Gain insights into Pandas' powerful tools for data analysis. Learn about Series, DataFrames, and optimize performance for data operations, filtering, and file uploads in data science and machine learning.

Pandas is a very popular Python library that provides powerful, flexible and high-performance tools to process data.

If you’re looking to get into data science, machine learning, or you just want to brush up on your analytics skills, then look no further. In this course, you’ll learn how to process and analyze data with Pandas. 

You’ll start out by covering the basic data structures in Pandas: Series and dataframes. In the latter half of the course, you’ll learn how to perform operations on different data structures, and how to optimize performance for file uploads, data filtering, and more.

Data Analysis & Processing with Pandas

# Convert data to list without index

Both Series and DataFrame have a very simple function, `tolist()`. From the name you can immediately tell what the function does. 

You can call the function directly from a Series or from the attribute **values**. They are the same.

>**Notice**: 
>- The `DataFrame` only supports the second method, which is called from the attribute `values`.
>- The type of the result from DataFrame is a nested list.
>- The values attribute does not include labels like row or column names, so, you wouldn't get the index. If you want to keep the labels, just see the next section.

DataFrames or Series are very useful data structures, but not all Python libraries use these types. So, you need to convert them to other Python types, such as a list.

In previous lessons, we learned how to create Series and DataFrames from a Python list. Here, we see how to convert them back.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Convert DataFrame to a List

Convert data to list without index