I'm currently having an issue with Python. I have a Pandas DataFrame and one of the columns is a string with a date. The format is :
"%Y-%m-%d %H:%m:00.000". For example : "2011-04-24 01:30:00.000"
I need to convert the entire column to integers. I tried to run this code, but it is extremely slow and I have a few million rows.
for i in range(calls.shape[0]):
calls['dateint'][i] = int(time.mktime(time.strptime(calls.DATE[i], "%Y-%m-%d %H:%M:00.000")))
Do you guys know how to convert the whole column to epoch time?
convert the string to a datetime using to_datetime and then subtract datetime 1970-1-1 and call dt.total_seconds():
In [2]:
import pandas as pd
import datetime as dt
df = pd.DataFrame({'date':['2011-04-24 01:30:00.000']})
df
Out[2]:
date
0 2011-04-24 01:30:00.000
In [3]:
df['date'] = pd.to_datetime(df['date'])
df
Out[3]:
date
0 2011-04-24 01:30:00
In [6]:
(df['date'] - dt.datetime(1970,1,1)).dt.total_seconds()
Out[6]:
0 1303608600
Name: date, dtype: float64
You can see that converting this value back yields the same time:
In [8]:
pd.to_datetime(1303608600, unit='s')
Out[8]:
Timestamp('2011-04-24 01:30:00')
So you can either add a new column or overwrite:
In [9]:
df['epoch'] = (df['date'] - dt.datetime(1970,1,1)).dt.total_seconds()
df
Out[9]:
date epoch
0 2011-04-24 01:30:00 1303608600
EDIT
better method as suggested by @Jeff:
In [3]:
df['date'].astype('int64')//1e9
Out[3]:
0 1303608600
Name: date, dtype: float64
In [4]:
%timeit (df['date'] - dt.datetime(1970,1,1)).dt.total_seconds()
%timeit df['date'].astype('int64')//1e9
100 loops, best of 3: 1.72 ms per loop
1000 loops, best of 3: 275 µs per loop
You can also see that it is significantly faster