我需要根据以下结构基于文本文件创建Pandas DataFrame：
Alabama[edit]
Auburn (Auburn University)[1]
Florence (University of North Alabama)
Jacksonville (Jacksonville State University)[2]
Livingston (University of West Alabama)[2]
Montevallo (University of Montevallo)[2]
Troy (Troy University)[2]
Tuscaloosa (University of Alabama, Stillman College, Shelton State)[3][4]
Tuskegee (Tuskegee University)[5]
Alaska[edit]
Fairbanks (University of Alaska Fairbanks)[2]
Arizona[edit]
Flagstaff (Northern Arizona University)[6]
Tempe (Arizona State University)
Tucson (University of Arizona)
Arkansas[edit]
带＆＃34; [edit]＆＃34;的行是状态，行[编号]是区域。我需要拆分以下内容，然后重复每个Region Name的State name。
Index State Region Name
0 Alabama Aurburn...
1 Alabama Florence...
2 Alabama Jacksonville...
...
9 Alaska Fairbanks...
10 Alaska Arizona...
11 Alaska Flagstaff...
Pandas DataFrame
我不确定如何根据＆＃34; [edit]＆＃34;分割文本文件。和＆＃34; [编号]＆＃34;或＆＃34;（字符）＆＃34;进入相应的列并重复每个Region Name的State Name。请任何人都可以给我一个起点来开始完成以下任务。
答案 0 :(得分：7)
对于列name的创建DataFrame，您可以先使用参数Region Name read_csv，分隔符是值中的值（如;）：
df = pd.read_csv('filename.txt', sep=";", names=['Region Name'])
然后insert新列State包含extract行，其中文本[edit]和replace的所有值都从(到结尾到列{ {1}}。
Region Name最后删除boolean indexing文字df.insert(0, 'State', df['Region Name'].str.extract('(.*)\[edit\]', expand=False).ffill())
df['Region Name'] = df['Region Name'].str.replace(r' \(.+$', '')
的行，str.contains创建了掩码：
[edit]如果需要所有价值解决方案更容易：
df = df[~df['Region Name'].str.contains('\[edit\]')].reset_index(drop=True)
print (df)
State Region Name
0 Alabama Auburn
1 Alabama Florence
2 Alabama Jacksonville
3 Alabama Livingston
4 Alabama Montevallo
5 Alabama Troy
6 Alabama Tuscaloosa
7 Alabama Tuskegee
8 Alaska Fairbanks
9 Arizona Flagstaff
10 Arizona Tempe
11 Arizona Tucson
答案 1 :(得分：6)
您可以先将文件解析为元组：
import pandas as pd
from collections import namedtuple
Item = namedtuple('Item', 'state area')
items = []
with open('unis.txt') as f:
for line in f:
l = line.rstrip('\n')
if l.endswith('[edit]'):
state = l.rstrip('[edit]')
else:
i = l.index(' (')
area = l[:i]
items.append(Item(state, area))
df = pd.DataFrame.from_records(items, columns=['State', 'Area'])
print df
输出：
State Area
0 Alabama Auburn
1 Alabama Florence
2 Alabama Jacksonville
3 Alabama Livingston
4 Alabama Montevallo
5 Alabama Troy
6 Alabama Tuscaloosa
7 Alabama Tuskegee
8 Alaska Fairbanks
9 Arizona Flagstaff
10 Arizona Tempe
11 Arizona Tucson
答案 2 :(得分：4)
假设你有以下DF：
In [73]: df
Out[73]:
