Statistical data analysis is the process of collating, preparing, analyzing, and visualizing the data in a manner that helps businesses to make informed decisions. In this article, we’ll discuss how to use Python for data analysis.
With data-oriented decisions, businesses make predictions about customer demand, trends, behaviors, and much more.
For analyzing large volumes of data, your business needs appropriate tools and programming languages to work efficiently. Python for data analysis is one of the most popular programming languages that contain data-oriented libraries to simplify the task.
How to analyze the data with Python
The three most important steps in statistical data analysis are:
- Data mining
- Data processing and modeling
- Data visualization
Let’s go step by step and understand how you can perform the same with the use of Python.
1. Data mining
Data mining is the process that extracts usable data from a large set of raw data.
The results of any data analysis are only as good as the data that is fed into the processing. A data analyst can use Python pre-built libraries like Scrapy for data collection and mining. With Scrapy you get all the tools that you need to extract data from websites, process them, and store them in a preferred structure and format.
Beautiful Soup is another Python library used for web scraping and pulling data out of HTML and XML files. It creates a parse tree from page source code to extract data in a hierarchical and more readable manner.
Below are the main steps involved in web scraping:
- Installing the required third-party libraries like Scrapy or Beautiful Soup
- Accessing the HTML content from the target web page
- Parsing the HTML content
- Searching and navigating through the parse tree
2. Data processing and modeling
The two most-commonly-used Python libraries for data processing are NumPy (Numerical Python) and pandas.
NumPy – NumPy consists of multi-dimensional array objects for performing mathematical and logical operations on arrays. NumPy gives you an enormous range of fast and efficient ways of creating arrays and manipulating numerical data inside them.
You can easily import NumPy and initialize a NumPy array in your code.
import numpy as np
A = np.array([1,2,3])
pandas – pandas is an open-source library that enables you to perform data manipulation and analysis in Python. Pandas offer data manipulation and data operations for numerical tables and time series.
Python is capable of running different types of predictive models efficiently like regression, decision trees, SVM, Random Forest, etc.
With the help of these models, you have the power to predict the future outcomes of the events based on historical data. It helps businesses to make more informed and proactive data-based decisions.
If you want to build a predictive model using Python, you will need to:
- Import respective Python packages and read data into your environment
- Divide your data into 2 parts — training data and test data
- Train your model on the training data
- Test the efficacy of the model against the test data
- If you’re satisfied with your model, start to make predictions on a new data set
3. Data visualization
Data visualization gives us a clear picture of the analysis by giving it visual context through maps, pie charts, or graphs. It makes the data easier for the human mind to comprehend.
The most popular Python libraries used for data visualization are Matplotlib and seaborn. These libraries help to convert numbers into graphs, charts, diagrams for better understanding and explanation.
Now, let’s understand why Python is popular in the data analytics world.
Why Python for data analysis
Python has pre-built libraries and packages that help in simplifying data mining, data processing, and data visualization.
Here are a few top-most points that make Python the most popular choice for statistical data analysis:
Programming languages with a strong developer community are not only easy to learn, but they also help you to grow as an expert developer.
Communities provide you with the debugging solutions, the best practices, the possibilities, and whatnot.
Python has been around for a while now and that’s why on GitHub, it ranks no. 1 amongst all the programming languages (in terms of pull requests).
2. Easy to learn
Python is an easy language to learn, as its syntax resembles the English language and is easy to read and understand. You can learn Python on your own as there are lots of tutorials and videos available for learning Python.
3. Wide range of libraries
Python hosts several powerful libraries specifically designed to simplify the most frequent tasks in data analysis.
These libraries are open-sourced and available for anyone to import and start using inside their code.
The use of the pre-built libraries not only speeds up your work but also makes it more error-free.
4. Versatility, efficiency and speed
Python is efficient, reliable, and faster than other modern programming languages. It can be used in different environments, and th
is versatility of Python makes it more attractive to use in varied applications like:
- Mobile applications
- Desktop applications
- Web development
- Hardware programming
Look no further for statistical data analysis
In the present digital world, data has become an important part of businesses. It is the responsibility of the business owners to analyze their data and make informed decisions to achieve a competitive advantage.
In the field of data analytics, Python is one of the most popular and powerful programming languages. It supports numerous functional libraries that are easy to plug and play into your code. It has a great community, and you’re never short on debugging solutions or advice on best practices when it comes to Python programming.
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