Complete Guide: Working and CSV / Excel files and Edge Eppython

This manual lesson will visit the entire process of CSV / Excel files and to make analysis of the test data (FA) Epython. We will use the logical dataset of e-commerce that includes transactions, customer information, inventory data, and more.

Introduction

Data analysis is an important skill for today's data. In this lesson, we will learn how:

• Import data from Excel files
• Clean and pre-optimal data
• Check and analyze data through figures and visual observation
• Draw a logical understanding from business information

We will be using a number of important Python libraries:

• Pings to the head: Data and analysis
• destruction: Finding numbers
• Matplotlib including it is born: In view of data

To Set Your Nature

First, let us include the required libraries:

• Openpyxl and XLR R Reverse Pandas Using Excel Files
• Import your information libraries to your Python Script:

Understanding our data

Our sample data represents the sales of e-commerce company. It has five sheets:

1. Sales_data: The great deal of exchange with 1,000 orders
2. Customer_Data: Details of customer Demographic
3. Innovation: Product inventory information
4. Every month_summary: Modified background data
5. Data_issues: A data sample with quality problems with intent to practice

You can download the dataset here

Learned Excel Files

Now that we have our data, let's start by reading an Excel file:

You should see the output that shows the available sheets and size.

Reading some lines or columns

Sometimes you might want to read some parts of Excel File Excel:

To check the basic data

Let's examine our sales data to understand its composition and content:

Let us look at the distribution of commands in all areas of different and circuits:

Cleaning data and preparation

Let us use data cleaning using a sheet “data data” sheet, directly built on normal data problems:

Now let's clean the details:

Let us clean our main selling data:

Combining and joining data

Now let's combine data from different sheets to get rich insight:

Let's also join the data for analysis of the product level:

Analysis of the test data analysis

Let us now make analyzing something intended to understand our business insight:

Analysis of the sale of sales

The analysis of the customer's part

The analysis of the payment method

Analysis of Return

Cross-Tabition Analysis

Analysis of a meeting

Data observation

Now let's build a visual seeing to better understand our data:

Basic observation

Advanced observation and sea

Complicated observation

Store

In this study, we examined the full movement of managing CSV and Excel of Python files, from entering and cleaning raw data to the accrual data analysis (DA). Using logical e-commerce data, we read to integrate and join information, manage regular data quality issues, and remove important business issues with math analysis and view. We have also covered the keynics of the important Python such as pandas, Ince, matplotlib, and the sea. At the end, you should also get active EDA skills to convert raw data into an active understanding of real world applications.