Python used in Data Analytics

Introduction

Python is simply a high-level programming language used in data analytics,web development,automation,AI and so many more fields.
It was created by Guido van Rossum and released in 1991.
I will walk you through on how it's used in Data Analytics.

Why is python popular in data analytics

Python consistently ranks among the world’s most popular programming languages because it balances simplicity,power and flexibility which is often rare in programming languages.

1.Python looks simple and easy to read

Compared to other programming languages like Java or C++,Python code is much simple and usually takes fewer lines.It makes it easier for beginners to learn.

unlike java:

2.Used in data analyics

You can calculate the average of sales or anything by writing a simple code:

3.It has plenty of libraries

A good example is the 'Pandas'.It lets you load a spreadsheet(or CSV file) and start exploring it immediately.

4.Productivity

Despite being slower than other langages,pthon tends to be more productive since it only need few lines of code and as a developer,you can build a project much faster.

Python libraries usedin data analytics

These are basically the tools you'll use.

1.Pandas

Pandas lets you load a spreadsheet (or CSV file) and start exploring it immediately.It is also so powerful since it helps you clean,organize,filter and analyze data with very little code.

It is widely used by data analysts and data scientists to work with tables and large datasets efficiently.Learning Pandas is one of the most important steps in becoming comfortable with data analytics using Python.

3.Numpy

Numpy handles mathematical opperations.

In the above photo,i calculated the mean in less than a minute.It's incredibly useful when you're crunching hundreds of values.You can also calculate the median,standard deviation without writing loops.Another reason why analysts loves it.

4.Matplotlib and Seaborn

These are the best for visualization in Python by turning your data into charts.Matplotlib is the foundation and Seaborn sits on top of it and organises things in a nicer way with less effort.

A chart is worth a thousand numbers.These libraries turn your boring tables into something you can actually see and understand.
1.Bar charts for comparisons - plt.bar(['A','B','C'], [10,25,15]) shows which category wins.
2.Histograms for distributions - plt.hist(ages) reveals if your customers are mostly young or old.
3.Seaborn makes everything prettier - sns.barplot(data=df, x='city', y='sales') gives you professional colors and cleaner layouts without fiddling.

Using python in data cleaning,analyzing and visulizing

When you're working as a data analyst(or even just exploring data for fun),you'll follow the same process almost every time:
Clean the data,analyze the data then visualize the data

1.Data cleaning

An original data is alwas full of messy staff like duplicates,wrong capitilizaion,empty cells,wrong data types and many more.It is your job to clean it.So python allowys you to clean it in a much easier way

By running that,you can save alot of time that you could have spent in excel.

I also love python since you can save the code and still run it months later.In simple terms,i mean Excel forces you to repeat the same clicks every time.Python remembers.

2.Analyzing the data

once your data is clean,you can now solve every question you want.Python gives you answers fast and the more specific your questions,the more useful the answers become.

You don't need to memorize all these.Just know they exist.Knowing you can answer almost anything in seconds is what makes Python fun.

3.Creating visuals

Numbers in a table are hard to understand and confusing especially thousands of rows.Charts make things click immediately.

That creates a bar chart showing which regions are selling the most.When you bring a chart like that into a meeting,people get it way faster than if you'd read the numbers aloud.
You can create more than a bar graph:

Most people in meetings don't care about your math.They care about what they can see.A clean chart does the talking for you. You just point and say"Look at this."
Once you write the code for a chart,you can reuse it on next month's data with zero extra work.