An Introduction about what is Data Analysis and Visualization using Python?
Let’s Discover with an example.
What is Data Analysis?
Cleaning, altering, and modeling data for meaningful corporate decision-making information is defined as data analysis.
Data analysis is used to extract meaningful information from data and make decisions based on that knowledge.
Why Python?, we already said that Python is a widely-used programming language. When it concerns data analysis, it isn’t the only option, however, it’s a great one. Another reason is that it is more commonly used! Python is simple to use and has a huge developer community to assist you with data analysis. Furthermore, data analysis using Python is highly entertaining due to a large number of creative libraries for data analysis and visualization that it provides.
Pandas is the Python foundation library for data analysis.
It’s a high-level library for scientific computing and numerical analysis based on the NumPy library.
Pandas make working with data easier by providing the DataFrame data structure. DataFrame is a tool for reading and storing data. It includes the basic capabilities for reading and writing the dataset, as well as metadata viewing and querying to extract every nugget of information from it.
The very first task is to determine where you plan to go. It’s crucial to have the appropriate place to store all of your work when doing data analysis in Python. Python data analysis will be more than just text; it will also be your link to the database, so you’ll need a solid working environment.
Anaconda Distribution provides that service in Python. The Jupyter notebook is Anaconda’s ideal workspace. Well, it allows you to have visuals in your notebook immediately.
It also has additional magical features that allow you to see the output without having to explicitly state where you want it.
Let’s understand by one example.
First, reading datasets. For loading the dataset into its fundamental data structure, Pandas provides certain basic operations: DataFrame. We can use it as follows.
What are Data Visualizations?
Data visualization is the process of converting information into a visual representation, such as a map or graph, to make data easier to comprehend and extract insights from. Data visualization’s principal goal is to make it easier to discover patterns, trends, and outliers in enormous data sets. Information graphics, information visualization, and statistical graphics are all terms that are frequently used interchangeably.
Data visualization is one of the processes in the data science process, according to which data must be visualized after it has been collected, processed, and modeled to conclude. Data visualization is part of the larger data presentation architecture (DPA) discipline, which strives to efficiently identify, find, modify, prepare, and transmit data.
Why Python for Data Visualizations?
Python has several charting libraries, including Matplotlib, Seaborn, and several other data visualization tools, all of which have distinct features for constructing useful, personalized, and appealing plots to show data most simply and effectively possible.
Seaborn and Matplotlib
The python libraries Matplotlib and Seaborn are used for data visualization. They provide modules for plotting various graphs.
Seaborn is mostly used for statistical graphs, whereas Matplotlib is used to embed graphs into programs.
|It’s used to make simple graphs like line charts and bar graphs.||It’s primarily used for statistics visualization, and it’s capable of performing sophisticated visualizations with fewer commands.|
|It works primarily with datasets and arrays.||It can handle entire datasets.|
|Matplotlib is a useful tool for working with data arrays and frames. It considers aces and figures to be objects.||Seaborn is far more organized and functional than Matplotlib because it treats the entire dataset as if it were a single entity.|
|For exploratory data analysis, Matplotlib is more customizable and interacts well with Pandas and Numpy.||Seaborn features a wider range of pre themes and is mostly used for data analysis.|
For instance, Let’s take a look at this with an example. Matplotlib and Seaborn, are two well-known Python visualization libraries.
In conclusion, Reference for Data Analysis and Visualization