python共词矩阵分析结果一步到位

import os
import re
import pandas as pd
from PyPDF2 import PdfFileReader
import string
import yake

if __name__ == '__main__':
    
    # 运行第一部分代码
    pdf_files_path = 'C:/Users/win10/Documents/美国智库/pdf_files'

    # 定义一个函数，用于读取PDF文件并将其转化成文本
    def read_pdf(filename):
        filepath = os.path.join(pdf_files_path, filename)
        with open(filepath, 'rb') as f:
            pdf = PdfFileReader(f)
            text = ''
            for i in range(pdf.getNumPages()):
                page = pdf.getPage(i)
                text += page.extractText()
            return text


    # 读取Excel文件
    df = pd.read_excel(r'C:\Users\win10\Desktop\2022.xlsx')

    # 循环遍历每行数据
    for index, row in df.iterrows():
        # 判断是否有PDF文件名
        if pd.notna(row['pdf_filename']):
            try:
                # 尝试读取PDF文件并识别成文字
                text = read_pdf(row['pdf_filename'])
            except:
                # 读取失败则跳过
                continue
            # 替换art_content列的值为PDF中的文字
            df.at[index, 'art_content'] = text

    # 对art_content列进行分词
    stop_words = ["d"]

    def phrase_extract(text):
        text = text.lower()
        custom_kw_extractor = yake.KeywordExtractor(top=10, lan="en")
        keywords = custom_kw_extractor.extract_keywords(text)
        phrase_list = []
        for keyword, score in keywords:
            if len(keyword.split(' ')) > 1:
                phrase_list.append(keyword.lower())
        phrases_list = []
        for phrase in phrase_list:
            for i in range(0, len(text.split(phrase)) - 1):
                phrases_list.append(phrase)
        return phrases_list

    def segment(text):
        phrases_list = phrase_extract(text)
        # word_tokens = nltk.tokenize.word_tokenize(text.strip())
        word_list = []
        for i in text.split(' '):
            word_list.append(i)
        table = str.maketrans('', '', string.punctuation)
        tokens = [w.translate(table) for w in word_list]
        # tokens = [w.translate(table) for w in word_tokens]
        tokens = [word for word in tokens if word.isalpha()]
        tokens = [w for w in tokens if not w in stop_words]
        tokens = [word for word in tokens if len(word) > 1]
        tokens = list(set(tokens+phrases_list))
        return tokens

    df['col'] = df['art_content'].apply(lambda x: '/'.join(segment(x)).lower())
    df['col'].to_excel(r'C:\Users\win10\Desktop\结果P1.xlsx', index=False, header=False)

# -*- coding: utf-8 -*-
import numpy as np
import time
from pprint import pprint as p
import pandas as pd


def log(func):
    def wrapper(*args, **kwargs):
        now_time = str(time.strftime('%Y-%m-%d %X', time.localtime()))
        print('------------------------------------------------')
        print('%s func [%s] called' % (now_time, func.__name__))
        print('# %s' % func.__doc__)
        print('%s returns:' % func.__name__)
        re = func(*args, **kwargs)
        p(re)
        return re

    return wrapper


def readxls(path):
    import xlrd
    xl = xlrd.open_workbook(path)
    sheet = xl.sheets()[0]
    data = []
    for i in range(0, sheet.ncols):
        data.append(list(sheet.col_values(i)))
    return (data[0])


@log
def get_set_key(data, threshold=2):
    '''选取频数大于等于Threshold的关键词构建一个集合，用于作为共现矩阵的首行和首列'''
    all_key = '/'.join(data)
    key_list = [it.strip() for it in all_key.strip().split('/')]
    keys = set(key_list)
    dic = dict(zip(keys, [key_list.count(k) for k in keys]))

    wf = {k: v for k, v in dic.items() if k != '' and v >= threshold}
    set_key_list = []
    for a in sorted(wf.items(), key=lambda item: item[1], reverse=True):
        set_key_list.append(a[0])
    return set_key_list


@log
def format_data(data, set_key_list):
    '''格式化需要计算的数据，将原始数据格式转换成二维数组'''
    formated_data = []
    for ech in data:
        ech_line = str(ech).split('/')

        temp = []  # 筛选出format_data中属于关键词集合的词
        for e in ech_line:
            if e in set_key_list:
                temp.append(e)
        ech_line = temp

        ech_line = list(set(filter(lambda x: x != '', ech_line)))  # set去掉重复数据
        formated_data.append(ech_line)
    return formated_data


