import seaborn as snsimport matplotlib.pyplot as pltplt.rcParams['font.family'] = ['Times New Roman']plt.rcParams["axes.labelsize"] = 18#palette=['#0073C2FF','#EFC000FF']palette=['#E59F01','#56B4E8']#palette = ["white","black"]fig,ax = plt.subplots(figsize=(5,4),dpi=100,facecolor="w")ax = sns.barplot(x="order",y="value",hue="class",data=group_data_p,palette=palette,ci="sd", capsize=.1,errwidth=1,errcolor="k",ax=ax, **{"edgecolor":"k","linewidth":1})# 添加P值box_pairs = [(("one","type01"),("two","type01")), (("one","type02"),("two","type02")), (("one","type01"),("three","type01")), (("one","type02"),("three","type02")), (("two","type01"),("three","type01")), (("two","type02"),("three","type02"))]annotator = Annotator(ax, data=group_data_p, x="order",y="value",hue="class", pairs=box_pairs)annotator.configure(test='t-test_ind', text_format='star',line_height=0.03,line_width=1)annotator.apply_and_annotate()如何将ns以及ns对应的横线去掉，但是其他星号以及相对应的横线保留

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

4. import matplotlib.pyplot as plt：导入 Matplotlib 库中的 pyplot 模块并将其命名为 plt，用于绘制各种类型的图表； 5. import seaborn as sns：导入 Seaborn 库并将其命名为 sns，用于进行更加美观的数据...

import seaborn as sns import matplotlib.pyplot as plt import pandas as pd plt.rcParams['font.sans-serif']=['Arial Unicode MS'] #设置中文字体为Arial Unicode MS plt.rcParams['axes.unicode_minus']=False #解决保存图像是负号'-'显示为方块的问题 df = pd.read_csv("vehicles-数据.csv",encoding='gbk') y = df["year"].values包含几年的数据？起始年份是哪一年？展示每年汽车的数量

import matplotlib.pyplot as plt # 读取CSV文件 df = pd.read_csv("vehicles-数据.csv", encoding='gbk') # 获取起始年份和终止年份 start_year = df['year'].min() end_year = df['year'].max() num_years = end...

import seaborn as sns sns.heatmap(correlation_table, cmap='RdBu_r') plt.show()如何更换显示字体

import matplotlib.pyplot as plt import matplotlib as mpl # 设置字体 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...

import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] X = data.drop(['y'], axis=1) print('输出新的特征集数据***') print(x.head()) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) data = np.random.rand(42, 42) data_flattened = data.flatten(data)

import matplotlib.pyplot as plt import numpy as np import seaborn as sns from sklearn.model_selection import train_test_split data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') fig = plt....

import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 data=pd.read_csv('D:/23实训/实训项目/data_sample.csv',encoding='gb18030',# 修正文件编码 index_col=0,) # 计算每年销售总量 # car_data.groupby('年度')['销量'].sum() # 1 # car_data.groupby('年度').agg({'销量':'sum'}) #2 data_A=(data[['city'=='北京','rent_price_listing']].groupby('北京').sum())/data('city'=='北京').sum() print(data_A)

import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 data = pd.read_csv('D:/23实训/实训...

import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 #创建一个画布 fig = plt.figure(figsize=(6,6)) a = [138, 165] b = [207, 96] label1 = ['没患病人数','患病人数'] label2 = ['男性','女性'] #间隔 explode=(0,0.1) #颜色 colors = ['#6c5ce7','#81ecec'] plt.pie(a,labels=label1, explode=explode ,colors=colors, autopct='%1.1f%%',shadow=False,startangle=150) plt.show()请找出这段代码中的问题

1. import seaborn as sns 和 %matplotlib inline 这两行代码应该在 import matplotlib.pyplot as plt 之前。 2. explode 变量在代码中未定义。 3. label2 变量在代码中未使用。 4. 在 plt.pie() 函数中...

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。...

from sklearn.datasets import load_iris, fetch_20newsgroups import seaborn as sns import matplotlib.pyplot as plt import pandas as pd from pylab import mpl mpl.rcParams["font.sans-serif"] = ["SimHei"] mpl.rcParams["axes.unicode_minus"] = False iris = load_iris() iris_data = pd.DataFrame(data=iris.data, columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width']) iris_data['target'] = iris.target print(iris_data) def plot_iris(iris, col1, col2): sns.lmplot(x=col1, y=col2, data=iris, hue="target", fit_reg=False) plt.xlabel(col1) plt.ylabel(col2) plt.title('鸢尾花种类分布图') plt.show() plot_iris(iris_data, 'Sepal_Width', 'Petal_Length')请根据上述代码写一份详细解析

import matplotlib.pyplot as plt import pandas as pd from pylab import mpl mpl.rcParams["font.sans-serif"] = ["SimHei"] mpl.rcParams["axes.unicode_minus"] = False 首先导入了 load_iris 和 fetch_...

import matplotlib.pyplot as plt import seaborn as sns import pandas as pd plt.rcParams['font.sans-serif']=['SimHei'] #设置中文显示 plt.rcParams['axes.unicode_minus']=False #解决中文显示为方块的问题 sales = pd.read_csv('a_test/car.csv', encoding='utf-8') df = pd.DataFrame(sales) pivot_df = df.pivot(index='地区', columns='品牌', values='销量') sns.heatmap(pivot_df, annot=True, fmt="d", cmap="YlGnBu") plt.title('BBA在四大城市的月销量热力图') plt.figure(figsize=(10,10)) plt.show()根据这个代码，设置热力图的大小

import matplotlib.pyplot as plt import seaborn as sns import pandas as pd plt.rcParams['font.sans-serif']=['SimHei'] #设置中文显示 plt.rcParams['axes.unicode_minus']=False #解决中文显示为方块的...