text
0 Alabama[edit]
1 Auburn (Auburn University)[1]
2 Florence (University of North Alabama)
3 Jacksonville (Jacksonville State University)[2]
4 Livingston (University of West Alabama)[2]
5 Montevallo (University of Montevallo)[2]
6 Troy (Troy University)[2]
7 Tuscaloosa (University of Alabama, Stillman Co...
8 Tuskegee (Tuskegee University)[5]
9 Alaska[edit]
10 Fairbanks (University of Alaska Fairbanks)[2]
11 Arizona[edit]
12 Flagstaff (Northern Arizona University)[6]
13 Tempe (Arizona State University)
14 Tucson (University of Arizona)
15 Arkansas[edit]
您可以使用Series.str.extract()方法：
In [117]: df['State'] = df.loc[df.text.str.contains('[edit]', regex=False), 'text'].str.extract(r'(.*?)\[edit\]', expand=False)
In [118]: df['Region Name'] = df.loc[df.State.isnull(), 'text'].str.extract(r'(.*?)\s*[\(\[]+.*[\n]*', expand=False)
In [120]: df.State = df.State.ffill()
In [121]: df
Out[121]:
text State Region Name
0 Alabama[edit] Alabama NaN
1 Auburn (Auburn University)[1] Alabama Auburn
2 Florence (University of North Alabama) Alabama Florence
3 Jacksonville (Jacksonville State University)[2] Alabama Jacksonville
4 Livingston (University of West Alabama)[2] Alabama Livingston
5 Montevallo (University of Montevallo)[2] Alabama Montevallo
6 Troy (Troy University)[2] Alabama Troy
7 Tuscaloosa (University of Alabama, Stillman Co... Alabama Tuscaloosa
8 Tuskegee (Tuskegee University)[5] Alabama Tuskegee
9 Alaska[edit] Alaska NaN
10 Fairbanks (University of Alaska Fairbanks)[2] Alaska Fairbanks
11 Arizona[edit] Arizona NaN
12 Flagstaff (Northern Arizona University)[6] Arizona Flagstaff
13 Tempe (Arizona State University) Arizona Tempe
14 Tucson (University of Arizona) Arizona Tucson
15 Arkansas[edit] Arkansas NaN
In [122]: df = df.dropna()
In [123]: df
Out[123]:
text State Region Name
1 Auburn (Auburn University)[1] Alabama Auburn
2 Florence (University of North Alabama) Alabama Florence
3 Jacksonville (Jacksonville State University)[2] Alabama Jacksonville
4 Livingston (University of West Alabama)[2] Alabama Livingston
5 Montevallo (University of Montevallo)[2] Alabama Montevallo
6 Troy (Troy University)[2] Alabama Troy
7 Tuscaloosa (University of Alabama, Stillman Co... Alabama Tuscaloosa
8 Tuskegee (Tuskegee University)[5] Alabama Tuskegee
10 Fairbanks (University of Alaska Fairbanks)[2] Alaska Fairbanks
12 Flagstaff (Northern Arizona University)[6] Arizona Flagstaff
13 Tempe (Arizona State University) Arizona Tempe
14 Tucson (University of Arizona) Arizona Tucson
答案 3 :(得分：2)
<强> TL; DR
s.groupby(s.str.extract('(?P<State>.*?)\[edit\]', expand=False).ffill()).apply(pd.Series.tail, n=-1).reset_index(name='Region_Name').iloc[:, [0, 2]]
regex = '(?P<State>.*?)\[edit\]' # pattern to match
print(s.groupby(
# will get nulls where we don't have "[edit]"
# forward fill fills in the most recent line
# where we did have an "[edit]"
s.str.extract(regex, expand=False).ffill()
).apply(
# I still have all the original values
# If I group by the forward filled rows
# I'll want to drop the first one within each group
pd.Series.tail, n=-1
).reset_index(
# munge the dataframe to get columns sorted
name='Region_Name'
)[['State', 'Region_Name']])
State Region_Name
0 Alabama Auburn (Auburn University)[1]
1 Alabama Florence (University of North Alabama)
2 Alabama Jacksonville (Jacksonville State University)[2]
3 Alabama Livingston (University of West Alabama)[2]
4 Alabama Montevallo (University of Montevallo)[2]
5 Alabama Troy (Troy University)[2]
6 Alabama Tuscaloosa (University of Alabama, Stillman Co...
7 Alabama Tuskegee (Tuskegee University)[5]
8 Alaska Fairbanks (University of Alaska Fairbanks)[2]
9 Arizona Flagstaff (Northern Arizona University)[6]
10 Arizona Tempe (Arizona State University)
11 Arizona Tucson (University of Arizona)
<强> 设置
txt = """Alabama[edit]
Auburn (Auburn University)[1]
Florence (University of North Alabama)
Jacksonville (Jacksonville State University)[2]
Livingston (University of West Alabama)[2]
Montevallo (University of Montevallo)[2]
Troy (Troy University)[2]
Tuscaloosa (University of Alabama, Stillman College, Shelton State)[3][4]
Tuskegee (Tuskegee University)[5]
Alaska[edit]
Fairbanks (University of Alaska Fairbanks)[2]
Arizona[edit]
Flagstaff (Northern Arizona University)[6]
Tempe (Arizona State University)
Tucson (University of Arizona)
Arkansas[edit]"""
s = pd.read_csv(StringIO(txt), sep='|', header=None, squeeze=True)
答案 4 :(得分：0)
在将文件放入数据框之前，您可能需要对文件执行一些额外的操作。
一个起点是将文件拆分成行，在每一行中搜索字符串[edit]，将字符串名称作为字典的关键字放在那里......
我不认为Pandas有任何内置的方法可以处理这种格式的文件。
答案 5 :(得分：0)
您似乎来自Coursera的“数据科学概论”课程。通过此解决方案我的测试。我建议不要复制整个解决方案，而只是出于参考目的：）
lines = open('university_towns.txt').readlines()
l=[]
lofl=[]
flag=False
for line in lines:
l = []
if('[edit]' in line):
index = line[:-7]
elif('(' in line):
pos = line.find('(')
line = line[:pos-1]
l.append(index)
l.append(line)
flag=True
else:
line = line[:-1]
l.append(index)
l.append(line)
flag=True
if(flag and np.array(l).size!=0):
lofl.append(l)
df = pd.DataFrame(lofl,columns=["State","RegionName"])