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matplotlib & pandas
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Python/pandas/user_guide/timedeltas.md
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832
Python/pandas/user_guide/timedeltas.md
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# 时间差
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`Timedelta`，时间差，即时间之间的差异，用 `日、时、分、秒` 等时间单位表示，时间单位可为正，也可为负。
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`Timedelta` 是 `datetime.timedelta` 的子类，两者的操作方式相似，但 `Timedelta` 兼容 `np.timedelta64` 等数据类型，还支持自定义表示形式、能解析多种类型的数据，并支持自有属性。
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## 解析数据，生成时间差
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`Timedelta()` 支持用多种参数生成时间差：
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``` python
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In [1]: import datetime
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# 字符串
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In [2]: pd.Timedelta('1 days')
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Out[2]: Timedelta('1 days 00:00:00')
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In [3]: pd.Timedelta('1 days 00:00:00')
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Out[3]: Timedelta('1 days 00:00:00')
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In [4]: pd.Timedelta('1 days 2 hours')
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Out[4]: Timedelta('1 days 02:00:00')
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In [5]: pd.Timedelta('-1 days 2 min 3us')
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Out[5]: Timedelta('-2 days +23:57:59.999997')
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# datetime.timedelta
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# 注意：必须指定关键字参数
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In [6]: pd.Timedelta(days=1, seconds=1)
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Out[6]: Timedelta('1 days 00:00:01')
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# 用整数与时间单位生成时间差
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In [7]: pd.Timedelta(1, unit='d')
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Out[7]: Timedelta('1 days 00:00:00')
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# datetime.timedelta 与 np.timedelta64
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In [8]: pd.Timedelta(datetime.timedelta(days=1, seconds=1))
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Out[8]: Timedelta('1 days 00:00:01')
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In [9]: pd.Timedelta(np.timedelta64(1, 'ms'))
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Out[9]: Timedelta('0 days 00:00:00.001000')
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# 用字符串表示负数时间差
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# 更接近 datetime.timedelta
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In [10]: pd.Timedelta('-1us')
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Out[10]: Timedelta('-1 days +23:59:59.999999')
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# 时间差缺失值
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In [11]: pd.Timedelta('nan')
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Out[11]: NaT
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In [12]: pd.Timedelta('nat')
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Out[12]: NaT
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# ISO8601 时间格式字符串
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In [13]: pd.Timedelta('P0DT0H1M0S')
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Out[13]: Timedelta('0 days 00:01:00')
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In [14]: pd.Timedelta('P0DT0H0M0.000000123S')
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Out[14]: Timedelta('0 days 00:00:00.000000')
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```
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*0.23.0 版新增*：增加了用 [ISO8601 时间格式](https://en.wikipedia.org/wiki/ISO_8601#Durations)生成时间差。
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[DateOffsets](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offsets)（`Day`、`Hour`、`Minute`、`Second`、`Milli`、`Micro`、`Nano`）也可以用来生成时间差。
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``` python
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In [15]: pd.Timedelta(pd.offsets.Second(2))
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Out[15]: Timedelta('0 days 00:00:02')
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```
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标量运算生成的也是 `Timedelta` 标量。
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``` python
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In [16]: pd.Timedelta(pd.offsets.Day(2)) + pd.Timedelta(pd.offsets.Second(2)) +\
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....: pd.Timedelta('00:00:00.000123')
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....:
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Out[16]: Timedelta('2 days 00:00:02.000123')
