我们将file_content传递给StringIO()类,将其转换为olefile库可以读取的类文件对象,然后将其返回给父函数: defcreate_file_like_obj(note_file): file_size = note_file.info.meta.size file_content = note_file.read_random(0, file_size)returnStringIO.StringIO(file_content) parse_snt_file()函数接受类...
AI检测代码解析 fromdatetimeimportdatetimeimportpytz# 定义 UTC 时间字符串utc_time_str='2023-10-01 12:00:00'# 将字符串转换为 datetime 对象utc_time=datetime.strptime(utc_time_str,'%Y-%m-%d %H:%M:%S')utc_time=pytz.utc.localize(utc_time)# 获取 Unix 时间戳timestamp=utc_time.timestamp()prin...
def convert_enddate_to_seconds(self, ts): """Takes ISO 8601 format(string) and converts into epoch time.""" dt = datetime.datetime.strptime(ts[:-7],'%Y-%m-%dT%H:%M:%S.%f')+\ datetime.timedelta(hours=int(ts[-5:-3]), minutes=int(ts[-2:]))*int(ts[-6:-5]+'1') seconds...
1、Timestamp或DatetimeIndex:它的功能类似于其他索引类型,但也具有用于时间序列操作的专门函数。 t = pd.to_datetime("29/10/1923", dayfirst=True)#Timestamp('1923-10-29 00:00:00')t = pd.Timestamp('2019-01-01', tz = 'Europe/Berlin')#Timestamp('2019-01-01 00:00:00+0100', tz='Europe...
#convert datetime to unix time import time from datetime import datetime t = datetime.now() unix_t = int(time.mktime(t.timetuple())) #1672055277 #convert unix time to datetime unix_t = 1672055277 t = datetime.fromtimestamp(unix_t) #2022-12-26 14:47:57 使用dateutil模块来解析日期字符...
UTC Universal Time Coordinated 又叫协调世界时, UTC用数值记录了时间. 时间记录的是0时区从1972年开始共计走过了多少秒. 所以本地时间与UTC时间的转换需要考虑时差. 一般说来, UTC时间的计算不外乎三种情况. 1: UTC <---> UTC 2: UTC ---> LocalTime 3: Local...
time.gmtime() # convert seconds since Epoch to UTC tuple 结构化时间(UTC英国格林尼治天文台为零时区,北京在东八区,时差为8 个小时) time.localtime()#convert seconds since Epoch to local time tuple(本地时间)结构化时间 time.strftime()#convert time tuple to stringaccording to format specification格式...
" said the countess was a strange the same time the countess was already been and said that he was so strange to the countess was already been and the same time the countess was already been and said Epoch 00011: loss improved from 1.26144 to 1.24933, saving model to weights/weights_...
convert date def parser(x): return datetime.strptime(x,"%Y-%m-%d") #supervised def timeseries_to_supervised(data, lag=1): df = DataFrame(data) columns = [df.shift(1) for i in range(1, lag+1)] columns.append(df) df = concat(columns, axis=1) df.fillna(0, inplace=True) ...
to_clipboard to_csv to_dict to_excel to_frame to_hdf to_json to_latex to_list to_markdown to_numpy to_period to_pickle to_sql to_string to_timestamp to_xarray tolist transform transpose truediv truncate tshift tz_convert tz_localize unique unstack update value_counts values var view ...