请你给下面的代码每行写出注释，要求详细易懂。 import matplotlib.pyplot as plt %matplotlib inline from matplotlib import gridspec fig = plt.figure(figsize=(20, 4.5)) gs = gridspec.GridSpec(1, 2, width_ratios=[1, 2.5]) ax1 = plt.subplot(gs[0]) ax2 = plt.subplot(gs[1]) counts = [data["num_train"], data["num_test"]] colors = ['silver', 'purple'] explode = (0.1, 0) # explode 1st slice labels = ['train','test'] ax1.pie(counts, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140) counts = [] labels = [] for namecode in data["class_name"].keys(): counts.append(data["class_info"][namecode]) labels.append(data["class_name"][namecode]) print(len(counts),len(labels)) print(counts) print(labels) df = pd.DataFrame({"labels": labels, "counts": counts}) ax2.bar(df["labels"], df["counts"]) ax2.set_title("nums") ax2.set_ylabel("% nums") # ax2.set_xticks(rotation=-15) ax2.set_xticklabels(labels = labels, rotation=-15) plt.show()

import matplotlib.pyplot as plt import numpy as np import netCDF4 as nc import cartopy.crs as ccrs from cartopy.util import add_cyclic_point

import matplotlib.pyplot as plt import numpy as np import netCDF4 as nc import cartopy.crs as ccrs from cartopy.util import add_cyclic_point # Load the NetCDF data data = nc.Dataset('data.nc') ...

data = np.random.rand(80640, 3)使用import matplotlib.pyplot as plt可视化

import matplotlib.pyplot as plt # 生成数据 data = np.random.rand(80640, 3) # 取出x,y,z坐标 x = data[:, 0] y = data[:, 1] z = data[:, 2] # 绘制散点图 fig = plt.figure() ax = fig.add_subplot(111, ...

1设计并实现带标签的分组条形图此示例显示如何创建分组条形图以及如何使用标签注释条形图。import matplotlib.pyplot as pltimport numpy as np

import matplotlib.pyplot as plt import numpy as np # 数据 labels = ['A', 'B', 'C', 'D', 'E'] men_means = [20, 35, 30, 35, 27] women_means = [25, 32, 34, 20, 25] men_std = [2, 3, 4, 1, 2] women_std = ...

matplotlib.pyplot as plt 雷达图

import matplotlib.pyplot as plt # 创建数据 categories = ['A', 'B', 'C', 'D', 'E'] values = [4, 3, 5, 2, 1] # 计算角度 angles = np.linspace(0, 2 * np.pi, len(categories), endpoint=False).tolist() ...

from matplotlib import pyplot as plt fig.gca

fig.gca()是matplotlib中的一个函数，用于获取当前画布（figure）的坐标系（axes）。它返回当前figure中的当前axes对象，如果没有，则创建一个新的。在matplotlib中，figure是整个图的最高级别的...

import matplotlib.pyplot as plt #【错误行】包含绘图模块 fig= plt.figure() #创建一个空白画布 ax1=fig.plot(1,1,1) #【错误行】添加一个子图 plt.show() #显示图表

1. 导入的 matplotlib 库中的 pyplot 模块应该使用 import matplotlib.pyplot as plt 的方式导入； 2. 绘图函数应该使用 plt.subplot(1, 1, 1) 而不是 fig.plot(1, 1, 1)。修改后的代码如下： python import ...

import matplotlib.pyplot as plt square=[1,4,9,16,25] fig,ax=plt.subplots() ax.plt(square) plt.show()

具体来说，它导入了 matplotlib 库的 pyplot 模块，并创建了一个名为 square 的列表，其中包含了一些数字的平方。然后，它调用 plt.subplots() 函数来创建一个包含单个子图的 Figure 对象和 Axes 对象，并将 Axes ...

import matplotlib.pyplot as plt squares = [1,4,9,16,25] fig,ax = plt.subplots() ax.plot(squares) plt.show()这段代码运行时报错无法哈希的类型是list，如何修改

import matplotlib.pyplot as plt squares = [1, 4, 9, 16, 25] squares = np.array(squares) fig, ax = plt.subplots() ax.plot(squares) plt.show() 这样就能正常作图了。 ### 回答2：在这段代码中，...

