Python For Excel: Mastering Pandas Dataframes

Python For Excel: Mastering Pandas Dataframes
Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English

| Size: 1.40 GB[/center]
| 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.