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

# Reading data from a csv file

In most cases, we’ll read data from a file. The most commonly used file formats are csv, excel, parquet, and json. In this course, we’ll use csv files. The pandas `read_csv` function  creates a `DataFrame` from a csv file. Pandas provides several other functions for reading data in files with different formats, such as `read_json`, `read_parquet`, `read_excel`, and so on.



Learn how to create a Pandas DataFrame from data in a file.

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

Reading Data from a File

Reading data from a csv file