使用matplotlib优化下面的代码，保证函数所占面积的颜色为紫色。import numpy as np import matplotlib.pyplot as plt n = 256 X = np.linspace(-np.pi, np.pi, n) Y = np.sin(2 * X) # YOUR CODE HERE plt.show()

import matplotlib.pyplot as plt n = 256 X = np.linspace(-np.pi, np.pi, n) Y = np.sin(2 * X) fig, ax = plt.subplots() ax.plot(X, Y, color='blue') ax.fill_between(X, 0, Y, where=(Y > 0), color='...

import matplotlib.pyplot as plt from matplotlib import cm ax = plt.figure().add_subplot(projection='3d') X, Y, Z = axes3d.get_test_data(0.05) ax.contour(X, Y, Z, cmap=cm.coolwarm) # Plot contour curves plt.show()

import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import axes3d from matplotlib import cm # 创建3D图形对象 fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # 生成示例数据 X, Y, ...

from matplotlib.pyplot import MultipleLocator

import matplotlib.pyplot as plt from matplotlib.pyplot import MultipleLocator # 创建一个图表对象 fig, ax = plt.subplots() # 设置 x 轴刻度线间距 x_major_locator = MultipleLocator(2) ax.xaxis.set_...

from matplotlib import pyplot as plt

fig, ax = plt.subplots() # Plot the data ax.plot(x, y) # Customize the plot ax.set_title('Sample Plot') ax.set_xlabel('X-axis') ax.set_ylabel('Y-axis') # Show the plot plt.show()

import matplotlib.pyplot as plt import plotly.express as px data_canada = px.data.gapminder().query("country == 'Canada'") fig = px.bar( data_canada, # 数据集 x='year', # x轴 y='pop', # y轴 color ='pop' ) fig.show() 怎么让柱形图上面显示数据

import matplotlib.pyplot as plt import plotly.express as px data_canada = px.data.gapminder().query("country == 'Canada'") fig = px.bar(data_canada, x='year', y='pop', color='pop', text='year, pop')...

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解释这行代码import matplotlib.pyplot as plt import numpy as np fig,axe=plt.subplots()

import numpy as np import pandas as pd import matplotlib.pyplot as plt

import matplotlib.pyplot as plt控制窗口大小

import matplotlib.pyplot as plt import numpy as np import netCDF4 as nc import cartopy.crs as ccrs from cartopy.util import add_cyclic_point

data = np.random.rand(80640, 3)使用import matplotlib.pyplot as plt可视化

1设计并实现带标签的分组条形图此示例显示如何创建分组条形图以及如何使用标签注释条形图。import matplotlib.pyplot as pltimport numpy as np

matplotlib.pyplot as plt 雷达图

from matplotlib import pyplot as plt fig.gca

import matplotlib.pyplot as plt #【错误行】 包含绘图模块 fig= plt.figure() #创建一个空白画布 ax1=fig.plot(1,1,1) #【错误行】 添加一个子图 plt.show() #显示图表

import matplotlib.pyplot as plt square=[1,4,9,16,25] fig,ax=plt.subplots() ax.plt(square) plt.show()

import matplotlib.pyplot as plt squares = [1,4,9,16,25] fig,ax = plt.subplots() ax.plot(squares) plt.show()这段代码运行时报错无法哈希的类型是list，如何修改

使用matplotlib优化下面的代码，保证函数所占面积的颜色为紫色。import numpy as np import matplotlib.pyplot as plt n = 256 X = np.linspace(-np.pi, np.pi, n) Y = np.sin(2 * X) # YOUR CODE HERE plt.show()

import matplotlib.pyplot as plt from matplotlib import cm ax = plt.figure().add_subplot(projection='3d') X, Y, Z = axes3d.get_test_data(0.05) ax.contour(X, Y, Z, cmap=cm.coolwarm) # Plot contour curves plt.show()

from matplotlib.pyplot import MultipleLocator

from matplotlib import pyplot as plt

import matplotlib.pyplot as plt import plotly.express as px data_canada = px.data.gapminder().query("country == 'Canada'") fig = px.bar( data_canada, # 数据集 x='year', # x轴 y='pop', # y轴 color ='pop' ) fig.show() 怎么让柱形图上面显示数据

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