ydata, 'or') plt.plot(xfit, yfit, '-', color='gray') plt.fill_between(xfit, yfit - dyfit, yfit + dyfit, color='gray', alpha=0.2) plt.xlim(0, 10);注意上面我们调用fill_between函数：我们传递了的参数包括 x 值，y
8))scatter=plt.scatter(x,y,c=values,cmap='coolwarm',vmin=0,vmax=1)plt.colorbar(scatter)plt.title('Scatter Plot with Color Range - how2matplotlib.com')plt.xlabel('X-axis')plt.ylabel('Y-axis')plt.show()
data_a,width,label='category_A',color='#130074',ec='black',lw=.5)bar_b=ax.bar(x+width/2,data_b,width,label='category_B',color='#CB181B',ec='black',lw=.5)bar_c=ax.bar(x+width*3/2,data_c,width,label='category_C',color='white',ec='black',lw=.5)#定制化设计...
unique(midwest['category']) colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))] # Step 2: Draw Scatterplot with unique color for each category fig = plt.figure(figsize=(16, 10), dpi= 80, facecolor='w', edgecolor='k') for i, category in ...
in product_categories: if category in category_counts: category_counts[category] += 1 else: category_counts[category] = 1 #创建词云对象 wordcloud = WordCloud(font_path='simhei.ttf', background_color='white').generate_from_frequencies(category_counts) # 使用matplotlib绘制词云图...
['category'] categories = np.unique(midwest['category']) colors = [plt.cm.tab10(i / float(len(categories) - 1)) for i in range(len(categories))] # Step 2: Draw Scatterplot with unique color for each category fig = plt.figure(figsize=(16, 10), dpi=80, facecolor='w', ...
category_colors = plt.get_cmap('RdYlGn')(np.linspace(0.15, 0.85, data.shape[1])) fig, ax =plt.subplots(figsize=(9.2, 5)) ax.invert_yaxis() ax.xaxis.set_visible(False) ax.set_xlim(0, np.sum(data, axis=1).max()) for i, (colname, color) in enumerate(zip(category, category...
升级 pip: python3 -m pip install -U pip 安装 matplotlib 库: python3 -m pip install -U matplotlib 安装完成后,我们就可以通过 import 来导入 matplotlib 库: import matplotlib 以下实例,我们通过导入 matplotlib 库,然后查看 matplotlib 库的版本号: 实例 import matplotlib print(matplotlib.__version__) ...
16. 2, alpha=opacity, color='c', label='VotexF36') 17. 1.5*bar_width, means_VotexF50, bar_width/2, alpha=opacity, color='m', label='VotexF50') 18. 19. 'Category') 20. 'Scores') 21. 'Scores by group and Category') ...
# As many colors as there are unique midwest['category'] categories = np.unique(midwest['category']) colors = [plt.cm.tab10(i/float(len(categories)-1))foriinrange(len(categories))] # Step 2: Draw Scatterplot with unique color for each category ...