AI代码解释 # Import the image and convert toRGBimg=cv2.imread('text.jpg')img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)# Plot the imagewithdifferent kernel sizes kernels=[5,11,17]fig,axs=plt.subplots(nrows=1,ncols=3,figsize=(20,20))forind,sinenumerate(kernels):img_blurred=cv2.blur(img,ksi...
# Import Data df = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/mpg_ggplot2.csv") df_counts = df.groupby(['hwy','cty']).size().reset_index(name='counts') # Draw Stripplot fig, ax = plt.subplots(figsize=(16,10), dpi=80) sns.stripplot(df_counts.cty,...
'nearest', 'bilinear', 'bicubic', 'spline16', 'lanczos'] fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15, 30), subplot_kw={'xticks': [], 'yticks': []}) fig.subplots_adjust(hspace=0.05, wspace=0.05) for ax, interp_method in zip(axes...
# Import df_raw = pd.read_csv("https:///selva86/datasets/raw/master/mpg_ggplot2.csv") # Prepare Data df = df_raw.groupby('class').size() # Make the plot with pandas df.plot(kind='pie', subplots=True, figsize=(8, 8), dpi= 80) plt.title("Pie Chart of Vehicle Class - Bad...
def diplay_graph_degree(G): seq_degree = sorted((d for n, d in G.degree()), reverse=True) dmax = max(seq_degree) fig = plt.figure("Degree of the count graph", figsize=(8, 8)) # Create a gridspec for adding subplots of different sizes axgrid = fig.add_gridspec(5, 4) ax0...
# Plot the image with different kernel sizes kernels = [5,11,17] fig, axs = plt.subplots(nrows =1, ncols =3, figsize = (20,20)) forind, sinenumerate(kernels): img_blurred = cv2.blur(img, ksize = (s, s)) ax = axs[ind] ...
(num_categories),num_points)# 点的大小sizes=np.random.choice([9,18,27,36],num_categories*num_points)# 为每个点指定颜色值,这里我们简单地使用点的大小作为颜色值colors=sizes# 创建图形fig,ax=plt.subplots(figsize=(8,6))# 绘制散点图scatter=ax.scatter(x_values,y_values,s=sizes*10,c=colors...
The easiest way to create a new Figure is withpyplot: fig = plt.figure()# an empty figure with no Axes fig, ax = plt.subplots()# a figure with a single Axes fig, axs = plt.subplots(2,2)# a figure with a 2x2 grid of Axes ...
subplots(1, 2,figsize=(16,6), dpi= 80)plot_acf(df.traffic.tolist(), ax=ax1, lags=50)plot_pacf(df.traffic.tolist(), ax=ax2, lags=20) # Decorate# lighten the bordersax1.spines["top"].set_alpha(.3); ax2.spines["top"].set_alpha(.3)ax1.spines["bottom"].set_alpha(.3);...
多个图形描绘 subplots %pylab inline pylab.rcParams['figure.figsize'] = (10,6)# 调整图片大小# np.random.seed(19680801)n_bins =10x = np.random.randn(1000,3) fig, axes = plt.subplots(nrows=2, ncols=2) ax0, ax1, ax2, ax3 = axes.flatten() ...