# @log
def build_matirx(set_key_list):
    '''建立矩阵，矩阵的高度和宽度为关键词集合的长度+1'''
    edge = len(set_key_list) + 1
    # matrix = np.zeros((edge, edge), dtype=str)
    matrix = [[0 for j in range(edge)] for i in range(edge)]
    return matrix


@log
def init_matrix(matrix, set_key_list):
    '''初始化矩阵，将关键词集合赋值给第一列和第二列'''
    matrix[0][1:] = np.array(set_key_list)
    matrix = list(map(list, zip(*matrix)))
    matrix[0][1:] = np.array(set_key_list)
    return matrix


@log
def count_matrix(matrix, formated_data):
    '''计算各个关键词共现次数'''
    keywordlist = matrix[0][1:]  # 列出所有关键词
    appeardict = {}  # 每个关键词与 [出现在的行(formated_data)的list] 组成的dictionary
    for w in keywordlist:
        appearlist = []
        i = 0
        for each_line in formated_data:
            if w in each_line:
                appearlist.append(i)
            i += 1
        appeardict[w] = appearlist
    for row in range(1, len(matrix)):
        # 遍历矩阵第一行，跳过下标为0的元素
        for col in range(1, len(matrix)):
            # 遍历矩阵第一列，跳过下标为0的元素
            # 实际上就是为了跳过matrix中下标为[0][0]的元素，因为[0][0]为空，不为关键词
            if col >= row:
                # 仅计算上半个矩阵
                if matrix[0][row] == matrix[col][0]:
                    # 如果取出的行关键词和取出的列关键词相同，则其对应的共现次数为0，即矩阵对角线为0
                    matrix[col][row] = 0
                else:
                    counter = len(set(appeardict[matrix[0][row]]) & set(appeardict[matrix[col][0]]))

                    matrix[col][row] = counter
            else:
                matrix[col][row] = matrix[row][col]
    return matrix


def main():
    keyword_path = r'C:\Users\win10\Desktop\结果P1.xlsx'
    output_path = r'C:\Users\win10\Desktop\结果P2.xlsx'
    data = readxls(keyword_path)
    set_key_list = get_set_key(data)
    formated_data = format_data(data, set_key_list)
    matrix = build_matirx(set_key_list)
    matrix = init_matrix(matrix, set_key_list)
    result_matrix = count_matrix(matrix, formated_data)
    print(result_matrix)
    pd.DataFrame(result_matrix).to_excel(output_path,index=False)
    # np.savetxt(output_path, result_matrix, fmt=('%s,' * len(matrix))[:-1])


if __name__ == '__main__':
    main()

import pandas as pd

if __name__ == '__main__':

    # 读取 Excel 文件并将每个单元格转换为整数类型
    df = pd.read_excel(r'C:\Users\win10\Desktop\结果P2.xlsx', index_col=0)
    # df = df.astype(int)

    # 定义一个空字典用于存储词组搭配及其出现次数
    co_occurrence_dict = {}

    # 遍历共现矩阵中每个单元格，并将词组搭配及其出现次数存储到字典中
    for i in range(df.shape[0]):
        for j in range(i, df.shape[1]):  # 只遍历矩阵对角线以上的所有元素
            # 获取当前单元格的值
            count = df.iloc[i, j]
            # 获取当前单元格所对应的行和列的单词
            word1 = df.index[i]
            word2 = df.columns[j]
            # 构造词组搭配
            collocation = (word1, word2)
            # 更新字典中的词组搭配及其出现次数
            if collocation in co_occurrence_dict:
                co_occurrence_dict[collocation] += count
            else:
                co_occurrence_dict[collocation] = count

    # 将词组搭配及其出现次数转换为 DataFrame 类型
    result = pd.DataFrame([(collocation[0], collocation[1], count) for collocation, count in co_occurrence_dict.items()], columns=["Word1", "Word2", "Count"])

    # 将 DataFrame 写入到新的 Excel 表中
    result.to_excel(r'C:\Users\win10\Desktop\结果P3.xlsx', index=False)