参数说明：

• arg：int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like

　　The object to convert to a datetime. If a DataFrame is provided, the method expects minimally the following columns: "year", "month", "day". The column “year” must be specified in 4-digit format.

• errors：{‘ignore’, ‘raise’, ‘coerce’}, default ‘raise’

• If 'raise', then invalid parsing will raise an exception.
• If 'coerce', then invalid parsing will be set as NaT.
• If 'ignore', then invalid parsing will return the input.
• dayfirst：bool, default False

　　Specify a date parse order if arg is str or is list-like. If True, parses dates with the day first, e.g. "10/11/12" is parsed as 2012-11-10.

　　Warning：dayfirst=True is not strict, but will prefer to parse with day first.

• yearfirst：bool, default False

　　Specify a date parse order if arg is str or is list-like.

• If True parses dates with the year first, e.g. "10/11/12" is parsed as 2010-11-12.
• If both dayfirst and yearfirst are True, yearfirst is preceded (same as dateutil).

　　Warning：yearfirst=True is not strict, but will prefer to parse with year first.

• utc：bool, default False

　　Control timezone-related parsing, localization and conversion.

• If True, the function always returns a timezone-aware UTC-localized Timestamp, Series or DatetimeIndex. To do this, timezone-naive inputs are localized as UTC, while timezone-aware inputs are converted to UTC.
• If False (default), inputs will not be coerced to UTC. Timezone-naive inputs will remain naive, while timezone-aware ones will keep their time offsets. Limitations exist for mixed offsets (typically, daylight savings).

　　Warning：In a future version of pandas, parsing datetimes with mixed time zones will raise an error unless utc=True. Please specify utc=True to opt in to the new behaviour and silence this warning. To create a Series with mixed offsets and object dtype, please use apply and datetime.datetime.strptime.

• format：str, default None

　　The strftime to parse time, e.g."%d/%m/%Y".

• “ISO8601”, to parse any ISO8601 time string (not necessarily in exactly the same format);
• “mixed”, to infer the format for each element individually. This is risky, and you should probably use it along with dayfirst.

　　Warning：If a DataFrame is passed, then format has no effect.

• exact：bool, default True

　　Control how format is used:

• If True, require an exact format match.
• If False, allow the format to match anywhere in the target string.

　　Cannot be used alongside format='ISO8601' or format='mixed'.

• unit：str, *default ‘ns’

　　The unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or float number. This will be based off the origin. Example, with unit='ms' and origin='unix', this would calculate the number of milliseconds to the unix epoch start.

• infer_datetime_format：bool, default False

　　If Trueand no format is given, attempt to infer the format of the datetime strings based on the first non-NaN element, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by ~5-10x.

• origin：scalar, default ‘unix’

　　Define the reference date. The numeric values would be parsed as number of units (defined by unit) since this reference date.

• If 'unix'(or POSIX) time; origin is set to 1970-01-01.
• If 'julian', unit must be'D', and origin is set to beginning of Julian Calendar. Julian day number 0 is assigned to the day starting at noon on January 1, 4713 BC.
• If Timestamp convertible (Timestamp, dt.datetime, np.datetimt64 or date string), origin is set to Timestamp identified by origin.
• If a float or integer, origin is the difference (in units determined by the unitargument) relative to 1970-01-01.

• cache：bool, default True

　　If True, use a cache of unique, converted dates to apply the datetime conversion. May produce significant speed-up when parsing duplicate date strings, especially ones with timezone offsets. The cache is only used when there are at least 50 values. The presence of out-of-bounds values will render the cache unusable and may slow down parsing.

代码示例1：

 1 df = pd.DataFrame({'year': [2015, 2016],
 2                    'month': [2, 3],
 3                    'day': [4, 5]})
 4 res = pd.to_datetime(df)
 5 print(res)
 6 
 7 # 结果
 8 # 0   2015-02-04
 9 # 1   2016-03-05
10 # dtype: datetime64[ns]
11 
12 ###################################################
13 
14 res1 = pd.to_datetime(1490195805, unit='s')
15 res2 = pd.to_datetime(1490195805433502912, unit='ns')
16 print(res1)
17 print(res2)
18 
19 # 结果
20 # 2017-03-22 15:16:45
21 # 2017-03-22 15:16:45.433502912

 1 res = pd.to_datetime([1, 2, 3], unit='D',
 2                origin=pd.Timestamp('1960-01-01'))
 3 print(res)
 4 
 5 # 结果
 6 # DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], dtype='datetime64[ns]', freq=None)
 7 
 8 res = pd.to_datetime('2018-10-26 12:00:00.0000000011',
 9                format='%Y-%m-%d %H:%M:%S.%f')
10 print(res)
11 
12 # 结果
13 # 2018-10-26 12:00:00.000000001
14 
15 #################################################################
16 
17 res = pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
18 print(res)
19 
20 # 结果
21 # NaT

