The Ultimate Beginners Guide To Data Analysis With Pandas

Python for Data Science: Develop essential skills with Pandas, with practical exercises solved step by step

What you'll learn

Create, slice, and manipulate Series in Pandas, exploring from basic operations to grouping

Develop advanced skills in creating and manipulating DataFrames, mastering techniques for accessing and performing complex operations

Visualize data, create plots, and explore essential formatting techniques

Put your knowledge to the test with practical challenges, strengthening your skills in data manipulation and analysis

Explore the power of grouping in numerical and categorical data, as well as perform advanced operations for more sophisticated analyses

Requirements

Programming logic

Basic Python programming

Description

Welcome to the "Ultimate Beginners Guide to Pandas for Data Analysis" course, a comprehensive journey designed for beginners interested in exploring the Pandas library in the context of data analysis. This course has been carefully structured to provide a solid understanding of Pandas fundamentals and advanced techniques, empowering students to manipulate data with confidence and efficiency. Check out the modules and main topics below:Section 1: SeriesWe start with Pandas installation and the creation of Series, the essential one-dimensional structure for storing data. Throughout the module, we explore fundamental concepts such as slicing, copying, accessing with iloc and loc, sorting, filtering, mathematical operations, and string manipulations. We also cover advanced topics, including numerical and categorical grouping, handling missing values, functions, and practical challenges.Section 2: DataframeContinuing on, we delve into the creation and exploration of Dataframes, vital structures for analyzing more complex datasets. This module covers topics such as accessing with iloc and loc, manipulation of rows and columns, handling duplicate data and missing values, sorting, advanced filtering, creating and manipulating columns, aggregation, pivot tables, concatenation, joining, and import/export techniques. We include practical challenges to reinforce learning.Section 3: Data VisualizationIn the final module, we explore data visualization with Pandas. We cover the creation of line, bar, pie, scatter, and histogram plots, as well as formatting techniques and subplots. The module includes a practical challenge to apply the newly acquired skills in visualizing data.Upon completing this course, participants will be equipped with the practical skills necessary to effectively use Pandas in data analysis. Get ready for an hands-on learning experience, empowering you to tackle real-world challenges in data manipulation and interpretation.

Overview

Section 1: Introduction

Lecture 1 Course content

Lecture 2 Course materials

Section 2: Series

Lecture 3 Installation

Lecture 4 Creating series

Lecture 5 Slicing

Lecture 6 Copy, conversion, and concatenation

Lecture 7 Accessing elements with iloc

Lecture 8 Accessing elements with loc

Lecture 9 Ordering

Lecture 10 Counting

Lecture 11 Filtering

Lecture 12 Mathematical operations

Lecture 13 String operations

Lecture 14 Numerical grouping

Lecture 15 Categorical grouping

Lecture 16 Missing values

Lecture 17 Functions

Lecture 18 HOMEWORK

Lecture 19 Homework solution

Section 3: Dataframes

Lecture 20 Creating dataframes

Lecture 21 Exploring dataframes

Lecture 22 Accessing elements with iloc and loc

Lecture 23 Deleting rows and columns

Lecture 24 Duplicated rows

Lecture 25 Missing values

Lecture 26 Counting

Lecture 27 Ordering

Lecture 28 Filtering

Lecture 29 Rename and reorder columns

Lecture 30 Creating new columns

Lecture 31 Categorical features

Lecture 32 Aggregation

Lecture 33 Grouping

Lecture 34 Grouping with aggregation

Lecture 35 Aggregation with transform

Lecture 36 Pivot tables

Lecture 37 Concatenation and joining

Lecture 38 Date conversions

Lecture 39 Date indexes

Lecture 40 Importation and exportation

Lecture 41 HOMEWORK

Lecture 42 Homework solution

Section 4: Data visualization

Lecture 43 Line plot

Lecture 44 Formatting

Lecture 45 Subplots

Lecture 46 Bar and pizza plots

Lecture 47 Scatter plot

Lecture 48 Histogram

Lecture 49 HOMEWORK

Lecture 50 Homework solution

Section 5: Final remarks

Lecture 51 Final remarks

Lecture 52 BONUS

Individuals who are taking their first steps in Python programming and wish to delve into the world of data analysis in a practical manner,Students or early-career professionals in the field of data science seeking a solid understanding of data manipulation with Pandas,Professionals who already have basic knowledge in Python and want to enhance their skills in data manipulation and analysis using Pandas,Students looking for a practical introduction to data manipulation to complement their studies in statistics or related disciplines,Developers aiming to expand their skills to include data analysis, using Pandas as an essential tool in their projects