Python pandas 通过 dt 访问器有效地将日期时间转换为时间戳 [英] Python pandas convert datetime to timestamp effectively through dt accessor

问题描述

我有一个包含一些(数亿)行的数据框.我想有效地将​​日期时间转换为时间戳.我该怎么做?

我的示例df:

df = pd.DataFrame(index=pd.DatetimeIndex(start=dt.datetime(2016,1,1,0,0,1),结束=dt.datetime(2016,1,2,0,0,1), 频率='H')).reset_index().rename(columns={'index':'datetime'})df.head()约会时间0 2016-01-01 00:00:011 2016-01-01 01:00:012 2016-01-01 02:00:013 2016-01-01 03:00:014 2016-01-01 04:00:01

现在我使用 .apply() 将日期时间逐个转换为时间戳值，但如果我有一些(数亿)行，则需要很长时间(几个小时):

df['ts'] = df[['datetime']].apply(lambda x: x[0].timestamp(), axis=1).astype(int)df.head()日期时间 ts0 2016-01-01 00:00:01 14516028011 2016-01-01 01:00:01 14516064012 2016-01-01 02:00:01 14516100013 2016-01-01 03:00:01 14516136014 2016-01-01 04:00:01 1451617201

上面的结果就是我想要的.

如果我尝试使用 pandas.Series 的 .dt 访问器，则会收到错误消息:

df['ts'] = df['datetime'].dt.timestamp

<块引用>

AttributeError: 'DatetimeProperties' 对象没有属性'时间戳'

如果我尝试创建例如.使用 .dt 访问器的日期时间的日期部分比使用 .apply() 快得多:

df['date'] = df['datetime'].dt.datedf.head()日期时间 ts 日期0 2016-01-01 00:00:01 1451602801 2016-01-011 2016-01-01 01:00:01 1451606401 2016-01-012 2016-01-01 02:00:01 1451610001 2016-01-013 2016-01-01 03:00:01 1451613601 2016-01-014 2016-01-01 04:00:01 1451617201 2016-01-01

我想要类似时间戳的东西...

但我不太了解官方文档:它谈到转换为时间戳" 但我没有看到任何时间戳；它只是谈论使用 pd.to_datetime() 转换为日期时间，而不是时间戳...

pandas.Timestamp 构造函数也不起作用(返回以下错误):

df['ts2'] = pd.Timestamp(df['datetime'])

<块引用>

TypeError:无法将输入转换为时间戳

pandas.Series.to_timestamp 也做出了我想要的完全不同的东西:

df['ts3'] = df['datetime'].to_timestampdf.head()日期时间 ts ts30 2016-01-01 00:00:01 1451602801 <绑定方法 Series.to_timestamp of 0 2016...1 2016-01-01 01:00:01 1451606401 <绑定方法 Series.to_timestamp of 0 2016...2 2016-01-01 02:00:01 1451610001 <绑定方法 Series.to_timestamp of 0 2016...3 2016-01-01 03:00:01 1451613601 <绑定方法 Series.to_timestamp of 0 2016...4 2016-01-01 04:00:01 1451617201 <绑定方法 Series.to_timestamp of 0 2016...

谢谢！！

解决方案

我觉得你需要先转换成 numpy array by values 并转换为 int64 - 输出在 ns，所以需要除以10 ** 9:

df['ts'] = df.datetime.values.astype(np.int64)//10 ** 9打印 (df)日期时间 ts0 2016-01-01 00:00:01 14516064011 2016-01-01 01:00:01 14516100012 2016-01-01 02:00:01 14516136013 2016-01-01 03:00:01 14516172014 2016-01-01 04:00:01 14516208015 2016-01-01 05:00:01 14516244016 2016-01-01 06:00:01 14516280017 2016-01-01 07:00:01 14516316018 2016-01-01 08:00:01 14516352019 2016-01-01 09:00:01 145163880110 2016-01-01 10:00:01 145164240111 2016-01-01 11:00:01 145164600112 2016-01-01 12:00:01 145164960113 2016-01-01 13:00:01 145165320114 2016-01-01 14:00:01 145165680115 2016-01-01 15:00:01 145166040116 2016-01-01 16:00:01 145166400117 2016-01-01 17:00:01 145166760118 2016-01-01 18:00:01 145167120119 2016-01-01 19:00:01 145167480120 2016-01-01 20:00:01 145167840121 2016-01-01 21:00:01 145168200122 2016-01-01 22:00:01 145168560123 2016-01-01 23:00:01 145168920124 2016-01-02 00:00:01 1451692801

to_timestamp 用于将 从周期索引转换为日期时间索引.

