Question

我有一个CSV文件，基本上如下所示（我将其缩短为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

可以看出，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x_Name），由#分隔。

每个标题后面都是列标题（TIME_BIN, COUNT, AVG），所有列都保持不变。

然后按照属于列标题的一些数据行（例如TIME_BIN=09:00-12:00，COUNT=100，AVG=50）。

我想将此文件解析为Pandas数据框，如下所示：

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

这意味着标题可能不会被跳过，但必须由#拆分，然后链接到它所属的块中的数据。此外，列标题只需要一次，因为它们以后不会更改。

不知何故，我设法通过以下代码实现了我的目标。然而，这种方法看起来有点过于复杂，对我来说并不健全，我相信有更好的方法可以做到这一点。欢迎任何建议！

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'])

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

1 个答案: