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This article introduces the relevant knowledge of "how to draw a simple crease diagram with Python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Installation environment matplotlib

Personally, I also said earlier that it is strongly recommended to use Pycharm as the preferred IDE for Python beginners, mainly because of its powerful plug-in features, many environments can be installed with one click, such as matplotlib,numpy,requests in this article.

The following is directly on the effect picture:

Draw a simple line drawing using plot to draw a line import matplotlib.pyplot as plt# draw a line chart squares = [1, 4, 9, 16, 25] # plt.plot (squares, linewidth=5) # specify the weight of the line, # # plt.show () # modify label text and line weight # plt.title ("squre number", fontsize=24) # plt.xlabel ("Value", fontsize=14) # plt.ylabel ("square of value", fontsize=14) # plt.tick_params (axis='both', labelsize=14) # plt.show () # Correction graphic input_values = [1,2,3,4,5] plt.plot (input_values, squares, linewidth=5) plt.show ()

The resulting effect picture:

Use scatter to draw scatter plots and style import matplotlib.pyplot as plt# simple dots # plt.scatter (2,4) # plt.show () # modify label text and line weight plt.title ("squre number", fontsize=24) plt.xlabel ("Value", fontsize=14) plt.ylabel ("square of value", fontsize=14) # set the scale mark size plt.tick _ params (axis='both', which='major', labelsize=14) # draw scatter x_values = [1,2 3, 4, 5] y_values = [1, 4, 9, 16, 25] plt.scatter (x_values, y_values, 100) plt.show ()

Automatically calculate data import matplotlib.pyplot as pltx_values = list (range (1, 1001)) y_values = [x * * 2 for x in x_values] # y_values = [x * x for x in x_values] # y_values = [x ^ 2 for x in x_values] plt.scatter (x_values, y_values, swarm 40) # the value range of the axis # plt.axis (0, 1100, 0, 1100000) # is xmin xmax,ymin,ymaxplt.show ()

Random walk import matplotlib.pyplot as plyfrom random import choiceclass RandomWalk (): def _ init__ (self, num_points=5000): self.num_points = num_points self.x_values = [0] self.y_values = [0] def fill_walk (self): # keep walking Until the specified number of steps is reached while len (self.x_values) < self.num_points: # determines the direction of progress and the distance x_direction = choice ([1,-1]) x_distance = choice ([0,1,2,3,4,5,6,7,8) 9]) x_step = x_direction * x_distance y_direction = choice ([1,-1]) y_distance = choice ([0,1,2,3,4,5,6,7,8) 9]) y_step = y_direction * y_distance # cannot stand still if x_step = = 0 and y_step = = 0: continue next_x = self.x_values [- 1] + x_step next_y = self.y_values [- 1] + y_step self.x_values.append (next _ x) self.y_values.append (next_y) rw = RandomWalk () rw.fill_walk () ply.scatter (rw.x_values Rw.y_values, 15) ply.show () effect picture

Use Pygal to simulate rolling dice

The graphics that can be drawn by pygal can be accessed by pygal introduction.

Environment installation, install plug-ins directly on Pycharm.

Import pygalfrom random import randintclass Die (): def _ init__ (self, num_sides=6): self.num_sides = num_sides; def roll (self): # returns a random value between 1 and the number of dice faces return randint (1, self.num_sides) die = Die () results = [] # roll the dice 100 times and put the result in the list. For roll_num in range (10): result = die.roll () results.append (str (result)) print (results) # Analysis results frequencies = [] for value in range (1, die.num_sides + 1): frequency = results.count (value) frequencies.append (frequency) print (frequencies) # Visualize the results hist = pygal.Box () hist.title = "result of rolling one D6 1000 times" hist.x_labels = ['1th,' 2' Hist.x_title = "Result" hist.y_title = "frequency of result" hist.add ('D6mom, frequencies) hist.render_to_file (' die_visual.svg') install requests using Web API1.1

This plugin can be installed directly in Pycharm, which is very convenient.

1.2 process API response import requests# execute api call and store response url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'r = requests.get (url) print ("Status code:", r.status_code) # store api response in a variable response_dic = r.json () # processing result print (response_dic.keys ()) get the result: Status code: 200dict_keys ([' total_count', 'incomplete_results'') 'items']) 1.3 processing response dictionary # stores the api response in a variable response_dic = r.json () # processing result print (response_dic.keys ()) print ("Total repositories:", response_dic [' total_count']) repo_dics = response_dic ['items'] print ("repositories returned:" + str (len (repo_dics) # Research a warehouse repo_dic = repo_dics [0] print ("\ nKeys:" Str (len (repo_dic)) # for key in sorted (repo_dic.keys ()): # print (key) print ("Name:", repo_dic ['name']) print ("Owner:", repo_dic [' owner'] ['login']) print ("Starts:", repo_dic [' stargazers_count']) print ("Repository:", repo_dic ['html_url']) print ("Created:", repo_dic [' created_at']) print ("Updated:" Repo_dic ['updated_at']) print ("Description:", repo_dic [' description']) get the result: Total repositories: 2061622repositories returned:30Keys: 71Name: awesome-pythonOwner: vintaStarts: 40294Repository: https://github.com/vinta/awesome-pythonCreated: 2014-06-27T21:00:06ZUpdated: 2017-10-29T00:50:49ZDescription: A curated list of awesome Python frameworks, libraries, software and resources, this is the end of the introduction of "how to draw a simple folding figure with Python". Thank you for your reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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