Gain insights into data wrangling using Python. Learn about cleaning, transforming, and organizing data with libraries like NumPy, pandas, and scikit-learn for data science and machine learning projects.

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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

How do you perform a split operation in pandas?

How do you perform a split operation in pandas?

By enabling the creation of new columns based on existing data

By allowing you to focus on specific subsets of data and compare them to one another

In what ways can splitting a column improve the communication of results?

In what ways can splitting a column improve the communication of results?

You can use the addition operator and the `apply()` method with a custom function.

**(Select all that apply.)** What approaches can you use to join columns of a DataFrame?

(Select all that apply.) What approaches can you use to join columns of a DataFrame?

Which function appends DataFrames containing similar data rather than merges based on a common key?

Which function appends DataFrames containing similar data rather than merges based on a common key?

By specifying the column values as input arguments

By specifying the column indexes as input arguments

By specifying the column data types as input arguments

By specifying the column labels as input arguments

How do you specify the columns used for the merge operation in the `merge()` function in pandas?

How do you specify the columns used for the merge operation in the <code>merge()</code> function in pandas?

Quiz yourself on the knowledge you learned in the "Splitting, Combining, and Merging" 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: Splitting, Combining, and Merging