将多个csv文件合成一个csv_matlab 合并csv

1.python的writer做法

import csv
import pandas as pd
import os
from statistics import mean
#对dev测试生成的多个csv进行融合
def merge_different_csv():
    CSV_C0_NAME = 'file_name'
    CSV_C1_NAME = 'real_length'
    CSV_C2_NAME = 'dev_length'
    CSV_C3_NAME = 'dev_length_abs_error'
    CSV_C4_NAME = 'dev_length_relative_error'
    CSV_C5_NAME = 'prod_length'
    CSV_C6_NAME = 'prod_length_abs_error'
    CSV_C7_NAME = 'prod_length_relative_error'
 
    CSV_C8_NAME  = 'real_weight'
    CSV_C9_NAME  = 'dev_weight'
    CSV_C10_NAME = 'dev_weight_abs_error'
    CSV_C11_NAME = 'dev_weight_relative_error'
    CSV_C12_NAME = 'prod_weight'
    CSV_C13_NAME = 'prod_weight_abs_error'
    CSV_C14_NAME = 'prod_weight_relative_error'
    CSV_C15_NAME = 'dev_time'
    CSV_C16_NAME = 'prod_time'
 
    csv_path='./data/csv_data'
    every_csv__path_list_names=[os.path.join(csv_path,i) for i in os.listdir(csv_path)]
 
    dev_length_error=[]
    prod_length_error = []
    dev_weight_error = []
    prod_weight_error = []
   # 计算mean
    for every_csv_path in every_csv__path_list_names:
        with open(every_csv_path, "r") as csvfile:
            reader2 = csv.reader(csvfile)  # 读取csv文件，返回的是迭代类型
            # print(reader2)
            for i,csv_list in enumerate(reader2):
                # print(csv_list)
                if i:
                    csv_rows ={CSV_C0_NAME: csv_list[0],
                               CSV_C1_NAME: csv_list[1],
                               CSV_C2_NAME: csv_list[2],
                               CSV_C3_NAME: csv_list[3],
                               CSV_C4_NAME: csv_list[4],
                               CSV_C5_NAME: csv_list[5],
                               CSV_C6_NAME: csv_list[6],
                               CSV_C7_NAME: csv_list[7],
                               CSV_C8_NAME: csv_list[8],
                               CSV_C9_NAME: csv_list[9],
                               CSV_C10_NAME: csv_list[10],
                               CSV_C11_NAME: csv_list[11],
                               CSV_C12_NAME: csv_list[12],
                               CSV_C13_NAME: csv_list[13],
                               CSV_C14_NAME: csv_list[14],
                               CSV_C15_NAME: csv_list[15],
                               CSV_C16_NAME: csv_list[16]}
                    dev_length_error.append(float(csv_rows.get(CSV_C4_NAME)))
                    prod_length_error.append(float(csv_rows.get(CSV_C7_NAME)))
                    dev_weight_error.append(float(csv_rows.get(CSV_C11_NAME)))
                    prod_weight_error.append(float(csv_rows.get(CSV_C14_NAME)))
    # print(round(mean(dev_length_error),2))
    # print(round(mean(prod_length_error), 2))
    # print(round(mean(dev_weight_error), 2))
    # print(round(mean(prod_weight_error), 2))
    CSV_C4_NAME = CSV_C4_NAME + '=' + str(round(mean(dev_length_error), 2)) + '%'
    CSV_C7_NAME = CSV_C7_NAME + '=' + str(round(mean(prod_length_error), 2)) + '%'
    CSV_C11_NAME = CSV_C11_NAME + '=' + str(round(mean(dev_weight_error), 2)) + '%'
    CSV_C14_NAME = CSV_C14_NAME + '=' + str(round(mean(prod_weight_error), 2)) + '%'
    print(CSV_C4_NAME, CSV_C7_NAME, CSV_C11_NAME, CSV_C14_NAME)
    fieldnames_l = [CSV_C0_NAME, CSV_C1_NAME, CSV_C2_NAME, CSV_C3_NAME, CSV_C4_NAME, CSV_C5_NAME, CSV_C6_NAME,
                    CSV_C7_NAME, CSV_C8_NAME, CSV_C9_NAME, CSV_C10_NAME, CSV_C11_NAME, CSV_C12_NAME, CSV_C13_NAME,
                    CSV_C14_NAME, CSV_C15_NAME, CSV_C16_NAME]
    csvfile_all = open('./data/pig_dev_prod_data.csv', 'w')
    writer_pig_data = csv.DictWriter(csvfile_all, fieldnames=fieldnames_l)
    writer_pig_data.writeheader()
 
