There are many benefits of Pandas library, listing them all would probably take more time than what it takes to learn the library. Therefore, these are the core benefits of using the Pandas library:
1. The Pandas library provides a really fast and efficient way to manage and explore data.Series and DataFrames help us to represent and manipulate data efficiently.
2. Organization and labeling of data are perfectly taken care of by the intelligent methods of alignment and indexing, which can be found within Pandas.
3. It is easy to perform data cleaning with missing values handling with help of some functions provided by Pandas.
5. Pandas provide a wide array of built-in tools for the purpose of reading and writing data.
6. supports Multiple file formats.
7. Merging and joining of datasets is possible with help of pandas.
8. Pandas provides features likes moving window statistics and frequency conversion which is helpful for data scientists in time series analysis and relevant concepts.
9. Pandas have an in-built ability to help you plot your data and see the various kinds of graphs formed.
10. Pandas has ability of Grouping. It can separate data and grouping it according to the criteria we want. We can also find unique data in each feature.
11. Pandas allows you to implement a mathematical operation on the data.
The pandas forms a core component of the Python data analysis corpus. The distinguishing feature of pandas is the suite of data structures that it provides, which is naturally suited to data analysis, primarily the DataFrame and to a lesser extent Series (1-D vectors) and Panel (3D tables).
Simply put, pandas and statstools can be described as Python's answer to R, the data analysis and statistical programming language that provides both the data structures, such as R-data frames, and a rich statistical library for data analysis.
The benefits of pandas over using a language such as Java, C, or C++ for data analysis are manifold:
Data representation: It can easily represent data in a form naturally suited for data analysis via its DataFrame and Series data structures in a concise manner. Doing the equivalent in Java/C/C++ would require many lines of custom code, as these languages were not built for data analysis but rather networking and kernel development.
Data subsetting and filtering: It provides for easy subsetting and filtering of data, procedures that are a staple of doing data analysis.
Concise and clear code: Its concise and clear API allows the user to focus more on the core goal at hand, rather than have to write a lot of scaffolding code in order to perform routine tasks. For example, reading a CSV file into a DataFrame data structure in memory takes two lines of code, while doing the same task in Java/C/C++ would require many more lines of code or calls to non-standard libraries, as illustrated in the following table. Here, let's suppose that we had the following data:
Internet Usage Per 1000 People
In addition, pandas is built upon the NumPy libraries and hence, inherits many of the performance benefits of this package, especially when it comes to numerical and scientific computing. One oft-touted drawback of using Python is that as a scripting language, its performance relative to languages like Java/C/C++ has been rather slow. However, this is not really the case for pandas.