Python has become one of the most popular programming languages as compared to others in the world. It is widely and significant in various fields like data analysis, machine learning, web development, artificial intelligence, etc. We are going to discuss the key steps for effective data analysis of Python and why we should use Python. Let’s talk about why it is so popular nowadays.
Why Use Python for Data Analysis?
This language has very simple attributes and syntax which provide a large environment for large database and framework libraries that can perform various functions in data. This is the reason, why all data analytics courses have Python included. It is an excellent choice for beginner or expert data analysts to choose this in data analysis because it has a strong and versatile structure.
To understand how popular Python is, please check out the stats.
Let’s take a step back in history to begin.
Python has topped a survey on Stack Overflow on the Growth of Major Programming Languages from 2012 to 2018. As you can see, Python’s popularity has grown 2.5 times.
In a different survey on Future Traffic for Major Programming Languages from 2012 to 2020, Python was leading.
In another survey on small but growing technologies in World Bank high-income countries, Python performed outstandingly. It was making its space in the sector of information technology.
A survey was conducted by stackoverflow in 2024 on programming, scripting, and markup languages. You can see clearly that Python is in the top 3 after JavaScript and HTML/CSS.
In a survey on frameworks and libraries by the same, NumPy, Pandas, Scikit-learn, PyTorch, and Tensorflow are in the top 9. For your kind information, all these are the libraries of Python for data analysis.
In a recent survey on Top Programming Languages by Github, Python was the most popular and useful language for web development.
In another survey of the Top 10 Fastest Growing Languages in 2024 on the same, Python won the race and was on top.
Steps involved in effective data analysis using Python
Let’s focus on the topic again. Analyzing data is a long process that needs patience and a lot of focus. This process is divided into multiple steps and as it is I am explaining here.
1. Define the Problem and Objective
The very first step to commence any data analysis is to check the problem that we are trying to solve i.e.
- What is the purpose of the analysis? It should be very clear.
- What do we want from the data?
- Which type of data analysis should be done?
Types of data analysis in statistics
There are many types of data analysis in statistics descriptive, inferential, exploratory (EDA), predictive, diagnostic, and prescriptive.
Now we have prepared the roadmap for the rest of the analysis.
2. Set Up Your Development Environment
Now, that we have decided on the main objective of the data analysis, we need to set up an environment by which we can do data testing, modeling, visualization, and manipulation.
- Installation of Python
- Installation of Python libraries like NumPy is for numerical operations, Pandas is for data manipulation, Matplotlib and Seaborn is for data visualization, Jupyter Notebook.
3. Data Collection
Once the environment is set up, the next step is the data collection.
Data can be in from CSV, Excel, and Text files. Importing data in Python libraries like Pandas can read sources only from CSV, Excel, and Text files. The data is collected, and the next step is to prepare and process the data for deeper and clearer analysis. Proper data collection makes sure that you have the right information to get meaningful insights and make decisions correctly.
4. Data Explorer
This is a very crucial step while analyzing the data. We need to examine the data to convert it into well-structured data. The details need to be checked like rows, data types, null or missing values, and so on.
We need to identify the patterns or trends that can be done by exploratory data analysis so that we can know how much time taking this analysis.
5. Data Cleaning
As we know we get data in raw form rarely, so to get structured data, we need to format it. Then only we can do further analysis. The data we collect from scrapping, databases, or any other sources need cleaning. Python libraries like pandas are mostly known for handling and importing data.
Data cleaning is to make better quality of data.
It removes the duplicate values and corrects the null values.
6. Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) is a fundamental step to understanding the structure of the data. During this process, we need to analyze the data using different methods to identify the patterns, trends, and relationships between the data. It also helps in visualizing data distribution and correlations.
7. Testing of Data
After exploring the data, we understand what kind of data it is. what do we need to do from the data? and what we want from the data. Knowing the answers to these questions we format our data accordingly.
This is a very important step or integral part of data analysis it ensures the quality, reliability, and validity of the insights of data. Throughout this, we can make data modeling.
8. Data Visualization
Visualization of data enhances its more well-meaning structure, validity, and reliability. It gives a clearer and sorted understanding of the data. Also helps data give the best insights so that we can identify trends and patterns.
Bar graphs, pie charts, scatter plots, etc. mainly used in the Sea Horse Python library. By data visualization, we can represent data in a presentable way so that we can easily understand what data is about. It counts data types’ null values.
9. Model Validation and Tuning
Once data visualization is done then we can proceed to the most important and demanding step of modeling. Here, we need to select which model of data we need according to the data and according to what kind of analysis we need to do.
For example, classification model we need to do regression so that we can check the outcomes.
10. Deployment and Reporting
Deploy can be done in a Python environment like Flask or API, etc.
Then we need to deploy the model which is the final step of the data analysis. We do it to get better analysis which includes reports, visualization, and dashboards to present insights efficiently.
By this, we can easily or quickly make decisions and better recommendations from the analysis.
Conclusion
Python is now becoming a very important language to learn for beginners to experts in the technological world. Python’s simplicity, versatility, and strength in nature make it stand out as compared to other languages and due to this expertise recommended to learn and motivate.
There is a special course on data analysis using Python that students can join to master it.
Remember, Python has its own merits but there are some demerits like memory consumption. It needs more memory as compared to other languages like C and C++. So, if we provide the required memory which can be expensive, we will be able to use it in a meaningful way.
This language has grown its space in technology and successfully maintained its significant role in various IT fields. Web development, machine learning, data analysis, and business intelligence are a few examples of the uses of Python. It provides us with the tools and capabilities that are matched with the developers and programmers to analyze the data.
This is the reason people learn Python for web development and makes them stand out from others.
We can say that simplicity, libraries, and community support make it the first choice for data scientists or analysts. Python’s outstanding libraries for data analysis are one more reason that provides meaningful insights from the data.
