import pandas as pd import matplotlib.pyplot as plt from wordcloud import WordCloud def create_wordcloud(city_name): data_path = r"C:\Users\17116\Desktop\大一\去哪儿.xlsx" travel_data = pd.read_excel(data_path) city_dataframe = travel_data.groupby('地点').get_group(city_name) # 提取玩法列并合并为一个字符串 corpus = ' '.join(city_dataframe['玩法']) # 创建词云对象 wordcloud = WordCloud( width=2000, height=500, font_path="C:/Windows/Fonts/simfang.ttf", background_color='white').generate(corpus) # 在显示词云图之前设置图像大小 plt.figure(figsize=(60, 30)) plt.imshow(wordcloud.recolor(), interpolation="bilinear") plt.axis('off') plt.show() 基于上面的代码，如何实现把“玩法”那一列中的“深度游”剔除后生成词云图

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...

import pandas as pd import matplotlib.pyplot as plt path="src/step2/College.csv" def getDescribe():

import pandas as pd 和 import matplotlib.pyplot as plt 这两行代码导入了Python数据分析和可视化库Pandas和Matplotlib.pyplot。Pandas常用于数据处理和分析，它提供了一个高效、易用的数据结构DataFrame，...

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

import matplotlib.pyplot as plt %matplotlib inline from matplotlib.font_manager import FontProperties plt.rcParams['font.sans-serif'] = ['simhei'] data1 = pd.read_csv('G://data2.csv', index_col=0) ...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from collections import Counter

这是一段导入Python库的代码...- import matplotlib.pyplot as plt：导入Matplotlib库中的pyplot模块，并将其命名为plt。 - from collections import Counter：从Python标准库中的collections模块中导入Counter类。

import numpy as np import pandas import pandas as pd import matplotlib from sklearn import naive_bayes from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import normalize from sklearn.preprocessing import Binarizer from sklearn.impute import SimpleImputer from sklearn.preprocessing import OneHotEncoder import matplotlib.pyplot as plt from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix matplotlib.rc("font", family='Microsoft YaHei') data=pd.read_csv(r'D:\杂货铺\机器学习\银行数据集.csv',header=None)

这段代码导入了一系列的Python库，包括NumPy、Pandas、Matplotlib、scikit-learn等。其中，NumPy是Python科学计算的核心库，Pandas是数据处理的重要库，Matplotlib是绘图库，scikit-learn是机器学习库。接下来，使用...

## 获取数据 from sklearn.metrics import r2_score import statsmodels.api as sm import numpy as np import pandas as pd import matplotlib.pyplot as plt data = pd.read_table('C:/Users/lb/Desktop/test/k-means_data.txt',sep='\t',engine="python",encoding = 'gbk') data.columns.values data.head()改为读取csv文件

可以使用pd.read_csv()函数读取csv...import matplotlib.pyplot as plt data = pd.read_csv('C:/Users/lb/Desktop/test/k-means_data.txt', delimiter=',', encoding='gbk') data.columns.values data.head()

import pandas as pd from wordcloud import WordCloud import matplotlib.pyplot as plt import jieba df= pd.read_excel('海南疫情.xlsx') word_dict = dict(zip(df['地点'], df['感染人数'])) wc = WordCloud(width=800, height=400, background_color='white', max_words=100, font_path='msyh.ttc') wc.generate_from_frequencies(word_dict) plt.imshow(wc, interpolation='bilinear') plt.axis('off') plt.show()

这段代码是用来生成一个基于海南疫情数据的词云图。首先，它导入了 pandas ...需要注意的是，要使用该代码，需要确保安装了 pandas、WordCloud 和 matplotlib 库，并且确保有一个名为“海南疫情.xlsx”的 Excel 文件。

import xarray as xr import matplotlib.pyplot as plt from netCDF4 import Dataset import cartopy.crs as ccrs#投影方式 import cartopy.feature as cfeature import cartopy.io.shapereader as shpreader import numpy as np import pandas as pd from matplotlib.font_manager import fontManager da=pd.read_csv(r'E:\python11\STATION_58237.txt',sep='\s+') print(da) tem=da['TEM'] #print(tem.shape) rhu=da['RHU'] tem_ave=[] for i in range(0,24): tem_data=tem[i:147:24] print(tem_data) tem_ave[i]=np.mean(tem_data) print(tem_ave) plt.plot(tem_ave)帮我改一下

import matplotlib.pyplot as plt from netCDF4 import Dataset import cartopy.crs as ccrs import cartopy.feature as cfeature import cartopy.io.shapereader as shpreader import numpy as np import ...

