Python has become one of the most and popular programming languages as compare to others in the world. It is widely and significance in various fields like in data analysis, machine learning, web development, artificial intelligence, etc. As, 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 Using Python for Data Analysis?
This language has very simple attribute and syntax which provide 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 analyst to choose this in data analysis because it has strong and versatile structure.
To understand how Python is popular, check out the below given stats please.
Let’s take a step back in history to begin.
Python has topped in a survey on Stack Overflow on 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 same 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 outstanding. It was making its space in the sector of information technology.
A survey conducted by stackoverflow in 2024 on programming, scripting, and markup languages. You can see clearly that Python is in top 3 after JavaScript and HTML/CSS.
While in a survey on frameworks and libraries by same, NumPy, Pandas, Scikit-learn, PyTorch, and Tensorflow are in 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 languge for web development too.
In another survey on Top 10 Fastest Growing Languages in 2024 on the same, Python won the rase 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 which needs patience and a lot of focus. This process is divided in to 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 we need to check the problem which we are trying to solve i.e.
- What is the purpose of analysis? It should be very clear.
- What 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 like descriptive, inferential, exploratory (EDA), predictive, diagnostic, and prescriptive.
Now we have prepared the roadmap for rest of the analysis.
2. Set Up Your Development Environment
Now, we have decided the main objective of the data analysis, we need to set up an environment by which we can do data testing, modelling, 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 in Pandas can read sources only from CSV, Excel and Text files. The data is collected, the next step is to prepare and process the data for deeper and clear analysis. Proper data of collection make sures that you have the right information to get meaningful insights and make decisions correctly.
4. Data Explore
This is the very crucial step of do data analysis, we need to examine the data to convert into well-structured data the details need to check like rows, data types, null or missing values and so on.
We need to identify the patterns or trends 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 its 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 analyse the data using different methods to identify the patterns, trends, and relationships between the data. It also helps in visualizing data distribution, correlations, and understanding features and their importance.
7. Testing of Data
After exploration of data we understand that what kind of data it is? what we need to do from the data? and what we want from the data? Knowing the answers of these questions we format our data accordingly.
This is 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 modelling.
8. Data Visualisation
Visualisation of data enhance its more well manner structured, validity, reliability. It gives clearer and sorted understanding of the data. Also helps data gives best insights so that we can identify the trends and patterns.
Bar graph, pie charts, scatter plots and etc. mainly used in sea horse Python library. By data visualisation we can represent data in a presentable way so that we can easily understands the what data is about? Its counts, data types and null values and etc.
9. Model Validation and Tuning
Once data visualisation done then we can proceed the most important and demanding step to modelling. 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 Python environment like flask or API, etc.
Then we need to do 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 presents insights efficiently.
By this we can easily or quickly make decisions and better recommendation from the analysis.
Conclusion
Python is now becoming very important language to learn for beginners to expert in the technological world. Python simplicity, versatile and strong in nature makes standout as compare to other languages and due to this expertise recommends 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 consumptions. It needs more memory as compare to other language like C and C++. So, if we provide required memory which can be expensive, we will be able to use in a meaningful way.
This language has grown its space in the technology and successfully maintaining 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 the tools and capabilities that is matched with the developers and programmers to analysis the data.
This is the reason people learn Python for web development and make them stand out of others.
We can say that simplicity, libraries, and its community support make it first choice for the data scientists or analysts. Python’s outstanding libraries for data analysis is one more reason which provide meaningful insights from the data.
