31在 Python Matplotlib 中绘制饼图
import matplotlib.pyplot as plt labels = ['India', 'Canada', 'Japan', 'Australia', 'Russia'] sizes = [31, 19, 15, 14, 21] # Add upto 100% # Plot the pie chart plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90) # Equal aspect ratio ensures that pie is drawn as a circle. plt.axis('equal') # Display the graph onto the screen plt.show()
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32在 Matplotlib 饼图中为楔形设置边框
import matplotlib.pyplot as plt labels = ['India', 'Canada', 'Japan', 'Australia', 'Russia'] sizes = [31, 19, 15, 14, 21] # Add upto 100% # Plot the pie chart plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, wedgeprops={"edgecolor":"0",'linewidth': 1, 'linestyle': 'dashed', 'antialiased': True}) # Equal aspect ratio ensures that pie is drawn as a circle. plt.axis('equal') # Display the graph onto the screen plt.show()
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33在 Python Matplotlib 中设置饼图的方向
import matplotlib.pyplot as plt labels = ['India', 'Canada', 'Japan', 'Australia', 'Russia'] sizes = [31, 19, 15, 14, 21] # Add upto 100% # Plot the pie chart plt.pie(sizes, labels=labels, counterclock=False, startangle=90) # Equal aspect ratio ensures that pie is drawn as a circle. plt.axis('equal') # Display the graph onto the screen plt.show()
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34在 Matplotlib 中绘制具有不同颜色主题的饼图
import matplotlib.pyplot as plt sizes = [12, 23, 11, 17, 19, 24, 29, 11, 12, 9, 7, 5, 3, 2, 1] labels = ["Market %s" % i for i in sizes] fig1, ax1 = plt.subplots(figsize=(5, 5)) fig1.subplots_adjust(0.3, 0, 1, 1) theme = plt.get_cmap('copper') ax1.set_prop_cycle("color", [theme(1. * i / len(sizes)) for i in range(len(sizes))]) _, _ = ax1.pie(sizes, startangle=90, radius=1800) ax1.axis('equal') total = sum(sizes) plt.legend( loc='upper left', labels=['%s, %1.1f%%' % ( l, (float(s) / total) * 100) for l, s in zip(labels, sizes)], prop={'size': 11}, bbox_to_anchor=(0.0, 1), bbox_transform=fig1.transFigure ) plt.show()
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35在 Python Matplotlib 中打开饼图的轴
import matplotlib.pyplot as plt sizes = [12, 23, 11, 17, 19, 24, 29, 11, 12, 9, 7, 5, 3, 2, 1] labels = ["Market %s" % i for i in sizes] fig1, ax1 = plt.subplots(figsize=(5, 5)) fig1.subplots_adjust(0.1, 0.1, 1, 1) theme = plt.get_cmap('jet') ax1.set_prop_cycle("color", [theme(1. * i / len(sizes)) for i in range(len(sizes))]) _, _ = ax1.pie(sizes, startangle=90, radius=1800, frame=True) ax1.axis('equal') plt.show()
Output:
36具有特定颜色和位置的饼图
import numpy as np import matplotlib.pyplot as plt fig =plt.figure(figsize = (4,4)) ax11 = fig.add_subplot(111) # Data to plot labels = 'Python', 'C++', 'Ruby', 'Java' sizes = [250, 130, 75, 200] colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue'] # Plot w,l,p = ax11.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140, pctdistance=1, radius=0.5) pctdists = [.8, .5, .4, .2] for t,d in zip(p, pctdists): xi,yi = t.get_position() ri = np.sqrt(xi**2+yi**2) phi = np.arctan2(yi,xi) x = d*ri*np.cos(phi) y = d*ri*np.sin(phi) t.set_position((x,y)) plt.axis('equal') plt.show()
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37在 Matplotlib 中绘制极坐标图
