使用 Pandas,您可以执行以下操作:
import pandas as pd
df = pd.read_table('data', sep='\n', header=None, names=['town'])
df['is_state'] = df['town'].str.contains(r'\[edit\]')
df['groupno'] = df['is_state'].cumsum()
df['index'] = df.groupby('groupno').cumcount()
df['state'] = df.groupby('groupno')['town'].transform('first')
df['state'] = df['state'].str.replace(r'\[edit\]', '')
df['town'] = df['town'].str.replace(r' \(.+$', '')
df = df.loc[~df['is_state']]
df = df[['state','town']]
产生
state town
1 Alabama Auburn
2 Alabama Florence
3 Alabama Jacksonville
5 Alaska Fairbanks
7 Arizona Flagstaff
8 Arizona Tempe
9 Arizona Tucson
这是代码正在执行的操作的细分。将文本文件加载到 DataFrame 后,用于str.contains
识别哪些行是状态。用于cumsum
获取 True/False 值的累积和,其中 True 被视为 1,False 被视为 0。
df = pd.read_table('data', sep='\n', header=None, names=['town'])
df['is_state'] = df['town'].str.contains(r'\[edit\]')
df['groupno'] = df['is_state'].cumsum()
# town is_state groupno
# 0 Alabama[edit] True 1
# 1 Auburn (Auburn University)[1] False 1
# 2 Florence (University of North Alabama) False 1
# 3 Jacksonville (Jacksonville State University)[2] False 1
# 4 Alaska[edit] True 2
# 5 Fairbanks (University of Alaska Fairbanks)[2] False 2
# 6 Arizona[edit] True 3
# 7 Flagstaff (Northern Arizona University)[6] False 3
# 8 Tempe (Arizona State University) False 3
# 9 Tucson (University of Arizona) False 3
现在对于每个groupno
数字,我们可以为组中的每一行分配一个唯一的整数:
df['index'] = df.groupby('groupno').cumcount()
# town is_state groupno index
# 0 Alabama[edit] True 1 0
# 1 Auburn (Auburn University)[1] False 1 1
# 2 Florence (University of North Alabama) False 1 2
# 3 Jacksonville (Jacksonville State University)[2] False 1 3
# 4 Alaska[edit] True 2 0
# 5 Fairbanks (University of Alaska Fairbanks)[2] False 2 1
# 6 Arizona[edit] True 3 0
# 7 Flagstaff (Northern Arizona University)[6] False 3 1
# 8 Tempe (Arizona State University) False 3 2
# 9 Tucson (University of Arizona) False 3 3
同样对于每个groupno
数字,我们可以通过选择每个组中的第一个城镇来找到该州:
df['state'] = df.groupby('groupno')['town'].transform('first')
# town is_state groupno index state
# 0 Alabama[edit] True 1 0 Alabama[edit]
# 1 Auburn (Auburn University)[1] False 1 1 Alabama[edit]
# 2 Florence (University of North Alabama) False 1 2 Alabama[edit]
# 3 Jacksonville (Jacksonville State University)[2] False 1 3 Alabama[edit]
# 4 Alaska[edit] True 2 0 Alaska[edit]
# 5 Fairbanks (University of Alaska Fairbanks)[2] False 2 1 Alaska[edit]
# 6 Arizona[edit] True 3 0 Arizona[edit]
# 7 Flagstaff (Northern Arizona University)[6] False 3 1 Arizona[edit]
# 8 Tempe (Arizona State University) False 3 2 Arizona[edit]
# 9 Tucson (University of Arizona) False 3 3 Arizona[edit]
我们基本上有了想要的DataFrame;剩下的就是美化结果。我们可以使用 s[edit]
从s中删除state
第一个括号之后的所有内容:town
str.replace
df['state'] = df['state'].str.replace(r'\[edit\]', '')
df['town'] = df['town'].str.replace(r' \(.+$', '')
删除town
实际是状态的行:
df = df.loc[~df['is_state']]
最后,只保留所需的列:
df = df[['state','town']]