 1 from datetime import datetime
 2 res = pd.to_datetime(["2020-01-01 01:00:00-01:00",
 3                 datetime(2020, 1, 1, 3, 0)])
 4 print(res)
 5 
 6 # 结果
 7 # DatetimeIndex(['2020-01-01 01:00:00-01:00', '2020-01-01 02:00:00-01:00'], dtype='datetime64[ns, pytz.FixedOffset(-60)]', freq=None)
 8 
 9 ##################################################
10 
11 res = pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True)
12 print(res)
13 
14 # 结果
15 # DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)

2.3. 获取日期区间的数据

　　类似于列表切片。例如：

2.4. 按不同时期统计并显示数据

伪代码：

 1 import pandas as pd
 2 
 3 df = pd.DataFrame(pd.read_excel("文件"))
 4 df1=df[['订单付款时间','买家会员名','联系手机','买家实际支付金额']]
 5 df1 = df1.set_index('订单付款时间') # 将date设置为index
 6 
 7 print('---------按月统计数据-----------')
 8 #“MS”是每个月第一天为开始日期，“M”是每个月最后一天
 9 print(df1.resample('M').sum().to_period('M'))
10 
11 print('---------按季统计数据-----------')
12 #“QS”是每个季度第一天为开始日期，“Q”是每个季度最后一天
13 print(df1.resample('QS').sum())
14 
15 print('---------按年统计数据-----------')
16 #“AS”是每年第一天为开始日期，“A”是每年最后一天
17 print(df1.resample('AS').sum())
18 
19 
20 print('---------按年统计并显示数据-----------')
21 #“AS”是每年第一天为开始日期，“A”是每年最后一天
22 print(df1.resample('AS').sum().to_period('A'))
23 print('---------按季度统计并显示数据-----------')
24 print(df1.resample('Q').sum().to_period('Q'))
25 print('---------按月统计并显示数据-----------')
26 print(df1.resample('M').sum().to_period('M'))
27 df2=df1.resample('M').sum().to_period('M')
28 print('---------按星期统计并显示数据-----------')
29 print(df1.resample('w').sum().to_period('W').head())

DataFrame.resample(rule, axis=_NoDefault.no_default, closed=None, label=None, convention=_NoDefault.no_default, kind=_NoDefault.no_default, on=None, level=None, origin='start_day', offset=None, group_keys=False)

 1 index = pd.date_range('02/02/2020', periods=9, freq='T')
 2 series = pd.Series(range(9), index=index)
 3 print(series)
 4 print(series.resample('3T').sum())
 5 
 6 # 结果
 7 # 2020-02-02 00:00:00    0
 8 # 2020-02-02 00:01:00    1
 9 # 2020-02-02 00:02:00    2
10 # 2020-02-02 00:03:00    3
11 # 2020-02-02 00:04:00    4
12 # 2020-02-02 00:05:00    5
13 # 2020-02-02 00:06:00    6
14 # 2020-02-02 00:07:00    7
15 # 2020-02-02 00:08:00    8
16 # Freq: T, dtype: int64
17 # 2020-02-02 00:00:00     3
18 # 2020-02-02 00:03:00    12
19 # 2020-02-02 00:06:00    21
20 # Freq: 3T, dtype: int64

 1 index = pd.date_range('1/1/2000', periods=9, freq='min')
 2 series = pd.Series(range(9), index=index)
 3 
 4 def custom_resampler(arraylike):
 5     return np.sum(arraylike) + 5
 6 
 7 print(series.resample('3min').apply(custom_resampler))
 8 
 9 # 结果
10 # 2000-01-01 00:00:00     8
11 # 2000-01-01 00:03:00    17
12 # 2000-01-01 00:06:00    26
13 # Freq: 3T, dtype: int64

 1 import pandas as pd
 2 
 3 # 创建一个时间序列数据
 4 date_rng = pd.date_range(start='1/1/2020', end='1/10/2020', freq='H')
 5 df = pd.DataFrame(date_rng, columns=['date'])
 6 df['data'] = range(len(df))
 7 
 8 # 将日期设置为索引
 9 df = df.set_index('date')
10 
11 # 降采样：将数据从小时转换为天
12 downsampled = df.resample('D').mean()
13 print("降采样结果：")
14 print(downsampled)
15 
16 # 升采样：将数据从天转换为小时
17 upsampled = df.resample('H').ffill()
18 print("升采样结果：")
19 print(upsampled)

1 import pandas as pd
2 import numpy as np
3 
4 rng = pd.date_range('2/2/2020', periods=12, freq='T')
5 s1 = pd.Series(np.arange(12), index=rng)
6 print(s1.resample('5min').ohlc())

1 import pandas as pd
2 index = pd.date_range('20200201', '20200215')
3 data = [3, 6, 7, 4, 2, 1, 3, 8, 9, 10, 12, 15, 13, 22, 14]
4 s1_data = pd.Series(data, index=index)
5 print(s1_data)
6 print(s1_data.rolling(3).mean())