import,from...import,import...as的区别.pdf_fromimportas

python

package

需积分: 32 144 浏览量更新于2023-05-15 评论收藏 311KB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

资源详情

资源评论

资源推荐

Python中import, from...import,import...as的

区别

msqt

2018.11.26 15:49 字数 271

在python中import或者from…import是用来导入相应的模块。

举个例子：

import datetime

print(datetime.datetime.now())

以上代码实现输出系统当前时间，是引入整个datetime包，然后再调用datetime这个类中的now()方

法。

以上代码实现功能也可以用以下代码实现：

from datetime import datetime

print(datetime.now())

这里是从datetime包中只导入datetime这个类，让后再调用datetime这个类中的now()方法实现同样

的目的。

假如你嫌datetime这个包名称太长，想要给它取个别名，以后每次用到它的时候都用它的别名代替

它，这时就需要用到import…as，例子如下：

import datetime as dt

print(dt.datetime.now())

以上代码完成呢个与之前同样的功能，输出系统当前时间。

---------------------

本内容试读结束，登录后可阅读更多

下载后可阅读完整内容，剩余1页未读，立即下载

R for Data Science Visualize Model Transform Tidy and Import Data.pdf

Whenever you measure the same thing twice, you get two results—as long as you measure precisely enough. This phenomenon creates uncertainty and opportunity. Author Garrett Grolemund, Master ...

java pdf 查看器

Python 面试整理.pdf

import numpy as np import tensorflow as tf # 深度学习库 from captcha.image import ImageCaptcha # 用来生成验证码 import matplotlib.pyplot as plt # 用来将验证码可视化出来 from PIL import Image # 将...

from tkinter import * from pdfminer.pdfparser import PDFParser from pdfminer.pdfdocument import PDFDocument from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter from pdfminer.converter import PDFPageAggregator, TextConverter from pdfminer.layout import LAParams, LTTextBoxHorizontal from pdfminer.pdfpage import PDFPage class PDFViewer: def init(self, master): self.master = master master.title("PDF Viewer") # 创建文本框和滚动条 self.text = Text(master) self.scrollbar = Scrollbar(master, command=self.text.yview) self.text.configure(yscrollcommand=self.scrollbar.set) self.text.pack(side=LEFT, fill=BOTH, expand=True) self.scrollbar.pack(side=RIGHT, fill=Y) # 打开 PDF 文件 fp = open('x1/2023凯程333教育综合应试解析-外国教育史(OCR).pdf', 'rb') # 创建 PDF 解析器 parser = PDFParser(fp) # 创建 PDF 文档对象 document = PDFDocument(parser) # 获取页面布局参数 laparams = LAParams() # 创建 PDF 资源管理器 rsrcmgr = PDFResourceManager() # 创建聚合器对象 device = PDFPageAggregator(rsrcmgr, laparams=laparams) # 创建 PDF 解释器 interpreter = PDFPageInterpreter(rsrcmgr, device) # 处理每一页 PDF 页面 for page in PDFPage.create_pages(document): interpreter.process_page(page) # 获取页面布局 layout = device.get_result() # 提取每一页的文本内容 for x in layout: if isinstance(x, LTTextBoxHorizontal): text = x.get_text().strip() self.text.insert(END, text + '\n') # 关闭 PDF 文件 fp.close() root = Tk() pdf_viewer = PDFViewer(root) root.mainloop()上述文本框添加保存到本地功能的完整代码

from pdfminer.pdfpage import PDFPage from tkinter.filedialog import asksaveasfilename class PDFViewer: def __init__(self, master): self.master = master master.title("PDF Viewer") # 创建文本框和...

import matplotlib.pyplot as plt from mpl_toolkits. basemap import Basemap import xarray as xr import pandas as pd import numpy as np import netCDF4 as nc import cartopy.crs as ccrs ds = xr.open_dataset('correlation.1.30.160.200.191.3.13.51.nc') plt.subplot(1,1,1) ds.hgt.plot() plt.show() projection=ccrs.Orthographic(central_latitude=90, central_longitude=0) fig=plt.figure(figsize=(8,8)) ax=plt.axes(projection=projection) ax.coastlines() ax.set_global() ax.axhline(0,color='black') ax.axvline(0,color='black') ds=nc.Dataset('correlation.1.30.160.200.191.3.13.51.nc') lon=ds.variables['lon'][:] lat=ds.variables['lat'][:] time_index=0 variable=ds.variables['hgt'][time_index,:,:] lonlon,latlat=np.meshgrid(lon,lat) plt.scatter(lonlon,latlat) plt.contourf(lon,lat,variable,cmap='jet') data = ds.variables['time'][:] long = ds.variables['lon'][:] lati = ds.variables['lat'][:] plt.colorbar(label="Sif", orientation="horizontal") cbar=plt.colorbar plt.title('Jan 1948 to 2020: 1000mb Geopotential Height \n Seasonal Correlation w/ Jan SOI \n NCEP /NCAR Reanalysis') plt.show() plt.savefig('12.pdf')给代码改错

