The beauty of using Python is that unlike other programming languages, you get to cut down on development time. It is for this reason that many organizations are considering Python in their database development needs. However, in spite of all the benefits, using Python for data analysis is not without its flipsides. The features that make development easy may be a menace in most systems. Most people complain about slow running times, confusing libraries among other things. Even so, this does not mean you abandon Python all together.
The purpose of this article is to help you learn of the different things you can do to achieve better results with a Python database. The post also looks at a few mistakes you should always strive to avoid during database development and management.
The Python community has made available numerous resources that will be of great use when it comes to data analysis. The libraries are rich in functionality and have been tested extensively. With that being said, there is no need for you to waste time reinventing the wheel.
It is good to be unique, but you don’t necessarily need to do things your way just to look unique. A common mistake that most developers make is that of creating their own custom CSV. At the end of the day, they end up creating loading functions that are too unique to run properly on their platform. More often than not, their efforts lead to the creation of dictionaries which inevitably cause slow speed.
Most of the problems you may be trying to solve may already have been solved. Therefore, before you start working on your own custom query, check to make sure that it is not contained in the Python libraries.
In the case of handling large data sets, Python Pandas libraries are the best to focus on. There are great abstractions to help you deal with major challenges and numerous functionalities to make your work easier. Not reinventing the wheel will save you time and make sure your database analysis strategies run smoothly.
The last thing you want is to deal with a slow database. This is not only frustrating but will also impede the output of different departments in your organization. More often than not, developers focus more on functionality rather than performance.
There are several resources you can use to learn how to speed up your database. You need to identify the bottlenecks and fix the issues as soon as possible. Additionally, even if your code is at an optimal stage, you need to pythonize parts of the code for improved performance.
Python offers a myriad of resources that you can use to improve your code. Do not just rely on what you already know. Look at what other developers are doing.
Failing to grasp the concept of time
One area in which most developers fail in is when it comes to working with time. One point you have to understand is that the Epoch time is the same across the globe. How the number is translated into hours and minutes will depend on the time zone as well as the time of the year.
The time handling modules in Python can be confusing. It is imperative that you take time to understand how to work with time on Python. The proper use of the time command can make your code non-portable. Take some time to understand this feature and always counter-check your code for accuracy. The smallest of errors can make your database impractical. If you don’t fully understand this, it is better to outsource your work to remote DBA experts. They will save you from making serious mistakes.
Poor naming standards
Every good developer knows that poor naming standards can lead to serious problems. However, most people don’t pay much attention to the standards. This is more so because naming standards are more of a personal choice. The important thing is to ensure that your choices are logical, consistent and well documented.
For example, when working on customer address fields, you must never write it differently. You should not represent the address with both customer and address. Choose one and stick with it.
You have to ensure that all your naming standards make sense. This is more so when considering that with well documented work, there may be hundreds of pages. You need to save time by making sure that your standards are consistent. Once other programmers understand your method, it will be easier for them to go through your work.
Don’t misuse the primary keys
You will be amazed by the number of programmers who don’t really know how to use primary keys. One fact you need to remember is that you cannot use the primary key value of your data in the row. Second, the value should not have meaning and hence application data should not be used. The primary key values must not be changed at any one time. Primary key values are generated and managed by the system.
For the primary keys to be able to move data between systems, they need to have the aforementioned properties. Changing them will mean that you will be interfering with the data or even complicating the relationship between systems.
Poor documentation is the main cause of problems in data analysis. Although this is common knowledge, it is worth reminding you over and over again to always document your work properly. This should be done for both current and future use. The documentation should also spell out how the database structure needs to be used. This may take time but it will save you a lot more time and money later on.
The above are just some of the mistakes you should always avoid when working with Python in data analytics. The list is non-comprehensive. You need to keep an eye out. Nothing is set in stone when it comes to database management. You have to keep learning.