It is seen that many people start thinking about what data visualization is after listening to this fancy word. But in reality, it is much simpler, as it’s a concept in which we can make the data look beautiful and make it understandable. For achieving it we use graphs and charts. Therefore to let you know more about it. In today’s article, we will be discussing the details about data visualization in Python. So keep reading this article as we will discuss it in detail with the advantages and uses of it. Let’s start by discussing what exactly is data visualization.
What is data visualization in Python?
Python is a great choice for making applications and programs which consist of numbers and data. We can also make apps for making decisions based on numbers and data. But the data becomes hard to read and understand. Therefore the concept of data visualization comes to save you. With it, we can make the data look beautiful and make it understandable. Moreover, we use charts and graphs to do so.
By using these graphs, maps, charts we can analyze our data more effectively. We can understand the trends in data, the patterns and the faults or outliers.
How can we perform data visualization in Python?
We can achieve this by using some modules/ libraries. Some of them are:
- ggplot: It uses the concept of the grammar of graphics. Grammar of graphics in simple language means the use of aesthetic attributes such as colors, sizes and shapes etc and geometric attributes such as lines, bars etc.
- Seaborn is a Python package for creating statistical representations. The representations depend on datasets. To build informative graphics, the library conducts the necessary mapping and aggregation internally.
- Altair is a Python-based declarative statistical visualization framework. Declarative library indicates that we must define the linkages between the data columns and the channels before we can create any graphics. Altair has a declarative visualization and interaction grammar.
- Plotly: The graphs created using Plotly are dynamic, meaning you can easily find value at any point or session of the graphs. Plotly makes creating dashboards and deploying them on the server a breeze.
What are the data visualization techniques?
- Bar plot: The connection between a categorical variable and a numeric variable depicts by a barplot. Each categorical variable entity represents by a bar. The numeric value is represented by the size of the bar.
- Box Plot: It designed to show an overview of a group of data values with features such as minimum, first quartile, median, third quartile, and maximum.
- A time series is a collection of data points. In it, each of the data points is linked to a timestamp. The amount of rainfall in a region at different times of the year can be an example of it.
- Histogram: A histogram is a visual representation of data presented in the form of groupings. It is also a precise approach for displaying numerical data distribution graphically. It’s a type of bar plot in which the X-axis shows bin ranges and the Y-axis shows the frequency.
- A violin plot is comparable to a box and whisker plot in terms of function. It depicts the distribution of quantitative data across various levels of one (or more) categorical variables, allowing for comparison.
- Scatter plots help display how much one variable affects another by plotting data points on horizontal and vertical axes. Each row in the data table has a marker whose position is determined by the values of the columns on the Y and x axis.
What are the advantages of data visualization in Python?
- Analyzing Data More Effectively: We can monitor the data and use it for getting better day by day. It makes us ready for the future by making us eliminate our mistakes.
- Faster Decision Making: We can use the visuals to understand better and faster. if we supply people with the raw data written on the piece of paper and none would take interest. But through better graphics one can make better and faster decisions.
- Making better comparisons: If the data we have is in an organized way, then we can make better comparisons and choose the best for us.
Let’s wrap it up!
In today’s article, we learnt what is data visualization in Python programming language. We learnt in detail what it is, and how we can use it in python. We also learnt the advantages of using the data visualization concepts in the python programming language.
Therefore we hope you might have learnt something from it. If you found it helpful for you then you can let us know. And in case you have any queries even then let us know. We will try to help you out in the best possible way. Hope you remain healthy and keep learning.
