plt.title('Sin Function') plt.legend() plt.subplot(2, 1, 2) # 两行一列,当前选中第二个子图 plt.plot(x, y2, label='Cos') plt.title('Cos Function') plt.legend() plt.tight_layout() # 调整子图布局,防止重叠 plt.show() 在这个例子中,使用plt.subplot创建了两个子图,分别绘制了正弦和余...
importmatplotlib.pyplotaspltimportnumpyasnp# 创建数据x = np.linspace(0,2* np.pi,100) y1 = np.sin(x) y2 = np.cos(x)# 创建子图plt.subplot(2,1,1)# 两行一列，当前选中第一个子图plt.plot(x, y1, label='Sin') plt.title('Sin Function') plt.legend() plt.subplot(2,1,2)# 两...
In most cases, it is possible to use numpy or Python objects, but pandas objects are preferable because the associated names will be used to annotate the axes. Additionally, you can use Categorical types for the grouping variables to control the order of plot elements. This function always tre...
hue_order(list, optional): 指定hue变量的顺序，默认为None。estimator(function, optional): 在每个类...
plt.title('Cos Function') plt.legend() plt.tight_layout() # 调整子图布局,防止重叠 plt.show() 在这个例子中,使用plt.subplot创建了两个子图,分别绘制了正弦和余弦函数。 Matplotlib还提供了大量的定制化选项,包括颜色、线型、标记等。例如: plt.plot(x, y, color='red', linestyle='--', marker='o...
Subplot grid for plotting pairwise relationships in a dataset. p=sns.PairGrid() p.map_diag Plot with a univariate function on each diagonal subplot p.map_lower Plot with a bivariate function on the lower diagonal subplots. p.map_offdiag ...
plt.title('Sin Function') plt.legend() plt.subplot(2,1,2) # 两行一列,当前选中第二个子图 plt.plot(x, y2, label='Cos') plt.title('Cos Function') plt.legend() plt.tight_layout() # 调整子图布局,防止重叠 plt.show() 在这个例子中,使用plt.subplot创建了两个子图,分别绘制了正弦和余弦...
# Function to calculate correlation coefficient between two arrays def corr(x, y, **kwargs): # Calculate the value coef = np.corrcoef(x, y)[0][1] # Make the label label = r'$\rho$ = ' + str(round(coef, 2)) # Add the label to the plot ...
To help select good palettes or colormaps using this system, you can use thechoose_cubehelix_palette()function in a notebook to launch an interactive app that will let you play with the different parameters. Passas_cmap=Trueif you want the function to return a colormap (rather than a list...
Seaborn provides a scatterplot() axes-level function for this very purpose: Python In [2]: import matplotlib.pyplot as plt ...: import seaborn as sns ...: ...: ( ...: sns.scatterplot( ...: data=crossings, x="min_temp", y="max_temp" ...: ) ...: .set( ...: title=...