NumPy and Pandas Essentials Training

In this NumPy and pandas training course, attendees learn how to use these essential Python libraries to craft clean and organized datasets, use the quick calculations of NumPy for deeper insights, and tell visual stories through pandas manipulations. Attendees also master advanced techniques like time series analysis and data merging, all while learning efficient coding practices.

Course Details


2 days


Working knowledge of Python

Target Audience

  • Data Practitioners
  • Business Analysts
  • Software Engineers
  • IT Architects

Skills Gained

  • Unleash the power and computational efficiencies of NumPy and pandas
  • Learn the core features of both Python libraries
  • Become comfortable in navigating the related APIs
Course Outline
  • Essential NumPy
    • NumPy
    • The Python and C Connection
    • NumPy Characteristics
    • NumPy Efficiencies
    • The ndarray Object vs Python Sequence
    • The ndarray Data Structure Visually
    • The First Take on NumPy Arrays and the array() Method
    • Getting Help
    • The np.info() Function
    • The arange() Method
    • Re-Shaping, Take 1
    • Re-Shaping with Order
    • "Smart" Reshaping
    • Array Slicing
    • Array Slicing Visually
    • 2-D Array Slicing
    • Slicing and Stepping Through
    • Getting Last Row and Last Column
    • Indexing with Arrays of Indices
    • Understanding NumPy Types
    • Commonly Used Platform-Portable ndarray Numeric Data Types
    • Other Data Types
    • Unicode Strings
    • Changing the Data Type using astype()
    • Commonly Used Array Metrics
    • What is An ndarray Axis?
    • Commonly Used Aggregate (Reduction) Functions
    • Axis-Aware Aggregate Functions Visually
    • The NaN Value
    • The nan_to_num() Function
    • NaN in Aggregate Functions
    • The NaN-Tolerant Functions
    • The inf Value
    • The inf-Related Functions
    • Checking for Valid Numbers in an ndarray
    • The newaxis Attribute
    • Flattening the Matrices
    • The ravel() Method
    • Changing Order When Flattening with ravel()
    • ravel(): Things to be Aware of ...
    • The flatten() Method
    • Flattening with reshape(-1)
    • Flattening Using the [:,-1] Operator
    • Understaning Little-Endian and Big-Endian Byte Encodings
    • Handling Little-Endian and Big-Endian Byte Encodings in NumPy
    • Creating "Dummy" Arrays
    • "Dummy" Arrays Visually
    • The "Dummy-Like" Arrays
    • The view() Function
    • The copy() Function
    • The Issue of Shallow Copies of Python Lists
    • The True "Deep Copy"
    • Vectorization
    • Vectorization Visually
    • Broadcasting
    • Broadcasting Visually
    • Array Arithmetic Operations
    • Filtering
    • The any() and all() Functions
    • Combining Arrays
    • The append() Function
    • The insert() Function
    • The delete() Function
    • I/O Operations
    • I/O Operations Considerations
    • Using unique() and repeat()
    • Sundry Functions
    • Support for Generating Random Numbers
    • Seeding
    • The NumPy Random Generator's Methods
    • Generating Random Numbers
    • Descriptive Statistics
    • Sorting Arrays
    • Understanding argsort()
    • The argmin() and argmax() Functions
  • Essential pandas
    • What is pandas?
    • The Main Features and Capabilities
    • The Core High-Level Data Structures
    • The Series Object
    • Understanding the View and Copy Aspects of the Input Data
    • Accessing Values and Indexes in the Series Object
    • The Index Property
    • Using the Series Index as a Lookup Key
    • Useful Series Methods
    • The Series Object Supports NumPy Array Operations
    • Can I Pack a Python Dictionary into a Series?
    • The DataFrame Object
    • The DataFrame's Value Proposition
    • Creating a DataFrame
    • Creating a pandas DataFrame from a Python Dictionary
    • Plugging In Your Own Index
    • Getting DataFrame Metrics
    • Creating a Column with Auto-Incremented Values
    • The DataFrame info() Method
    • The describe() Method
    • Accessing DataFrame Columns
    • Accessing DataFrame Rows
    • Renaming DataFrame Columns
    • Accessing DataFrame Cells
    • The iloc[] Property
    • Using a Function in iloc
    • The Type of Object iloc Returns
    • The loc[] Property
    • Filtering in DataFrames
    • Using any() and all() with loc[]
    • The filter() Method
    • DataFrames are Mutable via Object Reference!
    • Iterating over DataFrame's Contents
    • The Axes
    • Deleting Rows and Columns
    • More on the drop() DataFrame Method
    • Adding a New Column to a DataFrame
    • Appending/Concatenating DataFrame and Series Objects
    • The concat() Method
    • Using the concat() Method
    • Reindexing
    • Re-indexing Series and DataFrames
    • Joining DataFrames
    • Understanding the get_dummies() DataFrame Method
    • What are Descriptive Statistics?
    • Calculating Descriptive Statistics and Summary Measures in pandas
    • Calculations Along axes
    • The nlargest() and nsmallest() Methods
    • Sorting DataFrame Values
    • The pandas I/O: Reading Methods
    • Reading From CSV Files
    • The pandas I/O: Writing Methods
    • Writing to a CSV File
    • Writing to the System Clipboard
    • The apply() Function
    • Minimizing DataFrames' Memory Footprint
  • Lab Exercises
    • Lab 1. Learning the Colab Jupyter Notebook Environment
    • Lab 2. Essential NumPy
    • Lab 3. Essential pandas
Upcoming Course Dates
USD $1,395
Online Virtual Class
Date: May 13 - 14, 2024
Time: 10 AM - 6 PM ET
USD $1,395
Online Virtual Class
Date: Jul 1 - 2, 2024
Time: 10 AM - 6 PM ET