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Python is soon gaining a lot of popularity as a tool for data analysis. Python has evolved as a more mature tool offering flexibility, which one can enjoy without really sacrificing the functionality of older programs. Python is considered as the language for people working in the field of data science more specifically for data analysis. The objective of initiating any data analysis project is to create the highest quality of data, in the shortest possible time. On understanding the basic concepts behind what you are doing, anyone can perform the data analysis by almost using any language, however, the USP of python is that it is one of the best options available for beginners.
For data analysis the two most popular languages of choice are ‘R’ and ‘Python’, both of them are easy and simple to install, are available for free, and to an extent are easy to get started. However, there are a few variables where Python takes over as the most preferred language, to begin with.
Python is a general-purpose programming language, which translates into the fact that it can be used for almost all purposes like, data munging, data engineering, data wrangling, also for web scraping, web crawling, to build a web-based application and many more uses. If you have any prior experience in object-based languages like Java or C++ then, it will be easier to gain flexibility in Python than in R.
Python is again preferred, as because it is an object-based language it is easier to write codes, especially in scenarios where one has to write large scale codes, which are sustainable and strong. One can duplicate the prototype code in python from your private computer, to be used as the production code if needed.
Python might not have libraries or an all-inclusive set of data, as compared to what other programming languages have to offer. Nevertheless, one can use python in collaboration with tools like Pandas or Numpy etc.., to get you the desired results.
Pandas is one of the most popular python libraries for data wrangling, which is used to deal with some of the most common data formats and their transformations.
Data wrangling is an essential part of any data analysis. Before any algorithms are applied to the data set it is crucial that the data is checked and ready for consumption. For example, if your data set is incomplete, or has null values, the analysis will not be complete and correct.
Data Wrangling with Pandas will help you drop Null Values, Filter Data.
Data wrangling with pandas can also be used for Grouping data, which is slightly more challenging than filtering data. Through Grouping one can find ways to correlate the data and discover trends. If you are working with financial data or weather data, with Pandas Time Series Analysis Tool, one could analyse events by hours or even by the minutes
Lastly, you can Export cleaned and filtered data to Excel or another format, basically share the data and present it in the best possible format.
So preparing the data is the first and most crucial task for data analysis, data wrangling with Pandas assures that any treatment that is applied to the data set will be effective.