import pandas as np import matplotlib.pyplot as plt from matplotlib import rcParams from wordcloud import WordCloud #设置编码 rcParams['font.family'] = 'SimHei' #获取数据 data = np.read_excel('海南疫情.xlsx') x_lable = data['地点'] y_lable = data['感染人数'] plt.plot(x_lable,y_lable) plt.xlabel('城市') plt.ylabel('感染人数') plt.title('截止2022-12-30 00:00:00海南省疫情总感染人数') plt.xticks(rotation=45) plt.show() 解释代码的意思

1. 导入必要的库：pandas 库、matplotlib.pyplot 库、matplotlib 库和 wordcloud 库。 2. 设置字体编码为 SimHei。 3. 读取 Excel 文件中的数据，存储在 data 变量中。 4. 从 data 变量中获取“地点”列和“感染...

解决这段代码的错误 ##chinese from wordcloud import WordCloud import matplotlib.pyplot as plt import pandas as pd # 打开文本 text = pd.read_excel("huati.xlsx", encoding="utf-8").read() # 生成对象 wc = WordCloud(font_path="msyh.ttc", width=800, height=600, mode="RGBA", background_color=None).generate(text) # 显示词云 plt.imshow(wc, interpolation='bilinear') plt.axis("off") plt.show() # 保存到文件 wc.to_file("bulletchinese.png")

import matplotlib.pyplot as plt import pandas as pd import jieba # 打开文本 text = pd.read_excel("huati.xlsx", encoding="utf-8").to_string() # 对文本进行分词处理 text = " ".join(jieba.cut(text)) # ...

import numpy as np import pandas as pd from wordcloud import WordCloud, ImageColorGenerator import matplotlib.pyplot as plt from PIL import Image def draw_cloud(read_name): image = Image.open('/Users/zhoulexin/downloads/学者.png') # 作为背景轮廓图 graph = np.array(image) # 参数分别是指定字体、背景颜色、最大的词的大小、使用给定图作为背景形状 wc = WordCloud(font_path='System/Library/Fonts/Supplemental/Arial Unicode.ttf', background_color='black', max_words=100, mask=graph) fp = pd.read_csv(read_name, encoding='gb18030') # 读取词频文件, 因为要显示中文，故编码为gbk name = list(fp["name"]) # 词 value = fp.val # 词的频率 for i in range(len(name)): name[i] = str(name[i]) dic = dict(zip(name, value)) # 词频以字典形式存储 wc.generate_from_frequencies(dic) # 根据给定词频生成词云 image_color = ImageColorGenerator(graph) plt.imshow(wc) plt.axis("off") # 不显示坐标轴 plt.show() wc.to_file('scholar词云.png') # 图片命名 if name == 'main': draw_cloud("/Users/zhoulexin/downloads/李学龙.csv")代码报错为KeyError: 'name'如何修改

这个错误可能是因为你的csv文件中没有名为"name"的列。你可以使用以下代码打印出所有的列名，检查一下是否有名为"name"的列： print(fp.columns) 如果没有名为"name"的列，那么你需要将下面的代码修改为你...

import pandas as pd import numpy as np import re import jieba from collections import Counter from wordcloud import WordCloud import matplotlib.pyplot as plt# 读取数据 # df = df[['text', 'label']] # df = df.dropna()# 分组 # groups = df.groupby('label')# 加载停用词 stop_words = set() with open('C:/Users/Administrator/Desktop/停用词/stopwords.txt', 'r', encoding='utf-8') as f: for line in f.readlines(): stop_words.add(line.strip())# 定义函数，用于去除停用词和提取关键词 def extract_keywords(text): words = [word for word in jieba.cut(text) if word not in stop_words] return Counter(words).most_common(10)# 提取关键词 keywords = {} for name, group in groups: keywords[name] = [] for text in group['text']: keywords[name].extend(extract_keywords(text)) keywords[name] = dict(keywords[name])# 绘制词云 for name, words in keywords.items(): wordcloud = WordCloud(width=800, height=400, font_path='msyh.ttc').generate_from_frequencies(words) plt.figure(figsize=(12, 8)) plt.imshow(wordcloud, interpolation='bilinear') plt.axis("off") plt.title(name) plt.show() 代码对吗

