Book Image

Hands-On Application Development with PyCharm - Second Edition

By : Bruce M. Van Horn II, Quan Nguyen
5 (1)
Book Image

Hands-On Application Development with PyCharm - Second Edition

5 (1)
By: Bruce M. Van Horn II, Quan Nguyen

Overview of this book

In the quest to develop robust, professional-grade software with Python and meet tight deadlines, it’s crucial to have the best tools at your disposal. In this second edition of Hands-on Application Development with PyCharm, you’ll learn tips and tricks to work at a speed and proficiency previously reserved only for elite developers. To achieve that, you’ll be introduced to PyCharm, the premiere professional integrated development environment for Python programmers among the myriad of IDEs available. Regardless of how Python is utilized, whether for general automation scripting, utility creation, web applications, data analytics, machine learning, or business applications, PyCharm offers tooling that simplifies complex tasks and streamlines common ones. In this book, you'll find everything you need to harness PyCharm's full potential and make the most of Pycharm's productivity shortcuts. The book comprehensively covers topics ranging from installation and customization to web development, database management, and data analysis pipeline development helping you become proficient in Python application development in diverse domains. By the end of this book, you’ll have discovered the remarkable capabilities of PyCharm and how you can achieve a new level of capability and productivity.
Table of Contents (24 chapters)
1
Part 1: The Basics of PyCharm
4
Part 2: Improving Your Productivity
9
Part 3: Web Development in PyCharm
15
Part 4: Data Science with PyCharm
19
Part 5: Plugins and Conclusion

Adding some sciency code

My editor keeps saying “sciency” isn’t a word. A wise man once said, “Science isn’t about why. It’s about why not!?” I’m going to keep using it and if you’re actually reading this, it means I got away with it.

We’ve set up the IDE, and installed our required packages. Let’s open up main.py and add some code so we can see PyCharm strut its stuff! In main.py, add this code:

import numpy as np
import matplotlib.pyplot as plt

These first two imports just bring in numpy and matplotlib with aliases. It turns out scientists hate typing more than normal developers:

N = 100
x = np.random.normal(0, 1, N)
y = np.random.normal(2, 3, N)
#%% plot data in histograms
plt.hist(x, alpha=0.5, label='x')
plt.hist(y, alpha=0.5, label='y')
plt.legend(loc='upper right')
plt.show()

Using NumPy, we are simply creating two sample 100-element datasets from normal distributions...