[英]How to add CSV file names as Column Headers in a dataframe using pandas?
[英]Read inconsistently formatted csv file into Pandas Dataframe (blocks with headline and repeating column headers)
我有一个CSV文件,该文件基本上如下所示(我将其简化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
),其中包含两个由( #
)分隔的信息( IDx
和x_Name
)。
每个标题后面紧跟着列标题( TIME_BIN, COUNT, AVG
),所有块均保持不变。
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
和AVG=50
)。
我想将此文件解析为如下所示的Pandas数据框:
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,而必须由#
分开,然后链接到其所属块中的数据。 此外,列标题仅需要一次,因为它们以后不会更改。
我设法通过以下代码实现了自己的目标。 但是,这种方法对我来说似乎过于复杂且不可靠,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]
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'])
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