python - 将日期时间格式转换为 Unix 时间戳 Pandas

Question

我正在尝试将正常的日期时间转换为 pandas 中的 unix 时间戳。在寻找周围的一些样本时，我只能找到一个例子 here但我无法在我的上下文中使用。数据集没有标题，最后的 2 列 需要转换 UNIX 时间戳 并生成新的输出以及前 3 列。

1466f7b93975983f6e292a8a4faaa4b2,1619b4d0d283c0dddb17d24a359a3b49,36db348cde68592a31d502366fc52932,2010-03-08 17:09:00.472544,2010-03-12 16:09:58.122987
367c13356a5d22158f0ae56977134e2c,eedb7d0714796b64767a8710ea3844a7,925476200929fd346ea312cbe9a046fe,2010-03-08 17:08:29.174236,2010-03-12 16:09:58.122987
edf6b1e4f67b0e8a5080d299c9f9aeb2,7cb7681b90388a7522d0f06578591567,ffde0649a72ded8e33522c503a4d5cbe,2010-03-08 17:08:22.030524,2010-03-12 16:09:58.122987
6bb2ad8bc78897e99072d4d76cf0f19c,b644947ac4db03bdb518cfa71765f8c8,eb25089d396c06255cbb5f1bad801cc4,2010-03-08 17:07:55.819137,2010-03-12 16:09:58.122987

输入文件有几百万行，我在这里发布的只有几行。任何建议都是有值(value)的。提前致谢

Answer 1

可以先read_csv然后通过 astype 将最后两列转换为 np.int64除以 10**9。写入文件使用 to_csv :

import pandas as pd
import numpy as np
import io

temp=u"""1466f7b93975983f6e292a8a4faaa4b2,1619b4d0d283c0dddb17d24a359a3b49,36db348cde68592a31d502366fc52932,2010-03-08 17:09:00.472544,2010-03-12 16:09:58.122987
367c13356a5d22158f0ae56977134e2c,eedb7d0714796b64767a8710ea3844a7,925476200929fd346ea312cbe9a046fe,2010-03-08 17:08:29.174236,2010-03-12 16:09:58.122987
edf6b1e4f67b0e8a5080d299c9f9aeb2,7cb7681b90388a7522d0f06578591567,ffde0649a72ded8e33522c503a4d5cbe,2010-03-08 17:08:22.030524,2010-03-12 16:09:58.122987
6bb2ad8bc78897e99072d4d76cf0f19c,b644947ac4db03bdb518cfa71765f8c8,eb25089d396c06255cbb5f1bad801cc4,2010-03-08 17:07:55.819137,2010-03-12 16:09:58.122987"""

#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp), 
                 header=None, #no header in csv
                 names=['a','b','c','d', 'e'], #set custom column names
                 parse_dates=['d','e']) #parse columns d, e to datetime
print df
                                   a                                 b  \
0  1466f7b93975983f6e292a8a4faaa4b2  1619b4d0d283c0dddb17d24a359a3b49   
1  367c13356a5d22158f0ae56977134e2c  eedb7d0714796b64767a8710ea3844a7   
2  edf6b1e4f67b0e8a5080d299c9f9aeb2  7cb7681b90388a7522d0f06578591567   
3  6bb2ad8bc78897e99072d4d76cf0f19c  b644947ac4db03bdb518cfa71765f8c8   

                                  c                          d  \
0  36db348cde68592a31d502366fc52932 2010-03-08 17:09:00.472544   
1  925476200929fd346ea312cbe9a046fe 2010-03-08 17:08:29.174236   
2  ffde0649a72ded8e33522c503a4d5cbe 2010-03-08 17:08:22.030524   
3  eb25089d396c06255cbb5f1bad801cc4 2010-03-08 17:07:55.819137   

                           e  
0 2010-03-12 16:09:58.122987  
1 2010-03-12 16:09:58.122987  
2 2010-03-12 16:09:58.122987  
3 2010-03-12 16:09:58.122987  


df['d'] = df['d'].astype(np.int64) // 10**9
df['e'] = df['e'].astype(np.int64) // 10**9

print df
                                  a                                 b  \
0  1466f7b93975983f6e292a8a4faaa4b2  1619b4d0d283c0dddb17d24a359a3b49   
1  367c13356a5d22158f0ae56977134e2c  eedb7d0714796b64767a8710ea3844a7   
2  edf6b1e4f67b0e8a5080d299c9f9aeb2  7cb7681b90388a7522d0f06578591567   
3  6bb2ad8bc78897e99072d4d76cf0f19c  b644947ac4db03bdb518cfa71765f8c8   

                                  c           d           e  
0  36db348cde68592a31d502366fc52932  1268068140  1268410198  
1  925476200929fd346ea312cbe9a046fe  1268068109  1268410198  
2  ffde0649a72ded8e33522c503a4d5cbe  1268068102  1268410198  
3  eb25089d396c06255cbb5f1bad801cc4  1268068075  1268410198  

df.to_csv('filename', header=None, index=False)