Pickling pandas dataframe multiplies by 5 the file size
Dont load 800MB file to memory. It will increase your loading time. Pickle objects too takes more time to load. Instead store the csv file as a sqlite3 (which comes along with python) table. And then query the table every time depending upon your need.
I am reading a 800 Mb CSV file with pandas.read_csv, and then use the original Python pickle.dump(datfarame) to save it. The result is a 4 Gb pkl file, so the CSV size is multiplied by 5. 
I expected pickle to compress data rather than extend it. Also because I can do a gzip on the CSV file which compress it to 200 Mb, dividing it by 4.
I am willing to accelerate the loading time of my program, and thought that pickling would help, but considering disk access is the main bottleneck I am understanding that I would rather have to compress the files and then use the compression option from pandas.read_csv to speed up the loading time.
Is that correct?
Is it normal that pickling pandas dataframe extend the data size?
How do you speed up loading time usually? 
What are the data-size limit would you load with pandas?