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

# Basic slicing and indexing

A series supports the basic slicing and indexing operations just like the Numpy `ndarray` and the native Python `list` types.

## Integer Index

With the `[]` operation,  you can access the specified value by its location, which is an integer index.  Just like accessing an element in a native Python `list`.

## Slice object

`start:stop:step` is an object that contains a portion of a sequence.

- If only `stop` is provided, it generates a portion of a sequence from index 0 to stop, where stop is excluded.
- If only  `start` is provided, it generates a portion of the sequence after the index `start` until the last element.
- If both start and stop are provided, it generates a portion of the sequence after the index `start` until the `stop` where the stop is excluded.
- If `start`, `stop`, and `step` are provided, it generates a portion of the sequence after the index `start` until `stop` with an increment of index `step`.

## List of indexes

Sometimes, we want to get multiple elements from a `Series` with multiple indexes; however, there is no pattern between them. You can pass a list of indexes to a Series object with the `[]` operation to get those elements.

Let’s see how we can use slicing and vectorization operations to manipulate the Series objects.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Manipulating Series Objects

Basic slicing and indexing

Integer Index

Slice object