将格式不一致的csv文件读入Pandas Dataframe（具有标题和重复列标题的块）

Question

我有一个CSV文件，基本如下所示（我将其缩短为一个显示结构的最小示例）：

ID1#First_Name
TIME_BIN,COUNT,AVG
09:00-12:00,100,50
15:00-18:00,24,14
21:00-23:00,69,47
ID2#Second_Name
TIME_BIN,COUNT,AVG
09:00-12:00,36,5
15:00-18:00,74,68
21:00-23:00,22,76
ID3#Third_Name
TIME_BIN,COUNT,AVG
09:00-12:00,15,10
15:00-18:00,77,36
21:00-23:00,55,18

可以看到，数据被分成多个块。每个块都有一个标题（例如ID1#First_Name），其中包含两个信息和平（IDx和{}），用#分隔。在

每个标题后面都是列标题（TIME_BIN, COUNT, AVG），对于所有块都是相同的。在

然后跟随属于列标题的一些数据行（例如TIME_BIN=09:00-12:00，COUNT=100，AVG=50）。在

我想将此文件解析为Pandas数据帧，如下所示：

^{pr2}$

这意味着标题不能被跳过，但必须被#分割，然后链接到它所属块中的数据。此外，列标题只需要一次，因为它们以后不会更改。在

不知怎么的，我用下面的代码实现了我的目标。然而，这种方法看起来有点过于复杂，对我来说并不健壮，我相信有更好的方法来实现这一点。欢迎提出任何建议！在

import pandas as pd
from io import StringIO (<- Python 3, for Python 2 use from StringIO import StringIO)

pathToFile = 'mydata.txt'

# read the textfile into a StringIO object and skip the repeating column header rows
s = StringIO()
with open(pathToFile) as file:
    for line in file:
        if not line.startswith('TIME_BIN'):
            s.write(line)

# reset buffer to the beginning of the StringIO object
s.seek(0)

# create new dataframe with desired column names
df = pd.read_csv(s, names=['TIME_BIN', 'COUNT', 'AVG'])

# split the headline string which is currently found in the TIME_BIN column and insert both parts as new dataframe columns.
# the headline is identified by its start which is 'ID'
df['ID'] = df[df.TIME_BIN.str.startswith('ID')].TIME_BIN.str.split('#').str.get(0)
df['Name'] = df[df.TIME_BIN.str.startswith('ID')].TIME_BIN.str.split('#').str.get(1)

# fill the NaN values in the ID and Name columns by propagating the last valid observation
df['ID'] = df['ID'].fillna(method='ffill')
df['Name'] = df['Name'].fillna(method='ffill')

# remove all rows where TIME_BIN starts with 'ID'
df['TIME_BIN'] = df['TIME_BIN'].drop(df[df.TIME_BIN.str.startswith('ID')].index)
df = df.dropna(subset=['TIME_BIN'])

# reorder columns to bring ID and Name to the front
cols = list(df)
cols.insert(0, cols.pop(cols.index('Name')))
cols.insert(0, cols.pop(cols.index('ID')))
df = df.ix[:, cols]

Answer 1

import pandas as pd
from StringIO import StringIO
import sys
pathToFile = 'mydata.txt'
f = open(pathToFile)
s = StringIO()
cur_ID = None
for ln in f:
    if not ln.strip():
            continue
    if ln.startswith('ID'):
            cur_ID = ln.replace('\n',',',1).replace('#',',',1)
            continue
    if ln.startswith('TIME'):
            continue
    if cur_ID is None:
            print 'NO ID found'
            sys.exit(1)
    s.write(cur_ID + ln)
s.seek(0)
# create new dataframe with desired column names
df = pd.read_csv(s, names=['ID','Name','TIME_BIN', 'COUNT', 'AVG'])