import matplotlib.pyplot as plt import xarray as xr import cartopy.crs as ccrs ds = xr.open_dataset('correlation.1.30.160.200.191.3.13.51.nc') plt.subplot(1, 1, 1) ds.hgt.plot() plt.show() ...

我使用python读取pdf文件，但部分内容缺失，如日期信息、保险金额信息等，仅读取到了可以直接从pdf文件中复制的表格框架信息。代码如下：from io import StringIO from pdfminer.pdfinterp import PDFResourceManager from pdfminer.pdfinterp import process_pdf from pdfminer.converter import TextConverter from pdfminer.layout import LAParams import re def read_from_pdf(file_path): """ 读取pdf文件 """ with open(file_path,'rb') as file: resource_manager = PDFResourceManager() return_str = StringIO() lap_params = LAParams() device = TextConverter(resource_manager,return_str,laparams=lap_params) process_pdf(resource_manager,device,file) device.close() content = return_str.getvalue() return_str.close() return re.sub('\s+','',content) 。请帮我看看为什么会这样，是否有方法用python代码解决，如果可以请帮我修改代码

with pdfplumber.open(file_path) as pdf: page = pdf.pages[0] # 读取第一页 text = page.extract_text() return text file_path = 'example.pdf' content = read_from_pdf(file_path) print(content) ``` ...

import numpy as np from scipy.stats import beta import matplotlib.pyplot as plt # ab_pairs = [(2.81,21.92), (14.19,123.57)] ab_pairs = [(1.369012558,56.67609926,'24_female'), (6.088662555,346.8019442,'2530_female')] #x = np.linspace(0, 1, 1002)[1:-1] x = np.linspace(0, 1, 10000)[1:-1] for a, b, c in ab_pairs: print(a, b, c) dist = beta(a, b) y = dist.pdf(x) plt.plot(x, y, label=r'$\alpha=%.1f,\ \beta=%.1f$'+ u'grp = %s' % (a, b, c)) # 设置标题 plt.title(u'313 Beta Distribution') #设置 x,y 轴取值范围 plt.xlim(0, 1) plt.ylim(0, 100) plt.legend() plt.savefig("./beta.svg", format="svg") 报错TypeError: not all arguments converted during string formatting

from scipy.stats import beta import matplotlib.pyplot as plt ab_pairs = [(1.369012558, 56.67609926, '24_female'), (6.088662555, 346.8019442, '2530_female')] x = np.linspace(0, 1, 10000)[1:-1] for a...

# -- coding: utf-8 -- import PyPDF2 import sys import io # 创建一个文本输出流，包装标准输出 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='gbk') # 创建一个txt文件并打开文件对象 with open('output7.txt', 'w', encoding='utf-8') as txt_file: # 打印包含特殊字符的字符串 s = 'PDF文件头信息：\xae' txt_file.write(s + '\n') # 打开PDF文件 pdf_file = open('x1/2023凯程333教育综合应试解析-外国教育史(OCR).pdf', 'rb') # 创建一个PDF读取器对象 pdf_reader = PyPDF2.PdfFileReader(pdf_file,strict=False) # 打印PDF文件头信息 pdf_info = pdf_reader.getDocumentInfo() txt_file.write('PDF文件头信息：\n') for key, value in pdf_info.items(): try: txt_file.write(f'{key}: {value}\n') except UnicodeEncodeError: txt_file.write(f'{key}: {" ".join(value.split())}\n') # 打印PDF文件体信息 for page_num in range(pdf_reader.getNumPages()): page = pdf_reader.getPage(page_num) txt_file.write(f'第{page_num+1}页的内容：\n{page.extractText()}\n') # 打印PDF交叉引用表信息 txt_file.write('PDF交叉引用表信息：\n' + str(pdf_reader.xref) + '\n') # 打印PDF文件尾信息 txt_file.write('PDF文件尾信息：' + str(pdf_reader.trailer) + '\n') # 关闭PDF文件和txt文件 pdf_file.close() txt_file.close()使用pdfminer修改上述代码

from pdfminer.pdfpage import PDFPage from pdfminer.pdfparser import PDFParser from pdfminer.pdfdocument import PDFDocument # 创建一个文本输出流，包装标准输出 sys.stdout = io.TextIOWrapper(sys.stdout....

