Matplotlib

 1: import matplotlib.pyplot as plt
 2: import numpy as np
 3: 
 4: x = np.arange(-3, 3, 0.1)
 5: plt.clf()
 6: plt.plot(x, np.sin(x))
 7: plt.show()
 8: 
 9: # 若要存圖，要先存檔再顯示 # for pycharm
10: # plt.savefig('images/SimpleSin.png', dpi=300)
11: # plt.show()   # for PyCharm

 1: import matplotlib.pyplot as plt
 2: import numpy as np
 3: 
 4: x = np.arange(-3, 3, 0.1)
 5: plt.clf()
 6: plt.plot(x, np.sin(x))
 7: plt.plot(x, np.cos(x))
 8: #加入圖例
 9: plt.legend(["sin", "cos"], loc="upper left")
10: 
11: 
12: # 若要存圖，要先存檔再顯示 # for PyCharm
13: plt.savefig('images/sincoslegend.png', dpi=300)

 1: import matplotlib.pyplot as plt
 2: import numpy as np
 3: 
 4: x1 = [1, 2, 3, 4, 5, 6]
 5: y1 = [1, 2, 3, 4, 5, 6]
 6: x2 = np.arange(0, 3, 0.3)
 7: x3 = np.arange(8).reshape(2, 4)
 8: print(x3)
 9: print(x3+3)
10: plt.plot(y1, 'c-p', x2, x2**2, 'ms:', x3, x3+3, '-*')
11: plt.savefig('line2.png', dpi=300)

 1: import matplotlib.pyplot as plt
 2: import numpy as np
 3: 
 4: def angdeg(x):
 5:     return 1.2*x+3
 6: 
 7: x = np.arange(0, 21, 1)
 8: plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # 步驟一（替換系統中的字型，這裡用的是Mac OSX系統）
 9: plt.rcParams['axes.unicode_minus'] = False  # 步驟二（解決座標軸負數的負號顯示問題）
10: ax = plt.figure().gca()
11: plt.plot(x, angdeg(x), color='r', linestyle='--', linewidth=3)
12: plt.xlabel('本月闖禍次數', fontsize=16)
13: plt.ylabel('媽媽生氣程度', fontsize=16)
14: plt.xlim(0,21)
15: ax.set_xticks(x)
16: plt.savefig('images/angrymam-1.png', dpi=300)

 1: import matplotlib.pyplot as plt
 2: import numpy as np
 3: 
 4: def angdeg(x):
 5:     return 500+(x**4)/100
 6: 
 7: x = np.arange(0, 21, 1)
 8: #plt.cla()
 9: plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # 步驟一（替換系統中的字型，這裡用的是Mac OSX系統）
10: plt.rcParams['axes.unicode_minus'] = False  # 步驟二（解決座標軸負數的負號顯示問題）
11: 
12: ax = plt.figure().gca()
13: plt.plot(x, angdeg(x), color='r', linestyle='--', linewidth=3)
14: 
15: plt.xlabel('本月闖禍次數', fontsize=16)
16: plt.ylabel('媽媽生氣程度', fontsize=16)
17: plt.xlim(0,21)
18: plt.ylim(0,2201)
19: ax.set_xticks(x)
20: plt.savefig('images/angrymam-2.png', dpi=300)

 1: import matplotlib.pyplot as plt
 2: import numpy as np
 3: import pandas as pd
 4: 
 5: def angdeg(x):
 6:     return x**4
 7: def l2c(x):
 8:     if x == True:
 9:         return 'r'
10:     else:
11:         return 'g'
12: 
13: np.random.seed(9527)
14: x = np.random.randint(21, size=20)
15: y = np.random.randint(21, size=20)
16: z = x + y < 20
17: colors = list(map(lambda x: 'g' if x == True else 'r', z))
18: #colors = list(map(l2c, z))
19: labels = list(map(lambda x: '挨揍' if x == True else '平安', z))
20: plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # 步驟一（替換系統中的字型，這裡用的是Mac OSX系統）
21: plt.rcParams['axes.unicode_minus'] = False  # 步驟二（解決座標軸負數的負號顯示問題）
22: 
23: ax = plt.figure().gca()
24: 
25: #plt.scatter(x, y, c = colors)
26: rc = [i for i in range(len(colors)) if colors[i] == 'r']
27: gc = [i for i in range(len(colors)) if colors[i] == 'g']
28: 
29: plt.scatter(x[rc], y[rc], c = 'r')
30: plt.scatter(x[gc], y[gc], c = 'g')
31: plt.xlabel('因闖禍受傷程度', fontsize=16)
32: plt.ylabel('因闖禍導致損失金額', fontsize=16)
33: ax.set_xticks(range(21))
34: ax.set_yticks(range(21))
35: plt.legend(['處罰','平安'])
36: plt.plot(x, 20-x, linewidth=3)
37: plt.savefig('images/angrymam-3.png', dpi=300)

