下面是一个使用pandas库的示例代码: importpandasaspddefget_previous_month(date):# 获取指定日期的前一个月previous_month=pd.to_datetime(date)-pd.DateOffset(months=1)returnprevious_month.to_period('M').to_timestamp()# 测试代码date=datetime.date(2022,1,15)previous_month=get_previous_month(date)p...
from datetime import datetime, timedelta def get_last_day_of_previous_month(): current_date = datetime.now() first_day_current_month = current_date.replace(day=1) last_day_previous_month = first_day_current_month - timedelta(days=1) return last_day_previous_month.strftime('%Y-%m-%d') p...
fromdatetimeimportdatetime,timedeltadefget_first_day_of_previous_month():# 获取当前日期current_date=datetime.now()year=current_date.year month=current_date.month# 计算前一个月的数据ifmonth==1:previous_month=12year-=1else:previous_month=month-1# 返回前一个月的第一天first_day_previous_month=date...
month[ , day[ , hour[ , minute[ , second[ , microsecond[ , tzinfo]]] ):datetime.timetuple ()datetime.utctimetuple ()datetime.toordinal ()datetime.weekday ()datetime.isocalendar ()datetime.isoformat ([ sep] )datetime.ctime
//根据字符日期返回星期几 * .Author:麦克劳林 * .@Date: 2018/12/29 */ public String get...
import datetime # 获取当前日期 current_date = datetime.date.today() # 获取指定类别的先前日期 def get_previous_date(category): if category == 'week': # 上一周的日期 previous_date = current_date - datetime.timedelta(days=7) elif category == 'month': # 上一个月的日期 previous_date ...
handForm = turtle.get_poly() turtle.register_shape(name, handForm) def Init(): global secHand, minHand, hurHand, printer # 重置Turtle指向北 turtle.mode("logo") # 建立三个表针Turtle并初始化 mkHand("secHand", 135) mkHand("minHand", 125) mkHand("hurHand", 90) secHand = turtle....
timedelta(days=1) first_day, last_day = get_month_range() while first_day < last_day: print(first_day) first_day += a_day # 使用生成器 for d in date_range(datetime(2012, 9, 1), datetime(2012, 10, 1), timedelta(hours=6)): print(d) if __name__ == '__main__': month...
#get google stock price dataimport yfinance as yfstart_date = '2020-01-01'end_date = '2023-01-01'ticker = 'GOOGL'df = yf.download(ticker, start_date, end_date)df.head()"""Date Open High Low Close Adj Close Volume 2020-01-02 67.420502 68.433998 67.324501 68.433998 68.433998 2727800020...
(Points get wide and scattered with increasing lag -> lesser correlation)\n',y=1.15)fig, axes = plt.subplots(1, 4, figsize=(10, 3), sharex=True, sharey=True, dpi=100)for i, ax in enumerate(axes.flatten()[:4]):lag_plot(a10.value, lag=i + 1, ax=ax, c='firebrick')ax....