Modifying input data to a usable list format using python-CodePudding

I have input data in the following format stored in a text file

I am trying to create separate lists with every column as a list with last element of the column as the first element as:

list1 = [D, T, W, F, J, S, H, N] . . list3 = [L, Q, V]

Tried reading contents and then splitting the list contents but end up losing the index as the spaces are ignored. How to approach it?

CodePudding user response：

Here is one way to create the lists dynamically based on your (.txt) file/dataframe :

import pandas as pd

_ = (
       pd.read_fwf("/tmp/file.txt" header=None)
            .set_axis(list(df.iloc[-1]), axis=1)
            .iloc[::-1][1:]
            .pipe(lambda df: [exec(f"globals()['list{k}'] = ([e.strip('[]') \
                                               for e in v if str(e) != 'nan'])")
                              for k,v in df.to_dict("list").items()])
    )

Output :

print([(var, val) for var, val in globals().items() if var.startswith("list")])

[('list1', ['D', 'T', 'W', 'F', 'J', 'S', 'H', 'N']),
 ('list2', ['H', 'R', 'P', 'Q', 'T', 'N', 'B', 'G']),
 ('list3', ['L', 'Q', 'V']),
 ('list4', ['N', 'B', 'S', 'W', 'R', 'Q']),
 ('list5', ['N', 'D', 'F', 'T', 'V', 'M', 'B']),
 ('list6', ['M', 'D', 'B', 'V', 'H', 'T', 'R']),
 ('list7', ['D', 'B', 'Q', 'J']),
 ('list8', ['D', 'N', 'J', 'V', 'R', 'Z', 'H', 'Q']),
 ('list9', ['B', 'N', 'H', 'M', 'S'])]

CodePudding user response：

Use read_fwf for read file first and then in list comprehension create list of lists:

df = pd.read_fwf(file, header=None)

L =  [v[1:].str.strip('[]').dropna().tolist() 
                   for k, v in df.iloc[::-1].to_dict('series').items()]

print (L)

[['D', 'T', 'W', 'F', 'J', 'S', 'H', 'N'],
 ['H', 'R', 'P', 'Q', 'T', 'N', 'B', 'G'],
 ['L', 'Q', 'V'],
 ['N', 'B', 'S', 'W', 'R', 'Q'],
 ['N', 'D', 'F', 'T', 'V', 'M', 'B'], 
 ['M', 'D', 'B', 'V', 'H', 'T', 'R'], 
 ['D', 'B', 'Q', 'J'], 
 ['D', 'N', 'J', 'V', 'R', 'Z', 'H', 'Q'],
 ['B', 'N', 'H', 'M', 'S']]