Here is what i got (time series) in pandas dataframe

There are several caveats to this solution.

First, your data has a high temporal resolution (seconds). However, there are hours-long gaps in between observations. You either coarsen the timestamp by rounding (e.g. to minutes or hours) or go along with the time series on a second-by-second resolution and accept the fact that most you balance values will be filled-in values rather than true observations.

Second, you have different balance values for the same timestamp which indicates faulty entries or a misspecified timestamp. I unified those entries via grouping by timestamp and averaged the balance over those non-unique timestamps.

Third, filled-up gaps and true observations both have the same visual representation in the plot (blue dots in the graph). As previously mentioned commenting out the fillna() line would only showcase true observations leaving everything in between white.

Finally, the missing values are merely filled in via padding. Look up different values of the argument method in the documentation in case you want to linearly interpolate etc.

Summary

The problems described above are typical for event-driven time series data. Since you deal with a (financial) balance that constitutes a state that is only changed by events (orders), I believe that the assumptions made above arew reasonable and can be adjusted easily for your or many other use cases.

CodePudding user response：

this helped

data = data.set_index('time').resample('1M').mean()