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How to convert a float to a Parquet TIMESTAMP Logical Type?

Let say I have a pyarrow table with a column Timestamp containing float64.
These floats are actually timestamps experessed in s.
For instance:
import pyarrow as pa
my_table = pa.table({'timestamp': pa.array([1600419000.477,1600419001.027])})
I read about Parquet Logical Type from documentation.
Please, how can I convert these float values to the Logical Type TIMESTAMP?
I see no documentation about the way to do this.
Thank you for your help.
Have a good day,
Bests,

You will need to convert the floats into an actual timestamp type in pyarrow, and then it will automatically be written to a paruet logical timestamp type.
Using the pyarrow.compute module, this conversion can also be done in pyarrow (a bit less ergonomic as doing the conversion in pandas, but avoiding a conversion to pandas and back):
>>> import pyarrow.compute as pc
>>> arr = pa.array([1600419000.477,1600419001.027])
>>> pc.multiply(arr, pa.scalar(1000.)).cast("int64").cast(pa.timestamp('ms'))
<pyarrow.lib.TimestampArray object at 0x7fe5ec3df588>
[
2020-09-18 08:50:00.477,
2020-09-18 08:50:01.027
]

I don't think you'll be able to convert within arrow from floats to timestamp.
Arrow assumes timestamp are 64 bit integers of a given precision (ms, us, ns). In your case you have to multiply your seconds floats by the precision you want (1000 for ms), then convert to int64 and cast into timestamp.
Here's an example using pandas:
(
pa.array([1600419000.477,1600419001.027])
.to_pandas()
.mul(1000)
.astype('long')
.pipe(pa.Array.from_pandas)
.cast(pa.timestamp('ms'))
)
Which gives you:
<pyarrow.lib.TimestampArray object at 0x7fb5025b6a08>
[
2020-09-18 08:50:00.477,
2020-09-18 08:50:01.027
]

Object to time conversion in pandas derived column

HI need help with datetime,
I have extracted minutes second value in the form of mm:ss eg(23:50)
but after that now I need to convert the same in '%H:%M:%S' format but it is giving error as it is in type dtype('o'), used below code but it is giving error, what to do
df_raw['Time-only'] = pd.to_datetime(df_raw['time2'], format='%H:%M:%S').dt.time

First, to_datetime is used to convert a given string format into DateTime. For example, if I had a string '2018-04-10 12:37:51.252'
df_raw['time2'] = pd.to_datetime(df_raw['time2'], format='%Y-%m-%d %H:%M:%S.%f')
doing the above will simply change the data types. You can verify them by doing
df_raw.dtypes
Now, to answer your question I believe you are trying to convert timestamp mm:ss to hh:mm:ss? If not, can you be more specific?
df_raw['Time-only'] = list(df_raw['time2'].map(lambda f: datetime.strftime(f, "%H:%M:%S")))
This piece of code will change the format of your timestamp to 12:37:51.

I believe you need convert values to timedeltas for next vectorized processing like subtract or sum:
df_raw = pd.DataFrame({'time2':['23:50','15:23']})
df_raw['Time-only'] = pd.to_timedelta('00:' + df_raw['time2'])
print (df_raw)
time2 Time-only
0 23:50 0 days 00:23:50
1 15:23 0 days 00:15:23
If convert to times then is not possible use vectorized operations, because get object python time.

How to efficiently convert milliseconds offset to np.datetime64 in an ndarray of np.void

Okay, bear with me, I've only just started using NumPy.
I have a time series split over multiple files. Each file captures the data for a single day. The time field in each record is simply the of milliseconds since the start of that day
Now I am trying to read this data in using numpy, but I have trouble to convert the milliseconds to an np.datetime64.
What I have sofar:
t_base = np.datetime64(<some_date>)
dtype = np.dtype([
("t", "i4"),
...<other fields here>...
])
data = np.fromfile(filename, dtype)
This gives me a one-dimensional ndarray of np.void. So far so good.
What I tried:
for record in np.nditer(data, op_flags["readwrite"]):
record["t"] = t_base + np.timedelta64(int(record["t"]), "ms")
Unfortunately, this does not change the type. Instead it converts the constructed datetime64 back to int32, which is incorrect because int32 does not have the range to capture time in milliseconds. So how do I get this done in an efficient manner? Obviously, after the conversion I have no need for the millisecond offset anymore.
Or alternatively, is there a way with fromfile to read an int32 into a int64 type? Then I can use an int64 as timestamp instead of datetime64.

pandas out of bounds nanosecond timestamp after offset rollforward plus adding a month offset

