Pandas库08_存取CSV文件

#学会csv、txt文件的读取与存储，了解pandas函数的参数的使用 #JSON与Excel数据，学会对JSON与Excel数据的读取与存储 import numpy as np import pandas as pd t_data={ "name":["唐浩","小王","老王","赵三","李四","王姐"], "sex":["男","女","男","女","男","女"], "year":[37,22,15,18,33,25], "city":["成都","北京","上海","成都","深圳","北京"] } #pandas将表格数据读取为DataFrame数据结构，常用的函数有 read_csv、read_table #创建一个csv文件 # fp=open("temp01.csv","w",newline="",encoding="utf-8") # writer=csv.writer(fp) # writer.writerow(("id","name","grade")) # writer.writerow(("1","唐浩","99")) # writer.writerow(("2","xiaowang","55")) # writer.writerow(("3","lisi","65")) # writer.writerow(("4","zhangshan","77")) # fp.close() """ id,name,grade 1,唐浩,99 2,xiaowang,55 3,lisi,65 4,zhangshan,77 """ #读取标准csv文件 read_csv() # df1_read=pd.read_csv("temp01.csv",encoding="utf-8") # print(df1_read) #也可以用read_table()读取 # df2_read=pd.read_table("temp01.csv",sep=",",encoding="utf-8") # print(df2_read) #可以指定某列做为索引标签 # df3_read=pd.read_csv(open("temp01.csv",encoding="utf-8"),index_col="id") # print(df3_read) #层次化索引数据 # df4_read=pd.read_csv(open("temp02.csv",encoding="utf-8"),index_col=[0,"id"]) # print(df4_read) #指定标题行 #当header=None,系统会多指定出一个标题列表出来，header=0为默认，第一行 #header=None,index_col=[0,"id"]这两个不能共存，在就要报错， #header=0,index_col=[0,"id"]可以共存 # df5_read=pd.read_csv(open("temp02.csv",encoding="utf-8"),header=0,index_col=[0,"id"]) # print(df5_read) #name指定列名 # df6_read=pd.read_csv(open("temp01.csv",encoding="utf-8"),index_col="id",header=0,names=["id","name","grade"]) # print(df6_read) #自定义读取，有时只读一行或几行，总之一句话，只读对我有用的数据 ,skiprows=[0,1,7,-1],列表中写不读的行数 # df7_r=pd.read_csv(open("temp03.csv",encoding="utf-8"),skiprows=[0,1,7,8],header=0) # print(df7_r) #只读取的前行数，nrows=n # df8_r=pd.read_csv(open("temp03.csv",encoding="utf-8"),skiprows=[0,1,7,8],header=0,nrows=3) # print(df8_r) #读取选择的列，usecols=["",""] # df9_r=pd.read_csv(open("temp03.csv",encoding="utf-8"),nrows=2,usecols=["id","name"],skiprows=[0,1,7,8]) # print(df9_r) #info()查看读取文件的信息 # df10_r=pd.read_csv(open("temp03.csv",encoding="utf-8"),nrows=2,usecols=["id","name"],skiprows=[0,1,7,8]) # print(df10_r.info()) # df11_r=pd.read_csv(open("temp01.csv",encoding="utf-8")) # # print(df11_r.info()) # # # # #如果文件信息很多，数据行数很多，我们可以像分页读一样，设个chunksize=n,来逐步读取 # # df12_r=pd.read_csv(open("temp01.csv",encoding="utf-8"),chunksize=2) # # #返回了一个可以迭代的对像df12_r,可以用for 循环把他的每一个元素都循环打出来，也可有用列表生成式如下面来拿到 # # print([x for x in df12_r][1]) #将以有的DataFrame数据存入csv文件中，用pandas里的to_csv() df=pd.DataFrame(t_data) # print(df) """ name sex year city 0 唐浩 男 37 成都 1 小王 女 22 北京 2 老王 男 15 上海 3 赵三 女 18 成都 4 李四 男 33 深圳 5 王姐 女 25 北京 """ df.to_csv("t_data.csv",encoding="utf-8",index=False) #就存进去了，以“，”号分隔

来源：https://www.cnblogs.com/yiyea/p/11441820.html