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```
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### to_timedelta
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`pd.to_timedelta()` 可以把符合时间差格式的标量、数组、列表、序列等数据转换为`Timedelta`。输入数据是序列，输出的就是序列，输入数据是标量，输出的就是标量，其它形式的输入数据则输出 `TimedeltaIndex`。
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`to_timedelta()` 可以解析单个字符串：
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``` python
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In [17]: pd.to_timedelta('1 days 06:05:01.00003')
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Out[17]: Timedelta('1 days 06:05:01.000030')
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In [18]: pd.to_timedelta('15.5us')
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Out[18]: Timedelta('0 days 00:00:00.000015')
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```
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还能解析字符串列表或数组：
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``` python
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In [19]: pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])
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Out[19]: TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015', NaT], dtype='timedelta64[ns]', freq=None)
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```
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`unit` 关键字参数指定时间差的单位：
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``` python
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In [20]: pd.to_timedelta(np.arange(5), unit='s')
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Out[20]: TimedeltaIndex(['00:00:00', '00:00:01', '00:00:02', '00:00:03', '00:00:04'], dtype='timedelta64[ns]', freq=None)
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In [21]: pd.to_timedelta(np.arange(5), unit='d')
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Out[21]: TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
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```
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### 时间差界限
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Pandas 时间差的纳秒解析度是 64 位整数，这就决定了 `Timedelta` 的上下限。
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``` python
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In [22]: pd.Timedelta.min
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Out[22]: Timedelta('-106752 days +00:12:43.145224')
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In [23]: pd.Timedelta.max
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Out[23]: Timedelta('106751 days 23:47:16.854775')
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```
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## 运算
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以时间差为数据的 `Series` 与 `DataFrame` 支持各种运算，`datetime64 [ns]` 序列或 `Timestamps` 减法运算生成的是`timedelta64 [ns]` 序列。
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``` python
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In [24]: s = pd.Series(pd.date_range('2012-1-1', periods=3, freq='D'))
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In [25]: td = pd.Series([pd.Timedelta(days=i) for i in range(3)])
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In [26]: df = pd.DataFrame({'A': s, 'B': td})
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In [27]: df
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Out[27]:
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A B
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0 2012-01-01 0 days
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1 2012-01-02 1 days
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2 2012-01-03 2 days
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In [28]: df['C'] = df['A'] + df['B']
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In [29]: df
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Out[29]:
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A B C
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0 2012-01-01 0 days 2012-01-01
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1 2012-01-02 1 days 2012-01-03
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2 2012-01-03 2 days 2012-01-05
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In [30]: df.dtypes
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Out[30]:
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A datetime64[ns]
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B timedelta64[ns]
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C datetime64[ns]
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dtype: object
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In [31]: s - s.max()
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Out[31]:
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0 -2 days
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1 -1 days
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2 0 days
