For clarification: mean,max,min,std are not "time series features", they are data features in general.
Assuming that you want to do it in python, you should take a look at pandas.DataFrame class. Once you initialize a Dataframe object with your tabular data, you can call its methods
DataFrame.std() for your purpose.
You can insert all these calculated characteristics into a new DataFrame and thereafter call
Dataframe.to_csv() to export them in a csv file.
Perhaps you need to look at this self-contained blogpost on Machine Learning with Signal Processing Techniques on how to prepare your time series data and extract useful statistical estimate and feature for machine learning models. At the end an example is given for classification. I found it super useful and straightforward.
Somewhere in the middle of the post, this great method for the Detection of peaks in data is introduced as well.
You can also use an open source python library called 'tsfresh' (https://tsfresh.readthedocs.io/en/latest/) to extract time series features
I don't have enough reputation to leave a comment, but could you please provide some sample data so that we can help you better?
When you say mean, max, min, are you trying to aggregate multiple rows of data on a date column with these functions? Or, do you have a timespan/ datetime/ timestamp column that you want to use?