This course aims to teach learners how to perform data analysis using Python Pandas. By the end of the course, students will be able to manipulate and analyze data efficiently using Pandas. The course covers topics such as DataFrame and Series basics, filtering, sorting, grouping, cleaning data, working with dates, and reading/writing data from different sources. The teaching method involves hands-on tutorials and practical examples. This course is intended for individuals interested in data analysis, data science, or anyone looking to enhance their Python skills for working with data.
Overview
Syllabus
Python Pandas Tutorial (Part 1): Getting Started with Data Analysis - Installation and Loading Data.
Python Pandas Tutorial (Part 2): DataFrame and Series Basics - Selecting Rows and Columns.
Python Pandas Tutorial (Part 3): Indexes - How to Set, Reset, and Use Indexes.
Python Pandas Tutorial (Part 4): Filtering - Using Conditionals to Filter Rows and Columns.
Python Pandas Tutorial (Part 5): Updating Rows and Columns - Modifying Data Within DataFrames.
Python Pandas Tutorial (Part 6): Add/Remove Rows and Columns From DataFrames.
Python Pandas Tutorial (Part 7): Sorting Data.
Python Pandas Tutorial (Part 8): Grouping and Aggregating - Analyzing and Exploring Your Data.
Python Pandas Tutorial (Part 9): Cleaning Data - Casting Datatypes and Handling Missing Values.
Python Pandas Tutorial (Part 10): Working with Dates and Time Series Data.
Python Pandas Tutorial (Part 11): Reading/Writing Data to Different Sources - Excel, JSON, SQL, Etc.
Taught by
Corey Schafer
