Use pandas to read the csv file with several uncertain factors - pandas

Question

I have asked the related question of string in: Find the number of \n before a given word in a long string. But this method cannot solve the complicate case I happened to. Thus I want to find out a solution of Pandas here.
I have a csv file (I just represent as a string):
csvfile = 'Idnum\tId\nkey:maturity\n2\nmaturity\tpara1\tpara2\n1Y\t0\t0\n2Y\t0\t0'
I want to use the pandas：
value = pandas.read_csv(csvfile, sep = '\t', skiprows = 3).set_index('maturity')
to obtain the table like:
and set the first columan maturity as index.
But there are several uncertain factors in the csvfile:
1..set_index('maturity'), the key maturity
of index is included in the row key: maturity. Then I should find the row key: xxxx and obtain the string xxxx
2.skiprows = 3: the number of skipped rows before the title:
is uncertain. The csvfile can be something like:
'Idnum\tId\nkey:maturity\n2\n\n\n\n\n\nmaturity\tpara1\tpara2\n1Y\t0\t0\n2Y\t0\t0'
I should find the row number of title (namely the row beginning with xxxx found in the rowkey: xxxx).
3.sep = '\t': the csvfile may use space as separator like:
csvfile = 'Idnum Id\nkey: maturity\n2\nmaturity para1 para2\n1Y 0 0\n2Y 0 0'
So is there any general code of pandas to deal with the csvfile with above uncertain factors?
Actually the string:
csvfile = 'Idnum\tId\nkey:maturity\n2\nmaturity\tpara1\tpara2\n1Y\t0\t0\n2Y\t0\t0'
is from a StringIO: data
data.getvalue() = 'Idnum\tId\nkey:maturity\n2\nmaturity\tpara1\tpara2\n1Y\t0\t0\n2Y\t0\t0'
I am not familiar with this structure and even I want to obtain a table of original data without any edition by using:
value = pandas.read_csv(data, sep = '\t')
There will be a error.

Answer 1

You can read the file line by line, collecting the necessary information and then pass the remainder to pd.read_csv with the appropriate arguments:
from io import StringIO
import re
import pandas as pd
with open('data.csv') as fh:
key = next(filter(lambda x: x.startswith('key:'), fh)).lstrip('key:').strip()
header = re.split('[ \t]+', next(filter(lambda x: x.startswith(key), fh)).strip())
df = pd.read_csv(StringIO(fh.read()), header=None, names=header, index_col=0, sep=r'\s+')
Example for data via StringIO:
fh = StringIO('Idnum\tId\nkey:maturity\n2\nmaturity\tpara1\tpara2\n1Y\t0\t0\n2Y\t0\t0')
key = next(filter(lambda x: x.startswith('key:'), fh)).lstrip('key:').strip()
header = re.split('[ \t]+', next(filter(lambda x: x.startswith(key), fh)).strip())
df = pd.read_csv(fh, header=None, names=header, index_col=0, sep=r'\s+')

Answer 2

If you do not mind reading the csv file twice you can try doing something like:
from io import StringIO
csvfile = 'Idnum\tId\nkey:maturity\n2\nmaturity\tpara1\tpara2\n1Y\t0\t0\n2Y\t0\t0'
data = pd.read_csv(StringIO(csvfile), sep='\t', error_bad_lines=False, header=None)
skiprows = len(data)
pd.read_csv(StringIO(csvfile), sep='\t', skiprows=skiprows)
same for you other example:
csvfile = 'Idnum\tId\nkey:maturity\n2\n\n\n\n\n\nmaturity\tpara1\tpara2\n1Y\t0\t0\n2Y\t0\t0'
data = pd.read_csv(StringIO(csvfile), sep='\t', error_bad_lines=False, header=None)
skiprows = len(data)
pd.read_csv(StringIO(csvfile), sep='\t', skiprows=skiprows)
This assumes that you know the sep of the file
Also if you want to find the key:
csvfile = 'Idnum\tId\nkey:maturity\n2\n\n\n\n\n\nmaturity\tpara1\tpara2\n1Y\t0\t0\n2Y\t0\t0'
data = pd.read_csv(StringIO(csvfile), sep='\t', error_bad_lines=False, header=None)
key = [x.replace('key:','') for x in data[0] if x.find('key')>-1]
skiprows = len(data)
pd.read_csv(StringIO(csvfile), sep='\t', skiprows=skiprows).set_index(key)