使用特定模式從txt文件創建Pandas DataFrame我需要基于以下結構的文本文件創建一個PandasDataFrame：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]帶有“[編輯]”的行是國家，行[數字]是區域。我需要將以下內容分開，然后對每個區域名稱重復國家名稱。Index State Region Name0 Alabama Aurburn...1 Alabama Florence...2 Alabama Jacksonville......9 Alaska Fairbanks...10 Alaska Arizona...11 Alaska Flagstaff...熊貓數據幀我不知道如何將基于“[編輯]”和“[數字]”或“(字符)”的文本文件拆分到相應的列中，并對每個區域名稱重復國家名稱。請任何人給我一個起點開始完成以下工作。
3 回答
千萬里不及你
TA貢獻1784條經驗 獲得超9個贊
import pandas as pdfrom collections import namedtupleItem = 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 dfState 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
汪汪一只貓
TA貢獻1898條經驗 獲得超8個贊
假設您有以下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]
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
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