Reputation: 4501
Can you store data as pandas HDFStore and open them / perform i/o using pytables? The reason this question comes up is because I am currently storing data as
pd.HDFStore('Filename',mode='a')
store.append(data)
However, as i understand pandas doesn't support updating records so much. I have a usecase where I have to update 5% of the data daily. Would pd.io.pytables work? if so I found no documentation on this? Pytables has a lot of documentation but i am not sure if i can open the file / update without opening using pytables when i didnt use pytables to save the file initially?
Upvotes: 4
Views: 539
Reputation: 210882
Here is a demonstration for the @flyingmeatball's answer:
Let's generate a test DF:
In [56]: df = pd.DataFrame(np.random.rand(15, 3), columns=list('abc'))
In [57]: df
Out[57]:
a b c
0 0.022079 0.901965 0.282529
1 0.596452 0.096204 0.197186
2 0.034127 0.992500 0.523114
3 0.659184 0.447355 0.246932
4 0.441517 0.853434 0.119602
5 0.779707 0.429574 0.744452
6 0.105255 0.934440 0.545421
7 0.216278 0.217386 0.282171
8 0.690729 0.052097 0.146705
9 0.828667 0.439608 0.091007
10 0.988435 0.326589 0.536904
11 0.687250 0.661912 0.318209
12 0.829129 0.758737 0.519068
13 0.500462 0.723528 0.026962
14 0.464162 0.364536 0.843899
and save it to HDFStore (NOTE: don't forget to use data_columns=True
(or data_columns=[list_of_columns_to_index]
) in order to index all columns, that we want to use in the where
clause):
In [58]: store = pd.HDFStore(r'd:/temp/test_removal.h5')
In [59]: store.append('test', df, format='t', data_columns=True)
In [60]: store.close()
Solution:
In [61]: store = pd.HDFStore(r'd:/temp/test_removal.h5')
The .remove()
method should return # of removed rows:
In [62]: store.remove('test', where="a > 0.5")
Out[62]: 9
Let's append changed (multiplied by 100
) rows :
In [63]: store.append('test', df.loc[df.a > 0.5] * 100, format='t', data_columns=True)
Test:
In [64]: store.select('test')
Out[64]:
a b c
0 0.022079 0.901965 0.282529
2 0.034127 0.992500 0.523114
4 0.441517 0.853434 0.119602
6 0.105255 0.934440 0.545421
7 0.216278 0.217386 0.282171
14 0.464162 0.364536 0.843899
1 59.645151 9.620415 19.718557
3 65.918421 44.735482 24.693160
5 77.970749 42.957446 74.445185
8 69.072948 5.209725 14.670545
9 82.866731 43.960848 9.100682
10 98.843540 32.658931 53.690360
11 68.725002 66.191215 31.820942
12 82.912937 75.873689 51.906795
13 50.046189 72.352794 2.696243
finalize:
In [65]: store.close()
Upvotes: 2
Reputation: 7997
Here are the docs I think you're after:
http://pandas.pydata.org/pandas-docs/version/0.19.0/api.html?highlight=pytables
See this thread as well:
Update pandas DataFrame in stored in a Pytable with another pandas DataFrame
Looks like you can load the 5% records into memory, remove them from the store then append the updated ones back
to replace the whole table
store.remove(key, where = ...)
store.append(.....)
You can also do outside of Pandas - see tutorial here on removal
http://www.pytables.org/usersguide/tutorials.html
Upvotes: 2