Data Visualization on Python


In today’s era, a bulk of data is being generated on daily basis. And if this data is in raw format, analysis of such data becomes very challenging. To overcome this problem, a process called Data Visualization is used.

Data Visualization is the process of conversion of data into visual representation like graphs, charts & other visuals.

How to do Data Visualization on Python?

Learning Data Visualization on Python will help you to gain several skills in the field of analysis, management & designing. This helps to convert enormous or large datasets into creative, innovative & easy to understand visual representatives, thus making it look meaningful.

Data Visualization on Python has numerous libraries for displaying data & each one are having unique features. Each of this Data Visualization on Python libraries can support various types of graphs. So if you want to create interactive, innovative, engaging, live or highly customised plot, Data Visualization on Python is the best option.

Career options in Data Visualization on Python

By expertise in Data Visualization in Python, one can begin his/her career as:

·        Data analyst

·        Financial data analyst

·        Business data analyst

·        Data engineer

·        Data scientist

·        Data conversion analyst

·        Data governance analyst

·        Healthcare data analyst

·        Data Visualization on Python developer

·        Data management analyst

Some of the well known Data Visualization on Python libraries are

1.     Data Visualization on Python via Matplotlib :

Matplotlib, the most well known & amazing graphing tool used in Data Visualization on Python. It was discovered by John Hunter in the year 2002. By this tool you can create

·        Line graphs

·        Bar charts

·        Pie graphs

·        Histograms

·        Add labels & styling to graphs

·        Add error bar to graphs

·        Scatter Plot

Thus Data Visualization on Python via Matplotlib offers low level data visualization that is easy to use. It also provides a lot of flexibility. Matplotlib library is basically built on NumPy arrays & created to operate with the broader SciPy stack. You can install Matplotlib on Windows, Linux & macOS.

2.     Data Visualization on Python via Seaborn:

Seaborn is basically Data Visualization on Python library based on Matplotlib. In other words, it is a high level interface built on top of the Matplotlib library.

Data Visualization on Python via Seaborn comes with excellent design options & colour palettes to help you create more appealing & attractive graphs. As it is built on top of Matplotlib so it can be used with Matplotlib as well. Using Seaborn along with Matplotlib is extremely simple too.

 

There are various plots used in Data Visualization on Python via Seaborn. These Plots are mostly used to depict the relationship between two or more variables. These variables can be either numerical or represent a category such as a group, class, or division. Seaborn categorises plot into the following

·        Relational plots

·        Categorical plots

·        Distribution plots

·        Matrix plots

·        Regression plots

·        Multi-plot grids

3.     Data Visualization on Python via Pandas

Data Visualization on Python via Pandas is a type of open source, high performance library which is easy to use. It is one of the most well known Python libraries. Data Visualization on Python via Pandas provides easy to use data structures, data frames & data analysis tools. It was started by Wes McKinney in the year 2008.

 

Data Visualization on Python along with Pandas is utilised in a variety academic & commercial areas including finance, economics & analysis

 

Data Visualization on Python via Pandas has a higher level API than Matplotlib which helps in getting the same results in less code. It is basically built on 2 Python libraries namely Matplotlib for data visualization & NumPy for mathematical operations.

 

Pandas established 2 new types of data storage objects:

·        Series: have a list like structure

·        Data frames: have a tabular structure

 

4.     Data Visualization on Python via Plotly:

Data Visualization on Python via Plotly is a free, open source graphing library that makes engaging, online publication quality graphs. Data Visualization on Python via Plotly supports over 40 unique chart types encompassing a broad range of financial, scientific & 3 dimensional graphs.

 

Data Visualization on Python via Plotly has a hovering feature that can assist us in finding helps any outliers or abnormalities in huge number of data points. It also makes the graph look more appealing & attractive.

 

Plotly supports variety of plots:

·        Line charts

·        Scatter plots

·        Histogram

·        Cox Plots

 

Data Visualization on Python via Plotly allows customization of graphs in an infinite number of ways, making plots more interesting and understandable to audience.

 

5.     Data Visualization on Python via Bokeh:

Data Visualization on Python via Bokeh, a type of data visualization library that creates interactive charts & plots. Bokeh renders its plot using mediums HTML, notebook & JavaScript. Bokeh plots can be installed in apps like flask & Django.

 

Data Visualization on Python via Bokeh mainly involves the use of modern web browsers for demonstrating innovative, concise & elegant graphics with high level interactivity.

 

Bokeh generally provides 2 types of visualization interfaces

1.     bokeh.models: A type of low level interface that gives application developers high flexibility

2.     bokeh.plotting: A type of high level interface for making visual glyphs

 

6.     Data Visualization on Python via Altair:

Data Visualization on Python via Altair is library for declarative data visualization which is basically based on Vega & Vega-Lite (Vega & Vega-lite are high visualization data tools).

 

Data Visualization on Python via Altair comes with a strong & concise visualization grammar that enables you to create a variety of statistical visualizations. The one who has already used Data Visualization on Python via Matplotlib will be able to appreciate the qualities of Altair library.

 

Altair’s API is consistent & very user friendly.  It is rapidly becoming the first choice of people for quick & effective approach to visualize data sets.

 

Essential elements of Data Visualization on Python via Altair

Altair chart require 3 important elements: Data, Mark & Encoding.

 

a)     Data:

As Data Visualization on Python via Altair is based on the Pandas Data frame, encoding with this become simple, and it can recognise the data types required for encoding. Thus one should use Data frames wherever possible because they make the process easier.

 

b)     Mark

Mark generally determines the visualization of data on the plot. In Altair, there are a variety of mark techniques available to choose from.

Area, bar, point, text, tick & line are some of the fundamental markers of Data Visualization on Python via Altair. Some compound markings like box plot, error band & error bar are also available in Altair

One of the biggest benefits of using Altair is that one can change the chart type simply by changing the mark type.

c)     Encoding

The mapping of data to add visual features to the chart is one of the most significant aspects of visualization. Encoding is the name given to this type of mapping in Altair, which is done with the Chart.encode() method.

 

Position channels, mark property channels, hyperlink channels, and other sorts of encoding channels are accessible in Altair.

 

Benefits

·        For all types of plots, the user only needs to alter the mark attribute to get different plots & core code remains the same.

·        In comparison to other visualisation libraries, the code is shorter and easier to develop.

·        The user can concentrate on the relationship between the data

columns and ignore the plot features that aren't necessary.

 

In today’s world, Data Visualization on Python is probably one of the most features for conversion of raw data into visualization format.