In businesses that make informed decisions, there are many ways to extract insights from data, one of them is using Python for data analysis .
Data analysis with Python has become very popular among data scientists and analysts , as it offers resources, vast libraries and good functionalities to data professionals who use this programming language.
In this article, understand what data analysis with Python is, why you should use this programming language for data analysis, the main Python libraries, and more!
What is data analysis with Python?
Python is a programming language that has become very popular in the data analyst and data scientist community.
Making decisions based on real business data has guided the practice of several companies that seek to be more assertive and strategic .
To achieve this, the collection, processing, analysis and visualization of data must be done in an optimized way and focused on extracting the most valuable insights for the business , while being efficient .
Thus, the Python programming language has been the main choice for data science professionals . This language has a vast ecosystem of libraries , which are ideal for supporting data manipulation and analysis.
In addition, it provides other resources that support this process, such as data visualization through easy-to-understand graphs , or support for machine learning and deep learning through libraries designed for this purpose.
Thus, Python is a programming language that is very suitable for carrying out data analysis processes in a simple and intuitive way, while also having complete libraries that optimize this process.
Why use Python for data analysis?
There are many advantages to using Python for data analysis. Check out some of them below!
Diversity of available libraries
One of the main advantages of using Python to analyze and visualize data is the availability of dedicated libraries for this purpose in this programming language.
Libraries offer tools to manipulate, organize and transform data, create visual graphics, among other essential resources for assertive data analysis.
Some libraries used in data analysis with Python are:
Pandas;
NumPy;
Seaborn;
Matplotlib,
TensorFlow;
PyTorch, etc.
Consolidated community of professionals
Another great benefit is the large and consolidated community of professionals who use Python for data analysis.
As mentioned, Python has become very popular among data analysts and related fields. This makes the community of professionals who use this programming language very strong.
Thus, data professionals, whether beginners or not, can count on the support of the community , in addition to being able to have meaningful exchanges to improve both their work and to contribute to the community.
Another advantage is that in addition to support, professionals also receive frequent updates to Python libraries , optimizing performance with this type of programming language.
In addition to being used for data analysis, the Python programming language also serves other purposes when it comes to programming. Thus, it is a versatile and multifunctional language, and can be widely used to carry out various types of projects.
For example, it is possible to use Python for web development , with libraries such as Flask and Django , as well as for the automation of tasks and processes , software and game development , among others.
Easy to learn
Many people consider Python to be an easier programming language to learn and execute. This is for several reasons, such as:
It has a clear and easy-to-understand syntax , being more readable and direct, unlike other programming languages such as C++;
It uses “indentation” , a hierarchy of elements that makes the language more organized and clean and therefore easier to learn and execute. While in other languages the use of sym why choose office 365 database service bols such as curly braces and parentheses is common to delimit the code, in Python, indentation makes this process more organized from the beginning;
It is open source , which means it operates on open code and offers a lot of materials, documentation, resources and libraries for those who are just starting out;
It has an engaged community willing to help beginner professionals, making the process more immersive and optimized.
Top Libraries for Data Analysis with Python
One of the factors that make the Python programming language excellent for data analysis is its well-structured, vast, constantly updated, and high-level libraries.
The main libraries for performing data analysis with Python are:
Pandas
One of the best-known and most used libraries by data professionals, it allows the user to manipulate, transform and analyze data in a very optimized way.
Pandas allows reading in various formats, such as SQL, CSV , Excel, etc., in addition to working mainly with two types of data structures: Series and DataFrames .
DataFrames follow a structure similar to an Excel spreadsheet , while Series refers to a one-dimensional array , which can be understood as a simple list of values. Other elements of Pandas are handling null data and merge and join operations .
NumPy
The NumPy library compiles functions related to linear algebra and numerical computation, working with multidimensional arrays , fast calculations, among other features.
Furthermore, the NumPy library is at the core of basically all programs and libraries that deal with mathematical operations and use the Python programming language.
For example, the Pandas library itself bases its data structure ( DataFrames and Series ) on NumPy arrays .
Matplotlib
Matplotlib is a library focused on data visualization, enabling the creation of 2D, 3D, line, bar, scatter plots, histograms, etc.
In it, data visualization can be customized according to the professional's needs, ensuring a lot of flexibility for creating graphs for data analysis.
Seaborn
The Seaborn library works on top of the Matplotlib library , meaning it can also be used for data visualization. The difference is that it allows you to create more visually pleasing graphs, making data analysis more intuitive.
Essential functions for performing data analysis with Python
There are some functions and commands in Python that are used to perform data analysis with this programming language.
These functions are related to the libraries used in this process and are used to import, read, manipulate, transform and visualize data. The most widely used library, Pandas, performs a large part of the data analysis process and has crucial functions for this process.
The first step to starting data analysis is to import a Python library into your current project code to add functionality and other elements to the code.
To do this, use the command: import. For example, if you are going to work with the Pandas library, use the function import pandas as pd to load this library into the code being used.
Next, the main commands for doing data analysis with Python using Pandas are:
read_*()
To start a data analysis project, you need to load the data into the DataFrame . The read_*() function will import data from a file in the chosen format so that it can be analyzed in the project.
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