How to split string with the values in their specific columns indexed on their label? - python-3.x

Question

I have the following data
Index Data
0 100CO
1 50CO-50PET
2 98CV-2EL
3 50CV-50CO
. .
. .
. .
I have to create split the data format into different columns each with their own header and their values, the result should be as below:
Index Data CO PET CV EL
0 100CO 100 0 0 0
1 50CO-50PET 50 50 0 0
2 98CV-2EL 0 0 98 2
3 50CV-50CO 50 0 50 0
. .
. .
. .
The data is not limited to CO/PET/CV/EL, will need as many columns needed each displaying its corresponding value.
The .str.split('-', expand=True) function will only delimit the data and keep all first values in same column and does not rename each column.
Is there a way to implement this in python?

Answer 1

You could do:
df.Data.str.split('-').explode().str.split(r'(?<=\d)(?=\D)',expand = True). \
reset_index().pivot('index',1,0).fillna(0).reset_index()
1 Index CO CV EL PET
0 0 100 0 0 0
1 1 50 0 0 50
2 2 0 98 2 0
3 3 50 50 0 0

Answer 2

Idea is first split values by -, then extract numbers and no numbers values to tuples, append to list and convert to dictionaries. It is passed in list comprehension to DataFrame cosntructor, replaced misisng values and converted to numeric:
import re
def f(x):
L = []
for val in x.split('-'):
k, v = re.findall('(\d+)(\D+)', val)[0]
L.append((v, k))
return dict(L)
df = df.join(pd.DataFrame([f(x) for x in df['Data']], index=df.index).fillna(0).astype(int))
print (df)
Data CO PET CV EL
0 100CO 100 0 0 0
1 50CO-50PET 50 50 0 0
2 98CV-2EL 0 0 98 2
3 50CV-50CO 50 0 50 0
If in data exist some values without number or number only solution should be changed for more general like:
print (df)
Data
0 100CO
1 50CO-50PET
2 98CV-2EL
3 50CV-50CO
4 AAA
5 20
def f(x):
L = []
for val in x.split('-'):
extracted = re.findall('(\d+)(\D+)', val)
if len(extracted) > 0:
k, v = extracted[0]
L.append((v, k))
else:
if val.isdigit():
L.append(('No match digit', val))
else:
L.append((val, 0))
return dict(L)
df = df.join(pd.DataFrame([f(x) for x in df['Data']], index=df.index).fillna(0).astype(int))
print (df)
Data CO PET CV EL AAA No match digit
0 100CO 100 0 0 0 0 0
1 50CO-50PET 50 50 0 0 0 0
2 98CV-2EL 0 0 98 2 0 0
3 50CV-50CO 50 0 50 0 0 0
4 AAA 0 0 0 0 0 0
5 20 0 0 0 0 0 20

Answer 3

Try this:
import pandas as pd
import re
df = pd.DataFrame({'Data':['100CO', '50CO-50PET', '98CV-2EL', '50CV-50CO']})
split_df = pd.DataFrame(df.Data.apply(lambda x: {re.findall('[A-Z]+', el)[0] : re.findall('[0-9]+', el)[0] \
for el in x.split('-')}).tolist())
split_df = split_df.fillna(0)
df = pd.concat([df, split_df], axis = 1)