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dtype: timedelta64[ns]
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In [32]: s - datetime.datetime(2011, 1, 1, 3, 5)
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Out[32]:
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0 364 days 20:55:00
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1 365 days 20:55:00
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2 366 days 20:55:00
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dtype: timedelta64[ns]
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In [33]: s + datetime.timedelta(minutes=5)
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Out[33]:
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0 2012-01-01 00:05:00
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1 2012-01-02 00:05:00
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2 2012-01-03 00:05:00
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dtype: datetime64[ns]
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In [34]: s + pd.offsets.Minute(5)
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Out[34]:
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0 2012-01-01 00:05:00
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1 2012-01-02 00:05:00
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2 2012-01-03 00:05:00
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dtype: datetime64[ns]
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In [35]: s + pd.offsets.Minute(5) + pd.offsets.Milli(5)
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Out[35]:
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0 2012-01-01 00:05:00.005
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1 2012-01-02 00:05:00.005
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2 2012-01-03 00:05:00.005
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dtype: datetime64[ns]
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```
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`timedelta64 [ns]` 序列的标量运算：
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``` python
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In [36]: y = s - s[0]
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In [37]: y
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Out[37]:
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0 0 days
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1 1 days
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2 2 days
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dtype: timedelta64[ns]
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```
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时间差序列支持 `NaT` 值：
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``` python
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In [38]: y = s - s.shift()
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In [39]: y
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Out[39]:
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0 NaT
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1 1 days
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2 1 days
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dtype: timedelta64[ns]
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```
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与 `datetime` 类似，`np.nan` 把时间差设置为 `NaT`：
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``` python
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In [40]: y[1] = np.nan
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In [41]: y
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Out[41]:
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0 NaT
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1 NaT
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2 1 days
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dtype: timedelta64[ns]
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```
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运算符也可以显示为逆序（序列与单个对象的运算）：
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``` python
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In [42]: s.max() - s
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Out[42]:
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0 2 days
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1 1 days
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2 0 days
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dtype: timedelta64[ns]
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In [43]: datetime.datetime(2011, 1, 1, 3, 5) - s
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Out[43]:
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0 -365 days +03:05:00
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1 -366 days +03:05:00
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2 -367 days +03:05:00
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dtype: timedelta64[ns]
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In [44]: datetime.timedelta(minutes=5) + s
