pandas python Reviews & Product Details

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.

Pandas is easy use,

can handle tabular data efficiently

very fast. Review collected by and hosted on G2.com.

it is in memory operations so it takes more memory and needs high configuration for the operations Review collected by and hosted on G2.com.

It’s great how there are so many libraries that the user can utilize for effective data manipulation. Great for company usage! Review collected by and hosted on G2.com.

There is nothing in particular that I dislike about the tool. Review collected by and hosted on G2.com.

Pandas used with Python is extremely intuitive, easy to use, robust, dealing with data-frames is simple, data subsetting and filtering features are cool, can support quite a large number of rows, very easy to learn with a large number of examples available online. Review collected by and hosted on G2.com.

- Panda only handles results that can fit in the memory, it can be a limitation sometimes.

- Though the documentation is largely available, it is sparse.

- Low performance and long runtime when you’re dealing with very large data sets. Review collected by and hosted on G2.com.

Pandas is by far one of the best open source Python libraries for Data manipulation and analysis. Pandas Data structure called Dataframe. I am truly in love with the Dataframe. It is really easy, data visualization is awesome, data frames are really fast in performance and many more such amazing features. Review collected by and hosted on G2.com.

I am a huge Pandas fan, there is nothing I dislike about it. Review collected by and hosted on G2.com.

I am literally in love with Pandas, just like I love panda animals.

Pandas provide excellent data structure(dataframe) for manipulation, analysing and cleaning the data.

It supports data in any format and give us in a nice table like structure. With the Dataframe you can manipulate data however you want. The plotting of data also becomes easier, to apply some statistics on data such as mean, standard deviation etc are just one line code.

Converting the datarame into csv, excel, json is super easy.

It makes life very very easier of Machine Learning and Data Science developers. Review collected by and hosted on G2.com.

Honestly, I love pandas there is nothing I dislike about it. It's just for smaller data you might want to use Python list or dictionary. Review collected by and hosted on G2.com.