Months-in-service Year of Production Date Running Sum of RateCalculated 2 2022 6.79E-05 4 2022 0.000203569 5 2022 0.000339282 7 2022 0.000407138 1 2021 3.84E-05 2 2021 0.000567183 3 2021 0.000794814 4 2021 0.001301464 5 2021 0.001904045 6 2021 0.002157183 7 2021 0.002394334 8 2021 0.002753294 9 2021 0.003121773 10 2021 0.0033909 11 2021 0.003506241 12 2021 0.003528699 13 2021 0.003720933 14 2021 0.003781838 15 2021 0.003820284 16 2021 0.003897178 17 2021 0.00399653 18 2021 0.00411187 19 2021 0.004365009 20 2021 0.004464361 import numpy as np import matplotlib.pyplot as plt from scipy.stats import weibull_min # 计算威布尔分布参数的最大似然估计 shape, loc, scale = weibull_min.fit(df1["Running Sum of RateCalculated"]) # 绘制威布尔分布的函数 x = np.linspace(0, max(df1["Months-in-service"]), 10) y = weibull_min.cdf(x, shape, loc, scale) plt.plot(x, y) plt.xlabel('Months-in-service') plt.ylabel('Running Sum of RateCalculated') plt.title('Weibull Distribution') plt.grid(True) plt.show() 这段代码让x轴变成Months-in-service， y轴变成Running Sum of RateCalculated

from scipy.stats import weibull_min # 原始数据 months_in_service = [2, 4, 5, 7, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20] running_sum_of_rate = [6.79E-05, 0.000203569, 0....

import os import io import tkinter as tk import tkinter.filedialog as filedialog from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter from pdfminer.converter import TextConverter from pdfminer.layout import LAParams from pdfminer.pdfpage import PDFPage def convert_pdf_to_txt(path): rsrcmgr = PDFResourceManager() laparams = LAParams() outfp = io.StringIO() device = TextConverter(rsrcmgr, outfp, laparams=laparams) fp = open(path, 'rb') interpreter = PDFPageInterpreter(rsrcmgr, device) password = "" maxpages = 0 caching = True pagenos = set() for page in PDFPage.get_pages(fp, pagenos, maxpages=maxpages, password=password, caching=caching, check_extractable=True): interpreter.process_page(page) fp.close() device.close() text = outfp.getvalue() outfp.close() return text def select_folder(): folder_path = filedialog.askdirectory() if folder_path: label.config(text=f'已选择文件夹：{folder_path}') convert_folder(folder_path) def convert_folder(folder_path): for file_name in os.listdir(folder_path): if file_name.endswith('.pdf'): pdf_path = os.path.join(folder_path, file_name) text = convert_pdf_to_txt(pdf_path) txt_name = file_name.replace('.pdf', '.txt') txt_path = os.path.join(folder_path, txt_name) with open(txt_path, 'w', encoding='utf-8') as f: f.write(text) label.config(text='转换完成！') root = tk.Tk() root.title('PDF转换器') root.geometry('300x100') button = tk.Button(root, text='选择文件夹', command=select_folder) button.pack(pady=10) label = tk.Label(root, text='请点击按钮选择文件夹') label.pack() root.mainloop()上述代码在控制台输出响应时间

例如，在convert_pdf_to_txt()函数中加入以下代码来测试执行时间： ``` import time start_time = time.time() # 原有的代码 end_time = time.time() print(f'执行时间：{end_time - start_time}秒') ``` ...

java 单张pdf url地址转png

// Load PDF document from URL URL url = new URL("http://example.com/document.pdf"); PDDocument document = PDDocument.load(url.openStream()); // Create PDF renderer PDFRenderer renderer = new PDF...

