How to Do Data Analytics in Python - Explained Every Steps

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Nowadays, there’s a huge wave of information and constantly growing data among companies. Businesses understand the importance of comprehending this data to make well-informed decisions. However, it is data analytics and helpful for companies. With the help of Python, a popular programming language, we can learn how to do data analytics in Python to help companies grow and make more money.

What is Data Analytics in Python?

Data analysis is a big toolbox with many different tools for different jobs. The steps you take will vary depending on the nature of your data and your objectives for analysis. To avoid getting lost in all this information, data analysis workflows are like a recipe for your project.

This workflow delivers your team a clear set of steps to follow, even though the specifics may vary depending on the data. Everyone involved knows what to do and how things are going. Workflows also help you avoid mistakes and make your analysis easier to repeat in the future, so you can use the same recipe on new data whenever you get it.
 

Benefits of Python in Data Analysis

Data analysis with Python is versatile and can be used for anything you can imagine, from building websites to crunching massive amounts of data. As a result, Python for data analysis makes it a great choice for beginners and experts alike.
 

• Easy to Learn and Use: Python reads almost like plain English, so it's easier to pick up than other languages. However, this lets you focus on your ideas and get things done faster.
• Powerful for Data Analysis: Python has advanced tools like Pandas and NumPy to work with data. You can analyze enormous datasets, create charts, and uncover hidden patterns.
• Fast Prototyping: The code is easy to write and change, so you can experiment quickly and see what works best.
• Big and Helpful Community: There are multiple Python experts online who can help you find solutions and learn new things.
• In-Demand Jobs: Companies use Python more and more, so there's an immense demand for skilled Python developers. In short, more job opportunities for you.

How to Do Data Analytics in Python?

In this Python data analysis tutorial, make sure that all the data is present and well-organized. You might sort them by type, clean off any smudges, and even combine a few fingerprints to get a clearer picture. Data pre-processing cleans and organizes the clues. They might create a new clue based on the existing ones. Both are crucial data analysis steps in Python in solving the case - you need clean, organized evidence to draw the right conclusions.

Step 1: Import Python Libraries

Before you dive into building a machine-learning model in Python, you need to get to know your data. Advanced tools are in Python called libraries that help you play around with your data. These libraries can help you load your data, do calculations, create charts, and even clean things up. Two necessary libraries are Pandas and NumPy for handling numbers and data, and Matplotlib and Seaborn for making those cool charts you see everywhere. By understanding your data through these tools, you'll be well on your way to building a great model.
 

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

#to ignore warnings

import warnings

warnings.filterwarnings('ignore')

Step 2: Understanding Dataset

A library, Pandas in data analytics in Python can take all that messy information and organize it into a neat table, like a giant spreadsheet. This table, called a DataFrame, is easy to work with and analyze. In this Python data analysis example, we use Pandas to examine the factors influencing the prices of pre-owned cars. We'll look at mileage and year to see how they impact the cost. By meticulously organizing the data, we can uncover recurring trends and accurately forecast future changes in used car prices.

data = pd.read_csv("used_cars.csv")

Before we move to conclusions, we need to get to know our data.
 

• We'll use functions like head() and tail() to see the first few and last few lines of data. As a result, this gives us a general idea of what's there.
• A function called info() gives us a summary of the data, like how many rows and columns there are, what kind of data is in each column (numbers, text, etc.), and if there's any missing information. Please note that some data for the 'New Price' and 'Price' fields is missing.
• We will determine if there is any repeated data by utilizing a function called nunique(). Duplicate data can mess up our analysis, so we'll decide how to handle it later.
• The isnull() function is like a detective tool to find any missing data in our dataset. We'll calculate the percentage of missing data in each column to see how much information is missing. In our example, it looks like "New Price" has a lot of missing data (around 86%).

Step 3: Data Reduction

A column listing a number for each car. This number probably won't help us guess the price, so we can just remove that column from our analysis. That way, we can focus on the data that matters. 

# Remove S.No. column from data

data = data.drop(['S.No.'], axis = 1)

data.info()

Step 4: Feature Engineering

Let's focus on the variables "Year" and "Name" in our dataset. When we look at the sample data, the "Year" column indicates the manufacturing year of the car.

It can be challenging to determine the car's age if it is in year format, even though the age of the car is an essential factor in determining the car's price.

Introducing a new column, “Car_Age” to know the age of the car 

from datetime import date

date.today().year

data['Car_Age']=date.today().year-data['Year']

data.head()

data['Brand'] = data.Name.str.split().str.get(0)

data['Model'] = data.Name.str.split().str.get(1) + data.Name.str.split().str.get(2)

data[['Name', 'Brand', 'Model']]

Step 5: Data Cleaning or Wrangling

Sometimes, the names of things might be confusing or there might be typos. Also, some data might be in the wrong format like text instead of numbers. We'll need to clean up these issues by renaming confusing names, fixing typos, and ensuring everything
is in the right format. However, this will make our data easier to work with and analyze. 

Python Data Analysis Tools

After understanding the steps of data analytics in Python, we will move further towards the lists of tools. The following section will elaborate on the tools:
 

• Matplotlib
• NumPy
• Pandas
• Python
• Scikit-learn
• Tableau

Conclusion

Data Analytics in Python has a toolbox for making sense of information. By using libraries like Pandas, you can organize messy data into neat tables and use advanced charts to see patterns. Then, you can explore your data to understand what information is missing and clean it up so it's all uniform. As a result, this prepares your data for the real star-building models to predict things and make informed decisions.

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