I am confused how pandas blew out of bounds for datetime objects with these lines:
import pandas as pd
BOMoffset = pd.tseries.offsets.MonthBegin()
# here some code sets the all_treatments dataframe and the newrowix, micolix, mocolix counters
all_treatments.iloc[newrowix,micolix] = BOMoffset.rollforward(all_treatments.iloc[i,micolix] + pd.tseries.offsets.DateOffset(months = x))
all_treatments.iloc[newrowix,mocolix] = BOMoffset.rollforward(all_treatments.iloc[newrowix,micolix]+ pd.tseries.offsets.DateOffset(months = 1))
Here all_treatments.iloc[i,micolix] is a datetime set by pd.to_datetime(all_treatments['INDATUMA'], errors='coerce',format='%Y%m%d'), and INDATUMA is date information in the format 20070125.
This logic seems to work on mock data (no errors, dates make sense), so at the moment I cannot reproduce while it fails in my entire data with the following error:
pandas.tslib.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2262-05-01 00:00:00

Since pandas represents timestamps in nanosecond resolution, the timespan that can be represented using a 64-bit integer is limited to approximately 584 years
In [54]: pd.Timestamp.min
Out[54]: Timestamp('1677-09-22 00:12:43.145225')
In [55]: pd.Timestamp.max
Out[55]: Timestamp('2262-04-11 23:47:16.854775807')
And your value is out of this range 2262-05-01 00:00:00 and hence the outofbounds error
Straight out of: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timestamp-limitations
Workaround:
This will force the dates which are outside the bounds to NaT
pd.to_datetime(date_col_to_force, errors = 'coerce')

Setting the errors parameter in pd.to_datetime to 'coerce' causes replacement of out of bounds values with NaT. Quoting the docs:
If ‘coerce’, then invalid parsing will be set as NaT
E.g.:
datetime_variable = pd.to_datetime(datetime_variable, errors = 'coerce')
This does not fix the data (obviously), but still allows processing the non-NaT data points.

The reason you are seeing this error message
"OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 3000-12-23 00:00:00" is because pandas timestamp data type stores date in nanosecond resolution(from the docs).
Which means the date values have to be in the range
pd.Timestamp.min(1677-09-21 00:12:43.145225) and
pd.Timestamp.max(2262-04-11 23:47:16.854775807)
Even if you only want the date with resolution of seconds or microseconds, pandas will still store it internally in nanoseconds. There is no option in pandas to store a timestamp outside of the above mentioned range.
This is surprising because databases like sql server and libraries like numpy allows to store date beyond this range. Also maximum of 64 bits are used in most of the cases to store the date.
But here is the difference.
SQL server stores date in nanosecond resolution but only up to a accuracy of 100 ns(as opposed to 1 ns in pandas). Since the space is limited(64 bits), its a matter of range vs accuracy. With pandas timestamp we have higher accuracy but lower date range.
In case of numpy (pandas is built on top of numpy) datetime64 data type,
if the date falls in the above mentioned range you can store
it in nanoseconds which is similar to pandas.
OR you can give up the nanosecond resolution and go with
microseconds which will give you a much larger range. This is something that is missing in pandas timestamp type.
However if you choose to store in nanoseconds and the date is outside the range then numpy will automatically wrap around this date and you might get unexpected results (referenced below in the 4th solution).
np.datetime64("3000-06-19T08:17:14.073456178", dtype="datetime64[ns]")
> numpy.datetime64('1831-05-11T09:08:06.654352946')
Now with pandas we have below options,
import pandas as pd
data = {'Name': ['John', 'Sam'], 'dob': ['3000-06-19T08:17:14', '2000-06-19T21:17:14']}
my_df = pd.DataFrame(data)
1)If you are ok with losing the data which is out of range then simply use below param to convert out of range date to NaT(not a time).
my_df['dob'] = pd.to_datetime(my_df['dob'], errors = 'coerce')
2)If you dont want to lose the data then you can convert the values into a python datetime type. Here the column "dob" is of type pandas object but the individual value will be of type python datetime. However doing this we will lose the benefit of vectorized functions.
import datetime as dt
my_df['dob'] = my_df['dob'].apply(lambda x: dt.datetime.strptime(x,'%Y-%m-%dT%H:%M:%S') if type(x)==str else pd.NaT)
print(type(my_df.iloc[0][1]))
> <class 'datetime.datetime'>
3)Another option is to use numpy instead of pandas series if possible. In case of pandas dataframe, you can convert a series(or column in a df) to numpy array. Process the data separately and then join it back to the dataframe.
4)we can also use pandas timespans as suggested in the docs. Do checkout the difference b/w timestamp and period before using this data type. Date range and frequency here works similar to numpy(mentioned above in the numpy section).
my_df['dob'] = my_df['dob'].apply(lambda x: pd.Period(x, freq='ms'))

You can try with strptime() in datetime library along with lambda expression to convert text to date values in a series object:
Example:
df['F'].apply(lambda x: datetime.datetime.strptime(x, '%m/%d/%Y %I:%M:%S') if type(x)==str else np.NaN)

