I observe many analytics and data experts wonders about the question: Why go from Excel to Python?
I understand this people very good. People have long experienced in Excel. They know how to rationally use limited memory resources and space for calculations. They know how to fill Excel's gaps with other external applications and systems.
There are two real barriers in launching Python in professional work in place of Excel: functionality and time.
In Python, we have to learn long time this actions that anyone can do it in Excel easily without any training. This is frustrating and unjust. Many easily functionalities is difficult in Python (for example calculation of a dominant).
The second reason is the lack of time. If we have to do something NOW, we have no time for experiments with uncertain solutions. Analytics always have no time!
Why go from Excel to Python?
My professional experience with Python Real problem appear in face of giant data bases, thousands dimensions and tens of thousands of entities and products that need to be analyzed on a regular basis. When in a short time we have to provide answers for astronomy scale problems.
We read a lot in Internet about huge, complex solutions, about machine learning and bi dashboards. From my professional experience: for cosmic problems, a simple solution is multiplied on a large scale. There may be conditions at work to deal with scientific work. I had conditions in which I had to monitor the business model in gigantic scale. This has been realized by simple Data Science tools.
The astronomy scale problems
Last four years I was working as a Data Scientist for big producer from branch wood processing. I was involved monitoring huge system of raw material and products flow. Thousands index of raw materials, 8 kinds of transports, own fleet of 300 of lorries and half thousand alien lorries. Raw materials were accepting four plants and six wood yards.
Similarly, a huge and complicated system was in sending products to the customers. I tried to carry out all the processes in Excel sheets. There was a very efficient database, and I was able to portion the information into Excel. In Excel there was a huge problem with the efficiency and time of the analysis. I tried to improve performance by increasing the memory in my laptop.
Many analytics don't want to better get to know and use Python. If expert use Excel during all his professional work, know many advanced tricks and complex applications, feels he have enough tools to solve every problem. When he meets the scale, he understands Why go from Excel to Python.
In next publication I show two main Data scientist tools used to control astronomical scale logistic model.