解析文本文件 python 并转换为 pandas 数据帧

Question

我正在尝试解析文本文件，将其转换为 pandas 数据帧。文件（包括空行）：

HEADING1
value 1

HEADING2
value 2

HEADING1,
value 11

HEADING2
value 12

应该转换成dataframe:

HEADING1, HEADING2
value 1, value 2
value 11, value 12

我试过下面的代码。但是，我不确定使用 converters 是否可行？

df = pd.read_table(textfile, header=None, skip_blank_lines=True, delimiter='\n',
                   # converters= 'what should I use?',
                   names= 'HEADING1, HEADING2'.split() )

Answer 1

您自己解析文本并在 '\n\n'

上拆分

# split file by `'\n\n'` to get rows
# split again by `'\n'` to get columns
# `zip` to get convenient lists of headers and values
cols, vals = zip(
    *[line.split('\n') for line in open(textfile).read().split('\n\n')]
)

# construct a `pd.Series`
# note: your index contained in the `cols` list will not be unique
s = pd.Series(vals, cols)

# we'll need to enumerate the duplicated index values so that we can unstack
# we do this by creating a `pd.MultiIndex` with `cumcount` then the header values
s.index = [s.groupby(level=0).cumcount(), s.index]

# finally, `unstack`
s.unstack()

   HEADING1  HEADING2
0   value 1   value 2
1  value 11  value 12

细分

list理解

[line.split('\n') for line in StringIO(txt).read().split('\n\n')]

[['HEADING1', 'value 1'],
 ['HEADING2', 'value 2'],
 ['HEADING1', 'value 11'],
 ['HEADING2', 'value 12']]

和zip

list(zip(*[line.split('\n') for line in StringIO(txt).read().split('\n\n')]))

[('HEADING1', 'HEADING2', 'HEADING1', 'HEADING2'),
 ('value 1', 'value 2', 'value 11', 'value 12')]

设置cols和vals

cols, vals = zip(*[line.split('\n') for line in StringIO(txt).read().split('\n\n')])

print(cols)
print()
print(vals)

('HEADING1', 'HEADING2', 'HEADING1', 'HEADING2')

('value 1', 'value 2', 'value 11', 'value 12')

制作系列

s = pd.Series(vals, cols)
s

HEADING1     value 1
HEADING2     value 2
HEADING1    value 11
HEADING2    value 12
dtype: object

枚举索引值

s.index = [s.groupby(level=0).cumcount(), s.index]
s

0  HEADING1     value 1
   HEADING2     value 2
1  HEADING1    value 11
   HEADING2    value 12
dtype: object

unstack

s.unstack()

   HEADING1  HEADING2
0   value 1   value 2
1  value 11  value 12

完整演示

import pandas as pd
from io import StringIO

txt = """HEADING1
value 1

HEADING2
value 2

HEADING1
value 11

HEADING2
value 12"""

cols, vals = zip(*[line.split('\n') for line in StringIO(txt).read().split('\n\n')])

s = pd.Series(vals, cols)
s.index = [s.groupby(level=0).cumcount(), s.index]

s.unstack()

   HEADING1  HEADING2
0   value 1   value 2
1  value 11  value 12

Answer 2

使用defaultdict

from collections import defaultdict
from io import StringIO
import pandas as pd

txt = """HEADING1
value 1

HEADING2
value 2

HEADING1
value 11

HEADING2
value 12"""

d = defaultdict(list)
[
    d[k].append(v)
    for k, v in [line.split('\n')
    for line in StringIO(txt).read().split('\n\n')]
];
pd.DataFrame(d)

   HEADING1  HEADING2
0   value 1   value 2
1  value 11  value 12

解析文本文件 python 并转换为 pandas 数据帧

Parse text file python and covert to pandas dataframe

python

parsing

converters

pandas