Pandas

Pandas is a library kit for the Python programming language that can help manipulate data tables or other key tasks in this type of object-oriented programming environment. Pandas may be useful in the design of certain machine learning and neural network projects or other major innovations where the Python programming language plays a role.

Why should we use Pandas ?

we take simple example: Let’s say we received an excel file with thousands of lines of invoices, invoice date, invoice amounts, and payment terms; and every week we need to change that into an aging report.

we could create formulas in excel, but with thousand of rows the file would get big quick, and we would may need to worry about the number of rows changing week over week, etc. Or we could do it with Pandas

Pandas provides Clean data, prep data, automate frequent tasks, manage large data sets, analyse data, summarize data.

Pandas vs Numpy ?

The Pandas module mainly works with the tabular data, whereas the NumPy module works with the numerical data. The Pandas provides some sets of powerful tools like DataFrame and Series that mainly used for analyzing the data, whereas in NumPy module offers a powerful object called Array

Pandas applications :


Pandas features:

• Handling of data : The Pandas library provides a really fast and efficient way to manage and explore data. It does that by providing us with Series and DataFrames, which help us not only to represent data efficiently but also manipulate it in various ways.

• Alignment and indexing : Having data is useless if you don’t know where it belongs and what it tells us about. Therefore, labeling of data is of utmost importance. Another important factor is an organization, without which data would be impossible to read. These two needs: Organization and labeling of data are perfectly taken care of by the intelligent methods of alignment and indexing, which can be found within Pandas.

• Input and output tools : Pandas provide a wide array of built-in tools for the purpose of reading and writing data. While analyzing you will obviously need to read and write data into data structures, web service, databases, etc.

• Multiple file formats supported : Data these days can be found in so many different file formats, that it becomes crucial that libraries used for data analysis can read various file formats. Pandas aces this sector with a huge scope of file formats supported. Whether it is a JSON or CSV, Pandas can support it all, including Excel and HDF5.

Pandas pros & cons:

Pros

• Data representation : The Pandas library is the perfect tool for anyone who wants to get into data science or data analysis because of the different ways it can represent and organize data.

• Handling of huge data : time is very important when it comes to data science. Therefore it becomes extremely important for the library being used to be very efficient in time. This is the front that Pandas excels in

• Extensive feature set : Pandas is really robust. This library provides the user with a large set of commands and amazing features that can be used to analyze the given data with the utmost ease.

• Merging and joining of datasets : While analyzing data we constantly need to merge and join multiple datasets to create a final dataset to be able to properly analyze it. This is important because if the datasets aren’t merged or joined properly, then it is going to affect the results adversely and we do not want that. Pandas can help to merge various datasets, with extreme efficiency so that we don’t face any problems while analyzing the data.

Cons

• Poor documentation : This is a big problem with pandas, especially for beginners.

• Poor 3D Matrix compatibility : This is one of the most visible drawbacks that the Pandas library suffers from. If your work deals with two dimensional (2D) matrices, then there could be nothing better than pandas for you.

• Learning curve : Pandas have a very steep learning curve.

Happy Learning ✌✌