1,1000)data2=np.random.normal(3,1,1000)# 创建直方图plt.figure(figsize=(10,6))plt.hist(data1,bins=30,alpha=0.5,color='blue',label='Group 1')plt.hist(data2,bins=30,alpha=0.5,color='red',label='Group 2')plt.title('Histogram with Two Groups - how2matplotlib.com')plt.xlabel('...
unique().__len__(), stacked=True, density=False, color=colors[:len(vals)]) # Decoration plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])}) plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", font...
unique().__len__(), stacked=True, density=False, color=colors[:len(vals)]) # Decoration plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])}) plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", font...
直方图(Histogram),又称质量分布图,它是一种条形图的一种,由一系列高度不等的纵向线段来表示数据分布的情况。直方图用于概率分布,它显示了一组数值序列在给定的数值范围内出现的概率;而柱状图则用于展示各个类别的频数。可以使用 matplotlib.pyplot.hist()函数 来绘制直方图。 下面是绘制直方图的代码实例,供大家参考。s...
hist(vals, 30, stacked=True, density=False, color=colors[:len(vals)]) # Decorationplt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])})plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$", fontsize=22)plt....
n, bins, patches = plt.hist(vals, 30, stacked=True, density=False, color=colors[:len(vals)]) # Decoration plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])}) plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby...
(vals,30,stacked=True,density=False,color=colors[:len(vals)])# Decorationplt.legend({group:colforgroup,colinzip(np.unique(df[groupby_var]).tolist(),colors[:len(vals)])})plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby_var}$",fontsize=22)plt.xlabel(x_var)plt....
n, bins, patches = plt.hist(vals, 30, stacked=True, density=False, color=colors[:len(vals)]) # Decoration plt.legend({group:col for group, col in zip(np.unique(df[groupby_var]).tolist(), colors[:len(vals)])}) plt.title(f"Stacked Histogram of ${x_var}$ colored by ${groupby...
color='red') plt.xlabel("Weight") plt.ylabel("Probability") plt.title("Red Histogram Plot") ...
plt.title('Movie Rating Histogram') plt.show() importpandasaspdimportmatplotlib.pyplotasplt df = pd.read_csv(r'IMDB_Movie.csv', header=0, sep=',')# 散点图df.plot(kind='scatter', x='Votes', y='Rating') plt.title('Scatter of Movie Votes and Rating') ...