By Cyrille Rossant
Learn IPython for interactive Python programming, high-performance numerical computing, and knowledge visualization.
• a realistic step by step instructional with the intention to assist you to switch the Python console with the strong IPython command-line interface;
• Use the IPython pc to modernize how you engage with Python;
• practice hugely effective computations with NumPy and Pandas;
• Optimize your code utilizing parallel computing and Cython.
You already use Python as a scripting language, yet were you aware it's also more and more used for medical computing and knowledge research? Interactive programming is vital in such exploratory initiatives and IPython is the correct software for that. as soon as you’ve learnt it, you won't manage to dwell with out it.
Learning IPython for Interactive Computing and information Visualization is a realistic, hands-on, example-driven instructional to significantly enhance your productiveness in the course of interactive Python classes, and exhibits you the way to successfully use IPython for interactive computing and information analysis.
This booklet covers all elements of IPython, from the hugely robust interactive Python console to the numerical and visualization beneficial properties which are generally linked to IPython.
You will learn the way IPython allows you to practice effective vectorized computations, via examples masking numerical simulations with NumPy, info research with Pandas, and visualization with Matplotlib. additionally, you will realize how IPython should be very easily used to optimize your code utilizing parallel computing and dynamic compilation in C with Cython.
Learning IPython for Interactive Computing and information Visualization will let you optimize your productiveness in interactive Python sessions.
What you are going to study from this book
• Debug your code from the IPython console;
• Benchmark and profile your code from IPython;
• practice effective vectorized computations with NumPy;
• study facts tables with Pandas;
• Create visualizations with Matplotlib;
• Parallelize your code simply with IPython;
• customise IPython and create your individual magic commands;
• speed up your Python code utilizing dynamic C compilation with Cython.
A functional hands-on consultant which specializes in interactive programming, numerical computing, and information research with IPython.
Who this e-book is written for
This booklet is for Python builders who use Python as a scripting language or for software program improvement, and have an interest in studying IPython for expanding their productiveness in the course of interactive periods within the console.
Knowledge of Python is needed, while no wisdom of IPython is necessary.
Read Online or Download Learning IPython for Interactive Computing and Data Visualization PDF
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Additional info for Learning IPython for Interactive Computing and Data Visualization
We also use the keyword “elif”. This defines an alternate branch. Be careful with the order of branches! If more than one branch is True, only the first one will be used. For example: 1. 2. 3. 4. 5. 6. 7. 8. 9. ”) This code will only ever print “Big”, and the other statements will be ignored. Quiz Try to answer these questions without using the interpreter. 1. What are the boolean values - bool(a) - for the following variables: 1. 2. 3. 4. a = False a = “0” a = 0 * 18 a = “None” 2. What is the output of the following statement: (2 ** 2 == 4 and False) or (not (16 > 15 or 1 == 3)) 3.
ANSWER: You just need to make sure that max speed is capped by 30 km/h in Bike’s constructor 1. class Bike(Vehicle): 2. def __init__(self, name, max_speed): 3. max_speed = min(max_speed, 30) 4. __init__(name, max_speed) Exercises PROBLEM: Add to your code from the previous exercise a new method called show_status that prints something like “The bike is BMX, its speed is 27 km/h” ANSWER: Method’s implementation is supposed to look the following way: 1. def show_status(self): 2. name + “, its speed is “ 3.
They are useful for adding small comments at the beginning of functions. 1. # This is a valid single line comment. You can also add inline comments after a line of code, which can be useful for clarifying statements. 1. ”) 2. # This is another. # This would be an inline comment in Python Multi-line Comments Rather than chaining several single line comments together, for larger comments, you can use three consecutive quotation marks on the first and last line. 1. 2. 3. 4. 5. “”” This is a better way of using comments that span over multiple lines without having to use lots of hashes.