import matplotlib.pyplot as plt import numpy as np employee = ["Sam", "Rony", "Albert", "Chris", "Jahrum"] actual = [45, 53, 55, 61, 57, 45] expected = [50, 55, 60, 65, 55, 50] # Initialise the spider plot by setting figure size and polar projection plt.figure(figsize=(10, 6)) plt.subplot(polar=True) theta = np.linspace(0, 2 * np.pi, len(actual)) # Arrange the grid into number of sales equal parts in degrees lines, labels = plt.thetagrids(range(0, 360, int(360/len(employee))), (employee)) # Plot actual sales graph plt.plot(theta, actual) plt.fill(theta, actual, 'b', alpha=0.1) # Plot expected sales graph plt.plot(theta, expected) # Add legend and title for the plot plt.legend(labels=('Actual', 'Expected'), loc=1) plt.title("Actual vs Expected sales by Employee") # Dsiplay the plot on the screen plt.show()
Output:
38在 Matplotlib 中绘制半极坐标图
import matplotlib.pyplot as plt import numpy as np theta = np.linspace(0, np.pi) r = np.sin(theta) fig = plt.figure() ax = fig.add_subplot(111, polar=True) c = ax.scatter(theta, r, c=r, s=10, cmap='hsv', alpha=0.75) ax.set_thetamin(0) ax.set_thetamax(180) plt.show()
Output:
39Matplotlib 中的极坐标等值线图
import numpy as np import matplotlib.pyplot as plt # Using linspace so that the endpoint of 360 is included actual = np.radians(np.linspace(0, 360, 20)) expected = np.arange(0, 70, 10) r, theta = np.meshgrid(expected, actual) values = np.random.random((actual.size, expected.size)) fig, ax = plt.subplots(subplot_kw=dict(projection='polar')) ax.contourf(theta, r, values) plt.show()
Output:
40绘制直方图
import numpy as np import matplotlib.pyplot as plt # Data in numpy array exp_data = np.array([12, 15, 13, 20, 19, 20, 11, 19, 11, 12, 19, 13, 12, 10, 6, 19, 3, 1, 1, 0, 4, 4, 6, 5, 3, 7, 12, 7, 9, 8, 12, 11, 11, 18, 19, 18, 19, 3, 6, 5, 6, 9, 11, 10, 14, 14, 16, 17, 17, 19, 0, 2, 0, 3, 1, 4, 6, 6, 8, 7, 7, 6, 7, 11, 11, 10, 11, 10, 13, 13, 15, 18, 20, 19, 1, 10, 8, 16, 19, 19, 17, 16, 11, 1, 10, 13, 15, 3, 8, 6, 9, 10, 15, 19, 2, 4, 5, 6, 9, 11, 10, 9, 10, 9, 15, 16, 18, 13]) # Plot the distribution of numpy data plt.hist(exp_data, bins = 19) # Add axis labels plt.xlabel("Year") plt.ylabel("Salary") plt.title("Example of Histogram Plot") plt.show()
Output:
41在 Matplotlib 直方图中选择 bins
import numpy as np import matplotlib.pyplot as plt # Data in numpy array data = np.array([12, 15, 13, 20, 19, 20, 11, 19, 11, 12, 19, 13, 12, 10, 6, 19, 3, 1, 1, 0, 4, 4, 6, 5, 3, 7, 12, 7, 9, 8, 12, 11, 11, 18, 19, 18, 19, 3, 6, 5, 6, 9, 11, 10, 14, 14, 16, 17, 17, 19, 0, 2, 0, 3, 1, 4, 6, 6, 8, 7, 7, 6, 7, 11, 11, 10, 11, 10, 13, 13, 15, 18, 20, 19, 1, 10, 8, 16, 19, 19, 17, 16, 11, 1, 10, 13, 15, 3, 8, 6, 9, 10, 15, 19, 2, 4, 5, 6, 9, 11, 10, 9, 10, 9, 15, 16, 18, 13]) # Plot the distribution of numpy data ax = plt.hist(data, bins=np.arange(min(data), max(data) + 0.25, 0.25), align='left') # Add axis labels plt.xlabel("Year") plt.ylabel("Salary") plt.title("Example of Histogram Plot") plt.show()
Output:
42在 Matplotlib 中绘制没有条形的直方图
import numpy as np import matplotlib.pyplot as plt # Data in numpy array data = np.array([12, 15, 13, 20, 19, 20, 11, 19, 11, 12, 19, 13, 12, 10, 6, 19, 3, 1, 1, 0, 4, 4, 6, 5, 3, 7, 12, 7, 9, 8, 12, 11, 11, 18, 19, 18, 19, 3, 6, 5, 6, 9, 11, 10, 14, 14, 16, 17, 17, 19, 0, 2, 0, 3, 1, 4, 6, 6, 8, 7, 7, 6, 7, 11, 11, 10, 11, 10, 13, 13, 15, 18, 20, 19, 1, 10, 8, 16, 19, 19, 17, 16, 11, 1, 10, 13, 15, 3, 8, 6, 9, 10, 15, 19, 2, 4, 5, 6, 9, 11, 10, 9, 10, 9, 15, 16, 18, 13]) bins, edges = np.histogram(data, 21, normed=1) left, right = edges[:-1], edges[1:] X = np.array([left, right]).T.flatten() Y = np.array([bins, bins]).T.flatten() plt.plot(X, Y) plt.show()
