Python For Excel: Mastering Pandas Dataframes
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.40 GB | Duration: 2h 7m
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.40 GB | Duration: 2h 7m
Transform Your Excel Analysis with Efficient and Advanced Data Manipulation Techniques.
What you'll learn
Leverage Python libraries like pandas within Excel to enhance data analysis capabilities.
Work with pandas DataFrames in Excel for efficient data analysis.
Convert different Excel data sources into pandas DataFrames.
Techniques such as data filtering, removing duplicates, and adding new columns to a DataFrame.
Combine and reindex DataFrames for more complex analysis.
Use Pandas, Seaborn, Matplotlib & more directly in Excel
Time Series Analysis with Pandas in Excel
Requirements
A Windows desktop computer with a valid Microsoft 365 Subscription installed (MAC & Linux not supported)
An internet connection capable of streaming HD videos.
Basic Excel and Python Coding skills
Description
Python for Excel: Mastering Pandas DataFrames is a comprehensive course designed to enhance your data analysis skills by integrating Python and Excel functionalities. Python and Excel are prominent tools in data analytics and science, and this course demonstrates the amplified capabilities when they are used together.The course starts with fundamental concepts, introducing Python's integration with Excel and troubleshooting common errors. You'll learn how to leverage your data seamlessly within Python using the xl() function. Moving into Pandas basics, you'll explore DataFrames and Series, along with techniques for data selection, calculations, and manipulation, all within the Python editor.As you progress, the focus shifts to advanced data analysis with Pandas, covering data cleaning, text manipulation, DataFrame combination, and data aggregation techniques. The course also delves into plotting essentials, demonstrating basic plotting techniques and creating scatter plots using Seaborn.A significant portion of the course is dedicated to time series analysis using Pandas, covering topics like shifting data, calculating percentage changes, comparing time series, resampling, and correlation.Throughout the course, you'll work through practical examples tailored for Excel, such as fixing dates and creating sales dashboards. By the end, you'll have a solid understanding of leveraging Python's Pandas library within Excel for effective data analysis and visualization. This course is ideal for data analysts, and anyone seeking to streamline their data workflows using Python and Excel together.
Overview
Section 1: Getting started
Lecture 1 Course Introduction
Lecture 2 IMPORTANT: What you should know
Section 2: Python in Excel: The Basics
Lecture 3 Python in excel
Lecture 4 Getting set up
Lecture 5 Download exercise files
Lecture 6 Fixing errors and troubleshooting
Lecture 7 Using Python in Excel
Lecture 8 Using your data in Python
Lecture 9 The xl() function
Section 3: Pandas: The Basics
Lecture 10 DataFrame and Series
Lecture 11 Data Selection
Lecture 12 Calculations
Lecture 13 Rows: Filtering & Sorting
Lecture 14 Manipulating DataFrames
Lecture 15 Working with Python Editor
Section 4: Pandas: Data Analysis
Lecture 16 Data Cleaning
Lecture 17 Text Data Manipulation
Lecture 18 DataFrame Combination
Lecture 19 Data Aggregation
Section 5: Plotting
Lecture 20 Plotting Basics
Lecture 21 Scatter plot
Section 6: Pandas: Time series analysis
Lecture 22 Time Series Basics
Lecture 23 Time Series Analysis Using pandas DataFrames
Lecture 24 Shifting and Percentage Changes
Lecture 25 Comparing Time Series Data
Lecture 26 Resampling and Correlation
Section 7: Practical Python in Excel Examples
Lecture 27 Dashboard Sales
Data professionals looking to enhance their data analysis skills using Python and Excel.,Students or researchers interested in learning how to work with DataFrames for data analysis.,Individuals already familiar with Excel but wanting to explore how Python can enhance their data analysis capabilities.