Welcome to ITVersity’s ‘Data Analysis Using Pandas’ Tutorial!
In this lesson, we compare two essential methods for accessing data in Pandas: loc (label-based indexing) and iloc (position-based indexing). By the end, you’ll understand the differences between these methods and know exactly when to use each in your data analysis workflow.
* What You’ll Learn in This Video:*
* The difference between loc and iloc in Pandas.
* How to select rows and columns using labels (loc) and positions (iloc).
* Using loc for conditional filtering and column selection.
* Applying iloc for slicing data by integer ranges.
* Updating data using both loc and iloc.
* Practical examples with Toyota Sales Data and Sales Reps Data for clarity.
* When to Use loc vs iloc:*
* Use loc when:
* You know the labels for rows and columns.
* You need to filter data based on conditions.
* Your workflow involves label-based selections.
* Use iloc when:
* You know the positions of rows and columns.
* You’re slicing data by integer ranges.
* Your workflow involves position-based selections.
By the end of this video, you’ll confidently use loc and iloc for selecting, filtering, and updating data in Pandas.
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Continue Your Learning Journey with Pandas! 🚀
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What’s Next?
In upcoming videos, we’ll explore additional file formats and advanced data manipulation techniques. Stay tuned to master the full capabilities of Python Pandas!
#DataEngineering #Pandas #Python #Analytics #DataAnalysis #programming
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