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Take the Leap From Excel to Python: A Hands-On Guide

Name: Take the Leap From Excel to Python: A Hands-On Guide
Rating: 4.4 (91 reviews)

Use Python data analysis to re-create Excel features like pivot tables, VLOOKUP, charts, and more!

Created byCory Stahle

Last updated 12/2020

English

What you'll learn

You will know how to use "notebooks" to create well-documented and re-usable data analysis.
You will know how to re-create major Excel features like cell formulas and pivot tables in Python.
You will know how to use Python and pandas to perform VLOOKUP style operations
You will know how to use matplotlib to re-create Excel charts.
You will know how Python packages and functions are used
You will know how to build Excel pivot tables in Python

Course content

12 sections • 50 lectures • 8h 15m total length

Welcome to the Course!4:47
Introduction to why Python is so useful, the structure of the course, and how you can get the most out of the material.
Using Google Colaboratory15:25
How to use Google Colab, a free Python coding tool that doesn't require you to install anything on your computer!
Download Course Notebooks0:36
Download dozens of hands-on notebooks so you can work along with the videos and test your learning.

Packages6:56
Install and import Python packages into a Google Colab notebook.
Functions12:38
Use functions in Python to make calculations and processes easier!
Variables and Data Types18:18
How to save data into variables and basic overview of Python's data types.
Importing and Exporting Data20:41
How to import CSV's and Excel files from your computer, a website, or Google Drive. You will then learn how to save your results back to a CSV or XLSX file.
Dataframe Basics21:34
How to select columns, what an index is, and what are .loc[ ] and .iloc[ ] are.
Exploring Dataframes16:59
How to use dataframe methods and attributes to perform basic analysis on a dataframe.

Handling Missing Data12:44
How to identify, replace or remove missing data in Python using: isnull( ), fillna( ), bfill( ), ffill( ) and dropna().
Deleting Rows or Columns14:15
How to remove rows or columns using the drop( ) function.
Removing Duplicate Values4:00
How to use pandas drop_duplicates( ) function.
Changing Data Types17:55
How to change data types using type functions and the astype( ) method.
Sorting Your Data12:49
How to use sort_values( ), order by descending or ascending order, and how to sort by index.
Filtering Rows15:15
How to use boolean indexing to select rows based on one or more condition(s).
Finding and Replacing Text5:23
How to use replace( ) to find and replace data.
Text to Columns Splitting7:47
How to split one column into several like Excel's text-to-columns functionality.
Grouping Data14:07
How to use the groupby( ) function to perform calculations for sub-categories within a variable.

SUM( ), COUNT( ), AVERAGE() and more!10:53
How to perform basic Excel calculations in Python.
IF() Function7:35
How to replicate Excel's IF( ) function in Python using np.where( ).
SUMIF( ), COUNTIF( ), and AVERAGEIF( )13:10
How to perform calculations based on IF conditions within the data.
AND( ) / OR( )14:10
How to use logical operators to check more complex conditions.
CONCATENATE( )7:42
How to join text data together in a fashion similar to Excel's CONCATENATE( ) function.
LOWER( ), UPPER( ), and PROPER( )7:45
How to change the capitalization of text data in Python.
TRIM( )3:43
How to remove whitespaces from text.

Project Overview8:34
An overview of the Kiva loans data and template used in our work-a-long project.
Import Data4:53
Importing the Kiva loans data into our notebook from GitHub.
Explore and Clean the Data10:48
Using what we have learned to explore and clean the data.
Analyze (Question 1)4:05
How much money has been loaned through the Kiva platform?
Analyze (Question 2)7:38
How has the amount of money lent changed over time?
Analyze (Question 3)4:14
How does lending vary by activity sector?
Analyze (Question 4)7:43
How does lending vary by geographical region?
Analyze (Question 5)4:27
What is the most funded sector in each major region?
Communicate Results: Make a PDF or Save to GitHub5:53
How to create a PDF of our notebook or save it to GitHub to share.

Requirements

Familiarity with basic data analysis in Microsoft Excel.
No prior experience with programming is required — this course is geared for beginners.

Description

****INCLUDES DOZENS OF HANDS-ON CODING NOTEBOOKS****

This course will use hands-on exercises to teach you how to apply your Excel knowledge in Python.

Several years ago, I was in your shoes. I tried learning Python but would go back to do things in Excel because I was more comfortable with it. My goal in this course is to help you become as comfortable in Python as you are in Excel. To become comfortable, you need to have opportunities to participate in hands-on exercises and projects. You will have the chance to do both in this course.

This course is designed for students who want to grow their careers or improve their analysis by using Python.

What will you learn:

How to use Google Colab to begin using Python without installing software
How to use Python packages and functions
How to import and export data from Python
How to perform basic tasks like data cleaning
How Python can save time by creating scripts which can be re-run easily
How notebooks can be used to create well-documented analysis
How to replicate many Excel features

Excel features we will replicate in Python:

Pivot tables
VLOOKUP
Charts
Cell formulas like SUM( ), IF( ) and CONCATENATE( )
Filtering rows
Performing text to columns splits
and more!

Content

This course is divided into three parts.

The first part is the introduction to the course and an overview of the essential concepts you need to know to use Python and pandas.

In part 2, you will have the flexibility to take the lessons in whatever order best suits your needs. If you need to learn how to build a pivot table in Python, you can skip straight to that lesson. Want to build charts in python… you can start there instead.

In part 3 we will work on an analytics project using real-world data together. You will then apply everything you have learned to build your own project.

The course also has a 30-day 100% money-back guarantee. If you aren't happy with your purchase, you can refund the course with no questions asked.

Enroll now to make the leap from Excel to Python!

- Cory

Who this course is for:

Spreadsheet users who want to grow their careers or improve their analysis by using Python.

Take the Leap From Excel to Python: A Hands-On Guide

What you'll learn

Explore related topics

Course content

Welcome to the course!3 lectures • 21min

Quick Start Concepts6 lectures • 1hr 37min

PART 2 -----------------From Excel to Python-----------------1 lecture • 1min

Clean Data without the Excel Ribbon9 lectures • 1hr 44min

Use Python to Run "Cell Formulas''7 lectures • 1hr 5min

Transform Your Data with Pivot Tables and More!3 lectures • 44min

Working with Multiple Dataframes: Replacing VLOOKUP3 lectures • 43min

Building Charts and Visualizations5 lectures • 1hr

PART 3 -----------------Projects-----------------1 lecture • 1min

Bringing It All Together: Project Demo9 lectures • 58min

Requirements

Description

Who this course is for: