1 import pandas as pd
2
3 time_sentences = ["Monday: The doctor's appointment is at 2:45pm.",
4 "Tuesday: The dentist's appointment is at 11:30 am.",
5 "Wednesday: At 7:00pm, there is a basketball game!",
6 "Thursday: Be back home by 11:15 pm at the latest.",
7 "Friday: Take the train at 08:10 am, arrive at 09:00am."]
8
9 df = pd.DataFrame(time_sentences, columns=['text'])
10 df
1 # find the number of characters for each string in df['text']
2 df['text'].str.len()
0 46
1 50
2 49
3 49
4 54
Name: text, dtype: int64
1 # find the number of tokens for each string in df['text']
2 df['text'].str.split().str.len()
0 7
1 8
2 8
3 10
4 10
Name: text, dtype: int64
1 # find which entries contain the word 'appointment'
2 df['text'].str.contains('appointment')
0 True
1 True
2 False
3 False
4 False
Name: text, dtype: bool
1 # find how many times a digit occurs in each string
2 df['text'].str.count(r'd')
0 3
1 4
2 3
3 4
4 8
Name: text, dtype: int64
1 # find all occurances of the digits
2 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
1 # group and find the hours and minutes
2 df['text'].str.findall(r'(d?d):(dd)')
0 [(2, 45)]
1 [(11, 30)]
2 [(7, 00)]
3 [(11, 15)]
4 [(08, 10), (09, 00)]
Name: text, dtype: object
1 # replace weekdays with '???'
2 df['text'].str.replace(r'w+day', '???')
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
1 # replace weekdays with 3 letter abbrevations
2 df['text'].str.replace(r'(w+day)', 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
1 # create new columns from first match of extracted groups
2 df['text'].str.extract(r'(d?d):(dd)')
1 # extract the entire time, the hours, the minutes, and the period
2 df['text'].str.extractall(r'((d?d):(dd) ?([ap]m))')
1 # extract the entire time, the hours, the minutes, and the period with group names
2 df['text'].str.extractall(r'(?P<time>(?P<hour>d?d):(?P<minute>dd) ?(?P<period>[ap]m))')
