I was working with the Crunchbase dataset. I have an entry of Harvard University which was founded in 1636. This entry is giving me an error when I am trying to convert string to DateTime.
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1636-09-08 00:00:00
I found out that pandas support timestamp from 1677
>>> pd.Timestamp.min
Timestamp('1677-09-21 00:12:43.145225')
I checked out some solutions like one suggesting using errors='coerce' but dropping this entry/ making it null is not an option.
Can you please suggest a way to handle this issue?
As mentioned in comments by Henry, there is limitation of pandas timestamps because of its representation in float64, you could probably work around it by parsing the date-time using datetime library when needed, otherwise letting it stay as string or convert it to an integer
Scenario 1: If you plan on showing this value only when you print it
datetime_object = datetime.strptime('1636-09-08 00:00:00', '%Y-%m-%d %H:%M:%S')
Scenario 2: If you want to use it as a date column to retain information in the dataframe, you could additionally
datetime_object.strftime("%Y%m%d%H%M%S")
using it on a column in a pandas dataframe would yield this
df=pd.DataFrame([['1636-09-08 00:00:00'],['1635-09-09 00:00:00']], columns=['dates'])
df['str_date']=df['dates'].apply(lambda x:datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))
df.head()
dates
str_date
0
1636-09-08 00:00:00
1636-09-08 00:00:00
1
1635-09-09 00:00:00
1635-09-09 00:00:00
pandas treats this column as a object column, but when you access it, it is a datetime column
df['str_date'][0]
>>datetime.datetime(1636, 9, 8, 0, 0)
also, adding this for the sake of completeness: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-oob
I have a pandas column called 'date'
which has values and type like 2014-07-30 00:00:00 <class 'datetime.datetime'>.
I want to remove the time from the date.The end result being `2014-07-30' in datetime.datetime format.
I tried a bunch of solutions like-
df['PSG Date '] = df['PSG Date '].dt.date
but its giving me error-
AttributeError: Can only use .dt accessor with datetimelike values
I believe need first to_datetime and for dates use dt.date:
df['PSG Date '] = pd.to_datetime(df['PSG Date '], errors='coerce').dt.date
If want datetimes with no times use dt.floor:
df['PSG Date '] = pd.to_datetime(df['PSG Date '], errors='coerce').dt.floor('d')
First, you should begin with a datetime series; if you don't have one, use pd.to_datetime to force this conversion. This will permit vectorised computations:
df = pd.DataFrame({'col': ['2014-07-30 12:19:22', '2014-07-30 05:52:05',
'2014-07-30 20:15:00']})
df['col'] = pd.to_datetime(df['col'])
Next, note you cannot remove time from a datetime series in Pandas. By definition, a datetime series will include both "date" and "time" components.
Normalize time
You can use pd.Series.dt.floor or pd.Series.dt.normalize to reset the time component to 00:00:00:
df['col_floored'] = df['col'].dt.floor('d')
df['col_normalized'] = df['col'].dt.normalize()
print(df['col_floored'].iloc[0]) # 2014-07-30 00:00:00
print(df['col_normalized'].iloc[0]) # 2014-07-30 00:00:00
Convert to datetime.date pointers
You can convert your datetime series to an object series, consisting of datetime.date objects representing dates:
df['col_date'] = df['col'].dt.date
print(df['col_date'].iloc[0]) # 2014-07-30
Since these are not held in a contiguous memory block, operations on df['col_date'] will not be vectorised.
How to check the difference
It's useful to check the dtype for the series we have derived. Notice the one option which "removes" time involves converting your series to object.
Computations will be non-vectorised with such a series, since it consists of pointers to datetime.date objects instead of data in a contiguous memory block.
print(df.dtypes)
col datetime64[ns]
col_date object
col_floored datetime64[ns]
col_normalized datetime64[ns]
You can convert a datetime.datetime to date time.date by calling the .date() method of the object. eg
current_datetime = datetime.datetime.now()
date_only = current_datetime.date()
I have a column of the below type in dataframe.
MAT_DATE object
The values in this column are something like
42872
42741
...
...
How can I convert them to datetime ?
These are essentially future dates.
Using pd.to_datetime() converts them to year 1970
df['MAT_DATE1'] = pd.to_datetime(df['MAT_DATE'], errors='coerce')
If I use the excel to change to short date, it does well to convert the dates.
However I want to use it on the dataframe directly.
