import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec plt.rcParams["font.sans-serif"] = ["SimHei"] x_month = np.array(['1月', '2月', '3月', '4月', '5月', '6月']) y_sales = np.array([2150, 1050, 1560, 1480, 1530, 1490]) x_citys = np.array(['北京', '上海', '广州', '深圳', '浙江', '山东']) y_sale_count = np.array([83775, 62860, 59176, 64205, 48671, 39968]) # 创建画布和布局 fig = plt.figure(constrained_layout=True) gs = fig.add_gridspec(2, 2) ax_one = fig.add_subplot(gs[0, :]) ax_two = fig.add_subplot(gs[1, 0]) ax_thr = fig.add_subplot(gs[1, 1])优化为matplotlib饼状图

import numpy as np import matplotlib.pyplot as plt import math

import numpy as np import matplotlib.pyplot as plt import math # 解决图标题中文乱码问题 import matplotlib as mpl mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体 mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 #原数据 data=np.array([[72.03,241.2,1592.74],[73.84,241.2,1855.36],[74.49,244.8,2129.60],[76.68,250.9,2486.86],[78.00,250.9,2728.94],[79.68,252.2,3038.90]]) #要预测数据的真实值 data_T=np.array([[81.21,256.5,3458.05],[82.84,259.4,3900.27],[84.5,262.4,4399.06],[86.19,265.3,4961.62],[87

Matplotlib二维箭头图制作.docx

plt.rcParams['font.sans-serif'] = ['SimHei'] # 步骤一（替换sans-serif 字体） plt.rcParams['axes.unicode_minus'] = False # 步骤二（解决负号显示问题） x, y = np.meshgrid(np.arange(-2, 2, .2), np.arange...

python中import numpy as np np.seterr(all='ignore') import pandas as pd import matplotlib.pyplot as plt import seaborn as sns plt.rcParams['font.sans-serif']=['SimHei'] plt.rcParams['axes.unicode_minus'] = False sns.set_style("whitegrid",{'font.sans-serif':['simhei','Arial']})是什么意思

6. plt.rcParams['font.sans-serif']=['SimHei']：设置 Matplotlib 中的字体为中文黑体； 7. plt.rcParams['axes.unicode_minus'] = False：设置 Matplotlib 中的负号显示为正常符号，避免出现乱码； 8. sns....

import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline from matplotlib.font_manager import FontProperties plt.rcParams['font.sans-serif'] = ['simhei'] import pandas as pd data1=pd.read_csv('G://data2.csv',index_col=0) data1.head() X = (data1 - data1.mean()) / data1.std() from sklearn.decomposition import PCA n = 5 pca = PCA(n_components = n) pca.fit(X)可以帮我生成一段代码吗

plt.rcParams['font.sans-serif'] = ['simhei'] data1 = pd.read_csv('G://data2.csv', index_col=0) data1.head() X = (data1 - data1.mean()) / data1.std() from sklearn.decomposition import PCA n = 5 pca...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import warnings % matplotlib inline plt.rcParams[‘font.sans - serif’] = [‘SimHei’] plt.rcParams[‘axes.unicode_minus’] = False warnings.filterwarnings(‘ignore’) pd.set_option(‘display.precision’,3) pd.options.display.float_format = ‘{:.3f}’.format df = pd.read_excel(‘作业数据.xls’) print(df.shape) display(df.head())这段代码有什么问题怎么解决

用户写的是plt.rcParams[‘font.sans - serif’] = [‘SimHei’]。这里有几个问题。首先，引号用的是中文的左右引号‘ ’，而Python中应该使用英文的单引号'或者双引号"。所以这里的引号会导致语法错误。其次，键名...

import numpy as np import pandas as pd import matplotlib import matplotlib.pyplot as plt import seaborn as sns import chardet

plt.rcParams['font.sans-serif'] = 'SimHei' # 设置默认字体为黑体以支持中文显示 plt.rcParams['axes.unicode_minus'] = False # 解决负号无法正确显示的问题上述代码片段展示了如何准备环境，使得后续的...

帮我修改错误import pandas import pandas as pd import np import numpy np_load_old = np.load np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k) import matplotlib.pyplot matplotlib.pyplot.rcParams['font.sans-serif'] = 'SimHei' matplotlib.pyplot.rcParams['axes.unicode_minus'] = False data = numpy.load('D:\\temp\\test\\国民经济核算季度数据.npz') name = data['columns'] values = data['values'] matplotlib.pyplot.figure(figsize = (8,7)) matplotlib.pyplot.scatter(values[:,0],values[:,3], marker = 'o',c = '#0099FF') matplotlib.pyplot.scatter(values[:,0],values[:,4], marker = '*',c = '#00FF00') matplotlib.pyplot.scatter(values[:,0],values[:,5], marker = '+',c = '#FF3300') matplotlib.pyplot.xlabel('年份') matplotlib.pyplot.ylabel('生产总值（亿元）') matplotlib.pyplot.xticks(range(0,70,4),values[range(0,70,4),1],rotation=45) matplotlib.pyplot.title('2000-2017年各产业季度生产总值散点图') matplotlib.pyplot.legend(['第一产业','第二产业','第三产业']) matplotlib.pyplot.savefig('D:\\temp\\test\\2022231168许开国c.png') matplotlib.pyplot.show name1 = pandas.DataFrame(list(data['columns'])) values1 = pandas.DataFrame(list(data['values'])) with pd.ExcelWriter('D:\\temp\\test\\2022231201许开国c.xlsx') as writer: name1.to_excel(writer, sheet_name= 'Sheet1', index=False, header=True) values1.to_excel(writer, sheet_name='Sheet2', index=False, header=True)