import matplotlib.pyplot as plt # 读取数据 df = pd.read_csv('data.csv') df = df[['text', 'label']] df = df.dropna() # 分组 groups = df.groupby('label') # 加载停用词 stop_words = set() with open('C:...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import jieba import requests import re from io import BytesIO import imageio # 设置城市和时间 city = '上海' year = 2021 quarter = 2 # 爬取数据 url = f'http://tianqi.2345.com/t/wea_history/js/{city}/{year}/{quarter}.js' response = requests.get(url) text = response.content.decode('gbk') # 正则表达式匹配 pattern = re.compile(r'(\d{4}-\d{2}-\d{2})\|(\d{1,2})\|(\d{1,2})\|(\d{1,3})\|(\d{1,3})\|(\D+)\n') result = pattern.findall(text) # 数据整理 data = pd.DataFrame(result, columns=['日期', '最高温度', '最低温度', '空气质量指数', '风力等级', '天气']) data[['最高温度', '最低温度', '空气质量指数', '风力等级']] = data[['最高温度', '最低温度', '空气质量指数', '风力等级']].astype(int) data['日期'] = pd.to_datetime(data['日期']) # 可视化分析 # 统计天气情况 weather_count = data['天气'].value_counts() weather_count = weather_count[:10] # 分词统计 seg_list = jieba.cut(' '.join(data['天气'].tolist())) words = {} for word in seg_list: if len(word) < 2: continue if word in words: words[word] += 1 else: words[word] = 1 # 绘制柱状图和词云图 plt.figure(figsize=(10, 5)) plt.bar(weather_count.index, weather_count.values) plt.title(f'{city}{year}年第{quarter}季度天气情况') plt.xlabel('天气') plt.ylabel('次数') plt.savefig('weather_bar.png') wordcloud = pd.DataFrame(list(words.items()), columns=['word', 'count']) mask_image = imageio.imread('cloud_mask.png') wordcloud.plot(kind='scatter', x='count', y='count', alpha=0.5, s=300, cmap='Reds', figsize=(10, 5)) for i in range(len(wordcloud)): plt.text(wordcloud.iloc[i]['count'], wordcloud.iloc[i]['count'], wordcloud.iloc[i]['word'], ha='center', va='center', fontproperties='SimHei') plt.axis('off') plt.imshow(mask_image, cmap=plt.cm.gray, interpolation='bilinear') plt.savefig('weather_wordcloud.png')这个python代码有错误，请改正以使该代码运行成功

import matplotlib.pyplot as plt import jieba import requests import re from io import BytesIO import imageio # 设置城市和时间 city = '上海' year = 2021 quarter = 2 # 爬取数据 url = f'...

import pandas as pd.docx

import pandas as pd import numpy as np - **Pandas**: 一个强大的数据分析与处理库。 - **NumPy**: 用于进行数值计算的 Python 库。 #### 2. 读取 CSV 文件到 DataFrame python wine_data = pd.read_csv('...

【matplotlib绘图】matplotlib.pyplot与axes的关系

最近在学习数据可视化，梳理一下其中一些诸如pandas绘图、matplotlib绘图、pyplot(plt)、axes等概念。重要的事情说三遍：axes不是axis！axes不是axis！axes不是axis！重要的事情说三遍：pyplot是接口不是对象！...

matplotlib.pyplot画图并导出保存的实例

1. 导入必要的库：文档中首先展示了如何导入pandas、numpy和matplotlib.pyplot模块。这些库分别用于数据处理、数值计算和绘图功能。 2. 创建图表和坐标轴：使用plt.subplots()函数创建了一个图表对象fig和一个...

Pandas读取Excel参数详解：pd.read_excel操作与实践

import pandas as pd # 基本用法，读取Excel文件的默认第一个工作表 data = pd.read_excel('file.xlsx') # 指定工作表名称 data = pd.read_excel('file.xlsx', sheet_name='Sheet2') # 使用索引列 data = pd.read...

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