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43使用 Matplotlib 同时绘制两个直方图
import numpy as np import matplotlib.pyplot as plt age = np.random.normal(loc=1, size=100) # a normal distribution salaray = np.random.normal(loc=-1, size=10000) # a normal distribution _, bins, _ = plt.hist(age, bins=50, range=[-6, 6], density=True) _ = plt.hist(salaray, bins=bins, alpha=0.5, density=True) plt.show()
Output:
44绘制具有特定颜色、边缘颜色和线宽的直方图
import numpy as np import matplotlib.pyplot as plt # Data in numpy array exp_data = np.array([12, 15, 13, 20, 19, 20, 11, 19, 11, 12, 19, 13, 12, 10, 6, 19, 3, 1, 1, 0, 4, 4, 6, 5, 3, 7, 12, 7, 9, 8, 12, 11, 11, 18, 19, 18, 19, 3, 6, 5, 6, 9, 11, 10, 14, 14, 16, 17, 17, 19, 0, 2, 0, 3, 1, 4, 6, 6, 8, 7, 7, 6, 7, 11, 11, 10, 11, 10, 13, 13, 15, 18, 20, 19, 1, 10, 8, 16, 19, 19, 17, 16, 11, 1, 10, 13, 15, 3, 8, 6, 9, 10, 15, 19, 2, 4, 5, 6, 9, 11, 10, 9, 10, 9, 15, 16, 18, 13]) # Plot the distribution of numpy data plt.hist(exp_data, bins=21, align='left', color='b', edgecolor='red', linewidth=1) # Add axis labels plt.xlabel("Year") plt.ylabel("Salary") plt.title("Example of Histogram Plot") plt.show()
Output:
45用颜色图绘制直方图
import numpy as np import matplotlib.pyplot as plt # Data in numpy array data = np.array([12, 15, 13, 20, 19, 20, 11, 19, 11, 12, 19, 13, 12, 10, 6, 19, 3, 1, 1, 0, 4, 4, 6, 5, 3, 7, 12, 7, 9, 8, 12, 11, 11, 18, 19, 18, 19, 3, 6, 5, 6, 9, 11, 10, 14, 14, 16, 17, 17, 19, 0, 2, 0, 3, 1, 4, 6, 6, 8, 7, 7, 6, 7, 11, 11, 10, 11, 10, 13, 13, 15, 18, 20, 19, 1, 10, 8, 16, 19, 19, 17, 16, 11, 1, 10, 13, 15, 3, 8, 6, 9, 10, 15, 19, 2, 4, 5, 6, 9, 11, 10, 9, 10, 9, 15, 16, 18, 13]) cm = plt.cm.RdBu_r n, bins, patches = plt.hist(data, 25, normed=1, color='green') for i, p in enumerate(patches): plt.setp(p, 'facecolor', cm(i/25)) # notice the i/25 plt.show()
Output:
46更改直方图上特定条的颜色
import pandas as pd import matplotlib.pyplot as plt s = pd.Series([12, 15, 13, 20, 19, 20, 11, 19, 11, 12, 19, 13, 12, 10, 6, 19, 3, 1, 1, 0, 4, 4, 6, 5, 3, 7, 12, 7, 9, 8, 12, 11, 11, 18, 19, 18, 19, 3, 6, 5, 6, 9, 11, 10, 14, 14, 16, 17, 17, 19, 0, 2, 0, 3, 1, 4, 6, 6, 8, 7, 7, 6, 7, 11, 11, 10, 11, 10, 13, 13, 15, 18, 20, 19, 1, 10, 8, 16, 19, 19, 17, 16, 11, 1, 10, 13, 15, 3, 8, 6, 9, 10, 15, 19, 2, 4, 5, 6, 9, 11, 10, 9, 10, 9, 15, 16, 18, 13]) p = s.plot(kind='hist', bins=50, color='orange') bar_value_to_label = 5 min_distance = float("inf") # initialize min_distance with infinity index_of_bar_to_label = 0 for i, rectangle in enumerate(p.patches): # iterate over every bar tmp = abs( # tmp = distance from middle of the bar to bar_value_to_label (rectangle.get_x() + (rectangle.get_width() * (1 / 2))) - bar_value_to_label) if tmp < min_distance: # we are searching for the bar with x cordinate # closest to bar_value_to_label min_distance = tmp index_of_bar_to_label = i p.patches[index_of_bar_to_label].set_color('b') plt.show()
Output:
47箱线图
