Python cheatsheet and snippets for beginners

• EPTA-Forge-Python

  2 0 0.0 Python

Github Repo (in PT-br)

• awesome-python

  86 205,996 7.0 Python

  An opinionated list of awesome Python frameworks, libraries, software and resources.

In this section, I'll introduce some modules and cool stuff that python provides for us to work with. These are just a few examples of things that Python can do, but in general, there are many things beyond that. You can check some things out here.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• NumPy

  272 26,459 10.0 Python

  The fundamental package for scientific computing with Python.

  

import math #math https://docs.python.org/3/library/math.html #matplotlib https://matplotlib.org/ #numpy https://numpy.org/ #math -> Some more complicated calculations print(math.sqrt(15)) print(math.sin(math.pi/6)) print(math.cos(math.radians(60)))) #numpy more advanced math and amazing helper functions #matplotlib plotting charts import matplotlib.pyplot as plt import numpy as np plt.style.use('_mpl-gallery') # make date x = np.linspace(0, 10, 100) y = 4 + np.sin(x) # plot fig, ax = plt.subplots() ax.plot(x, y, linewidth=2.0) ax.set(xlim=(0, 8), xticks=np.arange(1, 8), ylim=(0, 8), yticks=np.arange(1, 8)) # plt.show() plt.savefig("matplotlib.png")

• cheatsheets

  126 7,245 7.0 Python

  Official Matplotlib cheat sheets (by matplotlib)

  

import math #math https://docs.python.org/3/library/math.html #matplotlib https://matplotlib.org/ #numpy https://numpy.org/ #math -> Some more complicated calculations print(math.sqrt(15)) print(math.sin(math.pi/6)) print(math.cos(math.radians(60)))) #numpy more advanced math and amazing helper functions #matplotlib plotting charts import matplotlib.pyplot as plt import numpy as np plt.style.use('_mpl-gallery') # make date x = np.linspace(0, 10, 100) y = 4 + np.sin(x) # plot fig, ax = plt.subplots() ax.plot(x, y, linewidth=2.0) ax.set(xlim=(0, 8), xticks=np.arange(1, 8), ylim=(0, 8), yticks=np.arange(1, 8)) # plt.show() plt.savefig("matplotlib.png")

Suggest a related project