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meanfor 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()