# 导入包 import pandas as pd import matplotlib as mpl import numpy as np import matplotlib.pyplot as plt import seaborn as sns plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus']=False mpl.use('TkAgg') # 导入数据 df = pd.read_csv(r'C:\Users\F\Desktop\3变量\data/CS2_35.csv') df_coor = df.corr() print(df_coor) fig, ax = plt.subplots(figsize=(8,6),facecolor='w') # 指定颜色带的色系 sns.heatmap(df.corr(),annot=True, vmax=1, square=True, cmap="Blues", fmt='.3g') plt.title('CS2_38') plt.show() fig.savefig('./df_corr.png',bbox_inches='tight',transparent=True)，帮我解释下这个代码用的方法和原理

1. 导入包：分别导入了 pandas、matplotlib、numpy、seaborn 等常用的数据分析和可视化包，其中 plt.rcParams['font.sans-serif'] = ['SimHei'] 和 plt.rcParams['axes.unicode_minus']=False 用于解决中文显示和...

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt plt.rcParams['font.family'] = 'Fangsong' # 指定使用宋体字体 fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(30, 25), dpi=300) # 创建 9 个子图 # 读取数据 df = pd.read_excel("test_Fd.xlsx") # 按照 FenDMC 进行分组 groups = df.groupby("FenDMC") # 遍历每个分组，绘制子图 for idx, (name, group) in enumerate(groups): ax = axes[idx] sns.heatmap(groups.pivot(index=["FactoryName","JiJXH"], columns="shi", values="avg_sale"), cmap='YlGnBu', annot=True, fmt='.2f', cbar_kws={'label': '单台售价', 'shrink':0.8, 'aspect':40}, ax=ax) ax.set_xlabel(None) ax.set_ylabel(None) ax.set_title(name, fontsize=20) # 调整边距，使图像居中 plt.tight_layout() # 保存图像 plt.savefig('heatmap.png') # 显示图像 plt.show()提示'DataFrameGroupBy' object has no attribute 'pivot'

sns.heatmap(df_pivot, cmap='YlGnBu', annot=True, fmt='.2f', cbar_kws={'label': '单台售价', 'shrink':0.8, 'aspect':40}, ax=ax) 在这个例子中，我们首先使用groupby方法对DataFrame对象进行分组，并计算...

sns.set(style="darkgrid", font_scale=1.2) plt.rcParams["font.family"] = "SimHei" plt.rcParams["axes.unicode_minus"] = False

而 plt.rcParams["font.family"] = "SimHei" 和 plt.rcParams["axes.unicode_minus"] = False 是使用 matplotlib 库设置字体和解决负号显示问题的代码。其中，"SimHei" 是设置字体为中文黑体，axes.unicode_minus=...

代码解释from PIL import Image import matplotlib import pandas as pd import matplotlib.pyplot as plt from wordcloud import WordCloud import seaborn as sns import numpy as np from pyecharts import options as opts sns.set(font="Kaiti", style="ticks", font_scale=1.4) matplotlib.rcParams['axes.unicode_minus'] = False df = pd.read_excel('战狼2-观看后=清洗后的数据(1).xlsx') img = Image.open('zhanlang.jpg'） mask = np.array(img) column_name = '评论内容' text_data = ' '.join(df[column_name].dropna().astype(str).tolist()) print(text_data) wordcloud = WordCloud(font_path="msyh.ttc",mask=mask, width=800, height=400, background_color='white').generate(text_data) wordcloud.generate(text_data) plt.figure(figsize=(10, 5)) plt.imshow(wordcloud, interpolation='bilinear') plt.axis('off') plt.show() wordcloud.to_file("1_词云图.png")

首先导入了需要的库，包括PIL用于处理图片，matplotlib用于绘图，pandas用于数据处理，seaborn用于设置绘图样式，numpy用于处理数组，以及pyecharts和wordcloud库。然后读取了一个Excel文件中的数据，并选择了一个列...

sns.load_dataset(“iris”)报错原因探究+解决办法

import seaborn as sns data = sns.load_dataset(iris) 报错。加载不出来 import seaborn as sns data = sns.load_dataset(fights) 报错。也加载不出来 2 原因发现我的seaborn-data里面是空的（没有那些库文件...

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Python库是一组预先...例如，Matplotlib和Seaborn库在数据可视化领域内非常受欢迎，它们提供了广泛的工具和技术，可以创建高度定制化的图表和图形，帮助数据科学家和分析师在数据探索和结果展示中更有效地传达信息。

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

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