🧩 Part 8/8

✔ Summary of the blog series
✔ Benefits of using pandas and dataframes for data analysis
✔ How to access the official pandas documentation and tutorials
✔ Some recommended books, courses, and websites to learn more about pandas and dataframes
✔ Some practical examples and exercises to practice your skills
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🧩 Part 7/8

✔ Grouping dataframes with groupby
✔ Basic syntax and examples of groupby
✔ Applying multiple functions with agg
✔ Grouping by multiple columns and levels
✔ Aggregating dataframes with pivot_table
✔ Basic syntax and examples of pivot_table
✔ Specifying values, index, and columns
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🧩 Part 6/8

✔ The apply method
✔ Applying a function to each row or column of a dataframe
✔ Applying a function to each element of a dataframe
✔ The map method
✔ Mapping a dictionary or a function to a series
✔ Mapping a series to another series
✔ The lambda function
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🧩 Part 5/8

✔ Filtering dataframes
✔ Filtering by column values
✔ Filtering by multiple conditions
✔ Filtering by string methods
✔ Filtering by query method
✔ Sorting dataframes
✔ Sorting by column values
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🧩 Part 4/8

✔ Basic indexing and slicing
✔ Label-based indexing with loc
✔ Position-based indexing with iloc
✔ Boolean indexing with conditions
✔ Summary and exercises
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🧩 Part 3/8

✔ Accessing dataframe rows
✔ Using loc and iloc
✔ Using boolean indexing
✔ Updating dataframe rows
✔ Using assignment operator
✔ Using update method
✔ Appending dataframe rows
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🧩 Part 2/8

✔ Accessing dataframe columns
✔ Renaming dataframe columns
✔ Adding dataframe columns
✔ Deleting dataframe columns
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🧩 Part 1/8

✔ What is pandas?
✔ How to install pandas
✔ How to create a dataframe from a list of dictionaries
✔ Define a list of dictionaries
✔ Convert the list to a dataframe
✔ View and inspect the dataframe
✔ How to access and manipulate dataframe elements
✔ Select columns and rows
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