python实现表格图片转excel格式文件，excel表格取数据，表格PDF取数据

python实现表格图片转excel格式文件，excel表格取数据，表格PDF取数据

def Image_Excel(APP_ID,API_KEY,SECRET_KEY,img_response_body):
   #  调用百度AI接口
    client = AipOcr(APP_ID, API_KEY, SECRET_KEY)
    # 循环遍历文件家中图片
    # image=r'C:\Users\Administrator\Desktop\rhinoweCode\XnSpider\XnSpider\图片识别表格.png'#get_image(image_path)
    # # for image in images:
    # # 以二进制方式打开图片
    # img_open=open(image,'rb')
    # # 读取图片
    # img_read = img_open.read()
    # 调用表格识别模块识别图片
    table = client.tableRecognitionAsync(img_response_body)
    # 获取请求ID
    request_id = table['result'][0]['request_id']
    #获取表格处理结果
    result = client.getTableRecognitionResult(request_id)
    # 处理状态是“已完成”，获取下载地址
    # print(result)
    while result['result']['ret_msg'] != '已完成':
        time.sleep(2)  # 暂停2秒再刷新
        result = client.getTableRecognitionResult(request_id)
    download_url = result['result']['result_data']
    return download_url
    # print(download_url)
    # # 获取表格数据
    # excel_data = requests.get(download_url)
    #  # 根据图片名字命名表格名称
    # xlsx_name = "表"+ ".xlsx"#image.split(".")[0] + ".xlsx"
    # # 新建excel文件
    # xlsx = open(xlsx_name, 'wb')
    # # 将数据写入excel文件并保存
    # xlsx.write(excel_data.content)

#在开始前，需要检查图片的大小，必须在1K~4M之间，不然会报错“‘error_msg': ‘image size error, image is too big or too small, upper limit 4M, lower limit 1k, please check your param'”。


#ln
def img_2_table(img_read):
    table=Image_Excel(APP_ID,API_KEY,SECRET_KEY,img_read)
    return table

def get_excel_table(url, index=None, method=None, cols=None, keys=None, header=None, headers=USER_AGENT):#headers={'User-Agent': "pandas"}):#
    """
    传入excel的url地址，来获取excel中的表格数据，返回Json类型数据.
    数据格式：[{第一行kv键值对},{第二行kv键值对},{},...]
    返回的时间数据可能为字符串(2021-8-25)或者时间戳(int)
    """
    # # 显示所有列
    print(url, '正在处理...')
    # headers = {'User-Agent': "pandas"}
    excelDataFrames = pd.read_excel(url, sheet_name=None, header=header, storage_options=headers)  # index-1
    excelTableJson = {}
    # pprint(excelDataFrames)
    for tableName, excelDataFrame in excelDataFrames.items():
        excelDataFrame.replace(r'\s+|\n|\t', '', regex=True, inplace=True)
        if list(excelDataFrame.values):
            if keys:
                index = get_table_index(excelDataFrame, keys)
            if index != None:
                if index != -1:
                    excelDataFrame = change_df(df=excelDataFrame, index=index, method=method, cols=cols)  # , redu=1, index-1  ->:   header=None
                # print(excelDataFrame)
                elif index == -1 and header:
                    excelDataFrame = change_df(df=excelDataFrame, index=index, header=header, method=method, cols=cols)
                excelJson = json.loads(excelDataFrame.to_json(orient='records'))
                # excelJson = [json.loads(excelData.to_json(orient='records'))[0] for excelData in excelDataFrame]
                excelTableJson[tableName] = excelJson
                # print(excelTableJson)

            else:
                excelJson = json.loads(excelDataFrame.to_json(orient='records'))
                excelTableJson[tableName] = excelJson
    # pprint(excelTableJson)
    print(url, '处理完毕！！！')
    return excelTableJson

表格pdf取数据

http://www.xa.gov.cn/web_files/file/2023/01/11/202301111459070356479.pdf 加上headers

import pdfplumber
from PyPDF2 import PdfFileReader, PdfReader
from fake_useragent import UserAgent

USER_AGENT = UserAgent().random


def get_pdf_table1(url, headers={"User-Agent": USER_AGENT}):
    """
    传入pdf的url地址，来获取pdf中的表格数据，返回Json类型数据.
    数据格式：[{第一行kv键值对},{第二行kv键值对},{},...]`
    """
    print(url, '正在处理...')
    # 获取pdf页数
    if url.startswith("http"):
        a = requests.get(url, headers=headers).content
    else:
        a = open(url, "rb").read()
    # fp = tempfile.TemporaryFile()
    # fp.write(a)
    with open('test.pdf', 'wb', ) as f:
        f.write(a)
    # 读取pdf文件，保存为pdf实例
    pdf = pdfplumber.open("./test.pdf")

    # 访问第二页
    first_page = pdf.pages[0]
    # print(first_page)

    # 自动读取表格信息，返回列表
    table = first_page.extract_table()
    # print(table)

    # table
    df = pd.DataFrame(table[2:], columns=table[1])# 第一行为空 (table[2:], columns=table[1])  第一行是表列名(table[1:], columns=table[0])
    df=df[df.columns[1:]]
    df.columns=[i.replace("\n","") for i in df.columns]
    # print(df)
    result = df.to_dict(orient="records")
    # print(result)
    return result#table

#这里url必须是英文

get_pdf_table1(link_response.urljoin(parse.quote(img_url)), headers={})

excelJsonDict = get_pdf_table1(, headers={})

备用方法

def get_pdf_table(url, headers=None):
    """
    传入pdf的url地址，来获取pdf中的表格数据，返回Json类型数据.
    数据格式：[{第一行kv键值对},{第二行kv键值对},{},...]`
    """
    if headers is None:
        headers = {}
    headers.update({"User-Agent": USER_AGENT})
    print(url, '正在处理...')
    # 获取pdf页数
    a = requests.get(url, headers=headers).content
    fp = tempfile.TemporaryFile()
    fp.write(a)
    reader = PdfFileReader(fp)#PdfMerger
    # 不解密可能会报错：PyPDF2.utils.PdfReadError: File has not been decrypted
    # if reader.isEncrypted:
    #     reader.decrypt('')
    page = reader.getNumPages()
    pages = ",".join([str(i) for i in range(1, page + 1)])
    pdfs = camelot.read_pdf(url, pages=pages, flavor='stream', headers=headers)
    pdfDataFrame = pd.DataFrame()
    for pdf in pdfs:
        pdfData = pdf.df
        pdfData = pdfData[~pdfData[2].isin([''])]
        pdfDataFrame = pdfDataFrame.append(pdfData)
    pdfDataFrame = change_df(pdfDataFrame, 0)
    print(pdfDataFrame.columns[0])
    # print(pdfDataFrame)#960629
    pdfDataFrame = pdfDataFrame[~pdfDataFrame[pdfDataFrame.columns[0]].isin([pdfDataFrame.columns[0]])]
    # pdfDataFrame.to_csv('1.csv', encoding='utf_8_sig')
    pdfDataFrame = pdfDataFrame.fillna(method='pad')
    pdfJson = json.loads(pdfDataFrame.to_json(orient='records'))
    print(url, '处理完毕！！！')
    return pdfJson