Data visualization is essential to analyze massive amounts of information and make data-driven decisions. Data Visualization involves producing images that communicate relationships among the represented data to viewers of the images. By using visual elements like charts, graphs and maps. Data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
Matplotlib and seaborn are the two popular data science libraries used for visualization, We will examine them below:
Matplotlib: This is a standard data science library that helps to generate data visualizations such as two-dimensional diagrams and graphs (histograms, scatterplots, non-Cartesian coordinates graphs). Matplotlib also provide an object-oriented API for embedding plots into applications. However, data scientists need to write more code than usual while using this library for generating advanced visualizations.
Seaborn: Seaborn is based on Matplotlib and serves as a useful Python machine learning tool for visualizing statistical models – heatmaps and other types of visualizations that summarize data and depict the overall distributions. When using this library, you get to benefit from an extensive gallery of visualizations (including complex ones like time series, joint plots, and violin diagrams).
Importance of Data Visualizations
Data visualization is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results.
The main goal is to visualize data and statistics, interpreting the displays to gain information. Without data visualization, it is challenging to identify the correlations between the relationship of independent variables. By making sense of those independent variables, we can make better business decisions. Data Visualization shows us trends over time, It’s impossible to make predictions without having the necessary information from the past and present. Trends over time tell us where we were and where we can potentially go. Data visualization also takes the information from different markets to give us insights into which audiences to focus attention on and which ones to stay away from. We get a clearer picture of the opportunities within those markets by displaying this data on various charts and graphs.