Python Data Analysis with NumPy and Pandas Training in London United, Kingdom
Enroll in or hire us to teach our Python Data Analysis with NumPy and Pandas class in London United, Kingdom by calling us @303.377.6176. Like all HSG
classes, Python Data Analysis with NumPy and Pandas may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it
is scheduled: Public Training Classes
Provided there are enough attendees, Python Data Analysis with NumPy and Pandas may be taught at one of our local training facilities.
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We offer private customized training for groups of 3 or more attendees.
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Course Description |
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| This is a rapid introduction to NumPy, pandas and matplotlib for
experienced Python programmers who are new to those libraries. Students
will learn to use NumPy to work with arrays and matrices of numbers;
learn to work with pandas to analyze data; and learn to work with
matplotlib from within pandas.
Course Length: 2 Days
Course Tuition: $790 (US) |
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Prerequisites |
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| : Basic Python programming experience. In particular working with strings; working with lists, tuples and dictionaries; loops and conditionals; and writing your own functions. | |
Course Outline |
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Jupyter Notebook
Getting Started with Jupyter Notebook
Creating Your First Jupyter notebook
More Experimenting with Jupyter Notebook
Getting the Class Files
Markdown
Magic Commands
Automagic
Autosave
Directory Commands
Bookmarking
Command History
Last Three Inputs and Outputs
Environment Variables
Loading and Running Code from Files
Shell Execution
More Magic Commands
Getting Help
NumPy
Efficiency
NumPy Arrays
Getting Basic Information about an Array
np.arange()
Similar to Lists
Different from Lists
Universal Functions
Multiplying Array Elements
Multi-dimensional Arrays
Retrieving Data from an Array
Modifying Parts of an Array
Adding a Row Vector to All Rows
More Ways to Create Arrays
Getting the Number of Rows and Columns in an Array
Random Sampling
Rolling Doubles
Using Boolean Arrays to Get New Arrays
More with NumPy Arrays
pandas
Series
Other Ways of Creating Series
np.nan
Accessing Elements from a Series
Retrieving Data from a Series
Series Alignment
Using Boolean Series to Get New Series
Comparing One Series with Another
Element-wise Operations and the apply() Method
Series: A More Practical Example
DataFrame
Creating a DataFrame from a NumPy Array
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
Getting Columns
Exploring a DataFrame
Cleaning Data
Getting Rows
Combining Row and Column Selection
Scalar Data: at[] and iat[]
Boolean Selection
Using a Boolean Series to Filter a DataFrame
Series and DataFrames
Plotting with matplotlib
Inline Plots in Jupyter Notebook
Line Plot
Bar Plot
Annotation
Plotting a DataFrame
Other Kinds of Plots
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Python Programming Uses & Stats
Python Programming is Used For:
Web Development
Video Games
Desktop GUI's
Software Development
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Difficulty
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Popularity
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Year Created 1991 |
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Pros
Easy to Learn:
The learning curve is very mild and the language is versatile and fast to develop.
Massive Libraries:
You can find a library for basically anything: from web development, through game development, to machine learning.
Do More with Less Code:
You can build prototypes and test out ideas much quicker in Python than in other language
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Cons
Speed Limitations: It is an interpretive language and therefore much slower than compiled languages. Problems with Threading: Multi-threaded CPU-bound programs may be slower than single-threaded ones do to the Global Interpreter Lock (GIL) that allows only one thread to execute at a time. Weak on Mobile: Although, there are a number or libraries that provide a way to develop for both Android and iOS using Python currently Android and iOS don’t support Python as an official programming language. |
| Python Programming Job Market |
Average Salary
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Job Count
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Complimentary Skills to have along with Python Programming
The potential for career growth, whether you are new to the industry or plan to expand your current skills, depends upon your interests:
- For knowledge in building in PC or windows, phone apps or you are looking your future in Microsoft learn C#
- For android apps and also cross platform apps then learn Java
- If you are an Apple-holic and want to build iOS and MAC apps and then choose Objective C or Swift
- Interested in game development? C++
- Data mining or statistics then go with R programming or MATLAB
- Building an operating systems? C
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