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Best Practices for Handling Duplicate Elements in Python Lists

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Here are three awesome ways that you can use to remove duplicates in a list. These are helpful in resolving your data analytics solutions.  01. Using a Set Convert the list into a set , which automatically removes duplicates due to its unique element nature, and then convert the set back to a list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = list(set(original_list)) 02. Using a Loop Iterate through the original list and append elements to a new list only if they haven't been added before. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] for item in original_list:     if item not in unique_list:         unique_list.append(item) 03. Using List Comprehension Create a new list using a list comprehension that includes only the elements not already present in the new list. Solution: original_list = [2, 4, 6, 2, 8, 6, 10] unique_list = [] [unique_list.append(item) for item in original_list if item not in unique_list] All three methods will result in uni

Case Study On Cloud Computing

The concept of cloud computing is in use for many years, but in recent years has it become a highlighted and come in picture. In the year 1990s, cloud computing was developed by major IT providers such as Sun, Microsoft, Google, and Amazon.

Cloud computing
Cloud computing
Different products came into use for different levels of users. The most popular services for end users include web-based email systems (SaaS), e.g. AOL, Gmail, Hotmail, and Yahoo! Mail, and office applications such as Google Docs, Microsoft MS Office Online, Cloud-canvas.com, and Write.fm, etc.

Developers can run their programs on the cloud (PaaS) like Google AppEngine, Windows Azure, and Force.com. Companies or organizations store or backup their large data on remote servers (IaaS), for example, Rackspace, Microsoft Azure, Animoto, Jungle Disk and Amazon's EC2 or S3 servers.

In 2011, the Primary Research Group (PRG) published a report of its recently conducted survey on library use of cloud computing (Primary Research Group, 2011). Participants included 70 libraries worldwide with the majority from the United States. The survey report reveals that 61.97 percent of libraries in the sample used free SaaS while 22.54 percent of libraries sampled used paid subscription SaaS; less than 3 percent of libraries surveyed used PaaS, and 4.23 percent used IaaS.

Most libraries using PaaS or IaaS had annual budgets over $5,000,000. Smaller libraries usually used the servers of their parent organizations, while libraries with multi-million dollar budgets tended to use their own servers.

  • Few case studies also show the exploration and adoption of other models of cloud computing in libraries. For example, California State University libraries have migrated their key library systems to vendors' cloud-based servers (i.e. a public cloud) and to campus IT's internally virtualized environment (i.e. a private cloud) (Wang, 2012). 
  • The Burritt Library at Central Connecticut State University used Amazon's S3 to back up their high-resolution digital objects (Iglesias, 2011). Murray State University library experimented with Dropbox for library services (Bagley, 2011). University of Arizona Libraries have migrated their ILS, Digital Libraries website, Interlibrary Loan system and repository software to cloud-based services (Han, 2010). 
  • The Z. Smith Reynolds Library in Winston Salem, NC, started to use Amazon's EC2 for hosting its website, discovery services, and digital library services in 2009 (Mitchell, 2010b).

The service model represent server administration and maintenance responsibilities are moved from local personnel to the hosting vendor, while the management of the application remains in the traditional way, i.e. librarians are still able to access the backend of the system for local customizations as if they were managing the system locally.

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