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

When you want to easily reference them during analysis

When you want to generate a report about the column values

**(Select all that appply.)** When do you apply data wrangling techniques to column names?

(Select all that appply.) When do you apply data wrangling techniques to column names?

Which property do you use to retrieve column names from a DataFrame?

Which property do you use to retrieve column names from a DataFrame?

It removes leading and trailing spaces surrounding any text.

What does the <code>strip()</code> function do?

What is *not* a column data type in pandas?

What is not a column data type in pandas?

A `float` to an `integer` data type conversion

A `string` to an `integer` data type conversion 

An `integer` to a `float` data type conversion

A `string` to a `float` data type conversion

What is being performed in the code below where `salary` already has a `string` data type?

```
df['salary'] = df['salary'].apply(np.float64)
```

What is being performed in the code below where <code>salary</code> already has a <code>string</code> data type?
<pre><code>df['salary'] = df['salary'].apply(np.float64)
</code></pre>

You would get a column with a `string` data type.

You would get a column with an `integer` data type.

You would get a column with a `float` data type.

You would get an error because you can't combine columns with a `string` and an `integer` data type.

What would happen if you combined columns with a `string` and an `integer` data type?

What would happen if you combined columns with a <code>string</code> and an <code>integer</code> data type?

Test yourself on the knowledge you learned from the "Standardization" 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: Standardization