Python自学入门(七)：Pandas之文件操作

Python自学入门(一)：环境安装
Python自学入门(二)：数据类型和运算符
Python自学入门(三)：数据类型详解
Python自学入门(四)：流程控制
Python自学入门(五)：函数和模块
Python自学入门(六)：Pandas之数据结构

5.2 文件操作

5.2.1 文本文件

读取文件

f = open("D:/temp/test.txt", encoding='utf-8') # 返回一个文件对象
line = f.readline()             # 调用文件的 readline()方法
while line:
    print(line)               
    line = f.readline()
f.close()  #关闭

f = open("D:/temp/test.txt", encoding='utf-8') # 返回一个文件对象
line = f.readlines()  #读取所有
print(line)               
f.close()  #关闭

写入文件

#会清空文件中原来的内容，文件不存在会创建文件
f = open("D:/temp/test2.txt","w",encoding="utf-8")
f.write("我爱Python\n")
f.close()

#追加内容
f = open("D:/temp/test2.txt","a",encoding="utf-8")
f.write("我爱Python\n")
f.close()

5.2.2 CSV文件

CSV文件(Comma-Separated Values)，CSV文件是以纯文本形式存储的数据，列之间用逗号分隔。

date,open,high,low,close
2021/5/12,56670.02,56704.83,56555.12,56650.96
2021/5/11,55816.14,56862.43,54370,56670.02
2021/5/10,58240.83,59500,53400,55816.14
2021/5/9,58866.53,59300,56235.66,58240.84
2021/5/8,57315.49,59500,56900,58862.05
2021/5/7,56393.68,58650,55241.63,57314.75
2021/5/6,57436.11,58360,55200,56393.68

CSV文件可以用Excel打开

读取CSV，读取到的结果是一个DataFrame

df = pandas.read_csv('csv-data.csv')
print(df.shape)
print(df.head())

写入CSV

subjects=['Java','Python','HTML5']
teachers=['张','李','Rose']
dic={"subject":subjects,"teacher":teachers}
df=pandas.DataFrame(dic)
df.to_csv('data.csv')

5.2.3 Excel文件

需要安装openpyxl模块，pip install openpyxl

读取Excel

df = pandas.read_excel('excel-data.xlsx')
print(df.shape)
print(df.head())

写入Excel

subjects=['Java','Python','HTML5']
teachers=['张','李','Rose']
dic={"subject":subjects,"teacher":teachers}
df=pandas.DataFrame(dic)
df.to_excel('data.xlsx')

5.2.4 XML文件

读取XML

from xml.dom.minidom import parse


domTree = parse("xml-data.xml")
# 文档根元素
rootNode = domTree.documentElement
print(rootNode.nodeName)

# 所有顾客
docs = rootNode.getElementsByTagName("doc")
data=[]
for doc in docs:
    url= doc.getElementsByTagName("url")[0].childNodes[0].data
    docno= doc.getElementsByTagName("docno")[0].childNodes[0].data
    title= doc.getElementsByTagName("contenttitle")[0].childNodes[0].data
    content= doc.getElementsByTagName("content")[0].childNodes[0].data
    data.append([url,docno,title,content])
df=pandas.DataFrame(data,columns=['url','docno','title','content'])

写入XML

subjects=['Java','Python','HTML5']
teachers=['张','李','Rose']
dic={"subject":subjects,"teacher":teachers}
df=pandas.DataFrame(dic)
with open('data.xml', 'w',encoding='utf-8') as xmlfile:
    # 写头部
    xmlfile.write('<?xml version="1.0" encoding="UTF-8"?>\n')    
    xmlfile.write('<root>\n')   # 名称可以根据实际需要修改
    for i in df.index: # 写数据
        xmlfile.write('<data>\n')
        xmlfile.write('<subject>')
        xmlfile.write(df.iloc[i]['subject'])
        xmlfile.write('</subject>\n')
        xmlfile.write('<teacher>')
        xmlfile.write(df.iloc[i]['teacher'])
        xmlfile.write('</teacher>\n')
        xmlfile.write('</data>\n')  
    xmlfile.write('</root>')   # 名称可以根据实际需要修改

5.2.5 JSON文件

读取json

df = pandas.read_json('json-data.json')

写入json

subjects=['Java','Python','HTML5']
teachers=['张','李','Rose']
dic={"subject":subjects,"teacher":teachers}
df=pandas.DataFrame(dic)
df.to_json('data.json',orient='records',force_ascii=False)