 1: import matplotlib.pyplot as plt
 2: import numpy as np
 3: import pandas as pd
 4: 
 5: def angdeg(x):
 6:     return x**4
 7: def l2c(x):
 8:     if x == True:
 9:         return 'r'
10:     else:
11:         return 'g'
12: def mycurv(x):
13:     return 5 + (2 ** (19-float(x)))/6000
14: 
15: np.random.seed(9527)
16: x = np.random.randint(21, size=20)
17: y = np.random.randint(21, size=20)
18: y1 = list(map(mycurv, x))
19: print('y:',y)
20: print('y1:',y1)
21: z = y > y1
22: print('z:', z)
23: colors = list(map(lambda x: 'g' if x == True else 'r', z))
24: colors = list(map(l2c, z))
25: #labels = list(map(lambda x: '挨揍' if x == True else '平安', z))
26: plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] # 步驟一（替換系統中的字型，這裡用的是Mac OSX系統）
27: plt.rcParams['axes.unicode_minus'] = False
28: # 步驟二（解決座標軸負數的負號顯示問題）
29: ax = plt.figure().gca()
30: #
31: ##plt.scatter(x, y, c = colors)
32: rc = [i for i in range(len(colors)) if colors[i] == 'r']
33: gc = [i for i in range(len(colors)) if colors[i] == 'g']
34: 
35: plt.scatter(x[rc], y[rc], c = 'r')
36: plt.scatter(x[gc], y[gc], c = 'g')
37: plt.xlabel('因闖禍受傷程度', fontsize=16)
38: plt.ylabel('因闖禍導致金錢損失', fontsize=16)
39: ax.set_xticks(x)
40: plt.legend(['處罰
41: ','平安'])
42: x1=np.arange(21,0,-1, dtype=float)
43: print(x1)
44: 
45: plt.plot(x1, 5 + (2 ** (19-x1))/6000, linewidth=3)
46: plt.xlim(0,21)
47: plt.ylim(0,21)
48: ax.set_xticks(range(21))
49: ax.set_yticks(range(21))
50: plt.savefig('images/angrymam-4.png', dpi=300)

plt.pie( 比例列表 [, 參數 1, 參數 2, ...] )

 1: import matplotlib.pyplot as plt
 2: import numpy as np
 3: 
 4: parts = [35.35, 23, 26.65, 15]
 5: labels = ['Harrison', 'Vanessa', 'James', 'Ruby']
 6: colors = ['red', 'lightblue', 'purple', 'yellow']
 7: explodes = [0.1, 0, 0, 0.1]
 8: plt.pie(parts, colors = colors, labels = labels, explode = explodes, autopct = '%3.2f%%')
 9: plt.axis('equal')
10: plt.legend(loc='upper left')
11: 
12: #plt.show()
13: plt.savefig('simplePie.png', dpi=300)

plt.text(x相對座標, y相對座標, '文字字串', [其它參數])

1: import matplotlib.pyplot as plt
2: 
3: x = [1, 2, 3, 4, 5, 6, 7, 8]
4: y = [1, 4, 9, 16, 25, 36, 49, 64]
5: plt.plot(x, y, 'r--')
6: for x, y in zip(x, y):
7:     plt.text(x-0.2, y+0.6, '(%d, %d)' %(x, y))
8: #plt.show()
9: plt.savefig('simpleText.png', dpi = 300)

plt.subplot(numRows, numCols, plotNum)

plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)