#!/usr/bin/env python # -- coding:utf-8 -- from pyspark.sql import SparkSession import matplotlib.pyplot as plt # 指定Mysql的配置 options = { "url": "jdbc:mysql://localhost:3306/test?useSSL=true", "driver": "com.mysql.jdbc.Driver", "dbtable": "(SELECT * from travels_detail where price!='None') t1", "user": "root", "password": "root" } spark = SparkSession.builder.getOrCreate() # 加载Mysql数据 data = spark.read.format("jdbc").options(**options).load() # 对目的地列进行分组，调用聚合函数count获取每个组的个数 df = data.groupby("destination").count().orderBy("count", ascending=False) # 筛选游记中提到的前10个目的地，并将Spark数据帧转为Pandas数据帧 result_pdf = df.select("*").limit(10).toPandas() # 设置matplotlib支持中文 plt.rcParams['font.family'] = ['sans-serif'] plt.rcParams['font.sans-serif'] = ['SimHei'] plt.bar(result_pdf["destination"], result_pdf["count"], width=0.8) plt.legend() plt.show()逐行解释

3. `from pyspark.sql import SparkSession`：导入SparkSession类，用于创建SparkSession对象。 4. `import matplotlib.pyplot as plt`：导入matplotlib.pyplot库，用于绘制图表。 5. `options = {...}`：定义一个...

import pytesseract from PIL import Image from pdf2image import convert_from_path pdf_file = 'example.pdf' page = 0 try: # 将PDF文件转换为图像 images = convert_from_path(pdf_file) image = images[page] # 转换为灰度图像 gray_image = image.convert('L') # OCR文本识别，设置参数和预处理操作 text = pytesseract.image_to_string(gray_image, lang='eng', config='--psm 6', noise_filter=True) print(text) except Exception as e: print(f"Error: {e}")优化

from pdf2image import convert_from_path pdf_file = 'example.pdf' page = 0 try: # 将PDF文件转换为图像 images = convert_from_path(pdf_file, size=(800, None), grayscale=True) image = images[page] ...

如何解决 AttributeError: module 'pdfminer' has no attribute 'extract_tables'

from pdfminer.pdfpage import PDFPage from pdfminer.pdfpage import PDFTextExtractionNotAllowed from pdfminer.pdfdevice import PDFDevice from pdfminer.layout import * from pdfminer.pdfinterp import...

import numpy as npimport matplotlib.pyplot as pltfrom scipy.stats import norm# 生成随机数据data = np.random.randn(1000)# 绘制直方图plt.hist(data, bins=30, density=True, alpha=0.5)# 拟合正态分布曲线mu, std = norm.fit(data)x = np.linspace(-5, 5, 100)p = norm.pdf(x, mu, std)plt.plot(x, p, 'k', linewidth=2)# 显示图像plt.show()讲一下各变量

这是Python中导入NumPy、Matplotlib和SciPy库的代码。这些库都是用于科学计算和数据可视化的常用工具。其中NumPy提供了高效的数组和矩阵运算功能，Matplotlib用于绘制各种类型的图表，而SciPy则提供了许多科学计算的...

我的PDF路径在C:\Users\R17\Desktop\测试WPS云进销存1212.pdf,如何添加在代码里？

from pdfminer.pdfpage import PDFPage import io def pdf_to_text(path): manager = PDFResourceManager() output = io.StringIO() converter = TextConverter(manager, output, laparams=LAParams()) ...

phthon中stats.uniform.pdf(np.linespace)是什么意思

from scipy.stats import uniform # 生成100个等间隔的数据点（范围为[0,1]） samples = np.linspace(0, 1, 100) # 计算均匀分布的PDF值 pdf_values = uniform.pdf(samples) # 可视化PDF值 plt.plot(samples, pdf...

tt010203tt

粉丝: 0
资源: 2

会员权益专享

import, from...import,import...as的区别.pdf

评论0

会员权益专享

最新资源

import, from...import,import...as的区别.pdf

评论0

R for Data Science Visualize Model Transform Tidy and Import Data.pdf

java pdf 查看器

Python 面试整理.pdf

java 单张pdf url地址转png

java httppost 获取pdf文件流

from scipy import stats as ss

pdf.savefig() 用法详解

如何解决 AttributeError: module 'pdfminer' has no attribute 'extract_tables'

我的PDF路径在C:\Users\R17\Desktop\测试WPS云进销存1212.pdf,如何添加在代码里？

phthon中stats.uniform.pdf(np.linespace)是什么意思

会员权益专享

最新资源