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Deep Learning Fundamentals (free course)
Are you interested in getting started with Deep Learning in Python? I haven't taken this course, but it looks promising!
I was thinking of starting a YouTube series in which I work through this course and share some of my learnings (and questions), and then you could follow along at home. Would that be of interest to you?
One aspect of Python's pandas library that can be confusing to newcomers is the concept of the "index". Every pandas DataFrame or Series has an index, and it's used for identification, selection, and alignment.
Here are some key facts about the index that will help you to work with pandas objects more effectively:
1️⃣ The index is on the left
The sequence of numbers on the left side is called the index, and they're used to identify the DataFrame rows. Sometimes they're called the "row labels".
2️⃣ By default, the index is a sequence of integers
Because I didn't define an index when creating the DataFrame, it assigned the numbers 0 through 192 as the index. They're stored in a special object called the "RangeIndex".
3️⃣ The column names are stored in an "Index" object
When people talk about "the index", they're talking about the row labels. But it turns out that the column names are stored in a special type of object called an "Index"!
That being said, you should never refer to the column names as "the index"... just call them "the columns" or "the column names".
4️⃣ The labels are not included in the shape
You should think of the index and columns as labels for the DataFrame. They're not part of the DataFrame "contents", and thus they're not included in its shape.
5️⃣ The index remains with each row
If you filter the DataFrame, the index remains with each row to identify it.
6️⃣ The index can be used for selection
You can use the loc accessor to select one or more rows or columns by label.
7️⃣ You can set a different column as the index
Notice that the default integer index no longer exists.
8️⃣ Setting a meaningful index allows for easier selection
Rather than referring to Brazil as "23" (its previous row label), you can refer to it as "Brazil".
If you want to learn more about the pandas index, check out these Data School videos:
🔗 What do I need to know about the pandas index? (Part 1)
🔗 What do I need to know about the pandas index? (Part 2)
Are you new to pandas? Stay tuned for my upcoming course, pandas in 30 days! Coming February 2024 🚀
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- Kevin
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