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Out[44]:
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0 2012-01-01 00:05:00
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1 2012-01-02 00:05:00
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2 2012-01-03 00:05:00
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dtype: datetime64[ns]
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```
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`DataFrame` 支持 `min`、`max` 及 `idxmin`、`idxmax` 运算：
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``` python
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In [45]: A = s - pd.Timestamp('20120101') - pd.Timedelta('00:05:05')
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In [46]: B = s - pd.Series(pd.date_range('2012-1-2', periods=3, freq='D'))
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In [47]: df = pd.DataFrame({'A': A, 'B': B})
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In [48]: df
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Out[48]:
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A B
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0 -1 days +23:54:55 -1 days
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1 0 days 23:54:55 -1 days
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2 1 days 23:54:55 -1 days
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In [49]: df.min()
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Out[49]:
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A -1 days +23:54:55
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B -1 days +00:00:00
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dtype: timedelta64[ns]
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In [50]: df.min(axis=1)
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Out[50]:
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0 -1 days
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1 -1 days
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2 -1 days
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dtype: timedelta64[ns]
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In [51]: df.idxmin()
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Out[51]:
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A 0
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B 0
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dtype: int64
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In [52]: df.idxmax()
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Out[52]:
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A 2
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B 0
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dtype: int64
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```
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`Series` 也支持`min`、`max` 及 `idxmin`、`idxmax` 运算。标量计算结果为 `Timedelta`。
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``` python
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In [53]: df.min().max()
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Out[53]: Timedelta('-1 days +23:54:55')
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In [54]: df.min(axis=1).min()
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Out[54]: Timedelta('-1 days +00:00:00')
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In [55]: df.min().idxmax()
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Out[55]: 'A'
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In [56]: df.min(axis=1).idxmin()
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Out[56]: 0
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```
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时间差支持 `fillna` 函数，参数是 `Timedelta`，用于指定填充值。
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``` python
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In [57]: y.fillna(pd.Timedelta(0))
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Out[57]:
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0 0 days
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1 0 days
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2 1 days
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dtype: timedelta64[ns]
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In [58]: y.fillna(pd.Timedelta(10, unit='s'))
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Out[58]:
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0 0 days 00:00:10
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1 0 days 00:00:10
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2 1 days 00:00:00
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dtype: timedelta64[ns]
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In [59]: y.fillna(pd.Timedelta('-1 days, 00:00:05'))
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Out[59]:
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0 -1 days +00:00:05
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1 -1 days +00:00:05
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2 1 days 00:00:00
