In [1]:
import pandas as pd
dates = ['2017-01-05', 'Jan 5, 2017', '01/05/2017', '2017.01.05', '2017/01/05','20170105']
pd.to_datetime(dates)
Out[1]:
DatetimeIndex(['2017-01-05', '2017-01-05', '2017-01-05', '2017-01-05',
'2017-01-05', '2017-01-05'],
dtype='datetime64[ns]', freq=None)
In [2]:
dt = ['2017-01-05 2:30:00 PM', 'Jan 5, 2017 14:30:00', '01/05/2016', '2017.01.05', '2017/01/05','20170105']
pd.to_datetime(dt)
Out[2]:
DatetimeIndex(['2017-01-05 14:30:00', '2017-01-05 14:30:00',
'2016-01-05 00:00:00', '2017-01-05 00:00:00',
'2017-01-05 00:00:00', '2017-01-05 00:00:00'],
dtype='datetime64[ns]', freq=None)
European style dates with day first
In [6]:
pd.to_datetime('01-07-2019')
Out[6]:
Timestamp('2019-01-07 00:00:00')
In [8]:
pd.to_datetime('01-07-2019', dayfirst=True)
Out[8]:
Timestamp('2019-07-01 00:00:00')
In [9]:
pd.to_datetime('2019$01$07', format='%Y$%m$%d')
Out[9]:
Timestamp('2019-01-07 00:00:00')
In [10]:
pd.to_datetime('2019#01#07', format='%Y#%m#%d')
Out[10]:
Timestamp('2019-01-07 00:00:00')
In [13]:
# Leave the invalid values (‘abc’)
pd.to_datetime(['2017-01-05', 'Jan 6, 2017', 'abc'], errors='ignore')
Out[13]:
Index(['2017-01-05', 'Jan 6, 2017', 'abc'], dtype='object')
In [14]:
# Assign NaT type for invalid values
pd.to_datetime(['2017-01-05', 'Jan 6, 2017', 'abc'], errors='coerce')
Out[14]:
DatetimeIndex(['2017-01-05', '2017-01-06', 'NaT'], dtype='datetime64[ns]', freq=None)
Epoch or Unix time means number of seconds that have passed since Jan 1, 1970 00:00:00 UTC time
In [21]:
# Search Online
current_epoch = 1563979818
pd.to_datetime(current_epoch, unit='s')
Out[21]:
Timestamp('2019-07-24 14:50:18')
In [22]:
pd.to_datetime(current_epoch*1000, unit='ms')
Out[22]:
Timestamp('2019-07-24 14:50:18')
In [25]:
# change format from tuple to list, if not can't convert it back
t = pd.to_datetime([current_epoch], unit='s')
t
Out[25]:
DatetimeIndex(['2019-07-24 14:50:18'], dtype='datetime64[ns]', freq=None)
Out[26]:
array([1563979818000000000], dtype=int64)
