pandas python Pros and Cons

Its ease of installation and integration with the rest of my applications created in python is something quite useful and practical, the fact that it allows me to perform data analysis in a much simpler, more precise and secure way is something that ultimately gives it A great plus to my developments, the fact that it supports a multiple number of files and files is something of great utility when I have the data in different file formats, so since it does not have exclusive use of a single format the data entry It can be much more comprehensive, its ease of use and implementation is so simple that even people who are not experts in data analysis can perform this type of task without any problem, the precision of the results is something really surprising and in fact allow making valuable decisions within the company. Review collected by and hosted on G2.com.

Its learning curve can be a bit slow at first, but nevertheless after you get used to using the application you realize that everything is really simple, easy and fast, sometimes it would get stuck, although I'm not sure if it was because of the application itself or because of my processor. Review collected by and hosted on G2.com.

This programming language has a learning curve for sure, but I really like that once you learn it, it’s pretty easy to remember. Pandas has a pretty simple language to write and code, but just like any other programming, you have to be careful about your language to get it to work properly. Review collected by and hosted on G2.com.

I dislike that it is hard to save my programming to a flash drive. I know it save son the internet but I do like a backup. Review collected by and hosted on G2.com.

Pandas is very useful and easy to use. It gives very high-performance. It is very easy to install and setup. We can read various kinds of files using it like ssv, xls, etc. It makes Data analysis very easy and we can play around with the data set to grasp maximum knowledge from it by its various useful functions and features. Review collected by and hosted on G2.com.

Pandas python is one of the best tools but sometimes it took very long time for large datasets Review collected by and hosted on G2.com.

Pandas is a great tool to visualize, import and analyze the data in many formats. Its support for different file formats and its self-explanatory commands makes it user-friendly even for an inexperienced user. Conversions from pandas dataframe to numpy arrays is also another great feature of Pandas since not all other python libraries have Pandas support. They have a really rich and useful documentation page. Its user base is huge compared to other similar libraries. Meaning you can find answer to your many questions. Review collected by and hosted on G2.com.

Pandas isn't great at memory handling. It imports all of the files even if you need just couple of rows from a file. It also doesn't support multiprocessing and its functions only runs on CPU, not GPU. Another thing that I don't like about Pandas is that its error messages. For instance, KeyError when it can't find the specified column in the dataframe. These kinds of errors must provide human-friendly errors instead of robotic messages like 'KeyError 'some_column'. Review collected by and hosted on G2.com.

Python Pandas is used to generate structured data from unstructed form of data like json data Review collected by and hosted on G2.com.

The most disliked thing is that in pandas the data is structured in slow pattern Review collected by and hosted on G2.com.

Easy to learn and understand. Learning it quite easily Review collected by and hosted on G2.com.

Sometimes it gets stuck without reason and also difficult to use GPU of AMD Review collected by and hosted on G2.com.

The best thing about the pandas, we can perform the data analytics operation with this in completion of data science process. it has couple of the function to perform the operation over the data frame(i.e. array or matrix). Review collected by and hosted on G2.com.

This is not dislike thing about the pandas. it is an requirement of analytics that this should have the memory optimization feature Review collected by and hosted on G2.com.

we can perform the data science operation below

we can do data cleansing with this python library

we can do data preprocessing and many more. Review collected by and hosted on G2.com.

This takes a bit more in memory to process the mass data that should be optimized.It should be version compatibility as well. Review collected by and hosted on G2.com.

- Ease of use: I can simply read a file by typing read_excel('name.xlsx') and that's it.

- Ability to manage all kinds of data for all kind of needs. You have multi-indexed data and you want to sort in a hierarchical way? No problem, pandas has a solution for that, just as it does for everything you do.

- It is based on NumPy so it works very efficiently thanks to vectorized background, that's very precious when working with huge amount of data.

- It is also based on Matplotlib which makes visualization very convenient. I can simply go write df['data'].hist() to plot histogram or df['data'].plot() for line plot or df['data'].plot(kind = 'bar') for bar plot, without being have to deal with a lot of parameters. Review collected by and hosted on G2.com.

As much as it's great to have matplotlib in the background of pandas, some features of matplotlib are not exactly available in pandas so we have to use matplotlib instead. To be able to use all features of matplotlib would be nice. Review collected by and hosted on G2.com.