 1: import numpy as np
 2: import matplotlib.pyplot as plt
 3: 
 4: from matplotlib.ticker import NullFormatter  # useful for `logit` scale
 5: 
 6: # Fixing random state for reproducibility
 7: np.random.seed(19680801)
 8: 
 9: # make up some data in the interval ]0, 1[
10: y = np.random.normal(loc=0.5, scale=0.4, size=1000)
11: y = y[(y > 0) & (y < 1)]
12: y.sort()
13: x = np.arange(len(y))
14: 
15: # plot with various axes scales
16: plt.figure()
17: 
18: # linear
19: plt.subplot(221)
20: plt.plot(x, y)
21: plt.yscale('linear')
22: plt.title('linear')
23: plt.grid(True)
24: 
25: 
26: # log
27: plt.subplot(222)
28: plt.plot(x, y)
29: plt.yscale('log')
30: plt.title('log')
31: plt.grid(True)
32: 
33: 
34: # symmetric log
35: plt.subplot(223)
36: plt.plot(x, y - y.mean())
37: plt.yscale('symlog', linthreshy=0.01)
38: plt.title('symlog')
39: plt.grid(True)
40: 
41: # logit
42: plt.subplot(224)
43: plt.plot(x, y)
44: plt.yscale('logit')
45: plt.title('logit')
46: plt.grid(True)
47: # Format the minor tick labels of the y-axis into empty strings with
48: # `NullFormatter`, to avoid cumbering the axis with too many labels.
49: plt.gca().yaxis.set_minor_formatter(NullFormatter())
50: 
51: # Adjust the subplot layout, because the logit one may take more space
52: # than usual, due to y-tick labels like "1 - 10^{-3}"
53: plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25, wspace=0.35)
54: 
55: plt.savefig('simpleSubplot.png', dpi = 300)

import numpy as np
scores = [[ 26,  53,  49,  72,  38,  14,  95,  49, 100,  77,  30,  23,  50,  47,  45,  44,  52,  10,  72,  11,  61,  91,   8,  39,  70,  74,  40,  45,  86,   9,],
          [ 68, 100,  97,  80,  63,  31, 100,  45,  92, 100,  54,  20,  78,  52,  62,  65,  85,  48,  90,  60, 100, 100,  56,  56,  62, 100,  37,  55,  91,  46,],
          [ 95,  24,  49,  78,  68,  28,  80,  85,  93,  84,  70,  95,  46,  12,  10,  38,   9,  97,  59,  85,  38,  87,  15,  76,  71,  59,  22,  11,  47,  63,],
          [100,  26,  50,  86,  84,  26,  93,  92,  92,  92,  96, 100,  68,  14,  50,  51,  44, 100,  69, 100,  35,  93,  40,  71,  91,  78,  54,  19,  43,  91,],
          [100,  48,  69,  97,  96,  58, 100,  92,  92,  84,  86, 100,  54,  33,  62,  67,  25, 100,  82, 100,  73,  91,  35,  82,  83,  75,  45,  41,  48,  76,]]

 1: import numpy as np
 2: import pylab as pl
 3: 
 4: x = np.arange(0.0, 2.0*np.pi, 0.01)
 5: print("x:", x[:5])
 6: y = np.sin(x)
 7: print("y:", y[:5])
 8: 
 9: pl.plot(x,y)
10: pl.xlabel('x')
11: pl.ylabel('y')
12: pl.title('sin')
13: pl.savefig("sinPlot.png")

 1: import numpy as np
 2: from bokeh.plotting import figure, show
 3: from bokeh.io import output_notebook, export_png, output_file, save
 4: import matplotlib.pylab as plt
 5: 
 6: N = 4000
 7: 
 8: x = np.random.random(size=N)*100
 9: y = np.random.random(size=N)*100
10: radii = np.random.random(size=N)*1.5
11: colors = ["#%02x%02x%02x" % (r, g, 150) for r, g in zip(np.floor(50+2*x).astype(int), np.floor(30+2*y).astype(int))]
12: 
13: output_notebook()
14: p = figure(title="Basic Title", plot_width=300, plot_height=300)
15: p.circle(x, y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)
16: output_file('bokehPlot.html', mode='inline')
17: save(p)

 1: import seaborn as sns
 2: sns.set(style="ticks")
 3: 
 4: #Load the example dataset for Anscombe's quartet
 5: df = sns.load_dataset("anscombe")
 6: 
 7: # Show the results of a linear regression within each dataset
 8: sns.lmplot(x="x", y="y", col="dataset", hue="dataset", data=df, col_wrap=2, ci=None, palette="muted", height=4, scatter_kws={'s' :50,'alpha':1})
 9: 
10: import matplotlib.pyplot as plt
11: #sns.sns_plot.savefig("seabornPlot.png")
12: plt.savefig("seabornPlot.png")

 1: import altair as alt
 2: from vega_datasets import data
 3: 
 4: cars = data.cars()
 5: 
 6: chart = alt.Chart(cars).mark_point().encode( x='Horsepower', y='Miles_per_Gallon', color='Origin').interactive()
 7: 
 8: #import pylab as pl
 9: #pl.savefig("altairPlot.png")
10: chart.save('altairPlot.png', webdriver='firefox')
11: chart.savechart('altairPlot.html')