If you are a data scientist, data analyst or just someone who loves to play with data and gather insights out of it then this article is for you. In this article, we discuss seven Python packages for data analysis and visualization which are popular and in-demand today and add significant value to the end result of a project by providing ease of access and smooth development experience.

What is Data Analysis?

Data Analysis is formally defined as the process of cleaning, searching, annotating, manipulating and modeling of data so as to identify relevant information and explain meaningful results out it. Today, when the world is driven by data, data analysis and its supporting tools are acquiring a significant place in the business ecosystem.

What is Data Visualization?

Data visualization is the presentation of data in a pictorial or graphical format. It allows to quickly interpret the data, manipulate different variables and see their effect at a glance and determine useful features of the data.

The following are some of the python packages which are quite in demand and are widely used to perform data analysis and visualization.

Pandas

Pandas is one of the fastest open-source data analysis and handling tools built in Python. On a broader level, it provides a whole ecosystem to play with data in a much easier fashion. It provides ideal data structures such as data frames and series which support data manipulation. Data loading, data handling, and powerful computations are some of the key features at which Pandas excel.

Installation

If you are using pip for package management, you can simply install Pandas by running the following command.

pip install pandas

Alternatively, you can also use Anaconda to install Pandas.

conda install pandas

NumPy

NumPy is a library to perform mathematical computations in Python. It is also a popular tool used for data analysis techniques. It enables the use of large arrays and matrices and provides a set of functionalities to perform complex mathematical operations on them.

NumPy has a lot of added advantages when compared to the conventional list data structure in Python. NumPy arrays are fast, convenient to use and consume less memory. All these properties of NumPy allow it to qualify for one of the most frequently used libraries while performing data analysis.

Installation

If you are using pip for package management, you can simply install NumPy by running the following command.

pip install numpy

Alternatively, you can also use Anaconda to install NumPy.

conda install numpy

SciPy

SciPy is an open-source Python library which is used to solve scientific and mathematical problems. It is built on NumPy extension and allows users to manipulate and visualize data with a wide range of high-level commands.

In contrast to NumPy, SciPy is more focused on scientific computations and hence contains fully-featured versions of mathematical and scientific functions.

Installation

If you are using pip for package management, you can simply install SciPy by running the following command.

pip install scipy

Alternatively, you can also use Anaconda to install SciPy.

conda install scipy

Matplotlib

Matplotlib is an open-source Python library used to draw graphics. It is an extremely rich package when it comes to exhaustive drawing and graphing. It supports nearly all forms of graphical interfaces and allows the users to utilize its functionalities to the maximum capacity. Since data visualization is one of the primary techniques to explain data, Matplotlib is quite high in demand.

Installation

If you are using pip for package management, you can simply install Matplotlib by running the following command.

pip install matplotlib

Alternatively, you can also use Anaconda to install Matplotlib.

conda install matplotlib

Seaborn

Seaborn is a data visualization library in Python. It is based on Matplotlib and allows the creation of statistical graphics. It has a number of functionalities which contribute to the ease of data visualization. An example of such functionalities includes an API that is based on datasets and allows comparison between multiple variables. It also supports multi-plot grids that provide great ease in building complex visualizations.

Installation

If you are using pip for package management, you can simply install Seaborn by running the following command.

pip install seaborn

Alternatively, you can also use Anaconda to install Seaborn.

conda install seaborn

Plotly

Plotly is another open-source graphing library and is extensively used for data visualization. The core feature which distinguishes Plotly from other graphing libraries is that it not just allows the users to create graphics but also offers to interact with them at run time. This creates a smooth visual experience when it comes to dealing with visualizations on large datasets.

Installation

If you are using pip for package management, you can simply install Plotly by running the following command.

pip install plotly

Alternatively, you can also use Anaconda to install Plotly.

conda install plotly

Bokeh

Bokeh is another interactive visualization package in Python and offers fairly good performance on large datasets in terms of interactivity. It offers another cool feature of accepting a large range of data input formats (CSV, JSON, etc) and generating visualizations for them.

Installation

If you are using pip for package management, you can simply install Bokeh by running the following command.

pip install bokeh

Alternatively, you can also use Anaconda to install Bokeh.

conda install bokeh

Key Note

Above mentioned are some of the popular and in-demand Python packages for data analysis and visualization. Each library comes with a different feature set and offers a great deal of functionalities to effectively perform desired operations on data. Readers are encouraged to explore these libraries and select the most suitable library according to their use case and application.

If you wish to learn more about Python, you can check out our collection of Python tutorials.

What’s your favorite Python package for analyzing data? Let us know in the comments below.