CSV文件如何读写

CSV文件如何读写

写出这篇文章的原因主要是最近在看一本书《「Python爬虫开发与实战-从入门到实战」》里面提到了CSV这个模块，我立马进行了尝试，发现非常好用，比之前的xlwt好用多了。

关键是爬取到数据之后，整个存储数据的逻辑更容易理解（可能还是自己太菜吧😭）本文中介绍的通过pandas和CSV 模块对数据进行读写操作

写入文件

pandas写入

# 1-pandas写入

import pandas as pd

data = [{"name":"yangming","age":32,"height":180,"address":"shenzhen"},
        {"name":"xiaoming","age":24,"height":168,"address":"guangzhou"},
        {"name":"zhoujun","age":29,"height":184,"address":"shanghai"},
        {"name":"zhangshan","age":20,"height":170,"address":"changsha"}
       ]

df = pd.DataFrame(data)
df

# 将DataFrame存储为csv,index表示是否显示行名，default=True

df.to_csv("tocsvfile-pandas.csv",sep=",")
pd.read_csv("tocsvfile-pandas.csv")

# ！！！如何理解index参数

df.to_csv("tocsvfile-pandas-1.csv",index=False,sep=",")
pd.read_csv("tocsvfile-pandas-1.csv")

csv写入

字典形式写入

### csv通过字典形式存储文件

import csv

data = [{"name":"yangming","age":32,"height":180,"address":"shenzhen"},
        {"name":"xiaoming","age":24,"height":168,"address":"guangzhou"},
        {"name":"zhoujun","age":29,"height":184,"address":"shanghai"},
        {"name":"zhangshan","age":20,"height":170,"address":"changsha"}
       ]

with open("information.csv","w",encoding="utf-8") as f:
    writer = csv.DictWriter(f,fieldnames=["name","age","height","address"])
    writer.writeheader()
    writer.writerows(data)  # 写入整个数据data-----用writerows
    writer.writerow({"name":"Peter","age":28,"height":176,"address":"shenzhen"})  # 单独写入一条数据----用writerow

import pandas as pd

data = pd.read_csv("information.csv")
data

列表形式写入

# with 语句写在开头，防止属性字段重复写入文件中
# 例子：简书APP之旅

with open("information-1.csv",'a',newline ='',encoding="utf-8") as f:
        writer = csv.DictWriter(f,fieldnames=["name","age","height","address"])
        writer.writeheader()   
        
        for i in range(1,5):
            name_list = ["xiaoming","yanghong","peter","Tom"] * i
            age_list = [19,27,32,24] * i
            height_list = [176,180,172,183] * i
            address_list = ["shenzhen","guangzhou","shanghai","changsha"] * i

            infomation_list = []
            for j in range(len(name_list)):
                information = {
                    "name":name_list[j],
                    "age":age_list[j],
                    "height":height_list[j],
                    "address":address_list[j]
                }
                infomation_list.append(information)

        writer.writerows(infomation_list)

# 读取数据
import pandas as pd

data = pd.read_csv("information-1.csv")
data

单行写入

import csv

# 1-设置文件头
fileHeader = ["name", "score"]

# 2-待写入3行数据
d1 = ["Wang", "100"]
d2 = ["Li", "80"]
d3 = ["xiaosi","92"]

# 3-写入数据
f = open("instance_1.csv", "w")
writer = csv.writer(f)   # 生成writer对象

# 写入的内容都是以列表的形式整体传入函数
# writer.writerows([fileHeader, d1, d2, d3])  # 这行等效于下面的三行代码

# 4-数据单个形式传入
writer.writerow(fileHeader)   
writer.writerow(d1)
writer.writerow(d2)
writer.writerow(d3)

# 5-需要关闭文件！！！
f.close()

pd.read_csv("instance_1.csv")

上下文写入-with

import csv

# 文件头
fileHeader = ["name", "score"]

# 写入的两行数据
d1 = ["Wang", "100"]
d2 = ["Li", "80"]
d3 = ["xiaosi","92"]

# 写入数据

with open("instance_2.csv", "a") as f:
    writer = csv.writer(f)
    writer.writerows([fileHeader, d1, d2, d3])

pd.read_csv("instance_2.csv")

读文件

pandas读取文件

# 1-通过pandas读取文件

import pandas as pd

csvfile = pd.read_csv("information.csv")
csvfile

csv模块读取文件

# 2-通过csv模块读取文件

import csv

with open("information-1.csv") as f:
    csvfile = csv.reader(f)
    for line in csvfile:   # 不需要用readlines
        print(line)

['name', 'age', 'height', 'address']
['xiaoming', '19', '176', 'shenzhen']
['yanghong', '27', '180', 'guangzhou']
['peter', '32', '172', 'shanghai']
['Tom', '24', '183', 'changsha']
['xiaoming', '19', '176', 'shenzhen']
['yanghong', '27', '180', 'guangzhou']
['peter', '32', '172', 'shanghai']
['Tom', '24', '183', 'changsha']
['xiaoming', '19', '176', 'shenzhen']
['yanghong', '27', '180', 'guangzhou']
['peter', '32', '172', 'shanghai']
['Tom', '24', '183', 'changsha']
['xiaoming', '19', '176', 'shenzhen']
['yanghong', '27', '180', 'guangzhou']
['peter', '32', '172', 'shanghai']
['Tom', '24', '183', 'changsha']

本文使用 mdnice 排版