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Python Data Analysis with JupyterLab

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Duration: 2 Days

Method: Instructor-Led, Hands On

Price: $1125.00

Course Code: PY1008


Audience

This is a rapid introduction to NumPy, pandas, and matplotlib for experienced Python programmers who are new to those libraries.

Description

If you or your team are using or plan to use Python for data science or data analytics, then this is the right Python course for you. The course assumes that you already have had a good amount of Python training and/or experience. Your live instructor will start the class by teaching you how to use Jupyter Notebook, a great tool for writing, testing, and sharing quick Python programs. Even if you do not end up using Jupyter Notebook as your main Python IDE, you will appreciate having it as a tool in your Python toolkit. You will learn NumPy, which makes working with arrays and matrices (in place of lists and lists of lists) much more efficient, and pandas, which makes manipulating, munging, slicing, and grouping data much easier. You will also learn some simple data visualization techniques with matplotlib.

Objectives

Upon successful completion of this course, the student will be able to:

  • Learn to work with Jupyter Notebook
  • Learn to use NumPy to work with arrays and matrices of numbers
  • Learn to work with pandas to analyze data
  • Learn to work with matplotlib from within pandas

Prerequisites

Basic Python programming experience. In particular, you should be very comfortable with Working with strings, Working with lists, tuples, and dictionaries, Loops, and conditionals, and writing your own functions. Courses that can help you meet these prerequisites: Introduction to Python 3 Training and Advanced Python 3 Training

Topics

  • I. JupyterLab
    • Exercise: Creating a Virtual Environment
    • Exercise: Getting Started with JupyterLab
    • Jupyter Notebook Modes
    • Exercise: More Experimenting with Jupyter Notebooks
    • Markdown
    • Exercise: Playing with Markdown
    • Magic Commands
    • Exercise: Playing with Magic Commands
    • Getting Help
  • II. NumPy
    • Exercise: Demonstrating Efficiency of NumPy
    • NumPy Arrays
    • Exercise: Multiplying Array Elements
    • Multi-dimensional Arrays
    • Exercise: Retrieving Data from an Array
    • More on Arrays
    • Using Boolean Arrays to Get New Arrays
    • Random Number Generation
    • Exploring NumPy Further
  • III. pandas
    • Getting Started with pandas
    • Introduction to Series
    • np.nan
    • Accessing Elements in a Series
    • Exercise: Retrieving Data from a Series
    • Series Alignment
    • Exercise: Using Boolean Series to Get New Series
    • Comparing One Series with Another
    • Element-wise Operations and the apply() Method
    • Series: A More Practical Example
    • Introduction to DataFrames
    • Creating a DataFrame using Existing Series as Rows
    • Creating a DataFrame using Existing Series as Columns
    • Creating a DataFrame from a CSV
    • Exploring a DataFrame
    • Exercise: Practice Exploring a DataFrame
    • Changing Values
    • Getting Rows
    • Combining Row and Column Selection
    • Boolean Selection
    • Pivoting DataFrames
    • Be careful using properties!
    • Exercise: Series and DataFrames
    • Plotting with matplotlib
    • Exercise: Plotting a DataFrame
    • Other Kinds of Plots