Data Cleaning and Preprocessing with pandas

with Martin Ganchev
4.7/5
(1,681)

Master Python’s quintessential pandas library and its core data structures – Series and DataFrame objects. Elevate your data analysis skills for real-world challenges

3 hours of content 17587 students

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 3 hours of content
  • 23 Interactive exercises
  • 18 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Data Cleaning and Preprocessing with pandas

A course by Martin Ganchev

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 3 hours of content
  • 23 Interactive exercises
  • 18 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 3 hours of content
  • 23 Interactive exercises
  • 18 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • Add the popular pandas library to your data analysis skillset to enhance your capabilities
  • Learn how to install and import Python packages
  • Gain proficiency in using pandas’ series and DataFrame objects to enhance your data analysis skillset
  • Explore and master different ways to clean and preprocess data in pandas
  • Solve real-world data preprocessing problems with pandas
  • Elevate your career with advanced pandas skills, making your resume stand out to recruiters and hiring managers

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

pandas is one of today’s most celebrated data analysis libraries. A favorite of many, its versatile functionalities can be leveraged for manipulation of many types of data - numeric, text, Boolean, and more. That’s one of the features that make pandas the go-to choice for analysts, especially during the data cleaning and preprocessing stages. pandas is built on NumPy and takes advantage of its computational power and abilities. But what sets pandas apart is its ability to operate with data in an easy-to-use way, allowing you to focus almost entirely on your analytic task. In this course, you will learn how to work with this powerful Python library and its core data structures – the pandas Series and DataFrames.

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Introduction to the pandas Library

1.1 Introduction to the pandas Library

6 min

Installing and Running pandas

1.3 Installing and Running pandas

6 min

Introduction to pandas Series

1.4 Introduction to pandas Series

9 min

Working with Attributes in Python

1.7 Working with Attributes in Python

5 min

Using an Index in pandas

1.10 Using an Index in pandas

4 min

Label-based vs Position-based Indexing

1.13 Label-based vs Position-based Indexing

5 min

Curriculum

  • 1. pandas - Basics
    15 Lessons 82 Min

    In this section, you will develop a basic understanding of the pandas library and practice with fundamental programming tools such as methods, parameters, arguments, attributes, and index values. You will also learn how to work with the pandas Series and DataFrame objects. In the end, we will present the pandas documentation and will show how you can navigate through it.

    Introduction to the pandas Library
    6 min
    Installing and Running pandas
    6 min
    Introduction to pandas Series
    9 min
    Working with Attributes in Python
    5 min
    Using an Index in pandas
    4 min
    Label-based vs Position-based Indexing
    5 min
    More on Working with Indices in Python
    6 min
    Using Methods in Python - Part I
    5 min
    Using Methods in Python - Part II
    3 min
    Parameters vs Arguments
    5 min
    The pandas Documentation
    10 min
    Introduction to pandas DataFrames
    5 min
    Creating DataFrames from Scratch - Part I
    6 min
    Creating DataFrames from Scratch - Part II
    5 min
    Additional Notes on Using DataFrames
    2 min
  • 2. Data Cleaning and Data Preprocessing
    1 Lesson 5 Min

    Only about 20% of the work of a data analytics or science team goes to statistical analysis, making visualization or predictive models. The bulk of the time is consumed by collecting, cleaning, and preprocessing data. That is why in this section, we’ve provided a single lecture that aims at clarifying the meaning of and difference between the data cleaning and data preprocessing stages.

    Data Cleaning and Data Preprocessing
    5 min
  • 3. pandas Series
    5 Lessons 21 Min

    Here, we will introduce you to working with one of the two core data structures of pandas – the pandas Series object. You will also discover several common methods and learn how to apply them to a pandas Series.

    .unique(), .nunique()
    4 min
    Converting Series into Arrays
    5 min
    .sort_values()
    4 min
    Attribute and Method Chaining
    4 min
    .sort_index()
    4 min
  • 4. pandas DataFrames
    6 Lessons 38 Min

    This section focuses on the other fundamental object in pandas - the DataFrame. The DataFrame is universally known as the most important structure in this library. Here, we will revise its characteristics as well as comment on several popular related methods. In addition, we will show you how to deal with various techniques for data selection in a DataFrame.

    A Revision to pandas DataFrames
    5 min
    Common Attributes for Working with DataFrames
    4 min
    Data Selection in pandas DataFrames
    7 min
    Data Selection - Indexing Data with .iloc[]
    6 min
    Data Selection - Indexing Data with .loc[]
    4 min
    A Few Comments on Using .loc[] and .iloc[]
    12 min

Topics

Pythondata analysisProgrammingdata preprocessingPandas

Tools & Technologies

python

Course Requirements

  • Highly recommended to take the Intro to Python course first
  • You will need to install the Anaconda package, which includes Jupyter Notebook

Who Should Take This Course?

Level of difficulty: Intermediate

  • Aspiring data analysts, data scientists, data engineers, AI engineers
  • Graduate students who need Python and pandas for their studies

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Martin Ganchev

Martin Ganchev

Worked at

15 Courses

36062 Reviews

530983 Students

Martin began working with 365 in 2016 as the company’s second employee. Martin’s resilience, hard-working attitude, attention to detail, and excellent teaching style played an instrumental role in 365’s early days. He authored some of the firm’s most successful courses. And besides teaching, Martin dreams about becoming an actor. In September 2021, he enrolled in an acting school in Paris, France.

What Our Learners Say

07.12.2024
03.12.2024
Soo many things to learn in short time, would have been great to have more deep coding exrcises between lessons
29.11.2024

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