In [1]: import pandas as pd In [2]: from io import StringIO In [3]: data = "col1,col2,col3\na,b,1\na,b,2\nc,d,3" In [4]: pd.read_csv(StringIO(data)) Out[4]: col1 col2 col3 0 a b 1 1 a b 2 2 c d 3 In [5]: pd.read_csv(StringIO(data), usecols=lam...
复制 In [50]: import io In [51]: data = io.StringIO("a,b\n,True\n2,") In [52]: df = pd.read_csv(data) In [53]: df.dtypes Out[53]: a float64 b object dtype: object In [54]: df_conv = df.convert_dtypes() In [55]: df_conv Out[55]: a b 0 <NA> True 1 2 ...
DatetimeIndex(['2011-07-06 12:00:00','2011-08-06 00:00:00'], dtype='datetime64[ns]', freq=None) It also handles values that should be condidered missing (None, empty string. etc.): idx = pd.to_datetime(datestrs + [None]) idx DatetimeIndex(['2011-07-06 12:00:00','2011-08...
they can also be days, hours, milliseconds, microseconds.""" # Two datetime objects can also be compared. # For example, using t and t_next above: print(t>t_next) """False""" # Converting datetime objects to strings """ If we have a string, how do we convert it to a datetime...
DatetimeIndex.strftime(date_format):使用指定的date_format转换为Index。 DatetimeIndex.snap([freq]):将时间戳记录到最近出现的频率 DatetimeIndex.tz_convert(tz):将tz-aware DatetimeIndex从一个时区转换为另一个时区。 DatetimeIndex.tz_localize(tz[, ambiguous, …]):将tz-naive DatetimeIndex本地化为tz- aware...
datetimestores(存储) both the date and time down to the microsecond timedelta reprecents the temporal(临时的) difference between two datetime objects: "cj 特方便, 在时间相加上" delta=datetime(2011,1,7)-datetime(2008,6,24,8,15) delta ...
In [203]: df = pd.read_csv("foo.csv", parse_dates=True)In [204]: df.indexOut[204]: DatetimeIndex(['2009-01-01', '2009-01-02', '2009-01-03'], dtype='datetime64[ns]', freq=None) 使用MultiIndex读取索引 假设您的数据由两列索引: ...
String column to datetime, custom format Pandas timestamp now Pandas timestamp to string Filter rows by date Filter rows where date in range Group by year Group by start of week For information on the advanced Indexes available on pandas, seePandas Time Series Examples: DatetimeIndex, PeriodIndex...
unit参数不使用与上面讨论的format参数相同的字符串。可用的单位在pandas.to_datetime()的文档中列出。 使用指定了tz参数的 epoch 时间戳构建Timestamp或DatetimeIndex会引发 ValueError。如果你有另一个时区中的墙上时间的 epoch,你可以将 epoch 读取为时区无关的时间戳,然后本地化到适当的时区: ...
print(pd.to_datetime(df['time_stamp'],unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')) #先赋予标准时区,再转换到东八区 print("-"*20) # 处理中文 print(pd.to_datetime('2019年10月10日',format='%Y年%m月%d日')) ...