基于PyTables将(高频)时序数据保存为HDF5文件高效访问的库：TsTables
jopen
11年前
TsTables —— Python下基于PyTables将(高频)时序数据保存为HDF5文件高效访问的库。
# Class to use as the table description class BpiValues(tables.IsDescription): timestamp = tables.Int64Col(pos=0) bpi = tables.Float64Col(pos=1) # Use pandas to read in the CSV data bpi = pandas.read_csv('bpi_2014_01.csv',index_col=0,names=['date','bpi'],parse_dates=True) f = tables.open_file('bpi.h5','a') # Create a new time series ts = f.create_ts('/','BPI',BpiValues) # Append the BPI data ts.append(bpi) # Read in some data read_start_dt = datetime(2014,1,4,12,00) read_end_dt = datetime(2014,1,4,14,30) rows = ts.read_range(read_start_dt,read_end_dt) # `rows` will be a pandas DataFrame with a DatetimeIndex.