_csv(...)