pandas.tar.gz

Data Wrangling

Data wrangling is cleaning, transforming, and organizing data for further analysis. In this course, you will learn how to use Python to effectively wrangle and prepare data for use in data science and machine learning projects.

Throughout the course, you will learn about the common challenges that arise when working with data and how to overcome them. You will use Python and several libraries commonly used in data wrangling, including NumPy and pandas. Then, you will learn how to use pandas to clean, transform, and aggregate data. Moreover, you will also use scikit-learn, a library for machine learning, to identify outliers in our data.

By the end of the course, you will be able to use Python to effectively wrangle and prepare data for use in data science and machine learning projects. With these tools at your disposal, you can efficiently apply machine learning models and get realistic predictions after applying various wrangling techniques to the dataset.

Data Wrangling With Python

What's the process of finding specific records in a DataFrame?

What’s the process of finding specific records in a DataFrame?

They allow us to chain multiple operations.

They are more efficient and easier to read than other methods.

Why is it best practice to use boolean indexing or the `loc()` method when filtering data?

Why is it best practice to use boolean indexing or the <code>loc()</code> method when filtering data?

Which method can you use to filter a DataFrame based on a boolean condition?

Which method can you use to filter a DataFrame based on a boolean condition?

Which method allows you to filter a DataFrame based on row and column labels?

Which method allows you to filter a DataFrame based on row and column labels?

**(Select all that apply.)** Which functions can you use to sort a DataFrame?

(Select all that apply.) Which functions can you use to sort a DataFrame?

What is the best practice for sorting data in pandas?

What is the best practice for sorting data in pandas?

Test yourself on the knowledge you learned in the "Filtering and Sorting" chapter.

About This Course

Introduction to Data Wrangling

Reading Data

Standardization

Syntax Errors and Irrelevant Data

Duplicate and Missing Data

Filtering and Sorting

Splitting, Combining, and Merging

Handling Outliers

Exporting Data

Humanitarian Aid Project

Conclusion

Quiz Yourself: Filtering and Sorting