pandas python Reviews & Product Details

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.

Pandas in Python have the ability to handle and manipulate large datasets with ease. It provides a rich set of functions and methods that make data cleaning, transformation, and analysis efficient and intuitive. Review collected by and hosted on G2.com.

Pandas work slowly for very large datasets, pandas data frames are mutable which means that can be changed anytime, this can be advantageous but can be confusing or wont work well if not handled properly Review collected by and hosted on G2.com.

Pandas is widely used for data manipulation and data analysis. We can read datasets files such as CSV, Excel and process those files. Panda has tabular data structures like dataframes, series. It has more functions for manipulation of data. Empty records are handled properly. Review collected by and hosted on G2.com.

Pandas consume more memory when working with larger datasets. That's why there are performance limitations. It is dependent on external libraries. Support and performance should be improved. Review collected by and hosted on G2.com.