Gain insights into using Python Pandas for efficient data analysis and manipulation. Delve into detailed functions, parameters, and methods to elevate your data handling skills.

A Comprehensive Practical Guide to Python Pandas_ .png

There are several exercises that focus on how to use a particular function and method. The functions are covered in detail by explaining the important parameters and how to use them. By completing this course, you will be able to do data analysis and manipulation with Pandas easily and efficiently.

Mastering Data Analysis with Python Pandas

# Data science and Pandas

Data science helps businesses extract insights from data and has become tremendously popular in recent years. An increasing number of businesses have begun to recognize the potential value of data, so they’ve invested more in the data science workforce and technology.

The main contributor to the popularity of data science is that it’s become easier than ever to collect, process, and transform data. Data comes from a variety of different resources, such as sensors, cash registers, credit card transactions, banks, and so on. These are all examples of **raw data**.

In most cases, there are some issues with raw data that need to be handled in order to make it usable. Some typical issues in raw data are:

- Missing values.
- Being stored with the wrong data type.
- Not having a standard on the same type of values.
- Noisy data.

We’ll solve these issues through data cleaning, though it isn’t an exhaustive process. We sometimes need to process the data to remove redundant information or extract more useful information. 

Suppose we’re analyzing a retailer’s data in the form of customer transactions. We have the customer ID, the purchased product information, purchase date, and so on. However, we’re also interested in how often customers visit the store. We can find this information by calculating the time difference between two consecutive purchases of each customer. The data is a highly valuable asset, but it needs lots of cleaning and processing.

Discover why you need to learn Python Pandas, one of the most popular libraries in the data science ecosystem.

Course Introduction

Pandas Data Structures

Creating a Data Frame

Exploring a Data Frame

Filtering a Data Frame

String Manipulation with Pandas

Date Manipulation with Pandas

Handling Missing Values with Pandas

Data Analysis with Pandas

Data Visualization with Pandas

Combining DataFrames with Pandas

Final Challenge and Quiz

Why Should You Take This Course?

Data science and Pandas