Python Data Analysis with JupyterLab
Course Description
The Python Data Analysis with JupyterLab course offers a comprehensive introduction to data analysis techniques using Python and JupyterLab. The course covers essential topics such as getting started with JupyterLab, using Markdown and Magic Commands, exploring NumPy for numerical computing, and delving into pandas for data manipulation and analysis. Students will also learn to create visualizations using matplotlib. Through hands-on exercises and real-world examples, learners will acquire the skills necessary to perform effective data analysis using Python and JupyterLab.
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.
About this Course
Course Outline
- JupyterLab
- Creating a Virtual Environment (Exercise)
- Getting Started with JupyterLab (Exercise)
- Jupyter Notebook Modes
- More Experimenting with Jupyter Notebooks (Exercise)
- Markdown
- Playing with Markdown (Exercise)
- Magic Commands
- Playing with Magic Commands (Exercise)
- Getting Help
- NumPy
- Demonstrating Efficiency of NumPy (Exercise)
- NumPy Arrays
- Multiplying Array Elements (Exercise)
- Multi-dimensional Arrays
- Retrieving Data from an Array (Exercise)
- More on Arrays
- Using Boolean Arrays to Get New Arrays
- Random Number Generation
- Exploring NumPy Further
- pandas
- Getting Started with pandas
- Introduction to Series
- np.nan
- Accessing Elements in a Series
- Retrieving Data from a Series (Exercise)
- Series Alignment
- Using Boolean Series to Get New Series (Exercise)
- 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
- Practice Exploring a DataFrame (Exercise)
- Changing Values
- Getting Rows
- Combining Row and Column Selection
- Boolean Selection
- Pivoting DataFrames
- Be careful using properties!
- Series and DataFrames (Exercise)
- Plotting with matplotlib
- Plotting a DataFrame (Exercise)
- Other Kinds of Plots
Duration
2 Days
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.
- Writing your own functions.
Benefits
- Comprehensive Coverage: The courseware covers a wide range of data analysis topics using Python, JupyterLab, NumPy, pandas, and matplotlib, ensuring students gain a thorough understanding of the tools and techniques.
- Practical Examples: Real-world examples are used throughout the course, helping students see how Python and JupyterLab can be applied to solve real data analysis problems.
- Hands-on Learning: Students will participate in numerous exercises, allowing them to practice and solidify their new skills.
- Engaging Content: The courseware is designed to be interactive and engaging, keeping students interested and motivated to learn.
- Experienced Authors: The courseware is created by experienced Python developers and data analysts, ensuring high-quality content that is both accurate and up-to-date.