import matplotlib.pyplot as plt import pandas as pd df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [15, 15, 8, 12], [15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]], columns=['Apple', 'Orange', 'Banana', 'Pear'], index=['Basket1', 'Basket2', 'Basket3', 'Basket4', 'Basket5', 'Basket6']) df.boxplot(['Apple', 'Orange', 'Banana', 'Pear']) plt.show()
Output:
48箱型图按列数据分组
import matplotlib.pyplot as plt import pandas as pd employees = pd.DataFrame({ 'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005', 'Emp006' , 'Emp007', 'Emp008', 'Emp009', 'Emp010', 'Emp011', 'Emp012' , 'Emp013', 'Emp014', 'Emp015', 'Emp016', 'Emp017', 'Emp018' , 'Emp019', 'Emp020'], 'Occupation': ['Chemist', 'Statistician', 'Statistician', 'Statistician', 'Programmer', 'Chemist', 'Statistician', 'Statistician', 'Statistician', 'Programmer', 'Chemist', 'Statistician', 'Statistician', 'Statistician', 'Programmer', 'Chemist', 'Statistician', 'Statistician', 'Statistician', 'Programmer' ], 'Age': [23, 24, 34, 29, 40, 25, 26, 29, 40, 41, 40, 35, 41, 29, 33, 35, 29, 30, 36, 37]}) employees.boxplot(column=['Age'], by=['Occupation']) plt.show()
Output:
49更改箱线图中的箱体颜色
import matplotlib.pyplot as plt import pandas as pd df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [15, 15, 8, 12], [15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]], columns=['Apple', 'Orange', 'Banana', 'Pear'], index=['Basket1', 'Basket2', 'Basket3', 'Basket4', 'Basket5', 'Basket6']) box = plt.boxplot(df, patch_artist=True) colors = ['blue', 'green', 'purple', 'tan', 'pink', 'red'] for patch, color in zip(box['boxes'], colors): patch.set_facecolor(color) plt.show()
Output:
50更改 Boxplot 标记样式、标记颜色和标记大小
import matplotlib.pyplot as plt import pandas as pd df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 128], [15, 15, 89, 12], [-15, 14, 1, 8], [7, -11, 1, 8], [5, 4, 9, 2]], columns=['Apple', 'Orange', 'Banana', 'Pear'], index=['Basket1', 'Basket2', 'Basket3', 'Basket4', 'Basket5', 'Basket6']) flierprops = dict(marker='+', markerfacecolor='g', markersize=15, linestyle='none', markeredgecolor='r') df.boxplot(['Apple', 'Orange', 'Banana', 'Pear'], flierprops=flierprops) plt.show()
Output:
51用数据系列绘制水平箱线图
import matplotlib.pyplot as plt data = [-12, 15, 13, -20, 19, 20, 11, 19, -11, 12, 19, 10, 12, 10, 6, 19, 3, 1, 1, 0, 4, 49, 6, 5, 3, 7, 12, 77, 9, 8, 12, 11, 11, 18, 19, 18, 19, 3, 6, 5, 6, 9, 11, 10, 18, 14, 16, 17, 17, 19, 0, 2, 0, 3, 1, 4, 6, 6, 8, 7, 7, 69, 79, 11, 11, 10, 11, 10, 13, 13, 15, 18, 20, 19, 1, 11, 8, 16, 19, 89, 17, 16, 11, 1, 110, 13, 15, 3, 8, 6, 99, 10, 15, 19, 2, 4, 5, 6, 9, 11, 10, 9, 10, 99, 15, 16, 18, 13] fig = plt.figure(figsize=(7, 3), dpi=100) ax = plt.subplot(2, 1,2) ax.boxplot(data, False, sym='rs', vert=False, whis=0.75, positions=[0], widths=[0.5]) plt.tight_layout() plt.show()
Output:
52箱线图调整底部和左侧
import matplotlib.pyplot as plt import pandas as pd x = [[1.2, 2.3, 3.0, 4.5], [1.1, 2.2, 2.9, 5.0]] df = pd.DataFrame(x, index=['Apple', 'Orange']) df.T.boxplot() plt.subplots_adjust(bottom=0.25) plt.show()
Output:
53使用 Pandas 数据在 Matplotlib 中生成热图
import matplotlib.pyplot as plt import pandas as pd df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12], [15, 14, 1, 8]], columns=['Apple', 'Orange', 'Banana', 'Pear'], index=['Basket1', 'Basket2', 'Basket3', 'Basket4'] ) plt.imshow(df, cmap="YlGnBu") plt.colorbar() plt.xticks(range(len(df)),df.columns, rotation=20) plt.yticks(range(len(df)),df.index) plt.show()
