pandas python Reviews & Product Details

What I like most about the pandas framework for Python is its ease of use and great documentation. Currently, being pandas an extension of numpy, it has one of the best documentations possible. Review collected by and hosted on G2.com.

Even with good documentation, the main problem with Python (and consequently pandas) is that they need to improve calculation efficiency. Sometimes it tends to be a bit slow. Review collected by and hosted on G2.com.

Pandas provide a lot of prebuild features to modify and play with data, one of the most used libraries in data science space, pandas is easy to install thanks to pip (package installer of python). Review collected by and hosted on G2.com.

Pandas is indeed a great library but there is a learning curve which is a really big problem for beginners, also documentation is not well written so its hard to refer and work with official documentation Review collected by and hosted on G2.com.

The superiority comes in the way of using pandas - user-friendly.

It provides an ample amount of flexibility to the user to use it the way he/she wants to.

The support is strong and huge. Review collected by and hosted on G2.com.

When it comes to disliking, it is confusing at the beginning.

A beginner requires guidance when he wants to start using pandas.

There are ample amount of resources which itself makes it confusing.

However, one can easily learn by investing time and getting hands dirty (coding). Review collected by and hosted on G2.com.

Pandas framework gives a variety of options to import data with a very simple function. Pandas have various small functions with minimal modifications that can be used to manipulate the data. Review collected by and hosted on G2.com.

Pandas should have some good visualization tools included. Like in the seaborn package, pandas library van also be upgraded and may include the options for colorful plots and other diagrams Review collected by and hosted on G2.com.

To read CSV or excel files, I generally use pandas library in python every time. Also, I sometimes prefer it for visualazation. Once I read csv file in python, So with the help of pandas dataframe, performing a statistical analysis is very easy, lots of built-in functions available to use. A single line of program can help you. Review collected by and hosted on G2.com.

as It is easy to use, almost all functions are helpful. Review collected by and hosted on G2.com.

There is a method for everything and an even efficient way to do what you already do in python! this is not just adding functionality but improving the functionality that you already have Review collected by and hosted on G2.com.

Nothing! I really love Pandas, I use it every day since a year now, and everything is so easy since then, and my code has improved so much in efficency that how could I dislike pandas? Review collected by and hosted on G2.com.

I like the numpy and ipython integration most, which is very useful for any application. I like the PANDA packages, which are helpful for multiple data processing and machine learning applications. Julia and scipy also I like it. Data frame is essential for data manipulation and easy to link with SQL. It gives the same output in fewer lines of code compared to C++ and C Review collected by and hosted on G2.com.

Students can not use it efficiently because the switch to panda from standard python is very tuff. Less effective documentation leads hard to understand library features compare to other packages. Not essential for IoT-based embedded applications. Review collected by and hosted on G2.com.

My most favorite thing about Pandas that how they can easily represent your data. By using only two lines of code, you can import your data. One more thing is it easily handles heavy data. It also provides a data visualization function that helps me to visualize my data. It offers a large number of functions to do data manipulations. For me, it is the best library for tabular data. Review collected by and hosted on G2.com.

One thing I wouldn't say I like is that some functions in pandas come with very complex syntax. I cannot remember it. So, sometimes I have to check the documentation of Pandas to use it. Review collected by and hosted on G2.com.

Pandas is the most common library in python when you have to deal with table like data. This makes of pandas a library with a lot of help available around the web. I like the way of importing data to pandas from text format, spreadsheets, csv, tsv, etc.

I also like the way to select rows and columns and to operate with them. Although it is a little bit confusing at the beginning, once you get used to the way to manage data with pandas DataFrames, it is quite easy to play with data. Review collected by and hosted on G2.com.

If you are not careful managing data with pandas, the internal structures of pandas can use a high amount of memory. This is because pandas uses, by default, the object type, which requires a lot of memory. To solve this issue, you have to convert numeric types to int types. Then, you can reduce space by more than 50%. Review collected by and hosted on G2.com.

At the heart of the Pandas library is the data frame, which makes using the Pandas framework interoperable from a skills-building standpoint. Not only will learning the methods in Pandas be valuable within Python, but you can quickly transfer your knowledge of the framework to R or even Spark (for big data applications). Further, the framework itself implemented in Python is beneficial for data analysis, providing numerous helper functions on the data frame object, that include aggregation methods, standard statistical calculation methods, and handy join/merge, and subsetting functionality that all data analysts will likely use. On top of that, it is built on top of Numpy for easy transference between those types for more heavy-duty/actual work or even pushing it up to a higher level of abstraction for more data-viz/communications/analysis work. Review collected by and hosted on G2.com.

There's not much to dislike, except perhaps memory and some run-time constraints. By adding a lot of 'extra' structure on top of the NumPy array, the data frame isn't the most efficient data type, but what you get is worth the extra resources needed to run it, though maybe not at extreme scale (several dozen gigs or more than a couple million rows depending on how many columns of data is included in your frame). Review collected by and hosted on G2.com.