None of above are so good, because it will delete your data. But, you can only mantain and edit your conversion:
# convertin from epoch to datatime mantainig the nanoseconds timestamp
xbarout= pd.to_datetime(xbarout.iloc[:,0],unit='ns')

Convert numpy.datetime64 to string object in python

I am having trouble converting a python datetime64 object into a string. For example:
t = numpy.datetime64('2012-06-30T20:00:00.000000000-0400')
Into:
'2012.07.01' as a string. (note time difference)
I have already tried to convert the datetime64 object to a datetime long then to a string, but I seem to get this error:
dt = t.astype(datetime.datetime) #1341100800000000000L
time.ctime(dt)
ValueError: unconvertible time

Solution was:
import pandas as pd
ts = pd.to_datetime(str(date))
d = ts.strftime('%Y.%m.%d')

If you don't want to do that conversion gobbledygook and are ok with just one date format, this was the best solution for me
str(t)[:10]
Out[11]: '2012-07-01'
As noted this works for pandas too
df['d'].astype(str).str[:10]
df['d'].dt.strftime('%Y-%m-%d') # equivalent

You can use Numpy's datetime_as_string function. The unit='D' argument specifies the precision, in this case days.
>>> t = numpy.datetime64('2012-06-30T20:00:00.000000000-0400')
>>> numpy.datetime_as_string(t, unit='D')
'2012-07-01'

t.item().strftime('%Y.%m.%d')
.item() will cast numpy.datetime64 to datetime.datetime, no need to import anything.

There is a route without using pandas; but see caveat below.
Well, the t variable has a resolution of nanoseconds, which can be shown by inspection in python:
>>> numpy.dtype(t)
dtype('<M8[ns]')
This means that the integral value of this value is 10^9 times the UNIX timestamp. The value printed in your question gives that hint. Your best bet is to divide the integral value of t by 1 billion then you can use time.strftime:
>>> import time
>>> time.strftime("%Y.%m.%d", time.gmtime(t.astype(int)/1000000000))
2012.07.01
In using this, be conscious of two assumptions:
1) the datetime64 resolution is nanosecond
2) the time stored in datetime64 is in UTC
Side note 1: Interestingly, the numpy developers decided [1] that datetime64 object that has a resolution greater than microsecond will be cast to a long type, which explains why t.astype(datetime.datetime) yields 1341100800000000000L. The reason is that datetime.datetime object can't accurately represent a nanosecond or finer timescale, because the resolution supported by datetime.datetime is only microsecond.
Side note 2: Beware the different conventions between numpy 1.10 and earlier vs 1.11 and later:
in numpy <= 1.10, datetime64 is stored internally as UTC, and printed as local time. Parsing is assuming local time if no TZ is specified, otherwise the timezone offset is accounted for.
in numpy >= 1.11, datetime64 is stored internally as timezone-agnostic value (seconds since 1970-01-01 00:00 in unspecified timezone), and printed as such. Time parsing does not assume the timezone, although +NNNN style timezone shift is still permitted and that the value is converted to UTC.
[1]: https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/datetime.c see routine convert_datetime_to_pyobject.

I wanted an ISO 8601 formatted string without needing any extra dependencies. My numpy_array has a single element as a datetime64. With help from #Wirawan-Purwanto, I added just a bit:
from datetime import datetime
ts = numpy_array.values.astype(datetime)/1000000000
return datetime.utcfromtimestamp(ts).isoformat() # "2018-05-24T19:54:48"

Building on this answer I would do the following:
import numpy
import datetime
t = numpy.datetime64('2012-06-30T20:00:00.000000000')
datetime.datetime.fromtimestamp(t.item() / 10**9).strftime('%Y.%m.%d')
The division by a billion is to convert from nanoseconds to seconds.

Here is a one liner (note the padding with extra zero's):
datetime.strptime(str(t),'%Y-%m-%dT%H:%M:%S.%f000').strftime("%Y-%m-%d")
code sample
import numpy
from datetime import datetime
t = numpy.datetime64('2012-06-30T20:00:00.000000000-0400')
method 1:
datetime.strptime(str(t),'%Y-%m-%dT%H:%M:%S.%f000').strftime("%Y-%m-%d")
method 2:
datetime.strptime(str(t)[:10], "%Y-%m-%d").strftime("%Y-%m-%d")
output
'2012-07-01'

Also, if someone want to apply same formula for any series of datetime dataframe then you can follow below steps
import pandas as pd
temp = []
for i in range(len(t["myDate"])):
ts = pd.to_datetime(str(t["myDate"].iloc[i]))
temp.append(ts.strftime('%Y-%m-%d'))
t["myDate"] = temp

datetime objects can be converted to strings using the str() method
t.__str__()