title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.
Python Data Analysis with JupyterLab
Duration
2
Course
PLPJ-175
Available Formats
Classroom Training, Online Training
Course Description
Overview
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 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. This title is not available in hard copy.Objectives
Audience
Individuals or teams using Python for data science or data analytics
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.
Topics
1. JupyterLab
- Exercise 1: Creating a Virtual Environment
- Exercise 2: Getting Started with JupyterLab
- Jupyter Notebook Modes
- Exercise 3: More Experimenting with Jupyter Notebooks
- Markdown
- Exercise 4: Playing with Markdown
- Magic Commands
- Exercise 5: Playing with Magic Commands
- Getting Help
- Exercise 6: Demonstrating Efficiency of NumPy
- NumPy Arrays
- Exercise 7: Multiplying Array Elements
- Multi-dimensional Arrays
- Exercise 8: Retrieving Data from an Array
- More on Arrays
- Using Boolean Arrays to Get New Arrays
- Random Number Generation
- Exploring NumPy Further
- Getting Started with pandas
- Introduction to Series
- np.nan
- Accessing Elements in a Series
- Exercise 9: Retrieving Data from a Series
- Series Alignment
- Exercise 10: 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 11: Practice Exploring a DataFrame
- Changing Values
- Getting Rows
- Combining Row and Column Selection
- Boolean Selection
- Pivoting DataFrames
- Be careful using properties!
- Exercise 12: Series and DataFrames
- Plotting with matplotlib
- Exercise 13: Plotting a DataFrame
- Other Kinds of Plots
Related Courses
-
Introduction to Python 3
PLPJ-145- Duration: 4 Days
- Delivery Format: Classroom Training, Online Training
- Price: 2,340.00 USD
-
Machine Learning With Spark
DCSK-110- Duration: 4 Days
- Delivery Format: Classroom Training, Online Training
- Price: 2,340.00 USD
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
Course Added To Shopping Cart
bla
bla
bla
bla
bla
bla
Self-Paced Training Terms & Conditions
??spvc-wbt-warning??
Exam Terms & Conditions
??exam-warning??
??group-training-form-area??
??how-can-we-help-you-area??
??personalized-form-area??
??request-quote-area??
Sorry, there are no classes that meet your criteria.
Please contact us to schedule a class.
STOP! Before You Leave
Save 0% on this course!
Take advantage of our online-only offer & save 0% on any course !
Promo Code skip0 will be applied to your registration
Nothing yet
Purchase Information
??elearning-coursenumber?? ??coursename??
View Cart
title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.