Python Fundamentals Part II: Lists, Tuples, Dictionaries, Sets, Array-Oriented Programming with Numpy, pandas Series, pandas DataFrames and Intro to Visualization

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The professional programmer’s Deitel® video guide to Python development with the powerful IPython and Jupyter Notebooks platforms.



Python Fundamentals LiveLessons with Paul Deitel is a code-oriented presentation of Python—one of the world’s most popular and fastest growing languages. In the context of scores of real-world code examples ranging from individual snippets to complete scripts, Paul will demonstrate coding with the interactive IPython interpreter and Jupyter Notebooks. You'll quickly become familiar with the Python language, its popular programming idioms, key Python Standard Library modules and several popular open-source libraries. In the Intro to Data Science videos, Paul lays the groundwork for later lessons in which he’ll introduce some of today's most compelling, leading-edge computing technologies, including natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, sentiment analysis through deep learning with recurrent neural networks, big data with Hadoop®, Spark™ streaming, NoSQL databases and the Internet of Things.


NOTE this LiveLesson is one in a series of five: 

  • Python Fundamentals LiveLessons Part I
  • Python Fundamentals LiveLessons Part II
  • Python Fundamentals LiveLessons Part III
  • Python Fundamentals LiveLessons Part IV
  • Python Fundamentals LiveLessons Part V

Skill Level 


What you will learn in Part II:

  • Lesson 5–Sequences: Lists and Tuples–Create, initialize and access the elements of lists and tuples;  sort and search lists, and search tuples; pass lists and tuples to functions and methods; list methods; functional-style programming (lambdas, filter, map, reduce, list comprehensions, generator expressions, 2D lists); static visualization with the Seaborn and Matplotlib visualization libraries.
  • Lesson 6–Dictionaries and Sets–Dictionaries of key—value pairs; sets of unique values;  iterating through keys, values and key—value pairs; adding, removing and updating key—value pairs; dictionary and set comparison operators; set operators and methods; operators in and not in for membership testing; mutable set operations; dictionary and set comprehensions; dynamic visualization with the Seaborn and Matplotlib visualization libraries.
  • Lesson 7–Array-Oriented Programming with NumPy–numpy module’s high-performance ndarrays; how ndarrays differ from lists; comparing list vs. ndarrayperformance with the IPython %timeit magic; one-dimensional and multidimensionalndarrays; common ndarray manipulations; introduction to the pandas data manipulation library; one-dimensional Series and two-dimensional DataFrames; custom Series and DataFrame indices; basic descriptive statistics for data in aSeries and a DataFrame; customizing pandas output formatting.
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