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dtype: timedelta64[ns]
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```
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`Timedelta` 还支持取反、乘法及绝对值（`Abs`）运算：
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``` python
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In [60]: td1 = pd.Timedelta('-1 days 2 hours 3 seconds')
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In [61]: td1
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Out[61]: Timedelta('-2 days +21:59:57')
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In [62]: -1 * td1
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Out[62]: Timedelta('1 days 02:00:03')
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In [63]: - td1
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Out[63]: Timedelta('1 days 02:00:03')
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In [64]: abs(td1)
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Out[64]: Timedelta('1 days 02:00:03')
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```
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## 归约
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`timedelta64 [ns]` 数值归约运算返回的是 `Timedelta` 对象。 一般情况下，`NaT` 不计数。
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``` python
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In [65]: y2 = pd.Series(pd.to_timedelta(['-1 days +00:00:05', 'nat',
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....: '-1 days +00:00:05', '1 days']))
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....:
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In [66]: y2
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Out[66]:
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0 -1 days +00:00:05
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1 NaT
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2 -1 days +00:00:05
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3 1 days 00:00:00
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dtype: timedelta64[ns]
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In [67]: y2.mean()
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Out[67]: Timedelta('-1 days +16:00:03.333333')
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In [68]: y2.median()
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Out[68]: Timedelta('-1 days +00:00:05')
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In [69]: y2.quantile(.1)
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Out[69]: Timedelta('-1 days +00:00:05')
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In [70]: y2.sum()
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Out[70]: Timedelta('-1 days +00:00:10')
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```
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## 频率转换
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时间差除法把 `Timedelta` 序列、`TimedeltaIndex`、`Timedelta` 标量转换为其它“频率”，`astype` 也可以将之转换为指定的时间差。这些运算生成的是序列，并把 `NaT` 转换为 `nan`。 注意，NumPy 标量除法是真除法，`astype` 则等同于取底整除（Floor Division）。
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::: tip 说明
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Floor Division ，即两数的商为向下取整，如，9 / 2 = 4。又译作地板除或向下取整除，本文译作**取底整除**；
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扩展知识：
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Ceiling Division，即两数的商为向上取整，如，9 / 2 = 5。又译作屋顶除或向上取整除，本文译作**取顶整除**。
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:::
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``` python
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In [71]: december = pd.Series(pd.date_range('20121201', periods=4))
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In [72]: january = pd.Series(pd.date_range('20130101', periods=4))
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In [73]: td = january - december
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In [74]: td[2] += datetime.timedelta(minutes=5, seconds=3)
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In [75]: td[3] = np.nan
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In [76]: td
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Out[76]:
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0 31 days 00:00:00
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1 31 days 00:00:00
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2 31 days 00:05:03
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3 NaT
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dtype: timedelta64[ns]
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# 转为日
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In [77]: td / np.timedelta64(1, 'D')
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Out[77]:
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0 31.000000
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1 31.000000
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2 31.003507
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3 NaN
