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

## Series
Series is a one-dimensional array consisting of elements of multiple data types such as int, float, string, and object. 

It’s just like an N-dimensional NumPy array (`ndarray`) with an index. You can explicitly specify an index for a Series object, which can be either **int** or **string** type. When the specified index is not provided, **Series** automatically starts at zero and creates an index for the data with a step size of one. 

You can create a series from:
- A Python `dictionary` or `list`.
- A Numpy `ndarray`.
- A scalar value.

# Series
Series is a one-dimensional array consisting of elements of multiple data types such as int, float, string, and object. 

It’s just like an N-dimensional NumPy array (`ndarray`) with an index. You can explicitly specify an index for a Series object, which can be either **int** or **string** type. When the specified index is not provided, **Series** automatically starts at zero and creates an index for the data with a step size of one. 

You can create a series from:
- A Python `dictionary` or `list`.
- A Numpy `ndarray`.
- A scalar value.

Let's start with two fundamental data structures in pandas: Series and DataFrame.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Fundamental Data Structures in pandas

Series