在 pandas 数据框中操纵时间范围 [英] Manipulate time-range in a pandas Dataframe

问题描述

需要清理csv导入，这给了我一段时间（以字符串形式）。代码在底部；我目前在df上使用正则表达式和 replace（）来转换其他字符。只是不确定如何：

Need to clean up a csv import, which gives me a range of times (in string form). Code is at bottom; I currently use regular expressions and replace() on the df to convert other chars. Just not sure how to:

1. 选择当前的24小时格式编号并添加：00


2. 如何选择12小时格式数字并使它们成为24小时格式。

输入（从csv导入）：

Input (from csv import):

   break_notes
0        15-18
1  18.30-19.00
2      4PM-5PM
3          3-4
4     4-4.10PM
5      15 - 17
6      11 - 13

到目前为止，我看起来像（删除空格，AM / PM，用冒号替换点）：

So far I have got it to look like (remove spaces, AM/PM, replace dot with colon):

   break_notes
0          15-18
1    18:30-19:00
2            4-5
3            3-4
4         4-4:10
5          15-17
6          11-13

但是，我希望它看起来像这样（'HH：MM-HH：MM'格式）：

However, I would like it to look like this ('HH:MM-HH:MM' format):

   break_notes
0    15:00-18:00
1    18:30-19:00
2    16:00-17:00
3    15:00-16:00
4    16:00-16:10
5    15:00-17:00
6    11:00-13:00

我的代码是：

data = pd.read_csv('test.csv')
data.break_notes = data.break_notes.str.replace(r'([P].|[ ])', '').str.strip()
data.break_notes = data.break_notes.str.replace(r'([.])', ':').str.strip()

推荐答案

根据请求的输入数据所需的转换器功能。 convert_entry 进行完整的值输入，将其拆分为一个破折号，然后将其结果传递给 convert_single ，因为两者的一半条目可以单独转换。每次转换后，它将它们与破折号合并。

Here is the converter function that you need based on your requested input data. convert_entry takes complete value entry, splits it on a dash, and passes its result to convert_single, since both halfs of one entry can be converted individually. After each conversion, it merges them with a dash.

convert_single 使用正则表达式搜索时间中的重要部分串。
它以一些数字 \d + （代表小时）开头，然后可选地是一个点或冒号以及一些其他的数字 [ 。：]？（\d +）？（代表分钟）。然后是可选的AM还是PM （AM | PM）？（在这种情况下，仅PM是相关的）

convert_single uses regex to search for important parts in the time string. It starts with a some numbers \d+ (representing the hours), then optionally a dot or a colon and some more number [.:]?(\d+)? (representing the minutes). And after that optionally AM or PM (AM|PM)? (only PM is relevant in this case)

import re


def convert_single(s):
    m = re.search(pattern="(\d+)[.:]?(\d+)?(AM|PM)?", string=s)
    hours = m.group(1)
    minutes = m.group(2) or "00"
    if m.group(3) == "PM":
        hours = str(int(hours) + 12)
    return hours.zfill(2) + ":" + minutes.zfill(2)


def convert_entry(value):
    start, end = value.split("-")
    start = convert_single(start)
    end = convert_single(end)
    return "-".join((start, end))


values = ["15-18", "18.30-19.00", "4PM-5PM", "3-4", "4-4.10PM", "15 - 17", "11 - 13"]

for value in values:
    cvalue = convert_entry(value)
    print(cvalue)

这篇关于在 pandas 数据框中操纵时间范围的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！