Output:
54带有中间颜色文本注释的热图
import pandas as pd import matplotlib.pyplot as plt data = { 'Basket1': [90, 95, 99, 50, 50, 45, 81], 'Basket2': [91, 98, 89, 75, 98, 49, 80], 'Basket3': [92, 97, 99, 85, 96, 75, 88], 'Basket4': [94, 96, 88, 79, 98, 69, 86] } fig, ax = plt.subplots(figsize=(9, 4)) df = pd.DataFrame.from_dict(data, orient='index') im = ax.imshow(df.values, cmap="YlGnBu") fig.colorbar(im) # Loop over data dimensions and create text annotations textcolors = ["k", "w"] threshold = 55 for i in range(len(df)): for j in range(len(df.columns)): text = ax.text(j, i, df.values[i, j], ha="center", va="center", color=textcolors[df.values[i, j] > threshold]) plt.show()
Output:
55热图显示列和行的标签并以正确的方向显示数据
import matplotlib.pyplot as plt import numpy as np column_labels = list('ABCDEFGH') row_labels = list('12345678') data = np.random.rand(8, 8) fig, ax = plt.subplots() heatmap = ax.pcolor(data, cmap=plt.cm.Reds) # Put the major ticks at the middle of each cell ax.set_xticks(np.arange(data.shape[0]), minor=False) ax.set_yticks(np.arange(data.shape[0]), minor=False) # Want a more natural, table-like display ax.invert_yaxis() ax.xaxis.tick_top() ax.set_xticklabels(row_labels, minor=False) ax.set_yticklabels(column_labels, minor=False) plt.show()
Output:
56将 NA cells 与 HeatMap 中的其他 cells 区分开来
import matplotlib.pyplot as plt import matplotlib.patches as patches import numpy as np column_labels = list('ABCDEFGH') row_labels = list('12345678') data = np.random.rand(8, 8) data = np.ma.masked_greater(data, 0.8) fig, ax = plt.subplots() heatmap = ax.pcolor(data, cmap=plt.cm.gray, edgecolors='blue', linewidths=1, antialiased=True) fig.colorbar(heatmap) ax.patch.set(hatch='..', edgecolor='red') # Put the major ticks at the middle of each cell ax.set_xticks(np.arange(data.shape[0]), minor=False) ax.set_yticks(np.arange(data.shape[0]), minor=False) # Want a more natural, table-like display ax.invert_yaxis() ax.xaxis.tick_top() ax.set_xticklabels(row_labels, minor=False) ax.set_yticklabels(column_labels, minor=False) plt.show()
Output:
57在 matplotlib 中创建径向热图
import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np fig = plt.figure() ax = Axes3D(fig) n = 12 m = 24 rad = np.linspace(0, 10, m) a = np.linspace(0, 2 * np.pi, n) r, th = np.meshgrid(rad, a) z = np.random.uniform(-1, 1, (n,m)) plt.subplot(projection="polar") plt.pcolormesh(th, r, z, cmap = 'Blues') plt.plot(a, r, ls='none', color = 'k') plt.grid() plt.colorbar() plt.show()
Output:
58在 Matplotlib 中组合两个热图
import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns df1 = pd.DataFrame(np.random.rand(20, 4), columns=list("ABCD")) df2 = pd.DataFrame(np.random.rand(20, 4), columns=list("WXYZ")) fig, (ax1, ax2) = plt.subplots(ncols=2) fig.subplots_adjust(wspace=0.01) sns.heatmap(df1, cmap="rocket", ax=ax1, cbar=False) fig.colorbar(ax1.collections[0], ax=ax1, location="left", use_gridspec=False, pad=0.2) sns.heatmap(df2, cmap="icefire", ax=ax2, cbar=False) fig.colorbar(ax2.collections[0], ax=ax2, location="right", use_gridspec=False, pad=0.2) ax2.yaxis.tick_right() ax2.tick_params(rotation=0) plt.show()
Output:
59使用 Numpy 和 Matplotlib 创建热图日历