    for every_csv_path in every_csv__path_list_names:
        with open(every_csv_path, "r") as csvfile:
            reader2 = csv.reader(csvfile)  # 读取csv文件，返回的是迭代类型
            # print(reader2)
            for i,csv_list in enumerate(reader2):
                # print(csv_list)
                if i:
                    csv_rows ={CSV_C0_NAME: csv_list[0],
                               CSV_C1_NAME: csv_list[1],
                               CSV_C2_NAME: csv_list[2],
                               CSV_C3_NAME: csv_list[3],
                               CSV_C4_NAME: csv_list[4],
                               CSV_C5_NAME: csv_list[5],
                               CSV_C6_NAME: csv_list[6],
                               CSV_C7_NAME: csv_list[7],
                               CSV_C8_NAME: csv_list[8],
                               CSV_C9_NAME: csv_list[9],
                               CSV_C10_NAME: csv_list[10],
                               CSV_C11_NAME: csv_list[11],
                               CSV_C12_NAME: csv_list[12],
                               CSV_C13_NAME: csv_list[13],
                               CSV_C14_NAME: csv_list[14],
                               CSV_C15_NAME: csv_list[15],
                               CSV_C16_NAME: csv_list[16]}
                    writer_pig_data.writerow(csv_rows)

2.pandas做法

import os
import pandas as pd
#对dev测试生成的多个csv进行融合
def merge_different_csv():
    """notice"""
    out_csv_path='pig_dev_prod_data.csv'
    csv_path = './out_csv'
    every_csv_path_list_names=[os.path.join(csv_path,i) for i in os.listdir(csv_path)]
 
    df=pd.read_csv(every_csv_path_list_names[-1])
    df.to_csv(out_csv_path, sep=',', encoding='utf-8', index=False)
 
    for i in range(len(every_csv_path_list_names)-1):
        df = pd.read_csv(every_csv_path_list_names[i])
        df.to_csv(out_csv_path,encoding='utf-8',index=False,header=False, mode='a+')
 
    df_all=pd.read_csv(out_csv_path)
    dev_length_relative_error=df_all['dev_length_relative_error']
    prod_length_relative_error=df_all['prod_length_relative_error']
    real_length=df_all['real_length']
    mean_dev_length=round(sum(dev_length_relative_error) / sum(real_length) * 100,2)
    mean_prod_length=round(sum(prod_length_relative_error) / sum(real_length) * 100,2)
 
    dev_weight_relative_error = df_all['dev_weight_relative_error']
    prod_weight_relative_error = df_all['prod_weight_relative_error']
    real_weight = df_all['real_weight']
    mean_dev_weight=round(sum(dev_weight_relative_error)/sum(real_weight)*100,2)
    mean_prod_weight=round(sum(prod_weight_relative_error) / sum(real_weight) * 100,2)
 
    print('测试模型长度误差 %.2f, 生产模型长度误差 %.2f' % (mean_dev_length, mean_prod_length))
    print('测试模型重量误差 %.2f, 生产模型重量误差 %.2f' % (mean_dev_weight, mean_prod_weight))
    dev_length_error_col_name = 'dev_length_relative_error {0}%'.format(mean_dev_length)
    prod_length_error_col_name = 'prod_length_relative_error {0}%'.format(mean_prod_length)
    dev_weight_error_col_name = 'dev_weight_relative_error {0}%'.format(mean_dev_weight)
    prod_weight_error_col_name = 'prod_weight_relative_error {0}%'.format(mean_prod_weight)
 
    df_all.rename(columns={'dev_length_relative_error': dev_length_error_col_name,
                           'prod_length_relative_error': prod_length_error_col_name,
                           'dev_weight_relative_error': dev_weight_error_col_name,
                           'prod_weight_relative_error': prod_weight_error_col_name
                           },inplace=True)
    df_all.to_csv(out_csv_path, encoding='utf-8',index=False)
 
 
if __name__ == '__main__':
    merge_different_csv()