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

By increasing the credibility of the data

By causing a lack of clarity in the collected data

By improving credibility in the eyes of the data stakeholder

How can grammatical errors in a dataset affect a project and its impact on an organization?


How can grammatical errors in a dataset affect a project and its impact on an organization?

A reduction in project costs associated with data cleaning, transformation, and storage

An increase in project costs associated with data cleaning, transformation, and storage

No change in project costs associated with data cleaning, transformation, and storage

What is the outcome of data validation?

A space located after the first character of a dataset value

A space located before the first character of a dataset value

A space located after the last character of a dataset value

A space located in the middle of a dataset value


Move the spaces to the middle of the data.

What is the most appropriate way to handle leading or trailing spaces in a dataset?


What is the most appropriate way to handle leading or trailing spaces in a dataset?

It increases memory space and complexity.

It increases the project's complexity and readability.

It decreases memory space and complexity.

It decreases the project's complexity and readability.

What are some benefits of deleting irrelevant data?

What are some benefits of deleting irrelevant data?

What function do you use to delete only the leading spaces?

What function do you use to delete only the leading spaces?

We delete the `firstname` and `lastname` columns of the DataFrame.

We delete the DataFrame rows with the `firstname` and `lastname` index values.

What happens when you use this line of code?
```
df.drop(["firstname", "lastname"], axis = 2, inplace = True)
```

What happens when you use this line of code?
<pre><code>df.drop([&quot;firstname&quot;, &quot;lastname&quot;], axis = 2, inplace = True)
</code></pre>

Test yourself on the knowledge you learned from the "Syntax Errors and Irrelevant Data" 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: Syntax Errors and Irrelevant Data