Data visualization makes it easier to identify recurring patterns, trends and any outliers from a large data set. It is used for translating numbers and information into a visual context such as a graph, to make data easier to understand.

Why is data visualization important

Data visualization is important across many functions. It can be used by companies for reporting, sharing information with stakeholders or for making simpler visual representation of large volume of data by plotting graphs.

Visualizing output becomes all the more important while using advanced machine learning or predictive analytics algorithms. It helps to track and monitor results and the output. This helps in ensuring that the models are being trained and performing as intended. Visualizations of complex data sets are easier for summarize findings and to understand and interpret them.

It is important to identify the data sets that best represent the information and also zero in on appropriate visualization styles for it. The insights provided by data visualizations will only be as accurate as the data and how crystal clear such visualizations are to interpret and understand. Therefore, it is essential to have accurate data visualization representation.

Top 5 python libraries for data visualization

1. Matplotlib

Matplotlib is the most widely used library for data visualization. Matplotlib can be easily integrated in python applications. It’s often used to create visualizations by researchers, data scientists for publication.

Matplotlib supports most popular charts such as histograms, bar charts, scatterplots etc. Also extensions are available for creating advanced visualizations like 3-D plots.

2. Seaborn

Seaborn is built upon Matplotlib. It provides a very easy to use interface for building complex visualizations. It is much easier to use as fewer lines of code are required to plot complex visualizations. Seaborn has an API that is based on datasets. It allows for quick comparison across multiple variables. It also makes use of different colors to highlight different patterns. It also estimates linear regression models automatically.

3. Plotly

Plotly as an online platform for data visualization, and can also be accessed from a Python notebook. Plotly supports some charts such as line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.

4. ggplot

ggplot is a Python implementation of the grammar of graphics. ggplot is commonly used for data analysis. ggplot is tightly integrated with Pandas making it necessary to use Pandas to make use of all the features.

5. Bokeh

Bokeh is a library designed for generating visualizations that are browser friendly. Bokeh allows for generating visualizations that are interactive in nature,. This means information can be conveyed in a more intuitive way.

Bokeh allows for unique visualizations through Network graphs, Geospatial plots etc. To show these visualizations in a browser, there are options to export them and display using just Javascript.