pandas（六）：pandas对excel进行读写

一、代码如下

import pandas as pd
import numpy as np

class Process():
    def __init__(self):
        self.path_1 = "3000条无意义.xlsx"
        self.path_2 = "录音跟听0526.xlsx"
        self.s = []
        self.l = []

    def read_path_1(self):
        readbook = pd.read_excel(self.path_1, sheet_name="Sheet1")
        s1 = readbook["语句"]
        y = readbook["语义"]
        for s,l in zip(s1, y):
            if str(l) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                self.s.append(s)
                self.l.append(l)

    def read_path_2(self):
        t1 = pd.read_excel(self.path_2, sheet_name="0526")
        t2 = pd.read_excel(self.path_2, sheet_name="0525")
        t3 = pd.read_excel(self.path_2, sheet_name="0524")
        s1 = t1["内容"]
        y1 = t1["正确标签"]
        for s,l in zip(s1, y1):
            if str(l) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                self.s.append(s)
                self.l.append(l)
        s2 = t2["内容"]
        y2 = t2["正确标签"]
        for s,l in zip(s2, y2):
            if str(l) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                self.s.append(s)
                self.l.append(l)
        s3 = t3["内容"]
        y3 = t3["正确标签"]
        for s,l in zip(s3, y3):
            if str(l) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                self.s.append(s)
                self.l.append(l)

    def noSemantic_1(self):
        path_in = "3000_1.xlsx"
        path_out = "new_data/3000_1.csv"
        t1 = pd.read_excel(path_in, sheet_name="Sheet1")
        s1 = t1["话术"]
        y1 = t1["语义"]
        sentence = []
        label = []
        predict = []
        for s, l in zip(s1, y1):
            if str(l) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                sentence.append(s)
                label.append(l)
                predict.append("无意义")
        dataframe = pd.DataFrame({'sentence': sentence,"predict":predict, 'label': label })
        dataframe.to_csv(path_out, index=False, sep='	', encoding="utf8")

    def noSemantic_2(self):
        path_in = "3000_2.xlsx"
        path_out = "new_data/3000_2.csv"
        t1 = pd.read_excel(path_in, sheet_name="对话文本")
        s1 = t1["客户语句"]
        y1 = t1["语义小类"]
        sentence = []
        label = []
        predict = []
        for s, l in zip(s1, y1):
            if str(l) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                sentence.append(s)
                label.append(l)
                predict.append("无意义")
        dataframe = pd.DataFrame({'sentence': sentence, "predict": predict, 'label': label})
        dataframe.to_csv(path_out, index=False, sep='	', encoding="utf8")

    def noSemantic_3(self):
        path_in = "3000_3.xlsx"
        path_out = "new_data/3000_3.csv"
        t1 = pd.read_excel(path_in, sheet_name="Sheet1")
        s1 = t1["语句"]
        y1 = t1["语义"]
        sentence = []
        label = []
        predict = []
        for s, l in zip(s1, y1):
            if str(l) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                sentence.append(s)
                label.append(l)
                predict.append("无意义")
        dataframe = pd.DataFrame({'sentence': sentence, "predict": predict, 'label': label})
        dataframe.to_csv(path_out, index=False, sep='	', encoding="utf8")

    def luyin_1(self):
        path_in = "录音跟听_1.xlsx"
        path_out = "new_data/record_1.csv"
        t1 = pd.read_excel(path_in, sheet_name="Sheet1")
        s1 = t1["内容"]
        p1 = t1["预测标签"]
        y1 = t1["正确标签"]
        sentence = []
        label = []
        predict = []
        for s, l, pre in zip(s1, y1, p1):
            if str(l) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                p = pre.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                sentence.append(s)
                label.append(l)
                predict.append(p)
        dataframe = pd.DataFrame({'sentence': sentence, "predict": predict, 'label': label})
        dataframe.to_csv(path_out, index=False, sep='	', encoding="utf8")
        dataframe.to_excel('new_data/record_1.xlsx', sheet_name='data', index=False, encoding="utf8")

