python 时间 日度 月度 季度
df['Time'] = pd.date_range(end='2021-11-10', periods=230, freq='MS').strftime("%Y-%b")
数据转化为月度数据， 日数据转换为周数据、月数据或季度数据
benchmark_.index.to_period('M')
df_chn_usa_link_m = df_chn_usa_link_m.resample('M').mean() # resample 月度数据，均值, 或者使用last、first
df_chn_usa_link_m.index.strftime('%Y-%m') # 转化为str
pandas获取月底最后一个交易日对应数据； pandas.tseries.offsets
benchmark_ = benchmark.copy()
benchmark_.index = pd.to_datetime(benchmark_.index)
benchmark_[benchmark_.index.day == benchmark_.index.days_in_month] # 只输出每个月最后一天 == 交易日
benchmark_.loc[benchmark_.groupby(benchmark_.index.to_period('M')).apply(lambda x: x.index.min())] # 只输出每个月交易日第一天
benchmark_.loc[benchmark_.groupby(benchmark_.index.to_period('M')).apply(lambda x: x.index.max())] # 只输出每个月最后一个交易日
字典值降序排列
[(k, v) for k, v in sorted(zip(del_var, del_variance), key=lambda x: -x[-1])]
对于公司、时间，去做时间窗的处理：
df.set_index(['cid', 'time'])
df_query = df.loc[df.index.isin(dateindexes, level=1), :].groupby(level=[0]).sum().astype(bool).astype('int8')
生成当前时刻，过去10天的数据：
from datetime import datetime, timedelta
values = range(10)
dates = [datetime.now()-timedelta(days=_) for _ in range(10)]
pandas对于时间序列数据处理的常用方法，可以构造一些细致的特征
# 导入相关库包
import pandas as pd
import numpy as np
import datetime
import time
import random
from calendar import monthrange
# 捏造数据
if __name__ == '__main__':
df = pd.DataFrame(
[['零售店01', '2021-10-01', '2021-10-01 11:47:34', '1993-11-03', '深圳', 100],
['零售店01', '2021-10-02', '2021-10-02 12:47:34', '1993-11-04', '深圳', 120],
['零售店01', '2021-10-03', '2021-10-03 11:47:34', '1993-10-03', '深圳', 140],
['零售店01', '2021-10-04', '2021-10-04 08:47:34', '1993-02-03', '深圳', 170],
['零售店01', '2021-10-05', '2021-10-05 11:47:34', '1993-02-03', '深圳', 190],
['零售店01', '2021-10-06', '2021-10-06 15:47:34', '1993-04-03', '深圳', 10],
['零售店01', '2021-10-07', '2021-10-07 17:47:34', '1993-02-03', '深圳', 20],
['零售店01', '2021-10-08', '2021-10-08 19:47:34', '1993-06-03', '深圳', 420],
['零售店01', '2021-10-09', '2021-10-09 11:47:34', '1993-03-03', '深圳', 230],
['零售店01', '2021-10-10', '2021-10-10 20:47:34', '1993-02-20', '深圳', 80]
], columns=['店铺名称', '统计日期', '大促开始时间', '店长出生日期', '店铺所在城市', '销量'])
df.head()
# 原先属于字符串，转datetime
df['datetime64'] = pd.to_datetime(df['统计日期'])
df['year'] = df['datetime64'].dt.year
df['quarter'] = df['datetime64'].dt.quarter
df['month'] = df['datetime64'].dt.month
df['week'] = df['datetime64'].dt.isocalendar().week
df['day'] = df['datetime64'].dt.day
df['hour'] = df['datetime64'].dt.hour
df['minute'] = df['datetime64'].dt.minute
df['second'] = df['datetime64'].dt.second
df['weekday'] = df['datetime64'].dt.weekday
df['dayofyear'] = df['datetime64'].dt.dayofyear
df['dayofweek'] = df['datetime64'].dt.dayofweek
# df['weekofyear'] = df['datetime64'].dt.weekofyear
################################
# 0-1 特征
################################
df['is_work_day'] = np.where(df['dayofweek'].isin([5, 6]), 0, 1) # 是否工作日
df['is_month_start'] = np.where(df['datetime64'].dt.is_month_start, 1, 0)
df['is_month_end'] = np.where(df['datetime64'].dt.is_month_end, 1, 0)
# 特殊日子/公众假日
special_day = ['2021-10-01', '2021-10-02']
df['is_special_day'] = np.where(df['统计日期'].isin(special_day), 1, 0)
# 是否凌晨
df['is_before_dawn'] = np.where(df['hour'].isin([0, 1, 2, 3]), 1, 0)
################################
# 时间差
################################
# 获取前一天日期
df['yesterday'] = df['datetime64'] - datetime.timedelta(days=1)
# 日期差计算(天)
df['day_dif'] = (df['datetime64'] - df['yesterday']).dt.days
# 日期差计算(小时)
df['hour_dif'] = (df['datetime64'] - df['yesterday']).values / np.timedelta64(1, 'h') # 换成 D 则为 天， D，h
################################
# 衍生特征
################################
df = df.loc[:, ['店铺名称', '统计日期', '销量']]
df['date'] = pd.to_datetime(df['统计日期'])
# 时序值特征衍生前记得排序
df.sort_values(['店铺名称', '统计日期'], ascending=[True, True], inplace=True)
# 衍生时间滑动窗口统计变量
f_min = lambda x: x.rolling(window=3, min_periods=1).min()
f_max = lambda x: x.rolling(window=3, min_periods=1).max()
f_mean = lambda x: x.rolling(window=3, min_periods=1).mean()
f_std = lambda x: x.rolling(window=3, min_periods=1).std()
f_median = lambda x: x.rolling(window=3, min_periods=1).median()
function_list = [f_min, f_max, f_mean, f_std, f_median]
function_name = ['min', 'max', 'mean', 'std', 'median']
for i in range(len(function_list)):
df[('stat_%s' % function_name[i])] = df.sort_values('统计日期', ascending=True).groupby(['店铺名称'])['销量'].apply(
function_list[i])
# 衍生lag变量
for i in [1, 2, 3]:
df["lag_{}".format(i)] = df['销量'].shift(i)
想获取交易所交易日的日期可以使用 tushare， click this, 没有注册可以点击 注册
pro.query('trade_cal', start_date='20180101', end_date='20181231')
