pandas python Pros and Cons

Data science is made easy with the pandas library because it works well and lets me get a lot of things done. Review collected by and hosted on G2.com.

Pandas is an open source library and the data structures are sometimes confusing to use with documentation. Review collected by and hosted on G2.com.

Ease and speed of data handling and manipulation Review collected by and hosted on G2.com.

Slow speed when working with element by element via for loop Review collected by and hosted on G2.com.

The ability to manipulate data with such ease by providing SO many operations to the user is just beyond doubt the best thing about it. Review collected by and hosted on G2.com.

Sometimes some operations like loc, iloc etc can get really confusing. The documentation is good but can be made way better. Review collected by and hosted on G2.com.

The applicability, portability to other modules, extremely useful in data science applications Review collected by and hosted on G2.com.

Some operations are bit rigid. Flexibility options are few Review collected by and hosted on G2.com.

Data comes in every form and needs to be handled and manipulated for further processing. Pandas is a pretty good library that can handle various file types and provides easy to use API for data manipulation.

The best thing I liked about pandas is the DataFrame concept, after lodaing data from any file irrespective of the type it brings all data to DataFrame object on which all the APIs can be easily applied. Review collected by and hosted on G2.com.

Pandas is the only library we use at Easesolution Pvt. Ltd. for data pre-processing and manipulation. So I personally have no dislike for it. Review collected by and hosted on G2.com.

It's basically a necessary package for anyone involved in Data Science/Analytics using Python. Review collected by and hosted on G2.com.

There's really nothing to dislike about it. Review collected by and hosted on G2.com.

Pandas Dataframe structure makes it very simple to arrange and adjust data. When used with SciPy it makes Machine Learning an easy process. Review collected by and hosted on G2.com.

Some of the code examples do not always make the process of manipulating the data easy. I have found examples on other websites that are more effective with Dataframes. So, yes, the documentation is sometimes lacking the reality of programming in Python. Review collected by and hosted on G2.com.

The library is very intuitive. Most operation follow verb_noun pattern and even if you are a new user, you can "guess" what possibly the operation will be. It makes working with series and data frames very easy and most of times, you will find a highly optimized function in the library for whatever operation you want to perform. Review collected by and hosted on G2.com.

When it comes to plotting, it can take some time getting used to the terminology and approach used. However, once you are familiar with the approach, then creating insightful and beautiful graphs becomes easy. Review collected by and hosted on G2.com.

To play with data one of the best modules in python. One can import data a lot of file formats and then use it as required. It has a lot of functionalities which helps to get the data in the required format Review collected by and hosted on G2.com.

Needs a lot of study to get used to it and manipulate data Review collected by and hosted on G2.com.

The built in function which helps to provide the insight Review collected by and hosted on G2.com.

Sometimes for complicated data, it seems bit confusing but depends person to person Review collected by and hosted on G2.com.