Using the origin parameter of the pandas.to_datetime that you are interested in and based on the days as the delta as #Wen suggested, this might work:
pd.to_datetime(df['MAT_DATE'],errors='coerce',unit='d',origin='1900-01-01')
The number is days delta to offset date , the default for excel is offset is 1990-01-01
s=pd.Series([42872,42741])
pd.TimedeltaIndex(s,unit='d')+pd.to_datetime('1900-01-01')
Out[88]: DatetimeIndex(['2017-05-19', '2017-01-08'], dtype='datetime64[ns]', freq=None)
I am trying to get the time index of a dataframe as a series, but it appears to be dropping the timezone when I call the method to_series. Below is an example. Is this a bug or am I doing something incorrectly?
rows = 50
df = pd.DataFrame(np.random.randn(rows,2), columns=list('AB'), index=pd.date_range('1/1/2000', periods=rows, freq='1H', tz=pytz.UTC))
print df.index[-1]
# 2000-01-03 01:00:00+00:00
print df.index.to_series()[-1]
# 2000-01-03 01:00:00
print df.index[-1].tzinfo
# UTC
print df.index.to_series()[-1].tzinfo
#None
No, you are not doing something wrong, and neither it is a bug.
It is a currently known limitation of pandas/numpy: timezones aware datetime data are only supported in the index. In a series, the data are stored as numpy datetime64 types, which does not support timezones. There is an open issue for adding this timezone feature also to series: https://github.com/pydata/pandas/issues/8260
A workaround is to store your data as object dtype instead of datetime64 (then they will be stored as Timestamp objects, a subclass of datetime.datetime). This will enable you to keep the timezone info.
to_series has a keep_tz keyword argument to obtain this (see docstring):
In [34]: df = df.tz_convert('US/Eastern')
In [35]: df.index.to_series()[-1]
Out[35]: Timestamp('2000-01-03 01:00:00')
In [36]: df.index.to_series(keep_tz=True)[-1]
Out[36]: Timestamp('2000-01-02 20:00:00-0500', tz='US/Eastern', offset='H')
You can use the function tz_localize to make a Timestamp or DateTimeIndex timezone aware, but how can you do the opposite: how can you convert a timezone aware Timestamp to a naive one, while preserving its timezone?
An example:
In [82]: t = pd.date_range(start="2013-05-18 12:00:00", periods=10, freq='s', tz="Europe/Brussels")
In [83]: t
Out[83]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-05-18 12:00:00, ..., 2013-05-18 12:00:09]
Length: 10, Freq: S, Timezone: Europe/Brussels
I could remove the timezone by setting it to None, but then the result is converted to UTC (12 o'clock became 10):
In [86]: t.tz = None
In [87]: t
Out[87]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-05-18 10:00:00, ..., 2013-05-18 10:00:09]
Length: 10, Freq: S, Timezone: None
Is there another way I can convert a DateTimeIndex to timezone naive, but while preserving the timezone it was set in?
Some context on the reason I am asking this: I want to work with timezone naive timeseries (to avoid the extra hassle with timezones, and I do not need them for the case I am working on).
But for some reason, I have to deal with a timezone-aware timeseries in my local timezone (Europe/Brussels). As all my other data are timezone naive (but represented in my local timezone), I want to convert this timeseries to naive to further work with it, but it also has to be represented in my local timezone (so just remove the timezone info, without converting the user-visible time to UTC).
I know the time is actually internal stored as UTC and only converted to another timezone when you represent it, so there has to be some kind of conversion when I want to "delocalize" it. For example, with the python datetime module you can "remove" the timezone like this:
In [119]: d = pd.Timestamp("2013-05-18 12:00:00", tz="Europe/Brussels")
In [120]: d
Out[120]: <Timestamp: 2013-05-18 12:00:00+0200 CEST, tz=Europe/Brussels>
In [121]: d.replace(tzinfo=None)
Out[121]: <Timestamp: 2013-05-18 12:00:00>
So, based on this, I could do the following, but I suppose this will not be very efficient when working with a larger timeseries:
In [124]: t
Out[124]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-05-18 12:00:00, ..., 2013-05-18 12:00:09]
Length: 10, Freq: S, Timezone: Europe/Brussels
In [125]: pd.DatetimeIndex([i.replace(tzinfo=None) for i in t])
Out[125]:
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-05-18 12:00:00, ..., 2013-05-18 12:00:09]
Length: 10, Freq: None, Timezone: None
To answer my own question, this functionality has been added to pandas in the meantime. Starting from pandas 0.15.0, you can use tz_localize(None) to remove the timezone resulting in local time.