import datetime as dt import matplotlib.pyplot as plt import numpy as np def main(): dates, data = generate_data() fig, ax = plt.subplots(figsize=(6, 10)) calendar_heatmap(ax, dates, data) plt.show() def generate_data(): num = 60 data = np.random.randint(0, 20, num) start = dt.datetime(2018, 1, 1) dates = [start + dt.timedelta(days=i) for i in range(num)] return dates, data def calendar_array(dates, data): i, j = zip(*[d.isocalendar()[1:] for d in dates]) i = np.array(i) - min(i) j = np.array(j) - 1 ni = max(i) + 1 calendar = np.nan * np.zeros((ni, 7)) calendar[i, j] = data return i, j, calendar def calendar_heatmap(ax, dates, data): i, j, calendar = calendar_array(dates, data) im = ax.imshow(calendar, interpolation='none', cmap='summer') label_days(ax, dates, i, j, calendar) label_months(ax, dates, i, j, calendar) ax.figure.colorbar(im) def label_days(ax, dates, i, j, calendar): ni, nj = calendar.shape day_of_month = np.nan * np.zeros((ni, 7)) day_of_month[i, j] = [d.day for d in dates] for (i, j), day in np.ndenumerate(day_of_month): if np.isfinite(day): ax.text(j, i, int(day), ha='center', va='center') ax.set(xticks=np.arange(7), xticklabels=['M', 'T', 'W', 'R', 'F', 'S', 'S']) ax.xaxis.tick_top() def label_months(ax, dates, i, j, calendar): month_labels = np.array(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']) months = np.array([d.month for d in dates]) uniq_months = sorted(set(months)) yticks = [i[months == m].mean() for m in uniq_months] labels = [month_labels[m - 1] for m in uniq_months] ax.set(yticks=yticks) ax.set_yticklabels(labels, rotation=90) main()
Output:
60在 Python 中创建分类气泡图
import numpy as np import matplotlib.pyplot as plt import pandas as pd df = pd.DataFrame({'Company1':['Chemist', 'Scientist', 'Worker', 'Accountant', 'Programmer', 'Chemist', 'Scientist', 'Worker', 'Statistician', 'Programmer', 'Chemist', 'Accountant', 'Statistician', 'Scientist', 'Accountant', 'Chemist', 'Scientist', 'Statistician', 'Statistician', 'Programmer'], 'Company2':['Programmer', 'Statistician', 'Scientist', 'Statistician', 'Worker', 'Chemist', 'Accountant', 'Accountant', 'Statistician', 'Chemist', 'Programmer', 'Scientist', 'Scientist', 'Accountant', 'Programmer', 'Chemist', 'Accountant', 'Scientist', 'Scientist', 'Worker'], 'Count':[53, 15, 1, 2, 4, 22, 6, 1, 15, 15, 1, 1, 2, 2, 4, 4, 22, 22, 6, 6] }) # Create padding column from values for circles that are neither too small nor too large df["padd"] = 2.5 * (df.Count - df.Count.min()) / (df.Count.max() - df.Count.min()) + 0.5 fig = plt.figure() # Prepare the axes for the plot - you can also order your categories at this step s = plt.scatter(sorted(df.Company1.unique()), sorted(df.Company2.unique(), reverse = True), s = 0) s.remove # Plot data row-wise as text with circle radius according to Count for row in df.itertuples(): bbox_props = dict(boxstyle = "circle, pad = {}".format(row.padd), fc = "w", ec = "b", lw = 2) plt.annotate(str(row.Count), xy = (row.Company1, row.Company2), bbox = bbox_props, ha="center", va="center", zorder = 2, clip_on = True) # Plot grid behind markers plt.grid(ls = "--", zorder = 1) # Take care of long labels fig.autofmt_xdate() plt.tight_layout() plt.show()
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61使用 Numpy 和 Matplotlib 创建方形气泡图
