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Tag: time-series

Pandas Aggregate Daily Data to Monthly Timeseries

I have a time series that looks like this (below) And I want to resample it monthly, so it has 2019-10 is equal to the average of all the values of october, November is the average of all the PTS values for November, etc. However, when i use the pd.resample(‘M’).mean() method, if the final day for each month does not

Is there any function to get multiple timeseries with .get and create a dataframe in Pandas?

I get multiple time series data in series format with datetimeindex, which I want to resample and convert to a dataframe with multiple columns with each column representing each time series. I am using separate functions to create the dataframe, for example, .get(), .resample(), pd.concat(). Since it is not following the DRY principle (Don’t Repeat Yourself) and I can be

Plotting time series directly with Pandas

In the above dataframe, all I want to create a line plot so that we have info on trends per year for each of the columns. I’ve read about pivot-table on related posts, but when I implement that, it says there are no numbers to aggregate. I don’t want to aggregate something. I just need the y-axis in terms of

Pandas – stack time columns with time and date

I have date and time data now I want to reduce this dataframe to two columns with Timestamp (date+time) in a column and value in another column current df – desired df – Here is original list from which I’m creating my dataframe – Answer Use melt to flatten your dataframe and set Time as a name of the variable

Setting Time with interval of 1 minute

I have a dataset which comprises of minutely data for 2 stocks over 3 months. I have to create date in the first column and time (with interval of 1 minute) in the next column for 3 months. I am attaching the snap of 1 such data set. Kindly help me to solve this problem. Data Format Answer -Create 3