Python读取和写入txt、Excel文件和JSON文件的方法

# 读取整个文件内容
with open('example.txt', 'r', encoding='utf-8') as file:
    content = file.read()
    print(content)

# 逐行读取文件内容
with open('example.txt', 'r', encoding='utf-8') as file:
    for line in file:
        print(line.strip())

# 写入文本到文件（覆盖模式）
with open('example.txt', 'w', encoding='utf-8') as file:
    file.write('Hello, World!\n')

# 追加文本到文件
with open('example.txt', 'a', encoding='utf-8') as file:
    file.write('Appending a new line.\n')

# 假设我们的文件内容如下：
# Name, Age, City
# Alice, 30, New York
# Bob, 25, Los Angeles

with open('example.txt', 'r', encoding='utf-8') as file:
    header = file.readline().strip().split(', ')
    data = []
    for line in file:
        values = line.strip().split(', ')
        record = dict(zip(header, values))
        data.append(record)
    print(data)

# 逐行读取大文件
with open('large_file.txt', 'r', encoding='utf-8') as file:
    for line in file:
        process_line(line)  # 自定义的处理函数

import pandas as pd

# 读取 Excel 文件
df = pd.read_excel('example.xlsx', sheet_name='Sheet1')
print(df)

from openpyxl import load_workbook

# 读取 Excel 文件
wb = load_workbook('example.xlsx')
sheet = wb['Sheet1']
for row in sheet.iter_rows(values_only=True):
    print(row)

import pandas as pd

# 创建一个 DataFrame
data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)

# 写入 Excel 文件
df.to_excel('example.xlsx', sheet_name='Sheet1', index=False)

from openpyxl import Workbook

# 创建一个新的 Excel 文件
wb = Workbook()
sheet = wb.active
sheet.title = 'Sheet1'

# 写入数据
sheet.append(['Name', 'Age'])
sheet.append(['Alice', 25])
sheet.append(['Bob', 30])

# 保存文件
wb.save('example.xlsx')

import pandas as pd

# 读取 Excel 文件的所有工作表
xls = pd.ExcelFile('example.xlsx')
sheets = {}
for sheet_name in xls.sheet_names:
    sheets[sheet_name] = pd.read_excel(xls, sheet_name=sheet_name)
    print(f"Sheet: {sheet_name}")
    print(sheets[sheet_name])

from openpyxl import load_workbook

# 读取 Excel 文件
wb = load_workbook('example.xlsx')
for sheet_name in wb.sheetnames:
    sheet = wb[sheet_name]
    print(f"Sheet: {sheet_name}")
    for row in sheet.iter_rows(values_only=True):
        print(row)

import pandas as pd

# 逐块读取大 Excel 文件
chunk_size = 1000
for chunk in pd.read_excel('large_file.xlsx', sheet_name='Sheet1', chunksize=chunk_size):
    process_chunk(chunk)  # 自定义的处理函数

import json

# 读取 JSON 文件
with open('example.json', 'r', encoding='utf-8') as file:
    data = json.load(file)
    print(data)

import json

# 数据
data = {'name': 'Alice', 'age': 25}

# 写入 JSON 文件
with open('example.json', 'w', encoding='utf-8') as file:
    json.dump(data, file, ensure_ascii=False, indent=4)

import json

# 假设我们的 JSON 文件内容如下：
# {
#     "name": "Alice",
#     "age": 30,
#     "address": {
#         "city": "New York",
#         "zipcode": "10001"
#     },
#     "phones": ["123-456-7890", "987-654-3210"]
# }

with open('example.json', 'r', encoding='utf-8') as file:
    data = json.load(file)
    print(data)

# 访问嵌套数据
print(data['address']['city'])  # 输出: New York
print(data['phones'][0])  # 输出: 123-456-7890

import json

# 嵌套数据
data = {
    "name": "Alice",
    "age": 30,
    "address": {
        "city": "New York",
        "zipcode": "10001"
    },
    "phones": ["123-456-7890", "987-654-3210"]
}

# 写入 JSON 文件
with open('example.json', 'w', encoding='utf-8') as file:
    json.dump(data, file, ensure_ascii=False, indent=4)

import json

# 假设我们的 JSON 文件内容如下，每行是一个独立的 JSON 对象：
# {"name": "Alice", "age": 30}
# {"name": "Bob", "age": 25}

with open('example.json', 'r', encoding='utf-8') as file:
    for line in file:
        data = json.loads(line.strip())
        print(data)

import ijson

# 假设我们的 JSON 文件内容如下：
# [
#     {"name": "Alice", "age": 30},
#     {"name": "Bob", "age": 25}
# ]

with open('example.json', 'r', encoding='utf-8') as file:
    parser = ijson.parse(file)
    for prefix, event, value in parser:
        if prefix.endswith('.name') and event == 'string':
            print(f"Name: {value}")
        elif prefix.endswith('.age') and event == 'number':
            print(f"Age: {value}")

import json

def process_chunk(chunk):
    for line in chunk:
        data = json.loads(line.strip())
        print(data)

chunk_size = 1000  # 每次读取1000行

with open('large_file.json', 'r', encoding='utf-8') as file:
    chunk = []
    for line in file:
        chunk.append(line)
        if len(chunk) >= chunk_size:
            process_chunk(chunk)
            chunk = []
    if chunk:
        process_chunk(chunk)

import jsonlines

# 假设我们的 JSON 文件内容如下，每行是一个独立的 JSON 对象：
# {"name": "Alice", "age": 30}
# {"name": "Bob", "age": 25}

with jsonlines.open('example.json') as reader:
    for obj in reader:
        print(obj)