Rajendra Gupta generates some graphics:
Data analysis requires analysts to handle structured, semi-structured, or unstructured data. Small datasets with few rows and columns are easy to understand. However, as the data complexity increases with many interlinked variables, getting data insights from tabular formatted data becomes challenging. According to a recent study from MIT, the human brain processes an entire image in just 13 milliseconds. Therefore, it is helpful to learn Python and visualization together.
How do we use Python to generate plots from the data to analyze patterns, correlations, and trends? What plots are available, and how do we use them with customizations? Let’s explore them in this tip.
There are a few visualization libraries in Python I prefer over matplotlib, and for static graphics, ggplot2 in R has pretty much everything else beat. But matplotlib is essentially the standard, so it’s important to know.