Data Wrangling with Python by Jacqueline Kazil, Katharine Jarmul

By Jacqueline Kazil, Katharine Jarmul

How do you're taking your facts research talents past Excel to the following point? via studying simply enough Python to get stuff performed. This hands-on advisor indicates non-programmers such as you the right way to technique details that's before everything too messy or tricky to entry. You don't want to know a specific thing concerning the Python programming language to get started.

Through a variety of step by step routines, you'll how you can gather, fresh, examine, and current facts successfully. You'll additionally realize tips on how to automate your facts technique, agenda dossier- enhancing and clean-up projects, approach greater datasets, and create compelling tales with facts you bought.

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It looks like we are importing some modules from the Python standard library here—sys and pprint. Let’s get some help on these. stdout]. Excellent. pprint() documentation, the method outputs an easy-to-read display of whatever was passed to it. path shows where Python looks to find modules. path? path) A list. We know how to use lists! pprint, it makes it look really nice. Let’s try to apply this to our list of lists holding animal names. First, let’s add a few more names to make it really messy: animal_names = [ ['Walter', 'Ra', 'Fluffy', 'Killer'], ['Joker', 'Simon', 'Ellie', 'Lishka', 'Fido'], ['Mr.

Csv'. When you create a variable, you are assigning what the program would normally out‐ put to the variable as its value. That is why nothing is returned when you create a new variable. csv') as its value. Object-Oriented Programming You may have heard of object-oriented programming, or OOP for short. Python is an object-oriented programming language. The “object” in OOP can be any of the data types we learned about in this chapter such as strings, variables, numbers or floats. In the example given in the text, our object is a string and it is stored now in filename.

We hope this approach allows you to learn by applying these new con‐ cepts to datasets and problems that interest you. Before we continue, let’s launch our Python interpreter. We will be using it to run our Python code throughout this chapter. It is easy to skim over an introductory chapter like this one, but we cannot emphasize enough the importance of physically typing what you see in the book. Similar to learning a spoken language, it is most useful to learn by doing. As you type the exercises in this book and run the code, you will encounter numerous errors, and debugging (working through these errors) will help you gain knowledge.

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