plt.rcParams['font.sans-serif'] = 'SimHei' plt.rcParams['axes.unicode_minus'] = False data = np.load('D:\\temp\\test\\国民经济核算季度数据.npz') name = data['columns'] values = data['values'] ...

#e19.1DrawRadar import numpy as np import matplotlib.pyplot as plt import matplotlib matplotlib.rcParams['font.family']='SimHei' matplotlib.rcParams['font.sans-serif'] = ['SimHei'] labels = np.array(['综合', 'KDA', '发育', '推进', '生存','输出']) nAttr = 6 data = np.array([7, 5, 6, 9, 8, 7]) #数据值 angles = np.linspace(0, 2np.pi, nAttr, endpoint=False) data = np.concatenate((data, [data[0]])) angles = np.concatenate((angles, [angles[0]])) fig = plt.figure(facecolor="white") plt.subplot(111, polar=True) plt.plot(angles,data,'bo-',color ='g',linewidth=2) plt.fill(angles,data,facecolor='g',alpha=0.25) plt.thetagrids(angles180/np.pi, labels) plt.figtext(0.52, 0.95, 'DOTA能力值雷达图', ha='center') plt.grid(True) plt.show()解析

matplotlib.rcParams['font.sans-serif'] = ['SimHei'] 该段代码设置了中文显示，使得图表中的中文能够正常显示。 3. 定义数据 python labels = np.array(['综合', 'KDA', '发育', '推进', '生存','输出']...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns plt.style.use('seaborn-v0_8') sns.set_palette("husl") # 使用统一色板 plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.titlepad'] = 20 # 标题间距 plt.rcParams['font.sans-serif'] = ['SimHei'] from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.preprocessing import StandardScaler from sklearn.feature_selection import SelectKBest, f_classif from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, classification_report, roc_curve, auc, confusion_matrix import statsmodels.api as sm from sklearn.metrics import precision_score, roc_curve plt.style.use('ggplot')可视图不显示中文

然后，用户尝试设置中文字体：plt.rcParams['font.sans-serif'] = ['SimHei']，并且重复了两次。还设置了标题间距plt.rcParams['axes.titlepad'] = 20。但是用户发现可视化图表仍然不显示中文。接下来，我需要考虑...

import numpy as np import matplotlib.pyplot as plt import matplotlib matplotlib.rcParams['font.family']='SimHei' matplotlib.rcParams['font.sans-serif'] = ['SimHei'] labels = np.array(['综合', 'KDA', '发育', '推进', '生存','输出']) nAttr = 6 data = np.array([7, 5, 6, 9, 8, 7]) #数据值 angles = np.linspace(0, 2np.pi, nAttr, endpoint=False) data = np.concatenate((data, [data[0]])) angles = np.concatenate((angles, [angles[0]])) fig = plt.figure(facecolor="white") plt.subplot(111, polar=True) plt.plot(angles,data,'bo-',color ='g',linewidth=2) plt.fill(angles,data,facecolor='g',alpha=0.25) plt.thetagrids([0,60,120,180,240,300], labels)#angles180/np.pi plt.figtext(0.52, 0.95, 'DOTA能力值雷达图', ha='center') plt.grid(True) plt.show()

这是一个使用 Python 中的 numpy 和 matplotlib 库绘制的雷达图，可以用来表示不同方面的能力值。其中，labels 是一个包含各个能力项名称的数组，nAttr 表示有几个能力项，data 是一个包含各项能力值的数组。通过...

