Single Pandas DataFrame or multiple DataFrames

Question

I am having millions of devices and from these millions of devices, I am going to get counter values at periodic intervals for those devices. This particular value has to be held in memory and i have to do further processing on top of this.

This is my problem. To handle this problem, I am using Pandas DataFrame with 2 columns. One column is time stamp and the other column being actual counter value. There are multiple options that I am looking at. 1.) One DataFrame for all these 1 million devices. 2.) The 1 million devices are logically grouped based on the location of the device. So instead of having all 1 million device counters in a single DataFrame, I can create multiple DataFrames based on the location of the server where device resides. If we do the 2nd way, it will come close to 1000 DataFrames. So I can have 1000 DataFrames based on the grouping of the devices on location. So it comes to 1 DataFrame for 1 group. The reason of splitting this into 1000 DataFrames is because of the parallelism (both read/write) which I can do (multi-processing). I can have parallelism implemented effectively. And also the time that it takes to do some calculations on the DataFrame will be a simple operation in smaller size of DataFrame.

Some of the questions that I have is:

1.) what is the maximum number of DataFrames that we can create in Pandas in a system that has 1-2GB of RAM
2.) Having more number of DataFrames, will it induce any overhead in pandas

I need some opinion on this like what is the best way - should I go with 1000 DataFrames or 1 huge DataFrame.

Please note: I am getting the data from the devices at a periodic interval (e.g. 5 minutes) which is in the network and the total devices approx. is 1000000. Also regularly, depending on some conditions, I need to delete the old data. Its not just reading the data and appending the data in DataFrame but also deletion of old data.