使用pandas对文本数据进行处理

import pandas as pd

time_sentences = ["Monday: The doctor's appointment is at 2:45pm.", 
                  "Tuesday: The dentist's appointment is at 11:30 am.",
                  "Wednesday: At 7:00pm, there is a basketball game!",
                  "Thursday: Be back home by 11:15 pm at the latest.",
                  "Friday: Take the train at 08:10 am, arrive at 09:00am."]

df = pd.DataFrame(time_sentences, columns=['text'])
df

# find the number of characters for each string in df['text']
df['text'].str.len()

0    46
1    50
2    49
3    49
4    54
Name: text, dtype: int64

# find the number of tokens for each string in df['text']
df['text'].str.split().str.len()

0     7
1     8
2     8
3    10
4    10
Name: text, dtype: int64

# find which entries contain the word 'appointment'
df['text'].str.contains('appointment')

0     True
1     True
2    False
3    False
4    False
Name: text, dtype: bool

# find how many times a digit occurs in each string
df['text'].str.count(r'\d')

0    3
1    4
2    3
3    4
4    8
Name: text, dtype: int64

# find all occurances of the digits
df['text'].str.findall(r'\d')

0                   [2, 4, 5]
1                [1, 1, 3, 0]
2                   [7, 0, 0]
3                [1, 1, 1, 5]
4    [0, 8, 1, 0, 0, 9, 0, 0]
Name: text, dtype: object

# group and find the hours and minutes
df['text'].str.findall(r'(\d?\d):(\d\d)')

0               [(2, 45)]
1              [(11, 30)]
2               [(7, 00)]
3              [(11, 15)]
4    [(08, 10), (09, 00)]
Name: text, dtype: object

# replace weekdays with '???'
df['text'].str.replace(r'\w+day\b', '???')

0          ???: The doctor's appointment is at 2:45pm.
1       ???: The dentist's appointment is at 11:30 am.
2          ???: At 7:00pm, there is a basketball game!
3         ???: Be back home by 11:15 pm at the latest.
4    ???: Take the train at 08:10 am, arrive at 09:...
Name: text, dtype: object

# replace weekdays with 3 letter abbrevations
df['text'].str.replace(r'(\w+day\b)', lambda x: x.groups()[0][:3])

0          Mon: The doctor's appointment is at 2:45pm.
1       Tue: The dentist's appointment is at 11:30 am.
2          Wed: At 7:00pm, there is a basketball game!
3         Thu: Be back home by 11:15 pm at the latest.
4    Fri: Take the train at 08:10 am, arrive at 09:...
Name: text, dtype: object

# create new columns from first match of extracted groups
df['text'].str.extract(r'(\d?\d):(\d\d)')

# extract the entire time, the hours, the minutes, and the period
df['text'].str.extractall(r'((\d?\d):(\d\d) ?([ap]m))')

# extract the entire time, the hours, the minutes, and the period with group names
df['text'].str.extractall(r'(?P<time>(?P<hour>\d?\d):(?P<minute>\d\d) ?(?P<period>[ap]m))')