使用Pandas，Python将数据附加到HDF5文件

Question

I have large pandas DataFrames with financial data. I have no problem appending and concatenating additional columns and DataFrames to my .h5 file.

The financial data is being updated every minute, I need to append a row of data to all of my existing tables inside of my .h5 file every minute.

Here is what i have tried so far, but no matter what i do, it overwrites the .h5 file and does not just append data.

HDFStore way:

#we open the hdf5 file
save_hdf = HDFStore('test.h5') 

ohlcv_candle.to_hdf('test.h5')

#we give the dataframe a key value
#format=table so we can append data
save_hdf.put('name_of_frame',ohlcv_candle, format='table',  data_columns=True)

#we print our dataframe by calling the hdf file with the key
#just doing this as a test
print(save_hdf['name_of_frame'])    

---

The other way I have tried it, to_hdf:

#format=t so we can append data , mode=r+ to specify the file exists and
#we want to append to it
tohlcv_candle.to_hdf('test.h5',key='this_is_a_key', mode='r+', format='t')

#again just printing to check if it worked 
print(pd.read_hdf('test.h5', key='this_is_a_key'))

---

Here is what one of the DataFrames looks like after being read_hdf:

           time     open     high      low    close     volume           PP  
0    1505305260  3137.89  3147.15  3121.17  3146.94   6.205397  3138.420000   
1    1505305320  3146.86  3159.99  3130.00  3159.88   8.935962  3149.956667   
2    1505305380  3159.96  3160.00  3159.37  3159.66   4.524017  3159.676667   
3    1505305440  3159.66  3175.51  3151.08  3175.51   8.717610  3167.366667   
4    1505305500  3175.25  3175.53  3170.44  3175.53   3.187453  3173.833333  

The next time I am getting data (every minute), i would like a row of it added to index 5 of all my columns..and then 6 and 7 ..and so on, without having to read and manipulate the entire file in memory as that would defeat the point of doing this. If there is a better way of solving this, do not be shy to recommend it.

PS sorry for the formatting of that table in here

Answer 1

pandas.HDFStore.put() has parameter append (which defaults to False ) - that instructs Pandas to overwrite instead of appending.

So try this:

store = pd.HDFStore('test.h5')

store.append('name_of_frame', ohlcv_candle, format='t',  data_columns=True)

we can also use store.put(..., append=True) , but this file should also be created in a table format:

store.put('name_of_frame', ohlcv_candle, format='t', append=True, data_columns=True)

NOTE: appending works only for the table ( format='t' - is an alias for format='table' ) format.

Answer 2

tohlcv_candle.to_hdf('test.h5',key='this_is_a_key', append=True, mode='r+', format='t')

You need to pass another argument append=True to specify that the data is to be appended to existing data if found under that key, instead of over-writing it.

Without this, the default is False and if it encounters an existing table under 'this_is_a_key' then it overwrites.

The mode= argument is only at file-level, telling whether the file as a whole is to be overwritten or appended.

One file can have any number of keys, so a mode='a', append=False setting will mean only one key gets over-written while the other keys stay.

I had a similar experience as yours and found the additional append argument in the reference doc. After setting it, now it's appending properly for me.

Ref: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_hdf.html

Note: hdf5 won't bother doing anything with the dataframe's indexes. We need to iron those out before putting the data in or when we take it out.