Stunning Python Libraries you should know to work on Data Science projects.
As a data scientist, data analysis and manipulation is a day-to-day job. Knowing the right library for the right tasks tremendously reduces the working hours. So, I thought to share the 3 primary libraries I used to work for my regular tasks for various purposes.
1. Pandas Library
pandas were released on 11 January 2008
pandas library 📚 is one of the most used and widespread libraries 📚 in Python. Pandas library is most required for data manipulation necessary for data analysis 🧐 or machine learning.
Pandas Library helps us to work on structured data optimally for data structures and functions. The name panda does not imply the animal 🐼 ; it expresses Panel Data which means a structured dataset. Data frame and series are the two main classes to work on pandas. Python with Pandas is used in a wide range of different fields.
Why should you use Pandas Library?
- The Pandas library provides a systematic method to manage and explore data.
- Alignment and indexing are one of the best methods in pandas library.
- Pandas library provides tools for loading data into in-memory data
objects from different file formats. - The different file format we can import into pandas are Comma-separated values (CSV), XLSX, ZIP, plain Text(txt), JSON, XML, HTML, Images, Hierarchical Data Format, PDF, DOCX, MP3, MP4, SQL
- Handling of missing data is integrated within pandas libraries.
- Handling of missing data is integrated within pandas libraries.
- Using pandas features, we can easily clean 🧼 up our data.
- Reshaping and pivoting of data sets.
- Label-based slicing, indexing, and subsetting of large data sets.
- Filter, Sort, and Transpose
- Function Application like Lambda, Aggregate, Group by, Map, Transform
And pipe. - Pandas can help to combine, concatenate, join and merge data.
- Pandas play an essential role in Descriptive Statistics and Random sampling.
The downside of the Pandas library
- Poor closeness for 3-Dimensional Matrices. We cannot efficiently process the image data using the Pandas library.
- Pandas have a very steep learning curve. There are too many functionalities available in Pandas, and it will be a time-consuming process to learn.
- To process big datasets is limited due to out-of-memory errors in pandas.
- Slow, limited multicore algorithms for large datasets
2. Dask Library
Dask library was released on 8 January 2015
To deal with extensive data sets and parallel computing, the best one ☝️ is with Dask Library.
For parallel computing, Dask is the extendable open source python 🐍 library.
Why should you use the Dask Library?
- Dask is familiar due to parallelizes NumPy and pandas data-frame.
- Dask Runs hardy on clusters with 1000s of cores.
- Dask is suitable for fast numerical algorithms.
- With python concurrent futures, Dask supports a real-time task framework.
- The higher-level Dask API is Dask Array or Dask Delayed, Dask ML, Dask Bags, and Dask DataFrame.
The downside of Dask
- Dask is not good at optimizing complex SQL queries.
- Index, Sort, and shuffle Operations are not good at Dask parallel computing.
3. Polars Library
The Polars project was started in March 2020 by Ritchie Vink.
Polars is a DataFrame library in the rust programming language and uses Apache Arrow as a foundation.
Polars is the wrap speed data frame library for python and rust.
Polars does not use an index for the data frame.; it utilizes an apache arrow why because the apache arrow is efficient in areas like load time, memory usage, and computation.
Why should you use Polars Library?
- Polars library provides a fast and easy way to work with a large dataset.
- Polars is a data manipulation and analysis library written in rust with APIs in Python.
- polars library gives full Support for numerical calculations.
- String manipulation and data frame operations like filtering, joining, intersection, and aggregations such as groupby can be made accessible using the polars library.
- Parallelization, optimizing CPU, Arrow2 framework makes polar so fast.
- when building data pipelines, polars is the best tool.
The downside of polars
- polars is not much efficient in compatibility.
I have written about 3 primary libraries I used to work on a day-to-day job, but it doesn’t mean I won’t use other libraries. As data scientists, we should always be up to date with the tech. Even there are dozens of libraries available on the market now. I have tried many, but these 3 are most attracted me to my data science job. What is your favorite library on data analysis tasks?
Thanks & Regards
Amsavalli
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