fastest data / database format for reading large files

Pradipto Banerjee pradipto.banerjee at adainvestments.com
Tue Oct 16 14:35:56 EDT 2012


I am working with a series of large files with sizes 4 to 10GB and may need to read these files repeated. What data format (i.e. pickle, json, csv, etc.) is considered the fastest for reading via python?

Thanks

 This communication is for informational purposes only. It is not intended to be, nor should it be construed or used as, financial, legal, tax or investment advice or an offer to sell, or a solicitation of any offer to buy, an interest in any fund advised by Ada Investment Management LP, the Investment advisor.  Any offer or solicitation of an investment in any of the Funds may be made only by delivery of such Funds confidential offering materials to authorized prospective investors.  An investment in any of the Funds is not suitable for all investors.  No representation is made that the Funds will or are likely to achieve their objectives, or that any investor will or is likely to achieve results comparable to those shown, or will make any profit at all or will be able to avoid incurring substantial losses.  Performance results are net of applicable fees, are unaudited and reflect reinvestment of income and profits.  Past performance is no guarantee of future results. All financial data and other information are not warranted as to completeness or accuracy and are subject to change without notice.

Any comments or statements made herein do not necessarily reflect those of Ada Investment Management LP and its affiliates. This transmission may contain information that is confidential, legally privileged, and/or exempt from disclosure under applicable law. If you are not the intended recipient, you are hereby notified that any disclosure, copying, distribution, or use of the information contained herein (including any reliance thereon) is strictly prohibited. If you received this transmission in error, please immediately contact the sender and destroy the material in its entirety, whether in electronic or hard copy format.



More information about the Python-list mailing list