import matplotlib.pyplot as plt import numpy as np import random xs = np.arange(1, 5, 1) ys = np.arange(0.5, 6, 1) colors = ["red", "blue", "green"] def square_size_color(value): square_color = random.choice(colors) if value < 1: square_size = random.choice(range(1, 10)) if 3 > value > 1: square_size = random.choice(range(10, 25)) else: square_size = random.choice(range(25, 35)) return square_size, square_color for x in xs: for y in ys: square_size, square_color = square_size_color(y) plt.plot(x, y, linestyle="None", marker="s", markersize=square_size, mfc=square_color, mec=square_color) plt.grid(visible=True, axis='y') plt.xlim(0.5, 4.5) plt.ylim(-0.5, 6.5) plt.show()
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62使用 Numpy 和 Matplotlib 创建具有气泡大小的图例
import numpy as np import matplotlib.pyplot as plt import pandas as pd N = 50 M = 5 # Number of bins x = np.random.rand(N) y = np.random.rand(N) a2 = 400*np.random.rand(N) # Create the DataFrame from your randomised data and bin it using groupby. df = pd.DataFrame(data=dict(x=x, y=y, a2=a2)) bins = np.linspace(df.a2.min(), df.a2.max(), M) grouped = df.groupby(np.digitize(df.a2, bins)) # Create some sizes and some labels. sizes = [50*(i+1.) for i in range(M)] labels = ['Tiny', 'Small', 'Medium', 'Large', 'Huge'] for i, (name, group) in enumerate(grouped): plt.scatter(group.x, group.y, s=sizes[i], alpha=0.5, label=labels[i]) plt.legend() plt.show()
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63使用 Matplotlib 堆叠条形图
import pandas as pd import numpy as np import matplotlib.pyplot as plt df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5, 5, 0, 0]], columns=['Apple', 'Orange', 'Banana', 'Pear'], index=['Basket1', 'Basket2', 'Basket3']) ax = df.plot(kind='bar', stacked=True) ax.set_xlabel('DataFrame Values') ax.set_ylabel('Basket') plt.show()
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64在同一图中绘制多个堆叠条
import pandas as pd import matplotlib.pyplot as plt df = pd.DataFrame(dict( A=[1, 2, 3, 4], B=[2, 3, 4, 5], C=[3, 4, 5, 6] )) fig, axes = plt.subplots(1, 2, figsize=(10, 4), sharey=True) df.plot.bar(ax=axes[0]) df.diff(axis=1).fillna(df).astype(df.dtypes).plot.bar(ax=axes[1], stacked=True) plt.show()
Output:
65Matplotlib 中的水平堆积条形图
import numpy as np import matplotlib.pyplot as plt people = ('A','B','C','D','E','F','G','H') segments = 4 # generate some multi-dimensional data & arbitrary labels data = 3 + 10* np.random.rand(segments, len(people)) percentages = (np.random.randint(5,20, (len(people), segments))) y_pos = np.arange(len(people)) fig = plt.figure(figsize=(10,8)) ax = fig.add_subplot(111) colors ='rgbwmc' patch_handles = [] left = np.zeros(len(people)) # left alignment of data starts at zero for i, d in enumerate(data): patch_handles.append(ax.barh(y_pos, d, color=colors[i%len(colors)], align='center', left=left)) # accumulate the left-hand offsets left += d # go through all of the bar segments and annotate for j in range(len(patch_handles)): for i, patch in enumerate(patch_handles[j].get_children()): bl = patch.get_xy() x = 0.5*patch.get_width() + bl[0] y = 0.5*patch.get_height() + bl[1] ax.text(x,y, "%d%%" % (percentages[i,j]), ha='center') ax.set_yticks(y_pos) ax.set_yticklabels(people) ax.set_xlabel('Distance') plt.show()
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