<h2>Introduction to the course</h2><p>Pandas is an open-source data manipulation and analysis library for Python. It provides data structures and manipulation tools for handling and analyzing structured data, such as numerical tables and time series data.</p><p>Pandas is a powerful tool for data manipulation and analysis, and it is widely used in various data science and machine learning tasks.</p><h2>Who is this course for?</h2><p>Pandas is famous among data scientists, analysts, and engineers for its powerful data manipulation capabilities, including data cleaning, filtering, and transformation, and its support for handling and analyzing large datasets. If you see yourself in that role, then this course is for you.</p>

Pandas

<h2>What does this lesson cover?</h2><p>In this lesson we will be covering three main topics:</p><p>Pandas in Python, how did it all happen? </p><p>Advantages of Pandas, why do we even need to use it?</p><p>Usage in Data Science, the deoth and importace of Pandas.</p>

Introduction

<h2>What does this lesson cover?</h2><p>The merge method is the most powerful method of the three combination methods that Pandas offer.</p><p>Why? We will get to know the answer of that question in this lesson.</p><p>The lesson comes equipt with lesson challenges, standard code examples, and quizzes.</p>

Merging

<h2>What does this lesson cover?</h2><p>In this lesson, we will be covering the most famous form of cleaning data. By replacing unwanted, false, or simply redundant data with new data.</p><p>Again, doing this by hand would prove time-consuming hence we will be using the replace() method.</p>

Replacing Values

<h1>What does this lesson cover?</h1><p>Working with DataFrames simply indicates creating a DataFrame =, which may seem simple from the beginning.</p><p>The trick here is there are three main methods to creating a DataFrame, depending on how we want to work with it.</p><p>The simplest of them all would be creating an empty DataFrame. From there we will escalate to creating a Dataframe from lists and finally by using the zip() method.</p>

Creating a DataFrame

<h2>What does this lesson cover?</h2><p>Pandas is built around two primary data structures: DataFrames and Series.</p><p>A DataFrame is a 2-dimensional tabular data structure that has rows and columns, and each column can have a different data type. </p><p>A Series is a 1-dimensional array-like object that can hold any data type. It is similar to a column in a DataFrame. </p>

Core Concepts

<h2>What does this lesson cover?</h2><p>In this lesson we will be referencing the remaining two methods used for combining large amounts of data: the concat() and join() methods.</p><p>Sometimes, developers mix the three methods up hence we&#x27;ve specifically outlined the main differences and specialties of each method with code examples and lesson quizzes that specify exactly when and how to use them correctly.</p>

Joining and Concatenating

<h2>What does this lesson cover?</h2><p>Another way to clean data is by setting a condition. Conditions can be as simple as True or False which indicates the outline of Boolean Indexing.</p><p>In this lesson, we will be covering the features and structure of Boolean indexing with various code examples and lesson quizzes.</p>

Boolean Indexing

<h2>What does this lesson cover?</h2><p>In this lesson, we will be discussing Series. Informally known as the second fundamental concept in Pandas, after DataFrames.</p><p>Not unlike Dataframes, there are multiple ways to create Series in Pandas as well.</p><p>This lesson will focus on all the methods with code examples and lesson quizzes.</p>

Creating a Series 

<h2>What does this lesson cover?</h2><p>To install pandas, you need to have Python and pip (the package installer for Python) installed on your computer. </p><p>The rest is pretty simple and will be covered in the lesson.</p>

Installation

Lesson Challenge

Pandas in Python

<p><strong>Pandas</strong> is an open-source, fast, and high-performing Python library that works with relational data. The library is built on the Python NumPy library and is known for managing numerical data sets.</p>

<p>Pandas permits developers to work with big data. This would entail cleaning messy data sets and making valid conclusions based on the given set.</p>

<p>Additionally, anything related to data sets can be uncovered with Pandas, this includes max and min values, the average value, and correlations.</p>

Select Correct Answer

Advantages

<p>There is a wide range of advantages to cooperating Pandas in your Python program. The main ones are:</p>

<p><strong>▸ </strong>The fast, smooth ability to merge, join, and concatenate data sets.</p><p><strong>▸ </strong>The indexing and grouping of data.</p><p><strong>▸ </strong>Efficient data set manipulation methods.</p><p><strong>▸ </strong>Time-series functionality.</p><p><strong>▸ </strong>Detection of data correlations and relationships.</p>

<p>The mission of this course will be to cover the crucial, above-mentioned benefits of Pandas.</p>

Usage in Data Science

<p>The primary usage of Pandas is in Data Science. The most valid duties in data, such as data cleaning, managing, and exploration, are executed with Pandas.</p>

<p>We’ve briefly touched on the advantages and some general aspects of Pandas, but now let’s get into the key “data-science” related features:</p>

<p><strong>▸ Data exploration</strong></p><p><strong>▸ Data handling</strong></p><p><strong>▸ Data visualization</strong></p><p><strong>▸ Data manipulation</strong></p>

<h2>What does this section cover?</h2><p>In this section, we will be getting to know the history and usage of Pandas. Discussing its benefits and advantages, its usage, and why we need it.</p><p>We&#x27;ll thus be moving onto the core concepts that Pandas revolves around: DataFrames and Series. </p><p>Finally, we&#x27;ll be getting into the simple and straight-to-the-point installation process.</p>

Get Started

<h2>What does this lesson cover?</h2><p>We&#x27;ve already referred to DataFrames and Series in previous lessons.</p><p>However, as they are the primary and fundamental characteristics of Pandas, we&#x27;ll need to study them a bit closer and slowly and surely begin to work with them.</p>

DataFrame and Series

<h2>What does this section cover?</h2><p>Pandas is known for working with data. When covering the depths of Pandas we touched on Data Manipulation, big data, and cleaning data.</p><p>Data Cleaning indicates working with messy and large amounts of data which would take days to filter by hand. That&#x27;s where Pandas comes in handy.</p>

Data Cleaning

<h2>What does this section cover?</h2><p>In this section, we will be referring to Data Manipulation.</p><p>Manipulating data usually means one thing: combining.</p><p>There are three main methods that Pandas offers to combine data and we will be considering each of them with examples and lesson quizzes.</p>

Overview

Pandas in Python

Advantages

Usage in Data Science