How to visualize data using Python
Data visualization is an important tool for analyzing and summarizing information. Python provides many libraries for creating a variety of graphs, charts, and other ways to visualize data. In this article, we'll look at the main data visualization libraries available for Python and give examples of how to display data.
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Matplotlib is one of the most popular libraries for data visualization in Python. It provides a wide range of tools for creating various graphs and charts.
Matplotlib is used to draw all kinds of graphs. It is an essential library for any data analyst. In addition, Matplotlib underlies other libraries. For example, Seaborn, which provides a high-level interface.
Plotly is a library for creating interactive data visualizations. It allows you to create graphs that can be explored, modified, and shared with others. Plotly supports line graphs, bar graphs, histograms, and more.
Humanize provides simple, easy and readable string formatting for various metrics: numbers, dates, time, etc. This library can format data in a variety of ways, including formatting with commas, converting timestamps to relative time, and much more.
Pendulum is a very handy library for everyday use. It extends a built-in Python module called DateTime, adding a more intuitive API for handling time zones and performing date operations. Overall, it provides a simple and easy-to-use API for formatting time-related metrics.
What are the best ways to visualize?
We've given examples of the best libraries for Python, but now let's get into how exactly you can visualize data in this language. Let's take the first library on the list (Matplotlib) as an example, since interacting with it will be the most visual and illustrative - largely due to its popularity.
Histograms are a type of visualization that looks at the distribution of values for a parameter. For example, you can use them to see what the distribution of values was by year of production of this or that thing: a movie, a game, etc.
Bar graphs are best for visualizing categorical variables. They are similar in appearance to bar histograms, but very different in meaning. For example, they are used when working with nominal scales instead.
Line graphs are used when you want to visualize quantitative indicators and show their relationship to each other. For example, they are useful when you want to calculate how many movies or games were produced in a year.
Scatter plots are useful when you want to show the relationship between two variables. With this type of visualization, you can also show the relationship between different groups of data by coloring them with different colors.
Earlier we looked at histograms, which are great for visualizing variables of the same type. But what if that's not enough and you need to display more information?
Box plots can help with that. The bottom and top of a box plotted with a solid line are always the first and third quartiles (25% and 75% of the data, respectively), and the bar inside the rectangle is always the second quartile (median). And the dashed lines with bars at the end start from the rectangle and show the range of the data. This allows you to fit many more variables, although it makes the graph less clear in general.
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