I have a dataframe with some (hundreds of) million of rows. And I want to convert datetime to timestamp effectively. How can I do it?

My sample df:

df = pd.DataFrame(index=pd.DatetimeIndex(start=dt.datetime(2016,1,1,0,0,1),
    end=dt.datetime(2016,1,2,0,0,1), freq='H'))
    .reset_index().rename(columns={'index':'datetime'})

df.head()

             datetime
0 2016-01-01 00:00:01
1 2016-01-01 01:00:01
2 2016-01-01 02:00:01
3 2016-01-01 03:00:01
4 2016-01-01 04:00:01

Now I convert datetime to timestamp value-by-value with .apply() but it takes a very long time (some hours) if I have some (hundreds of) million rows:

df['ts'] = df[['datetime']].apply(lambda x: x[0].timestamp(), axis=1).astype(int)

df.head()

             datetime          ts
0 2016-01-01 00:00:01  1451602801
1 2016-01-01 01:00:01  1451606401
2 2016-01-01 02:00:01  1451610001
3 2016-01-01 03:00:01  1451613601
4 2016-01-01 04:00:01  1451617201

The above result is what I want.

If I try to use the .dt accessor of pandas.Series then I get error message:

df['ts'] = df['datetime'].dt.timestamp

AttributeError: 'DatetimeProperties' object has no attribute 'timestamp'

If I try to create eg. the date parts of datetimes with the .dt accessor then it is much more faster then using .apply():

df['date'] = df['datetime'].dt.date

df.head()

             datetime          ts        date
0 2016-01-01 00:00:01  1451602801  2016-01-01
1 2016-01-01 01:00:01  1451606401  2016-01-01
2 2016-01-01 02:00:01  1451610001  2016-01-01
3 2016-01-01 03:00:01  1451613601  2016-01-01
4 2016-01-01 04:00:01  1451617201  2016-01-01

I want something similar with timestamps...

But I don't really understand the official documentation: it talks about "Converting to Timestamps" but I don't see any timestamps there; it just talks about converting to datetime with pd.to_datetime() but not to timestamp...

pandas.Timestamp constructor also doesn't work (returns with the below error):

df['ts2'] = pd.Timestamp(df['datetime'])

TypeError: Cannot convert input to Timestamp

pandas.Series.to_timestamp also makes something totally different that I want:

df['ts3'] = df['datetime'].to_timestamp

df.head()

             datetime          ts                                                ts3
0 2016-01-01 00:00:01  1451602801  <bound method Series.to_timestamp of 0    2016...
1 2016-01-01 01:00:01  1451606401  <bound method Series.to_timestamp of 0    2016...
2 2016-01-01 02:00:01  1451610001  <bound method Series.to_timestamp of 0    2016...
3 2016-01-01 03:00:01  1451613601  <bound method Series.to_timestamp of 0    2016...
4 2016-01-01 04:00:01  1451617201  <bound method Series.to_timestamp of 0    2016...

Thank you!!

解决方案

I think you need convert first to numpy array by values and cast to int64 - output is in ns, so need divide by 10 ** 9:

df['ts'] = df.datetime.values.astype(np.int64) // 10 ** 9
print (df)
              datetime          ts
0  2016-01-01 00:00:01  1451606401
1  2016-01-01 01:00:01  1451610001
2  2016-01-01 02:00:01  1451613601
3  2016-01-01 03:00:01  1451617201
4  2016-01-01 04:00:01  1451620801
5  2016-01-01 05:00:01  1451624401
6  2016-01-01 06:00:01  1451628001
7  2016-01-01 07:00:01  1451631601
8  2016-01-01 08:00:01  1451635201
9  2016-01-01 09:00:01  1451638801
10 2016-01-01 10:00:01  1451642401
11 2016-01-01 11:00:01  1451646001
12 2016-01-01 12:00:01  1451649601
13 2016-01-01 13:00:01  1451653201
14 2016-01-01 14:00:01  1451656801
15 2016-01-01 15:00:01  1451660401
16 2016-01-01 16:00:01  1451664001
17 2016-01-01 17:00:01  1451667601
18 2016-01-01 18:00:01  1451671201
19 2016-01-01 19:00:01  1451674801
20 2016-01-01 20:00:01  1451678401
21 2016-01-01 21:00:01  1451682001
22 2016-01-01 22:00:01  1451685601
23 2016-01-01 23:00:01  1451689201
24 2016-01-02 00:00:01  1451692801

to_timestamp is used for converting from period to datetime index.

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