See the whatsnew entry: http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#timezone-handling-improvements
So with my example from above:
In [4]: t = pd.date_range(start="2013-05-18 12:00:00", periods=2, freq='H',
tz= "Europe/Brussels")
In [5]: t
Out[5]: DatetimeIndex(['2013-05-18 12:00:00+02:00', '2013-05-18 13:00:00+02:00'],
dtype='datetime64[ns, Europe/Brussels]', freq='H')
using tz_localize(None) removes the timezone information resulting in naive local time:
In [6]: t.tz_localize(None)
Out[6]: DatetimeIndex(['2013-05-18 12:00:00', '2013-05-18 13:00:00'],
dtype='datetime64[ns]', freq='H')
Further, you can also use tz_convert(None) to remove the timezone information but converting to UTC, so yielding naive UTC time:
In [7]: t.tz_convert(None)
Out[7]: DatetimeIndex(['2013-05-18 10:00:00', '2013-05-18 11:00:00'],
dtype='datetime64[ns]', freq='H')
This is much more performant than the datetime.replace solution:
In [31]: t = pd.date_range(start="2013-05-18 12:00:00", periods=10000, freq='H',
tz="Europe/Brussels")
In [32]: %timeit t.tz_localize(None)
1000 loops, best of 3: 233 µs per loop
In [33]: %timeit pd.DatetimeIndex([i.replace(tzinfo=None) for i in t])
10 loops, best of 3: 99.7 ms per loop
Because I always struggle to remember, a quick summary of what each of these do:
>>> pd.Timestamp.now() # naive local time
Timestamp('2019-10-07 10:30:19.428748')
>>> pd.Timestamp.utcnow() # tz aware UTC
Timestamp('2019-10-07 08:30:19.428748+0000', tz='UTC')
>>> pd.Timestamp.now(tz='Europe/Brussels') # tz aware local time
Timestamp('2019-10-07 10:30:19.428748+0200', tz='Europe/Brussels')
>>> pd.Timestamp.now(tz='Europe/Brussels').tz_localize(None) # naive local time
Timestamp('2019-10-07 10:30:19.428748')
>>> pd.Timestamp.now(tz='Europe/Brussels').tz_convert(None) # naive UTC
Timestamp('2019-10-07 08:30:19.428748')
>>> pd.Timestamp.utcnow().tz_localize(None) # naive UTC
Timestamp('2019-10-07 08:30:19.428748')
>>> pd.Timestamp.utcnow().tz_convert(None) # naive UTC
Timestamp('2019-10-07 08:30:19.428748')
I think you can't achieve what you want in a more efficient manner than you proposed.
The underlying problem is that the timestamps (as you seem aware) are made up of two parts. The data that represents the UTC time, and the timezone, tz_info. The timezone information is used only for display purposes when printing the timezone to the screen. At display time, the data is offset appropriately and +01:00 (or similar) is added to the string. Stripping off the tz_info value (using tz_convert(tz=None)) doesn't doesn't actually change the data that represents the naive part of the timestamp.
So, the only way to do what you want is to modify the underlying data (pandas doesn't allow this... DatetimeIndex are immutable -- see the help on DatetimeIndex), or to create a new set of timestamp objects and wrap them in a new DatetimeIndex. Your solution does the latter:
pd.DatetimeIndex([i.replace(tzinfo=None) for i in t])
For reference, here is the replace method of Timestamp (see tslib.pyx):
def replace(self, **kwds):
return Timestamp(datetime.replace(self, **kwds),
offset=self.offset)
You can refer to the docs on datetime.datetime to see that datetime.datetime.replace also creates a new object.
If you can, your best bet for efficiency is to modify the source of the data so that it (incorrectly) reports the timestamps without their timezone. You mentioned:
I want to work with timezone naive timeseries (to avoid the extra hassle with timezones, and I do not need them for the case I am working on)
I'd be curious what extra hassle you are referring to. I recommend as a general rule for all software development, keep your timestamp 'naive values' in UTC. There is little worse than looking at two different int64 values wondering which timezone they belong to. If you always, always, always use UTC for the internal storage, then you will avoid countless headaches. My mantra is Timezones are for human I/O only.
The accepted solution does not work when there are multiple different timezones in a Series. It throws ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
The solution is to use the apply method.
Please see the examples below:
# Let's have a series `a` with different multiple timezones.
> a
0 2019-10-04 16:30:00+02:00
1 2019-10-07 16:00:00-04:00
2 2019-09-24 08:30:00-07:00
Name: localized, dtype: object
> a.iloc[0]
Timestamp('2019-10-04 16:30:00+0200', tz='Europe/Amsterdam')
# trying the accepted solution
> a.dt.tz_localize(None)
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
# Make it tz-naive. This is the solution:
> a.apply(lambda x:x.tz_localize(None))
0 2019-10-04 16:30:00
1 2019-10-07 16:00:00
2 2019-09-24 08:30:00
Name: localized, dtype: datetime64[ns]
# a.tz_convert() also does not work with multiple timezones, but this works:
> a.apply(lambda x:x.tz_convert('America/Los_Angeles'))
0 2019-10-04 07:30:00-07:00
1 2019-10-07 13:00:00-07:00
2 2019-09-24 08:30:00-07:00
Name: localized, dtype: datetime64[ns, America/Los_Angeles]
Setting the tz attribute of the index explicitly seems to work:
ts_utc = ts.tz_convert("UTC")
ts_utc.index.tz = None
Late contribution but just came across something similar in Python datetime and pandas give different timestamps for the same date.
