Python Data Analysis and Visualization: Cheat Sheet

Python is an extremely readable and vastly used programming language there is. It doesn't only provide dedicated diversity in its use but also the syntax for understanding the programming language is convenient, to begin with. Data scientists and analysts consistently use the python language because it is extremely straightforward and handy when it comes to constructing streams of data and also processing it.

This takes us to another application of the Python language and that is its use in prospects like data visualization. Data visualization refers to the digital interpretation of data in either two dimensional or a three-dimensional array with dedicated charts, diagrams, and tables as well. In order to begin with the concepts of data visualization, you need to work on a dedicated plot that provides the base for the whole system. Now in order to create the plot a lot of effort is required and lines of code that need to be incorporated consistently into the program until you get a plot only to begin with your data visualization project.          

As explained earlier the role of python in data analysis and visualization is extremely phenomenal due to a number of libraries that python provides access to which carry out different functions. But in order to extract everything that Python has to offer, you must work on your cheatsheet.

Python Cheatsheet; Dawn of convenience

A cheatsheet in literal terms is like having a little journal with you with everything that you require in order to proceed further with a dedicated project. Every cheatsheet can be different depending on the very requirements of the user but for this very article, a detailed cheatsheet on how to construct your plot is available. Go through every section delightedly and learn as much as you can;

Steps required to work on virtualization

1. Prepare data according to the number of dimensions that your plot has, you can easily understand what kind and number of dimensions your plot will be running by studying the project
2. Next thing is to initiate the graph world that literally translates into the overall design or construct/theme on which the plot has to stand
3. Creating the plot
4. Additional features need to be incorporated into the plot such as titles, labels and other stuff like this

Imports

The next thing after you have a clear indication of what you are going to work on is the start of the process of data importation. Matplotlib and Seaborn are the libraries of Python that should be loaded in here and their common alias can also be used. This would reduce the time which you otherwise will be spending on typing their long and non-required names.

Propagation of the graph world

It is important that you begin with the development or construction of a figure that would help in understanding the graph size. Seaborn library can be used here to add grids and styles to the graph space. There are four different styles in Seaborn that you can simply use here. After that, the lines, tables, elements of the plot as well as other style based elements can be loaded eventually.           

Creating the plot

The Python data analysis and visualization cheat sheet make it easier to create the plot. All the plots can be created using a single line of code running on the Seaborn library that is made available to you in this cheatsheet. The creation of the plot would literally depend on how many dimensions the plot is going to have.

Distribution plots

The distribution plots only provide the conception of data that has only a single function or carries a single variable. This data set only has one dimension and can elaborate rather easily where the concentration of the data points along a number line exists. Seaborn library of Python can on the other hand work aggressively on providing the results for the two-dimensional distribution plots and it will show two distribution plots simultaneously.

Following are some of the lines of coded depicted for you to use in your data visualization project that  you happen to be working with Python, the sole purpose is to present you with an idea such as what type of code executes what specific function and how you can get help from this cheatsheet easily;

• The distplot will plot a one-dimensional plot with a histogram while using the
• The rugplot will show ticks in the data point to show the clusters
• The kdeplot works extremely well with the one dimension inputted plots to show the curve of the distribution
• The jointplot will plot a scatterplot with its histograms on both sides to display the respective dimension

As you can already see; putting on these few lines of code can make the overall process of working or creating the plot very easy and once the plot has been created the visualization of the data is no big deal. Simply begin with the steps that are mentioned within this cheatsheet and have your prospects of working on large data virtualization projects simple and convenient.

In the long run, you will come to appreciate the context of having a data virtualization cheatsheet with you because it is time-saving, a jump starter, and very easy to work with.

The data analytics certification is required if you want to work as data analysis or data virtualization expert. Having complete this certification can help you to advance further in your career.