Storage as Pandas DataFrames and upgrade as Pytables

Question

Can you store data as pandas HDFStore and open it / do I / O using pytables? The reason this question arises is because I am currently storing data as

pd.HDFStore('Filename',mode='a')
store.append(data)

However, as I understand that pandas does not support updating records. I have a usecase where I have to update 5% of the data daily. Does pd.io.pytables work? if so, I have not found any documentation about this? Pytables has a lot of documentation, but I'm not sure if I can open a file / update without opening with pytables when I did not use pytables to save the file first?

Answer 1

@flyingmeatball:

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

HDFStore (. data_columns=True ( data_columns=[list_of_columns_to_index]), , where):

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()

:

In [61]: store = pd.HDFStore(r'd:/temp/test_removal.h5')

.remove() # :

In [62]: store.remove('test', where="a > 0.5")
Out[62]: 9

append ( 100) :

In [63]: store.append('test', df.loc[df.a > 0.5] * 100, format='t', data_columns=True)

:

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

:

In [65]: store.close()

Answer 2

, , , :

http://pandas.pydata.org/pandas-docs/version/0.19.0/api.html?highlight=pytables

:

pandas DataFrame Pytable pandas DataFrame

, 5% , ,

  store.remove(, =...)   store.append(.....)

pandas - .

http://www.pytables.org/usersguide/tutorials.html