In [48]: from pandas.api.indexers import VariableOffsetWindowIndexer In [49]: df = pd.DataFrame(range(10), index=pd.date_range("2020", periods=10)) In [50]: offset = pd.offsets.BDay(1) In [51]: indexer = VariableOffsetWindowIndexer(index=df.index, offset=offset) In [52]: df ...
df.columns = ['date','number'] df['date'] = pd.to_datetime(df['date']) #将数据类型转换为日期类型 df = df.set_index('date') # 将date设置为index print(df.head(2)) print(df.tail(2)) print(df.shape) number date 2013-10-24 3 2013-10-25 4 number date 2017-02-14 6 2017-...
如果要确保结果索引没有重复项并保留所有行,则可以使用ignore_index=True参数。 除了新的Int64Index之外,这基本上返回相同的结果: [外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-tjGS5G0y-1681365731657)(https://gitcode.net/apachecn/apachecn-ds-zh/-/raw/master/docs/learning-pa...
from pandas import date_range s = pd.Series([1, 2, 3, 4], index=date_range('20210101', periods=4)) s # 输出为: 2021-01-01 1 2021-01-02 2 2021-01-03 3 2021-01-04 4 Freq: D, dtype: int64 这种创建Series的方式还是比较优雅的,我们可以多借鉴。 三、Series常用属性 Series有...
index_col=1, parse_dates=True, date_parser=lambda date: pd.to_datetime(date, format='%Y年%m月%d日 %H时%M分')) print(df_csv_jp_i) # A # B # 2017-11-01 12:24:00 2017-11-01 12:24 # 2017-11-18 23:00:00 2017-11-18 23:00 ...
freq:一个字符串或者 DateOffset 对象,指定频率 tz: 指定时区 name:指定该 Index 的名字 closed:指定区间类型,为’left’(左闭右开),‘right’(左开右闭),None(左闭右闭) 示例如下: import pandas as pd p1 = pd.Period('20221113') # 从日期字符串创建 Period 对象,还可指定时分(秒),如:pd.Period(...
In [30]: df = pd.DataFrame(...: {"A": [0, 1, 2, 3, 4]}, index=pd.date_range("2020", periods=5, freq="1D")...: )...:In [31]: dfOut[31]:A2020-01-01 02020-01-02 12020-01-03 22020-01-04 32020-01-05 4In [32]: df.rolling("2D", center=False).mean()Out[...
stamp = ts.index[2] ts[stamp] 0.0770243257021936 As a convenience, you can also pass a string that is interpretable as a date: ts['1/10/2011'] -1.109911691867437 ts['20110110'] -1.109911691867437 For longer time series, a year or only a year and month can be passed to easly select sl...
df['date'] = pd.to_datetime(df['date'])#将数据类型转换为日期类型df = df.set_index('date')# 将date设置为indexprint(df.head(2))print(df.tail(2))print(df.shape) numberdate2013-10-2432013-10-254numberdate2017-02-1462017-02-226(425,1) ...
print(type(df.index)) Output: <class 'pandas.core.indexes.datetimes.DatetimeIndex'> 要返回在特定时间收集的服务器监控数据,无论日期如何,请使用 at_time 方法: display(df.at_time('09:00')) Output: datetime server_id cpu_utilization free_memory session_count ...