Jaroslav K.
After 365:
Senior Odoo Finance Consultant
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Online Course
Data Cleaning and Preprocessing with pandas
with
Martin Ganchev
Master Python’s quintessential pandas library and its core data structures – Series and DataFrame objects. Elevate your data analysis skills for real-world challenges
21,589 students already enrolled
Skill level:
Intermediate
Duration:
3 hours
-
Lessons (3 hours)
-
Practice exams (30 minutes)
- Lessons (3 hours)
- Practice exams (30 minutes)
CPE credits:
5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited
certificate
What you learn
- Add the pandas library to your data analysis toolkit.
- Learn how to install and import Python packages.
- Gain proficiency with pandas Series and DataFrame objects.
- Explore methods to clean and preprocess data using pandas.
- Solve real-world data preprocessing problems using pandas.
Topics & tools
PythonData AnalysisProgrammingData PreprocessingPandas
Your instructor
Content Creator & Content Manager at 365 Data Science | Data Science Instructor
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Economic and Social Sciences
Course OVERVIEW
Description
CPE Credits: 5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
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.
Prerequisites
- Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
- Completion of an introductory Python course is required.
- Familiarity with NumPy is helpful but not mandatory.
Advanced preparation
Curriculum
29 lessons
32 exercises
4 exams
- 2. Data Cleaning and Data Preprocessing5 minOnly 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.5 minOnly 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 PreprocessingExercise
- 3. pandas Series21 minHere, 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.21 minHere, 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()ExerciseCoding exerciseConverting Series into Arrays.sort_values()Attribute and Method ChainingCoding exerciseExercise.sort_index()Coding exercisePractice exam
- 4. pandas DataFrames44 minThis 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.44 minThis 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 DataFramesA Note on Working with the Anaconda AssistantUsing the Anaconda Assistant: Importing Data with pandasCommon Attributes for Working with DataFramesExerciseData Selection in pandas DataFramesPractice examData Selection - Indexing Data with .iloc[]Data Selection - Indexing Data with .loc[]Practice examA Few Comments on Using .loc[] and .iloc[]
- 5. Course exam45 min45 minCourse exam
Free lessons
1.1 Introduction to the pandas Library
6 min
1.3 Installing and Running pandas
6 min
1.4 Introduction to pandas Series
9 min
1.7 Working with Attributes in Python
5 min
1.10 Using an Index in pandas
4 min
1.13 Label-based vs Position-based Indexing
5 min
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of AI and data science graduates
successfully change
or advance their careers.
9 in 10
of our graduates landed a new AI & data job
after enrollment
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ACCREDITED certificates
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institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
Certificates are included with the Self-study learning plan.
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