This is represented by the fact that there is no line on the plot for first 900 days. The middle panel shows the shift(900) operation which shifts the data by 900 days, leaving NA values at early indices. The top panel in the plot shows ge data with a red line showing a local date. set(weight= 'heavy', color= 'red')Īx.axvline(local_max, alpha= 0.3, color= 'red')Īx.axvline(local_max + offset, alpha= 0.3, color= 'red')Īx.axvline(local_max + offset, alpha= 0.3, color= 'red') Timestamps can be sliced using the : notationįig, ax = plt.subplots( 3, figsize=( 15, 8), sharey= True)Īx.get_xticklabels(). ![]() Python's basic objects for working with time series data reside in the datetime module. In this notebook, we will briefly introduce date and time data types in native python and then focus on how to work with date/time data in Pandas.
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