首页import numpy as np import random import matplotlib.pyplot as plt是否有问题

import numpy as np import random import matplotlib.pyplot as plt是否有问题

时间: 2023-08-14 16:14:29 浏览: 169

Matplotlib scatter绘制散点图的方法实现

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前言考虑到很多同学可能还没有安装matplotlib包，这里给大家提供我常用的安装方法。首先Win键 + R，输入命令cmd打开命令行工具，再次在命令行工具中输入pip install matplotlib就可以直接安装了，安装后会提示安装成功。一、简单散点图 1.代码 import numpy as np import matplotlib.pyplot as plt #生成散点数据 n = 1024 X = np.random.normal(0,1,n) Y = np.random.normal(0,1,n) plt.scatter(X,Y) #输入散点数据 plt.show(

在Python中，通常每个导入语句都应该单独成行，因此在`import numpy as np import random import matplotlib.pyplot as plt`这行代码中，将三个导入语句放在同一行可能会导致语法错误。正确的写法应该是每个导入语句单独成行，例如： ``` import numpy as np import random import matplotlib.pyplot as plt ``` 这样写可以保证代码的可读性和易于维护。

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import numpy as np import random import matplotlib.pyplot as plt是否有问题

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import matplotlib.pyplot as plt import numpy as np x1 = np.random.randn(100) x2 = np.random.randn

解释代码import numpy as np import random import math import os import scipy.io import matplotlib.pyplot as plt import torch import torch.nn as nn import torch.nn.functional as F import torchvision import transformers %matplotlib inline

import os import matplotlib.pyplot as plt import numpy as np import random from PIL import Image, ImageOps, ImageFilter这些是什么意思

为代码添加注释import matplotlib.pyplot as plt import numpy as np x = np.random.rand(100) y = np.random.rand(100) plt.scatter(x,y) plt.show()

请解释import numpy as np from sklearn.model_selection import train_test_split import random from scipy.optimize import fsolve import matplotlib.pyplot as plt import heapq from tkinter import _flatten

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np import h5py import random import os from plyfile import PlyData import subprocess from scipy.spatial import KDTree

from tensorflow import keras from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt import tensorflow.keras as keras import tensorflow.keras.layers as layers import random import time from datetime import datetime import numpy as np

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np import h5py import random import os # import open3d as o3d from plyfile import PlyData import subprocess from scipy.spatial import KDTree

实验四 Python数据可视化该实验的实验目的（import matplotlib.pyplot as plt import numpy as np）列出四点实验目的

import matplotlib.pyplot as plt plt.plot(np.random.randn(50).cumsum())

import numpy as np import matplotlib.pyplot as plt x = np.random.uniform( -3,3, size=100) x = np.reshape(-1,1) y = 0.5 * x ** 2 + x + 2 + np.random.normal(0,1,size=100) plt.scatter(x,y) plt.show

import numpy as np import matplotlib.pyplot as plt np.random.seed(0) mu, sigma = 100,20 a = np.random.normal(mu,sigma, size=100) plt.hist(a,20, normed=0, histtype='stepfilled', facecolor='b',alpha=0.75) plt.title('Histogram') plt.show()

import numpy as np import matplotlib.pyplot as plt from math import pi k=2 M=2**k symLen=2**13 np.random.seed(2) txSymbols = np.random.randint(0,4,size=symLen) #plt.scatter(txSymbols.real,txSymbols.imag,c=txSymbols,cmap=plt.cm.Set1) rxSymbols=txSymbol

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

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import numpy as np import matplotlib.pyplot as plt from math import pi k=2 M=2k symLen=213 np.random.seed(2) txSymbols = np.random.randint(0,4,size=symLen) #plt.scatter(txSymbols.real,txSymbols.imag,c=txSymbols,cmap=plt.cm.Set1) rxSymbols=txSymbol