| 1. Data cleaning.mp4 | 19.7 MB | ||
| 1. Hello World.mp4 | 9.4 MB | ||
| 1. Introduction to time series.mp4 | 10.2 MB | ||
| 1. Python in Excel and pandas DataFrames.mp4 | 2.8 MB | ||
| 1. The next steps for learning more about Python in Excel.mp4 | 3.7 MB | ||
| 2. Time series analysis with pandas DataFrames.mp4 | 15.4 MB | ||
| 2. What you should know.mp4 | 4.3 MB | ||
| 2. Working with text data.mp4 | 21.5 MB | ||
| 2. pandas DataFrame and Series.mp4 | 17 MB | ||
| 3. About Python in Excel.mp4 | 3.8 MB | ||
| 3. Combining DataFrames.mp4 | 16.2 MB | ||
| 3. Data selection.mp4 | 26.6 MB | ||
| 3. Shifting and percentage changes.mp4 | 16.7 MB | ||
| 4. Calculations vectorization and empty cells.mp4 | 17.4 MB | ||
| 4. Comparing time series.mp4 | 17.1 MB | ||
| 4. Data aggregation.mp4 | 14.2 MB | ||
| 5. Plotting.mp4 | 15.4 MB | ||
| 5. Resampling and correlation.mp4 | 14 MB | ||
| 5. Row filtering.mp4 | 16.5 MB | ||
| 6. Case study- Sales dashboard.mp4 | 27.3 MB | ||
| 6. Manipulating DataFrames.mp4 | 22.4 MB | ||
| 7. Python editor and magic commands.mp4 | 16.4 MB | ||
| Ex_Files_Python_in_Excel_pandas_DataFrames.zip | 8.7 MB | ||
| ▲ 23 total files | |||
Quote:
Course details
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.
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