À propos de ce cours : This course will continue the introduction to Python programming that started with Python Programming Essentials and Python Data Representations. We'll learn about reading, storing, and processing tabular data, which are common tasks. We will also teach you about CSV files and Python's support for reading and writing them. CSV files are a generic, plain text file format that allows you to exchange tabular data between different programs. These concepts and skills will help you to further extend your Python programming knowledge and allow you to process more complex data. By the end of the course, you will be comfortable working with tabular data in Python. This will extend your Python programming expertise, enabling you to write a wider range of scripts using Python. This course uses Python 3. While most Python programs continue to use Python 2, Python 3 is the future of the Python programming language. This course uses basic desktop Python development environments, allowing you to run Python programs directly on your computer.
Les destinataires de ce cours : This class is for people who have have the background from our Python Data Representations course.
Enseigné par : Scott Rixner, Professor
Computer Science
Enseigné par : Joe Warren, Professor
Computer Science
| Informations de base | Cours 3 de 4 dans Introduction to Scripting in Python Specialization |
| Niveau | Débutant |
| Engagement | This is a four week class with an expected workload of 5-7 hours per week. |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Vidéo: Class Structure
- Vidéo: Python Dictionaries
- Vidéo: Defining a Dictionary
- Vidéo: Dictionary Lookup and Update
- Vidéo: Checking Keys
- Reading: Dictionaries - Example
- Vidéo: Handling Dictionary Errors
- Reading: Practice Exercises for Dictionaries
- Reading: Tabular Data
- Vidéo: Tabular Data as a Nested List
- Vidéo: Tabular Data as a Nested Dictionary
- Vidéo: Displaying Dictionaries
- Reading: Practice Exercises for Nested Data Structures
- Reading: CSV Files
- Vidéo: Parsing CSV Files
- Vidéo: Python's CSV Module
- Vidéo: CSV DictReader
- Vidéo: CSV Reader Options
- Vidéo: Experimenting with CSV Methods - Part 1
- Vidéo: Experimenting with CSV Methods - Part 2
- Reading: Practice Project: Loading Cancer-Risk Data
- Vidéo: Project Video for Part 1
- Reading: Project Description: Reading and Writing CSV Files
- Reading: OwlTest: Automated Feedback and Assessment
- LTI Item: Project Submission History
- Vidéo: Lambda
- Vidéo: Advanced Sorting
- Reading: Dictionaries vs. Lists for storing data
- Vidéo: Refactoring Your Code - Part 1
- Vidéo: Refactoring Your Code - Part 2
- Reading: Practice Project: Processing Cancer-Risk Data
- Vidéo: Project Video for Part 2
- Reading: Project Description: Analyzing Baseball Data
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
De mucha utilidad, permite ver la gran variedad de funciones que dispone Python.
It's fantastic learning opportunity, well taught, covered almost everything.Baseball project is cherry on the cake.
Great teachers I must say!
Learned many new things and cool teachers!
For foundation part to data analysis and concerning about it help me in the basic understanding.
Though with the pandas library make it simple that is some sort of drawback I find.
But it make my concept clear.
Thanks for the python Data Analysis course by Rice university.