English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 47m | 255 MB
Python and Excel are both some of the most popular “programming languages”, especially for data analytics/data science. Combined, they are even more powerful. In this course, author and Excel expert Felix Zumstein explains how to work with pandas DataFrames in Excel. pandas DataFrames are the backbone of every Python-based data analysis in Excel. Get a thorough introduction to DataFrames. Learn how to turn different sources—such as an Excel range, an Excel table, or a Power Query—into a DataFrame. Find out why and when it makes sense to use a DataFrame, as opposed to native Excel features like Power Query, Pivot Tables, or VLOOKUP formulas. Use a practical dataset to explore the basics of working with DataFrames, including an index, headers, filtering data, dropping duplicates, adding a new column, combining two DataFrames, and re-indexing. Plus, take a quick look at time series and visualizations.
Table of Contents
Introduction
1 Python in Excel and pandas DataFrames
2 What you should know
3 About Python in Excel
Introduction to pandas
4 Hello World
5 pandas DataFrame and Series
6 Data selection
7 Calculations, vectorization, and empty cells
8 Row filtering
9 Manipulating DataFrames
10 Python editor and magic commands
Data Analysis
11 Data cleaning
12 Working with text data
13 Combining DataFrames
14 Data aggregation
15 Plotting
Time Series Analysis
16 Introduction to time series
17 Time series analysis with pandas DataFrames
18 Shifting and percentage changes
19 Comparing time series
20 Resampling and correlation
21 Case study Sales dashboard
Conclusion
22 The next steps for learning more about Python in Excel
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