If you have timezone-aware datetime in pandas, technically, tz_localize(None) changes the POSIX timestamp (that is used internally) as if the local time from the timestamp was UTC. Local in this context means local in the specified timezone. Ex:
import pandas as pd
t = pd.date_range(start="2013-05-18 12:00:00", periods=2, freq='H', tz="US/Central")
# DatetimeIndex(['2013-05-18 12:00:00-05:00', '2013-05-18 13:00:00-05:00'], dtype='datetime64[ns, US/Central]', freq='H')
t_loc = t.tz_localize(None)
# DatetimeIndex(['2013-05-18 12:00:00', '2013-05-18 13:00:00'], dtype='datetime64[ns]', freq='H')
# offset in seconds according to timezone:
(t_loc.values-t.values)//1e9
# array([-18000, -18000], dtype='timedelta64[ns]')
Note that this will leave you with strange things during DST transitions, e.g.
t = pd.date_range(start="2020-03-08 01:00:00", periods=2, freq='H', tz="US/Central")
(t.values[1]-t.values[0])//1e9
# numpy.timedelta64(3600,'ns')
t_loc = t.tz_localize(None)
(t_loc.values[1]-t_loc.values[0])//1e9
# numpy.timedelta64(7200,'ns')
In contrast, tz_convert(None) does not modify the internal timestamp, it just removes the tzinfo.
t_utc = t.tz_convert(None)
(t_utc.values-t.values)//1e9
# array([0, 0], dtype='timedelta64[ns]')
My bottom line would be: stick with timezone-aware datetime if you can or only use t.tz_convert(None) which doesn't modify the underlying POSIX timestamp. Just keep in mind that you're practically working with UTC then.
(Python 3.8.2 x64 on Windows 10, pandas v1.0.5.)
Building on D.A.'s suggestion that "the only way to do what you want is to modify the underlying data" and using numpy to modify the underlying data...
This works for me, and is pretty fast:
def tz_to_naive(datetime_index):
"""Converts a tz-aware DatetimeIndex into a tz-naive DatetimeIndex,
effectively baking the timezone into the internal representation.
Parameters
----------
datetime_index : pandas.DatetimeIndex, tz-aware
Returns
-------
pandas.DatetimeIndex, tz-naive
"""
# Calculate timezone offset relative to UTC
timestamp = datetime_index[0]
tz_offset = (timestamp.replace(tzinfo=None) -
timestamp.tz_convert('UTC').replace(tzinfo=None))
tz_offset_td64 = np.timedelta64(tz_offset)
# Now convert to naive DatetimeIndex
return pd.DatetimeIndex(datetime_index.values + tz_offset_td64)
The most important thing is add tzinfo when you define a datetime object.
from datetime import datetime, timezone
from tzinfo_examples import HOUR, Eastern
u0 = datetime(2016, 3, 13, 5, tzinfo=timezone.utc)
for i in range(4):
u = u0 + i*HOUR
t = u.astimezone(Eastern)
print(u.time(), 'UTC =', t.time(), t.tzname())
How I handled this problem with a 15-min frequency datetimeindex in europe.
If you are in the situation where you have a timezone aware (Europe/Amsterdam in my case) index and want to convert it into a timezone naive index by transforming everything into local time, you will have dst problems, namely
there will be 1 hour missing on the last sunday of march (when europe switches to summer time)
there will be 1 hour duplicate on the last sunday of october (when europe switches to summer time)
Here is how you can handle it:
# make index tz naive
df.index = df.index.tz_localize(None)
# handle dst
if df.index[0].month == 3:
# last sunday of march, one hour is lost
df = df.resample("15min").pad()
if df.index[0].month == 10:
# in october, one hour is added
df = df[~df.index.duplicated(keep='last')]
Note: in my case, I run the above code on a df that contains only a single month, hence I do df.index[0].month to find out the month. If yours contains more months, you should probably index it differently to know when to do DST.
It consists of resampling from the last valid value in march, to avoid losing the 1 hour (in my case, all my data is in 15 min intervals, hence i resample like that. Resample for whatever your interval is). And for october, I drop duplicates.