Python Packages For Data Science

Python is the most preferred language used for data science and machine learning. It is quite simple and anyone with out any codding or programming background can learn it quite easily, when compared to other programming languages. 

Python is a free software. We don't have to pay any charges for using it on any online platform or installing it on our personal computers.

Today we shall see some most important and basic python packages used for data science projects.

1. Scientific Computing Libraries

i. Pandas

Pandas offers data structure and tools for effective data manipulation and analysis.

Following are some of their features in brief:
  • Tools for reading and writing data in between in-memory data structures and different file formats.
  • Data alignment and handling of missing data.
  • Reshaping and pivoting of data sets.
  • Label-based slicing, fancy indexing, and sub setting of large data sets.
  • Data set merging and joining.
  • Group by engine allowing split-apply-combine operations on data sets.
  • Column insertion and deletion.
  • Provides data filtration.
  • Hierarchical axis indexing to work with high-dimensional data  in a lower-dimensional data structure.
These are only few functions of pandas.

ii. NumPy

NumPy library adds support for large , multi-dimensional arrays and matrices , along with a large collection of high-level mathematical functions to operate on these arrays.  

iii. SciPy 

SciPy is a free and open source Python library used for scientific computing, technical computing and also for data visualization.

2. Visualization Libraries

Python Packages For Data Science
Python packages for data science

If we visualize the data, we come to know the trends that the data is showing quite quickly and in turn pick the right approach to solve the problem. It is also the best way to communicate our findings to the stakeholders.

i. Matplotlib

The Matplotlib package is the most well known library for data visualization . It is great for making graphs and plots. The graphs are highly customizable. 

ii. Seaborn

Seaborn is a python data visualization library based on matplotlib. It provides a high level interface for drawing attractive and informative statistical graphics. It's very easy to generate various plots such as heat maps, time series violin plots. 

3. Algorithmic Libraries 

With machine learning we are able to develop model using our data set and obtain predictions. The algorithmic libraries tackles the machine learning tasks from basic to complex.

i. Scikit-Learn

Scikit-Learn is a free machine learning library for python. It contains tools for statistical modeling , including regression, classification, clustering and so on. It also supports python numerical and scientific libraries like NumPy and SciPy.

ii. Statsmodels

Statsmodels is a python package that allows users to explore data, estimate statistical models and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions and result statistics are available for different types of data and each estimator. It complements SciPy's stats module.

Hope you enjoyed learning the Python libraries which shall be used for solving of data science assignments.


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