目录
# .txt
# .xlsx
# .mat
# .csv
# .yaml
# .npy&.npz
# .txt
# 读取path = r'C:\Users\Lenovo\Desktop\a.txt'with open(path,'r') as f:data = f.read().splitlines() # 去除换行符号\n# 写入result = [['a',1.5,2],['b',1.7,3],['c',9.3,4]]np.savetxt(path,result,'%s')
# .xlsx
# 读取data = pandas.read_excel(path,sheet_name='P_D',header=None) # header=None对应的是纯数据，无表头，正常有表头 有表头时读取某一列a = data.iloc[:,0].values # iloc用序号索引列 常用a = data.loc[:,'num'].values # loc是用str索引列读取某一行c = data.iloc[0,:].values # header无论是啥，读取的都是数据的第一行，不会出现表头的那一行常用：pp = [data.iloc[i,:].values for i in range(data.shape[0])] data.shape[0]从数据开始，不会算上表头的那一行# 写入import pandaspath = r'C:\Users\Lenovo\Desktop\a.xlsx'data = [['a',15],['b',23.4],['c',45]]df = pandas.DataFrame(data,columns=['alp','num']) # columns指定每一列的名称df.to_excel(path,sheet_name='a',index=False) # index=False，就会左紧邻，不会有额外的序号占一列
# .mat
# 读取import scipy.iodata = scipy.io.loadmat(path)['A']# 写入import scipy.ioresult = [[1,2,3],[4.5,6.5,2],[6,4,2.3]]scipy.io.savemat(path,{'A':result}) # 保存为字典格式：使用时用A索引
# .csv
import numpy as npdata_path = r'D:\毕业设计\数据采集平台\Time_Sequences\01-23-16-36-47_.csv'data = np.loadtxt(data_path,dtype=float,delimiter = ',')# 需要加delimiter = ',' 因为中间有逗号
# .yaml
# 读取import yamlwith open(path,'r') as f:data = yaml.load(f,Loader=yaml.FullLoader) # 注意：BaseLoader读出来时str类型，所以一般用FullLoader# 写入import yamla = {'name':'hjw','year':24,'hobby':'soccer','data':{'a':1.2,'b':3.2}}with open(path,'w') as f:yaml.dump(a,stream=f)
# .npy&.npz
# .npy文件就是np.save()之后产生的# .npz文件是多个numpy保存到一起 '''a = np.array([1,2,3,4,5])b = np.array([6,7,8,9,10])np.savez(r'D:\hjw.npz',n1=a,n2=b) # 修改名称为n1,n2np.savez_compressed() # 用法一样，只是耗时更长，有了压缩c = np.load(r'D:\hjw.npz')print(c.files) # ['n1', 'n2']print(c['n1']) # [1 2 3 4 5]print(c['n2']) # [ 6 7 8 9 10]
