listing deep learning for text and sequences inspect ing the dat of he jena weat her dat aset import os data_dir '/users/fchollet/downloads/jena_climatefname os path join(data_dir'jena_climate_ csv' open(fnamedata read( close(lines data split('\ 'header lines[ split(','lines lines[ :print(headerprint(len(lines)this outputs count of , lines of data (each line is timestepa record of date and weather-related values)as well as the following header["date time"" (mbar)"" (degc)""tpot ( )""tdew (degc)""rh (%)""vpmax (mbar)""vpact (mbar)""vpdef (mbar)""sh ( /kg)"" oc (mmol/mol)""rho ( / ** )""wv ( / )""max wv ( / )""wd (deg)"nowconvert all , lines of data into numpy array listing parsing he dat import numpy as np float_data np zeros((len(lines)len(header )for iline in enumerate(lines)values [float(xfor in line split(',')[ :]float_data[ :values for instancehere is the plot of temperature (in degrees celsiusover time (see figure on this plotyou can clearly see the yearly periodicity of temperature licensed to