用import numpy as np from skimage.io import imread from skimage.color import rgb2hsv,hsv2rgb from skimage.exposure import equalize_hist import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体 plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 A=np.clip(B, 0, 1.0) # 对数字B的每个元素值进行两头截断，确保输出数组A的元素取值在0~1之间

plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体 plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 # 对数字B的每个元素值进行两头截断，确保输出数组A的元素...

import pandas as pd import matplotlib.pyplot as plt # import numpy as np datas = pd.read_csv('jdsj.csv',encoding='utf-8') qsz = datas.sort_values(by=['月销量'],ascending=False) qsz =qsz[qsz['月销量'] != 0] plt.rcParams['font.sans-serif']=['SimHei'] qsz_s = qsz[0:10] plt.figure(figsize=(20,8),dpi = 80) br = plt.bar(x=qsz_s['景点名称'], height=qsz_s['月销量'],width=0.9) plt.xticks(rotation=40) plt.savefig('D:/BaiduNetdiskDownload/1.png') plt.show(br)

在绘制柱状图之前，你设置了中文字体为 SimHei，并且使用了 plt.xticks() 函数调整了 x 轴标签的旋转角度。在绘制图表之前，你使用了 plt.figure() 函数设置了图表的尺寸和 dpi。最后，你使用 show() 函数显示图表...

import os import pandas as pd import glob import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline plt.rcParams['font.sans-serif']=['SimHei'] plt.rcParams['axes.unicode_minus']=Falsecsv_list = glob.glob('all.csv') print(u'共发现%s个CSV文件'% len(csv_list)) print(u'正在处理............') for i in csv_list: fr = open(i,'rb').read() with open('result.csv','ab') as f: f.write(fr) print('合并完毕！')

这段代码的功能是将当前目录下所有后缀名为csv的文件合并成一个名为result.csv的文件。具体来说，首先使用glob.glob('all.csv')获取当前目录下所有后缀名为csv的文件，并将其保存到csv_list列表中。...

import scipy.io as sio import matplotlib.pyplot as plt import numpy as np data=sio.loadmat('D:\pycharm1\Cuprite.mat') print(data.keys()) print(data) plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False W = data['W'] x_axis = np.arange(len(W)) plt.plot(x_axis, W[:, 0], label='岩石') plt.plot(x_axis, W[:, 1], label='树') plt.plot(x_axis, W[:, 2], label='水') plt.legend() plt.show()

import matplotlib.pyplot as plt import numpy as np 加载MAT文件： mat_data = sio.loadmat('data.mat') 提取数据，假设x存在‘x’键，y1在‘y1’，y2在‘y2’： x = mat_data['x'].flatten() y1 = mat_data['y1...

这个代码错在哪里import matplotlib.pyplot as plt import numpy as np plt.rcParams['font.sans-serif']='SimHei' plt.rcParams['axes.unicode minus']=False plt.title('班级同学体育课统计') x=['乒乓球','羽毛球','篮球','瑜伽','足球'] y1=[13,15,18,10,11] plt.barh(x,y1) plt.show()

这段代码本身并没有语法错误，但是如果你没有安装 Matplotlib 库，那么就会出现 ModuleNotFoundError 错误。如果你已经安装了 Matplotlib 库，但是在运行这段代码时出现了问题，那么可能是因为你的数据有误，或者...

import numpy as np import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False data_a = np.array([85.5, 91, 72, 59, 66, 55]) data_b = np.array([94, 82, 89.5, 62, 49, 53]) plt.boxplot([data_a, data_b], labels=('男生各科平均成绩', '女生各科平均成绩'), meanline=True, widths=0.5, vert=False, patch_artist=True) plt.title('高二男生和女生各科平均成绩') plt.xlim(40, 100) plt.show()转为堆积面积图并美化

plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False data_a = np.array([85.5, 91, 72, 59, 66, 55]) data_b = np.array([94, 82, 89.5, 62, 49, 53]) fig, ax = plt....

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import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus']=False %matplotlib inline

import numpy as np import pandas as pd from tqdm import tqdm#进度条设置 import matplotlib.pyplot as plt from pylab import * import matplotlib; matplotlib.use('TkAgg') mpl.rcParams['font.sans-serif'] = ['SimHei'] mpl.rcParams['axes.unicode_minus'] = False

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import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus']=False %matplotlib inline

import numpy as np import pandas as pd from tqdm import tqdm#进度条设置 import matplotlib.pyplot as plt from pylab import * import matplotlib; matplotlib.use('TkAgg') mpl.rcParams['font.sans-serif'] = ['SimHei'] mpl.rcParams['axes.unicode_minus'] = False

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Matplotlib二维箭头图制作.docx

python中import numpy as np np.seterr(all='ignore') import pandas as pd import matplotlib.pyplot as plt import seaborn as sns plt.rcParams['font.sans-serif']=['SimHei'] plt.rcParams['axes.unicode_minus'] = False sns.set_style("whitegrid",{'font.sans-serif':['simhei','Arial']})是什么意思

import numpy as np import pandas as pd import matplotlib import matplotlib.pyplot as plt import seaborn as sns import chardet

这个代码错在哪里import matplotlib.pyplot as plt import numpy as np plt.rcParams['font.sans-serif']='SimHei' plt.rcParams['axes.unicode minus']=False plt.title('班级同学体育课统计') x=['乒乓球','羽毛球','篮球','瑜伽','足球'] y1=[13,15,18,10,11] plt.barh(x,y1) plt.show()

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