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dtype: float64
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In [78]: td.astype('timedelta64[D]')
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Out[78]:
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||||
0 31.0
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1 31.0
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||||
2 31.0
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||||
3 NaN
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||||
dtype: float64
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||||
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# 转为秒
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In [79]: td / np.timedelta64(1, 's')
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Out[79]:
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||||
0 2678400.0
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||||
1 2678400.0
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||||
2 2678703.0
|
||||
3 NaN
|
||||
dtype: float64
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||||
|
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In [80]: td.astype('timedelta64[s]')
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Out[80]:
|
||||
0 2678400.0
|
||||
1 2678400.0
|
||||
2 2678703.0
|
||||
3 NaN
|
||||
dtype: float64
|
||||
|
||||
# 转为月 （此处用常量表示）
|
||||
In [81]: td / np.timedelta64(1, 'M')
|
||||
Out[81]:
|
||||
0 1.018501
|
||||
1 1.018501
|
||||
2 1.018617
|
||||
3 NaN
|
||||
dtype: float64
|
||||
```
|
||||
|
||||
`timedelta64 [ns]` 序列与整数或整数序列相乘或相除，生成的也是 `timedelta64 [ns]` 序列。
|
||||
|
||||
``` python
|
||||
In [82]: td * -1
|
||||
Out[82]:
|
||||
0 -31 days +00:00:00
|
||||
1 -31 days +00:00:00
|
||||
2 -32 days +23:54:57
|
||||
3 NaT
|
||||
dtype: timedelta64[ns]
|
||||
|
||||
In [83]: td * pd.Series([1, 2, 3, 4])
|
||||
Out[83]:
|
||||
0 31 days 00:00:00
|
||||
1 62 days 00:00:00
|
||||
2 93 days 00:15:09
|
||||
3 NaT
|
||||
dtype: timedelta64[ns]
|
||||
```
|
||||
|
||||
`timedelta64 [ns]` 序列与 `Timedelta` 标量相除的结果为取底整除的整数序列。
|
||||
|
||||
|
||||
``` python
|
||||
In [84]: td // pd.Timedelta(days=3, hours=4)
|
||||
Out[84]:
|
||||
0 9.0
|
||||
1 9.0
|
||||
2 9.0
|
||||
3 NaN
|
||||
dtype: float64
|
||||
|
||||
In [85]: pd.Timedelta(days=3, hours=4) // td
|
||||
Out[85]:
|
||||
0 0.0
|
||||
1 0.0
|
||||
2 0.0
|
||||
3 NaN
|
||||
dtype: float64
|
||||
```
|
||||
|
||||
`Timedelta` 的求余（`mod(%)`）与除余（`divmod`）运算，支持时间差与数值参数。
|
||||
|
||||
``` python
|
||||
In [86]: pd.Timedelta(hours=37) % datetime.timedelta(hours=2)
|
||||
Out[86]: Timedelta('0 days 01:00:00')
|
||||
|
||||
# 除余运算的参数为时间差时，返回一对值（int, Timedelta）
|
||||
In [87]: divmod(datetime.timedelta(hours=2), pd.Timedelta(minutes=11))
|
||||
Out[87]: (10, Timedelta('0 days 00:10:00'))
|
||||
|
||||
# 除余运算的参数为数值时，也返回一对值（Timedelta, Timedelta）
|
||||
In [88]: divmod(pd.Timedelta(hours=25), 86400000000000)
|
||||
Out[88]: (Timedelta('0 days 00:00:00.000000'), Timedelta('0 days 01:00:00'))
|
||||
```
|
||||
|
||||
## 属性
|
||||
|
||||
`Timedelta` 或 `TimedeltaIndex` 的组件可以直接访问 `days`、`seconds`、`microseconds`、`nanoseconds` 等属性。这些属性与`datetime.timedelta` 的返回值相同，例如，`.seconds` 属性表示大于等于 0 天且小于 1 天的秒数。带符号的 `Timedelta` 返回的值也带符号。
|
||||
|
||||
`Series` 的 `.dt` 属性也可以直接访问这些数据。
|
||||
|
||||
::: tip 注意
|
||||
|
||||
这些属性**不是** `Timedelta` 显示的值。`.components` 可以提取显示的值。
|
||||
|
||||
:::
|
||||
|
||||
对于 `Series`：
|
||||
|
||||
``` python
|
||||
In [89]: td.dt.days
|
||||
Out[89]:
|
||||
0 31.0
|
||||
1 31.0
|
||||
2 31.0
|
||||
3 NaN
|
||||
dtype: float64
|
||||
|
||||
In [90]: td.dt.seconds
|
||||
Out[90]:
|
||||
0 0.0
|
||||
1 0.0
|
||||
2 303.0
|
||||
3 NaN
|
||||
dtype: float64
|
||||
```
|
||||
|
||||
直接访问 `Timedelta` 标量字段值。
|
||||
|
||||
``` python
|
||||
In [91]: tds = pd.Timedelta('31 days 5 min 3 sec')
|
||||
|
||||
In [92]: tds.days
|
||||
Out[92]: 31
|
||||
|
||||
In [93]: tds.seconds
|
||||
Out[93]: 303
|
||||
|
||||
In [94]: (-tds).seconds
|
||||
Out[94]: 86097
|
||||
```
|
||||
|
||||
`.components` 属性可以快速访问时间差的组件，返回结果是 `DataFrame`。 下列代码输出 `Timedelta` 的显示值。
|
||||
|
||||
``` python
|
||||
In [95]: td.dt.components
|
||||
Out[95]:
|
||||
days hours minutes seconds milliseconds microseconds nanoseconds
|
||||
0 31.0 0.0 0.0 0.0 0.0 0.0 0.0
|
||||
1 31.0 0.0 0.0 0.0 0.0 0.0 0.0
|
||||
2 31.0 0.0 5.0 3.0 0.0 0.0 0.0
|
||||
3 NaN NaN NaN NaN NaN NaN NaN
|
||||
|
||||
In [96]: td.dt.components.seconds
|
||||
Out[96]:
|
||||
0 0.0
|
||||
1 0.0
|
||||
2 3.0
|
||||
3 NaN
|
||||
Name: seconds, dtype: float64
|
||||
```
|
||||
|
||||
`.isoformat` 方法可以把 `Timedelta` 转换为 [ISO8601 时间格式](https://en.wikipedia.org/wiki/ISO_8601#Durations)字符串。
|
||||
|
||||
*0.20.0 版新增。*
|
||||
|
||||
``` python
|
||||
In [97]: pd.Timedelta(days=6, minutes=50, seconds=3,
|
||||
....: milliseconds=10, microseconds=10,
|
||||
....: nanoseconds=12).isoformat()
|
||||
....:
|
||||
Out[97]: 'P6DT0H50M3.010010012S'
|
||||
```
|
||||
|
||||
## TimedeltaIndex
|
||||
|
||||
