How can I read multiple text files and save them individually as a Pandas Dataframe?

Question

I have multiple txt files and I would like to convert them to a dataframe by creating a new column using header. My data looks like:

Person:?,?;F dob. ?  MT: ? Z:C NewYork Mon.:S St.?

144 cm/35 Kg/5 YearsOld






45,34,22,26,0
78,74,82,11,0

I use the following code to create a dataframe out of a single text file.

with open('file_directory', 'r') as f:
    heading_rows = [next(f) for _ in range(3)]



city = re.findall(pattern = ' w+ ', string = heading_rows[0])[0].strip()
numbers_list = [re.findall(pattern='d+', string=row) for row in heading_rows if 'cm' and 'kg' in row.lower()][0]

height, weight, age = [int(numbers_list[i]) for i in range(3)]
    
df = pd.read_csv('file_directory', sep='s+|;|,', engine='python', skiprows=8,comment='cm', index_col=None, names=list('ABCDEF'))
#df.dropna(inplace=True)
df['HEIGHT'] = height
df['WEIGHT'] = weight
df['AGE'] = age
df['CENTER'] = city

I tried to put the code (above) in a for loop so that I can read all text files in the folder so that I can convert them into a Pandas dataframe individually and save as a csv file.

lst = []
for name in glob.glob('my_directory/*'):

    with open(name, 'r') as f:
        heading_rows = [next(f) for _ in range(1)]
        lst.append(heading_rows)

Bu, I end up with StopIteration error in next(f) aprt of my code. How can I obtain the following dataframe while reading multiple text files? Then I would like to save each file as CSV file.

My expectation is to have the following dataframe type:

A, B, C, D, E, height, weight, age, city
45,34,22,26,0, 144,   35,      5,   NewYork 
78,74,82,11,0, 144,   35,      5,   NewYork

Asked By: dspractician

||

Source

Answer 1

Try:

import re
import pandas as pd


text = """
Person:?,?;F dob. ?  MT: ? Z:C NewYork Mon.:S St.?

144 cm/35 Kg/5 YearsOld


45,34,22,26,0
78,74,82,11,0
"""

pat = re.compile(
    r"(?sim)Z:C (S+).*(d+)s*cmD+(d+)s*kgD+(d+).*?((?:^[d,]+n)+)"
)

m = pat.search(text)
if m:
    city, height, weight, age, data = m.groups()
    all_data = []
    for row in data.splitlines():
        all_data.append(
            list(map(int, row.split(","))) + [height, weight, age, city]
        )

df = pd.DataFrame(
    all_data,
    columns=["A", "B", "C", "D", "E", "height", "weight", "age", "city"],
)
print(df)

Prints:

    A   B   C   D  E height weight age     city
0  45  34  22  26  0      4     35   5  NewYork
1  78  74  82  11  0      4     35   5  NewYork

Answered By: Andrej Kesely

Answer 2

You should use chardet which articulates encoding readings. Then add the read_Csv part in for loop.

import chardet
for name in glob.glob('file_directory/*'):
  with open(name, 'r') as f:
     heading_rows = [next(f) for _ in range(5)]
  #print(re.findall(pattern = ' w+ ', string = heading_rows[0])[0])

# to escape errors
  try:
     city = re.findall(pattern = ' w+ ', string = heading_rows[0])[0].strip()
  except IndexError:
     pass

  numbers_list = [re.findall(pattern='d+', string=row) for row in heading_rows if 'cm' and 'kg' in row.lower()][0]

  height, weight, age = [int(numbers_list[i]) for i in range(3)]

  with open(name, 'rb') as file:
     encodings = chardet.detect(file.read())["encoding"]
  df = pd.read_csv(name,sep='s+|;|,', engine='python', encoding=encodings, skiprows=1,comment='cm', index_col=None, names=list('ABCDEF'))


  df.to_csv(name+'.csv',index=False)

Answered By: mahmutoezmen

How can I read multiple text files and save them individually as a Pandas Dataframe?

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