    def luyin_2(self):
        path_in = "录音跟听_2.xlsx"
        path_out = "new_data/record_2.csv"
        t1 = pd.read_excel(path_in, sheet_name="Sheet1")
        s1 = t1["内容"]
        p1 = t1["预测标签"]
        y1 = t1["正确标签"]
        sentence = []
        label = []
        predict = []
        for s, l, pre in zip(s1, y1, p1):
            if str(l) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                p = pre.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                sentence.append(s)
                label.append(l)
                predict.append(p)
        dataframe = pd.DataFrame({'sentence': sentence, "predict": predict, 'label': label})
        dataframe.to_csv(path_out, index=False, sep='	', encoding="utf8")
        dataframe.to_excel('new_data/record_2.xlsx', sheet_name='data', index=False, encoding="utf8")

    def luyin_3(self):
        path_in = "录音跟听_3.xlsx"
        path_out = "new_data/record_3.csv"
        t1 = pd.read_excel(path_in, sheet_name="Sheet1")
        s1 = t1["内容"]
        p1 = t1["预测标签"]
        y1 = t1["正确标签"]
        sentence = []
        label = []
        predict = []
        for s, l, pre in zip(s1, y1, p1):
            if str(l) == "nan":
                continue
            if str(pre) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                p = pre.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                sentence.append(s)
                label.append(l)
                predict.append(p)
        dataframe = pd.DataFrame({'sentence': sentence, "predict": predict, 'label': label})
        dataframe.to_csv(path_out, index=False, sep='	', encoding="utf8")
        dataframe.to_excel('new_data/record_3.xlsx', sheet_name='data', index=False, encoding="utf8")

    def liucheng(self):
        path_in = "流程跟听.xlsx"
        path_out = "new_data/procedure.csv"
        t1 = pd.read_excel(path_in, sheet_name="Sheet1")
        s1 = t1["语句"]
        p1 = t1["预测标签"]
        y1 = t1["语义"]
        sentence = []
        label = []
        predict = []
        for s, l, pre in zip(s1, y1, p1):
            if str(l) == "nan":
                continue
            if str(pre) == "nan":
                continue
            else:
                s = s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                l = l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                p = pre.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
                sentence.append(s)
                label.append(l)
                predict.append(p)
        dataframe = pd.DataFrame({'sentence': sentence, "predict": predict, 'label': label})
        dataframe.to_csv(path_out, index=False, sep='	', encoding="utf8")
        dataframe.to_excel('new_data/procedure.xlsx', sheet_name='data',index=False, encoding="utf8")

    def main(self):
        self.read_path_1()
        self.read_path_2()
        dataframe = pd.DataFrame({'l': self.l, 's': self.s})
        dataframe.to_csv("all_0607.csv", index=False, sep='	', encoding="utf8")

    # def update_1(self):
    #     path = "excel/3000_拒识语料.csv"
    #     data = pd.read_csv(path, sep="	")
    #     sentence1 = data["sentence"].tolist()
    #     label1 = data["label"].tolist()
    #
    #     t2 = pd.read_excel("excel/语义优化_0608_1.xlsx", sheet_name="data")
    #     sentence2 = t2["sentence"].tolist()
    #     label2 = t2["label"].tolist()
    #
    #     t3 = pd.read_excel("excel/语义优化_0608_2.xlsx", sheet_name="data")
    #     sentence3 = t3["sentence"].tolist()
    #     label3 = t3["label"].tolist()
    #
    #     s = sentence1 + sentence2 + sentence3
    #     l = label1 + label2 + label3
    #     with open("all.txt", "a+", encoding="utf8") as f:
    #         for _l,_s in zip(l, s):
    #             _l = _l.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
    #             _s = _s.replace(" ", "").replace("
", "").replace("
", "").replace("	", "")
    #             line = _l + "  " + _s
    #             f.write(line + "
")
    def no_semantic(self):
        t1 = pd.read_csv("new_data/3000_1.csv", sep="	")

        t2 = pd.read_csv("new_data/3000_2.csv", sep="	")

        t3 = pd.read_csv("new_data/3000_3.csv", sep="	")

        df = pd.concat([t1, t2, t3], ignore_index=True)
        df.to_excel('excel/nosemantic.xlsx', sheet_name='data',index=False, encoding="utf8")
















if __name__ == '__main__':
    Process().no_semantic()