It has helped me a lot with data analysis and visualization. The syntax is easy to use and very coding-friendly, and it’s also straightforward to implement. I use it in almost every project, nearly every day. It’s especially easy to integrate when working with structured data. Review collected by and hosted on G2.com.

It’s a heavy library to implement, and it takes time. Review collected by and hosted on G2.com.

What I like best about pandas is how intuitive and powerful it makes data manipulation. Its DataFrame structure feels natural to work with, almost like handling an Excel sheet but with the full flexibility of Python. Operations that would take dozens of lines in raw Python—such as cleaning datasets, merging tables, filtering, grouping, or calculating statistics—can be done in just one or two lines with pandas.

I also appreciate how well pandas integrates with the entire Python data ecosystem, especially NumPy, Matplotlib, and scikit-learn. This seamless workflow makes pandas an essential tool for any data science or analytical project. Review collected by and hosted on G2.com.

One of my main frustrations with pandas is that it tends to become slow and consume a lot of memory when handling very large datasets, as it loads all the data into RAM. Certain operations, such as complex groupby tasks or applying custom Python functions, can be significantly slower than what you might experience with optimized databases or distributed systems. The learning curve can also be quite steep for newcomers, given the wide range of methods, various indexing options, and the distinctions between Series and DataFrames. On top of that, debugging chained operations is sometimes tricky, and getting pandas to work efficiently with data sources like SQL databases or cloud storage often requires additional configuration. Review collected by and hosted on G2.com.

Pandas is a mature, open-source Python library for data manipulation and analysis. Its core components, `DataFrame` and `Series`, provide robust abstractions for handling structured, labeled data.

Here’s what stands out from a developer’s perspective:

✅ Expressive Data Structures

• `DataFrame`: Two-dimensional, size-mutable, heterogeneous tabular data structure with labeled axes (rows and columns).

• `Series`: One-dimensional labeled array, capable of holding any data type.

✅ Comprehensive I/O Support

• Native functions for reading/writing CSV, Excel, SQL, JSON, Parquet, HDF5, and more. Methods like `read_csv()`, `to_excel()`, and `read_sql()` streamline integration with external data sources.

✅ Efficient Data Manipulation

• Powerful indexing, slicing, and subsetting using intuitive label-based or integer-based selectors.

• Vectorized operations built on top of NumPy enable fast, memory-efficient computations on large datasets.

• Built-in support for handling missing data (`NaN`, `NA`, `NaT`) without breaking workflows.

✅ Advanced Grouping and Aggregation

• Flexible `groupby` operations for split-apply-combine workflows, supporting complex aggregations and transformations.

✅ Time Series and Categorical Data

• Specialized types and methods for time series (e.g., `Timestamp`, `Period`, resampling) and categorical data, improving both performance and memory usage.

✅ Interoperability

• Seamless integration with the broader Python data stack: NumPy for numerical operations, Matplotlib and Seaborn for visualization, and scikit-learn for machine learning pipelines.

✅ Reshape, Merge, and Pivot

• Functions like `pivot_table`, `melt`, `merge`, and `concat` enable flexible data reshaping and joining.

✅ Extensive Documentation and Community

• Large, active community and extensive documentation, with a wealth of tutorials and examples for most use cases. Review collected by and hosted on G2.com.

Pandas is optimized for in-memory operations and single-threaded execution. Handling very large datasets (that don’t fit in RAM) or leveraging multi-core CPUs requires external tools or libraries (e.g., Dask, cuDF). Review collected by and hosted on G2.com.

Created visualization and reports using extensive python libraries, Pandas, Numpy, Matplotlib. Review collected by and hosted on G2.com.

Nothing as such, everything at par my expectation. Review collected by and hosted on G2.com.

Usability and Graphical representation of various data sets Review collected by and hosted on G2.com.

Nothing much to dislike about, It's still developing hoping to mature enough to be the best Review collected by and hosted on G2.com.

It is easy to understand. It is perfect for small-sized data manipulation. Review collected by and hosted on G2.com.

It tends to be slower as the size of the data increases. Review collected by and hosted on G2.com.

It has multiple functions for dataset processing Review collected by and hosted on G2.com.

Syntax keeps changing with updates, so that causes some confusion sometimes Review collected by and hosted on G2.com.

Pandas python is very powerful library in python,Pandas has incredible features like data analysis for file's like CSV file , Excel file, json file, dollar file, .text file etc it will convert all file types into dataframe and you can do easily operation on this dataframe. Review collected by and hosted on G2.com.

I'm using pandas since 1 year and no dislike about pandas because it is very powerful library.but i want to say pandas only visualise the data into dataframe if we want to visualise the data then we need to use another library for this,but rather than pandas is very great Library Review collected by and hosted on G2.com.

- Ease of use

- Ease of Implementation

- Ease of Integration

- Versatility

- Updated library Review collected by and hosted on G2.com.

There is no dislikes that I can think of. Review collected by and hosted on G2.com.

DataFrames in Pandas are useful to handle and analyse data very efficiently. Also pandas provides built-in methods to filter and sort data, handle missing data. Pandas allows/supports reading data from excel, CSV fil e etc which is another advantage. Review collected by and hosted on G2.com.

Pandas has few weak areas. When large datasets are provided as inputs, Pandas encounter performance issues as interacting over large DataFrames and performing operations on them is time consuming. Review collected by and hosted on G2.com.