[`TimedeltaIndex`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.TimedeltaIndex.html#pandas.TimedeltaIndex) 或 [`timedelta_range()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.timedelta_range.html#pandas.timedelta_range) 可以生成时间差索引。
|
||||
|
||||
`TimedeltaIndex` 支持字符串型的 `Timedelta`、`timedelta` 或 `np.timedelta64`对象。
|
||||
|
||||
`np.nan`、`pd.NaT`、`nat` 代表缺失值。
|
||||
|
||||
``` python
|
||||
In [98]: pd.TimedeltaIndex(['1 days', '1 days, 00:00:05', np.timedelta64(2, 'D'),
|
||||
....: datetime.timedelta(days=2, seconds=2)])
|
||||
....:
|
||||
Out[98]:
|
||||
TimedeltaIndex(['1 days 00:00:00', '1 days 00:00:05', '2 days 00:00:00',
|
||||
'2 days 00:00:02'],
|
||||
dtype='timedelta64[ns]', freq=None)
|
||||
```
|
||||
|
||||
`freq` 关键字参数为 `infer` 时，`TimedeltaIndex` 可以自行推断时间频率：
|
||||
|
||||
``` python
|
||||
In [99]: pd.TimedeltaIndex(['0 days', '10 days', '20 days'], freq='infer')
|
||||
Out[99]: TimedeltaIndex(['0 days', '10 days', '20 days'], dtype='timedelta64[ns]', freq='10D')
|
||||
```
|
||||
|
||||
### 生成时间差范围
|
||||
|
||||
与 [`date_range()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.date_range.html#pandas.date_range) 相似，[`timedelta_range()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.timedelta_range.html#pandas.timedelta_range) 可以生成定频 `TimedeltaIndex`，`timedelta_range` 的默认频率是日历日：
|
||||
|
||||
``` python
|
||||
In [100]: pd.timedelta_range(start='1 days', periods=5)
|
||||
Out[100]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq='D')
|
||||
```
|
||||
|
||||
`timedelta_range` 支持 `start`、`end`、`periods` 三个参数：
|
||||
|
||||
``` python
|
||||
In [101]: pd.timedelta_range(start='1 days', end='5 days')
|
||||
Out[101]: TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq='D')
|
||||
|
||||
In [102]: pd.timedelta_range(end='10 days', periods=4)
|
||||
Out[102]: TimedeltaIndex(['7 days', '8 days', '9 days', '10 days'], dtype='timedelta64[ns]', freq='D')
|
||||
```
|
||||
|
||||
`freq` 参数支持各种[频率别名](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases)：
|
||||
|
||||
``` python
|
||||
In [103]: pd.timedelta_range(start='1 days', end='2 days', freq='30T')
|
||||
Out[103]:
|
||||
TimedeltaIndex(['1 days 00:00:00', '1 days 00:30:00', '1 days 01:00:00',
|
||||
'1 days 01:30:00', '1 days 02:00:00', '1 days 02:30:00',
|
||||
'1 days 03:00:00', '1 days 03:30:00', '1 days 04:00:00',
|
||||
'1 days 04:30:00', '1 days 05:00:00', '1 days 05:30:00',
|
||||
'1 days 06:00:00', '1 days 06:30:00', '1 days 07:00:00',
|
||||
'1 days 07:30:00', '1 days 08:00:00', '1 days 08:30:00',
|
||||
'1 days 09:00:00', '1 days 09:30:00', '1 days 10:00:00',
|
||||
'1 days 10:30:00', '1 days 11:00:00', '1 days 11:30:00',
|
||||
'1 days 12:00:00', '1 days 12:30:00', '1 days 13:00:00',
|
||||
'1 days 13:30:00', '1 days 14:00:00', '1 days 14:30:00',
|
||||
'1 days 15:00:00', '1 days 15:30:00', '1 days 16:00:00',
|
||||
'1 days 16:30:00', '1 days 17:00:00', '1 days 17:30:00',
|
||||
'1 days 18:00:00', '1 days 18:30:00', '1 days 19:00:00',
|
||||
'1 days 19:30:00', '1 days 20:00:00', '1 days 20:30:00',
|
||||
'1 days 21:00:00', '1 days 21:30:00', '1 days 22:00:00',
|
||||
'1 days 22:30:00', '1 days 23:00:00', '1 days 23:30:00',
|
||||
'2 days 00:00:00'],
|
||||
dtype='timedelta64[ns]', freq='30T')
|
||||
|
||||
In [104]: pd.timedelta_range(start='1 days', periods=5, freq='2D5H')
|
||||
Out[104]:
|
||||
TimedeltaIndex(['1 days 00:00:00', '3 days 05:00:00', '5 days 10:00:00',
|
||||
'7 days 15:00:00', '9 days 20:00:00'],
|
||||
dtype='timedelta64[ns]', freq='53H')
|
||||
```
|
||||
|
||||
*0.23.0 版新增*。
|
||||
|
||||
用 `start`、`end`、`period` 可以生成等宽时间差范围，其中，`start` 与 `end`（含）是起止两端的时间，`periods` 为 `TimedeltaIndex` 里的元素数量：
|
||||
|
||||
``` python
|
||||
In [105]: pd.timedelta_range('0 days', '4 days', periods=5)
|
||||
Out[105]: TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'], dtype='timedelta64[ns]', freq=None)
|
||||
|
||||
In [106]: pd.timedelta_range('0 days', '4 days', periods=10)
|
||||
Out[106]:
|
||||
TimedeltaIndex(['0 days 00:00:00', '0 days 10:40:00', '0 days 21:20:00',
|
||||
'1 days 08:00:00', '1 days 18:40:00', '2 days 05:20:00',
|
||||
'2 days 16:00:00', '3 days 02:40:00', '3 days 13:20:00',
|
||||
'4 days 00:00:00'],
|
||||
dtype='timedelta64[ns]', freq=None)
|
||||
```
|
||||
|
||||
### TimedeltaIndex 应用
|
||||
|
||||
与 `DatetimeIndex`、`PeriodIndex` 等 `datetime` 型索引类似，`TimedeltaIndex` 也可当作 pandas 对象的索引。
|
||||
|
||||
``` python
|
||||
In [107]: s = pd.Series(np.arange(100),
|
||||
.....: index=pd.timedelta_range('1 days', periods=100, freq='h'))
|
||||
.....:
|
||||
|
||||
In [108]: s
|
||||
Out[108]:
|
||||
1 days 00:00:00 0
|
||||
1 days 01:00:00 1
|
||||
1 days 02:00:00 2
|
||||
1 days 03:00:00 3
|
||||
1 days 04:00:00 4
|
||||
..
|
||||
4 days 23:00:00 95
|
||||
5 days 00:00:00 96
|
||||
5 days 01:00:00 97
|
||||
5 days 02:00:00 98
|
||||
5 days 03:00:00 99
|
||||
Freq: H, Length: 100, dtype: int64
|
||||
```
|
||||
|
||||
选择操作也差不多，可以强制转换字符串与切片：
|
||||
|
||||
``` python
|
||||
In [109]: s['1 day':'2 day']
|
||||
Out[109]:
|
||||
1 days 00:00:00 0
|
||||
1 days 01:00:00 1
|
||||
1 days 02:00:00 2
|
||||
1 days 03:00:00 3
|
||||
1 days 04:00:00 4
|
||||
..
