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

# Why should we care about type?

- Reduce memory usage: Generally speaking, the numerical value would be regarded as `float64` or `int64`. In most cases, this is OK. However, imagine you have, say, 50 million rows, but the columns will only store numbers from 0 to 20. `int8` is quite enough in this case and saves a lot of memory.
- Unmatched type: As mentioned above, the numerical value would be regarded as **float64** or **int64**. In most cases, it's ok. However, if one column is an ID column, obviously, **float64** is not a suitable type.
- Specifical type: Datetime is one special case. If you don't specify it, the Datetime would be regarded as an **object** type.

Learn how to convert between types in pandas.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Type Conversion

Why should we care about type?