Python and its application in the field of data analysis.

Introduction
When human beings started using computers, they had to use simple functions like addition they had to write pages of cryptic symbols that looked like ancient runes. It took years to learn, and if you missed a single dot, the whole thing crashed.
In 1980, Guido van Rossum, decided to make life easier for everyone.

What is Python?
As scary as python sounds, Python is literally a programming language used to build software and websites, data analysis and automate task. With other programming languages at stake, python is known for ease of readability and use of simple English with influence from mathematics. It’s also used to perform software updates, help in debugging, gaming and developing desktop application.
Python is popular in data analysis because:

• Uses a syntax that is easy helping analyst to focus on data at hand.
• It has a diverse ecosystem with specialized libraries like Panda. With these libraries, they have pre-built functions which simplifies data analyst’s work. These libraries simplify complex data manipulation and visualization tasks.
• With a lot of documentation and tutorial of python, it’s easy to find solutions to problem encountered when using it.
• It has high ability to interact with other programming language like C, java. With this advantage, it gives data analysts leverage to capitalize in the strength of each language.
• It’s designed to handle large datasets efficiently.

Python libraries used in data analytics

A library is a collection of code that makes everyday tasks more efficient.

Pandas
It is used to work with data sets. It has function of cleaning, analyzing, exploring and manipulating data. It allows us to use in large data sets. Pandas provides two data structures for manipulating data: series and Data Frame. It also provides essential operations for working with structured data efficiently. The most common used functionalities:

• Loading data: Read files from CSV, excel, Json to data frame.
• Viewing and exploring data: Allows to inspect data and summarize statistics.
• Handling missing data: detects, replace or remove missing values.
• Selecting and Filtering Data: retrieves records or rows that matches a specific condition.
• Grouping Data: Organize data into categories.

NumPy
NumPy means numerical python. This means, this library works with arrays. A NumPy array is a structured collection of elements of the same data type stored in a table format. It provides efficient numerical computing features like:

• Array Manipulation: Create and reshape arrays.
• Mathematical functions: perform element wise operations.
• Linear Algebra: Perform matrix operations.
• Random number generations: Generate random data.

Matplotlib
It serves like a visualization utility whereby it creates interactive and animated visualization from data. It proves flexible plotting functions that can be used to understand trends, patterns and relationship effectively. Plots are basically used for visualizing the relationship between variables. The different types of plots in Matplotlib includes: line chart, bar chart, histogram, scatter plot, pie chat, box plot and heat map.
Seaborn
This is a data visualization library built on matplotlib and integrated with panda data structures. It makes data visualization easier and more efficient. Basic plots in seaborn are: line plot, scatterplot, bar plot, boxplot, histplot, heatmap and pair plot.
SciPy
SciPy stands for Scientific Python. It’s used to solve many mathematical equations and algorithm. It was built on NumPy Extension. SciPy is widely used in research, engineering and data analysis.
Scikit-learn
It’s a library popular for machine learning. It supports task like classification, regression and clustering of data. It makes data models more efficient and reliable. It also provides ready to use tools for training and evaluation. It allows the use of supervised and unsupervised algorithm which makes it easy to be used by beginners and experts. It’s also used for predictive data analysis.
Statsmodel
It’s a python library used for statistical modeling, hypothesis testing and exploring dataset. StatsModels provides several regression models to analyze relationships between variables and make prediction.
Plotly
Plotly is used for creating interactive, publication-quality visualizations. It is widely used in data science, analytics and machine learning for presenting data insights visually and interactively. Its fundamentals include basic plots, statistical plots, interactive graphs and 3D plotting.
Requests
Requests allow you to make HTTP requests in python. It’s used for web scraping. It’s easy to use and understand as it provides a simple API for HTTP request. It supports various HTTP methods like GET, POST, PUT, DELETE, etc., allowing you to interact with web services and APIs. This makes it best for retrieving data.

Python in Data analysis
Data cleaning
Data cleaning is primarily handled by panda and NumPy library. In the real world, the data tend to be messy i.e has missing values, inconsistent date format, duplicate entries, mixed cases in texts etc. With the use of simple codes in python, it’s able to:

• Identify missing values and either fill them or delete them.
• Remove duplicates.
• Correct data type by converting column to their appropriate format.
• Standardized text where there is inconsistence.
• Identify extreme values and decide whether to transform or remove them.
• Remove irrelevant features that do not contribute to data analysis.

Data analysis
After a cleanup of data, it’s important to assess relationship between variables. With the different libraries in python, they are able to perform mathematical equation and models that gives insights such as if there is any relationship between an amazing movie and the top cast. During analysis, it’s important to note that getting no relationship is also feedback from the analysis to avoid personal biases in the final part.

Data visualization
This is the final part whereby you showcase the analysis done in form of plots and other visuals. The core libraries for these are plotly, matplotlib and seaborn. Most data analyst prefer the plotly as it’s quite interactive and allows one to zoom, hover and beautify the visuals to appeal to eyes. Such visual representations help analysts uncover patterns and trends that might not be evident from raw numbers alone.

Real-world examples of Python in data analytics
As python is a versatile language, it’s used in various industries to serve different purposes.

In an entertainment industry it’s used to monitor content that people click or liked most. With that knowledge, it recommends similar content or gives ones that someone may like based on the past.

In a finance world, where we use cards to pay for things, it’s already aware of how clients use their card when transacting. Incase there is a discrepancy from the norm, python is able to detect and results in blocking it.

In supermarket, python uses historical data to check on what customer’s buy most and in what quantity. This help in the decision on what quantity to stock to avoid understocking and overstocking.

Banks and fintech industries use python to write trading bots that sell and buy stocks as it is faster than humans. It’s also used to analyze transactions for customers who are requesting for a loan to determine how much and at what interest they can give.

In law firm, python can be used to analyze the past ruling of a judge to determine how they may rule in a new case. Also, it can skim through a contract to find any hidden clause or mistakes made.

Python acts like a supervisor in a manufacturing industry. As some part of manufacturing uses a factory robot. It’s connected to python whereby it sends data to python script and python is able to pick any abnormality and raise an alarm when a machine/robot is faulty or almost faulty.

Why should beginners learn Python

1. Python syntaxes are easy to read and straight forward which makes it easy for learners to understand. Due to its simplicity, learners can focus on the basic fundamental of programming without feeling bombarded with complex information.
2. Python is free and can be used by anyone. It has countless tutorials, forum and libraries where people can learn it from. These resources ensure one does not stop learning by giving room to learn new codes and experiment old codes.
3. Learning Python helps develop essential problem-solving and logical thinking skills that are transferable to other programming languages. It provides a foundation that can be built on with other complex languages.
4. Due to python’s simplicity, you can write a code and gives feedback immediately giving ultimate satisfaction and motivation. This also makes it fun to use in during projects.
5. Python is the go-to language for AI and machine learning, making it an excellent starting point for those interested in these fields. It helps in understanding and using AI tools effectively.
6. Python is highly versatile and is used in various fields, including web development, data analysis, machine learning, and automation. Because of this, it can be used to in a non-tech career like banking and law.

In conclusion, Python's ease of learning, versatility, strong community support, and foundational benefits make it an ideal choice for beginners looking to dive into the world of programming.