|
||||
2 days 19:00:00 43
|
||||
2 days 20:00:00 44
|
||||
2 days 21:00:00 45
|
||||
2 days 22:00:00 46
|
||||
2 days 23:00:00 47
|
||||
Freq: H, Length: 48, dtype: int64
|
||||
|
||||
In [110]: s['1 day 01:00:00']
|
||||
Out[110]: 1
|
||||
|
||||
In [111]: s[pd.Timedelta('1 day 1h')]
|
||||
Out[111]: 1
|
||||
```
|
||||
|
||||
`TimedeltaIndex` 还支持局部字符串选择，并且可以推断选择范围：
|
||||
|
||||
``` python
|
||||
In [112]: s['1 day':'1 day 5 hours']
|
||||
Out[112]:
|
||||
1 days 00:00:00 0
|
||||
1 days 01:00:00 1
|
||||
1 days 02:00:00 2
|
||||
1 days 03:00:00 3
|
||||
1 days 04:00:00 4
|
||||
1 days 05:00:00 5
|
||||
Freq: H, dtype: int64
|
||||
```
|
||||
|
||||
### TimedeltaIndex 运算
|
||||
|
||||
`TimedeltaIndex` 与 `DatetimeIndex` 运算可以保留 `NaT` 值：
|
||||
|
||||
``` python
|
||||
In [113]: tdi = pd.TimedeltaIndex(['1 days', pd.NaT, '2 days'])
|
||||
|
||||
In [114]: tdi.to_list()
|
||||
Out[114]: [Timedelta('1 days 00:00:00'), NaT, Timedelta('2 days 00:00:00')]
|
||||
|
||||
In [115]: dti = pd.date_range('20130101', periods=3)
|
||||
|
||||
In [116]: dti.to_list()
|
||||
Out[116]:
|
||||
[Timestamp('2013-01-01 00:00:00', freq='D'),
|
||||
Timestamp('2013-01-02 00:00:00', freq='D'),
|
||||
Timestamp('2013-01-03 00:00:00', freq='D')]
|
||||
|
||||
In [117]: (dti + tdi).to_list()
|
||||
Out[117]: [Timestamp('2013-01-02 00:00:00'), NaT, Timestamp('2013-01-05 00:00:00')]
|
||||
|
||||
In [118]: (dti - tdi).to_list()
|
||||
Out[118]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2013-01-01 00:00:00')]
|
||||
```
|
||||
|
||||
### 转换
|
||||
|
||||
与 `Series` 频率转换类似，可以把 `TimedeltaIndex` 转换为其它索引。
|
||||
|
||||
``` python
|
||||
In [119]: tdi / np.timedelta64(1, 's')
|
||||
Out[119]: Float64Index([86400.0, nan, 172800.0], dtype='float64')
|
||||
|
||||
In [120]: tdi.astype('timedelta64[s]')
|
||||
Out[120]: Float64Index([86400.0, nan, 172800.0], dtype='float64')
|
||||
```
|
||||
|
||||
与标量操作类似，会返回**不同**类型的索引。
|
||||
|
||||
``` python
|
||||
# 时间差与日期相加，结果为日期型索引（DatetimeIndex）
|
||||
In [121]: tdi + pd.Timestamp('20130101')
|
||||
Out[121]: DatetimeIndex(['2013-01-02', 'NaT', '2013-01-03'], dtype='datetime64[ns]', freq=None)
|
||||
|
||||
# 日期与时间戳相减，结果为日期型数据（Timestamp）
|
||||
# note that trying to subtract a date from a Timedelta will raise an exception
|
||||
In [122]: (pd.Timestamp('20130101') - tdi).to_list()
|
||||
Out[122]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2012-12-30 00:00:00')]
|
||||
|
||||
# 时间差与时间差相加，结果还是时间差索引
|
||||
In [123]: tdi + pd.Timedelta('10 days')
|
||||
Out[123]: TimedeltaIndex(['11 days', NaT, '12 days'], dtype='timedelta64[ns]', freq=None)
|
||||
|
||||
# 除数是整数，则结果为时间差索引
|
||||
In [124]: tdi / 2
|
||||
Out[124]: TimedeltaIndex(['0 days 12:00:00', NaT, '1 days 00:00:00'], dtype='timedelta64[ns]', freq=None)
|
||||
|
||||
# 除数是时间差，则结果为 Float64Index
|
||||
In [125]: tdi / tdi[0]
|
||||
Out[125]: Float64Index([1.0, nan, 2.0], dtype='float64')
|
||||
```
|
||||
|
||||
## 重采样
|
||||
|
||||
与[时间序列重采样](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-resampling)一样，`TimedeltaIndex` 也支持重采样。
|
||||
|
||||
``` python
|
||||
In [126]: s.resample('D').mean()
|
||||
Out[126]:
|
||||
1 days 11.5
|
||||
2 days 35.5
|
||||
3 days 59.5
|
||||
4 days 83.5
|
||||
5 days 97.5
|
||||
Freq: D, dtype: float64
|
||||
```
|
||||
Reference in New Issue
Block a user