Here is a pandas solution for doing just this (which uses PyTables under the
hood):
# create a frame
In [45]: df = DataFrame(randn(1000,2),index=date_range('20000101',periods=1000))
In [53]: df
Out[53]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1000 entries, 2000-01-01 00:00:00 to 2002-09-26 00:00:00
Freq: D
Data columns (total 2 columns):
0 1000 non-null values
1 1000 non-null values
dtypes: float64(2)
# store it as a table
In [46]: store = pd.HDFStore('test.h5',mode='w')
In [47]: store.append('df',df)
# select out the index (a datetimeindex in this case)
In [48]: c = store.select_column('df','index')
# get the coordinates of matching index
In [49]: coords = c[pd.DatetimeIndex(c).month==5]
# select those rows
In [51]: from pandas.io.pytables import Coordinates
In [50]: store.select('df',where=Coordinates(coords.index,None,None))
Out[50]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 93 entries, 2000-05-01 00:00:00 to 2002-05-31 00:00:00
Data columns (total 2 columns):
0 93 non-null values
1 93 non-null values
dtypes: float64(2)
________________________________
From: Anthony Scopatz <scop...@gmail.com>
To: Discussion list for PyTables <pytables-users@lists.sourceforge.net>
Sent: Monday, August 5, 2013 2:54 PM
Subject: Re: [Pytables-users] dates and space
On Mon, Aug 5, 2013 at 1:38 PM, Oleksandr Huziy <guziy.sa...@gmail.com> wrote:
Hi Pytables users and developers:
>
>
>I have a few questions to which I could not find the answer in the
>documentation. Thank you in advance for any help.
>
>
>1. If I store dates in Pytables, does it mean I could write queries like
>table.where('date.month == 5')? Is there a common way to pass from python's
>datetime to pytable's datetime and inversely?
Hello Sasha,
Pytables times are the actual based off of C time, not Python's date times.
This is because they use the HDF5 time types. So unfortunately you can't write
queries like the one above. (You'd need to talk to numexpr about getting that
kind of query implemented ~_~.)
Instead I would suggest that you store your times as Float64Atoms and
Float64Cols and then use arithmetic to figure out the query:
table.where("(x / 3600 / 24)%12 == 5")
This is not perfect...
2. I have several variables stored in the same file in a separate table for
each variable. And I use separate columns year, month, day, hour, minute,
second - to mark the time for a record (the records are not necessarily
ordered in time) and this is for each variable. I was thinking to put all the
variables in the same table and put missing values for the variables which do
not have outputs for a given time step. Is it possible to put None as a default
value into a table (so I could easily filter dummy rows).
>
It is not possible to use "None" since that is a Python object of a different
type than the other integers you are trying to stick in the column. I would
suggest that you use values with no actual meaning. If you are using normal
ints you can use -1 to represent missing values. If you are using unsigned
ints you have to pick other values, like 13 for month on the Julian calendar.
But then again the data comes in chunks, does this mean I would have to check
if a row with the same date already exist for a different variable?
No you wouldn't you can store the same data multiple times in different rows.
I don't really like the ideas in 2, which are intended to save space, but maybe
all I need is a good compression level? Can somebody advise me on this?
>
Compression would definitely help here since the date numbers are all fairly
similar. Probably even a compression level of 1 would work. Keep in mind that
sometime using compression actually speeds things up (see the starving CPU
problem). You might just need to experiment with a few different compression
level to see how things go. 0, 1, 5, 9 gives you a good spread.
Be Well
Anthony
>
>
>
>
>
>Cheers
>--
>Oleksandr (Sasha) Huziy
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