python 读取多通道的tiff文件 python读取多个txt文件

1、txt
①读取全部内容

txt_filename = './files/python_baidu.txt'
# 打开文件
file_obj = open(txt_filename, 'r', encoding='utf-8')
# 读取整个文件内容
all_content = file_obj.read()
# 关闭文件
file_obj.close()
print(all_content)

②按行读取

txt_filename = './files/python_baidu.txt'
# 打开文件
file_obj = open(txt_filename, 'r', encoding='utf-8')
# 逐行读取
line1 = file_obj.readline()
print(line1)
# 继续读下一行，游标自动指向第二行
line2 = file_obj.readline()
print(line2)
# 关闭文件
file_obj.close()

③读取返回列表

txt_filename = './files/python_baidu.txt'
# 打开文件
file_obj = open(txt_filename, 'r', encoding='utf-8')
lines = file_obj.readlines()
for i, line in enumerate(lines):
    print ('{}: {}'.format(i, line))
# 关闭文件
file_obj.close()

④写操作

txt_filename = './files/test_write.txt'
# 打开文件
file_obj = open(txt_filename, 'w', encoding='utf-8')
# 写入全部内容
file_obj.write("《Python数据分析》")
file_obj.close()

⑤按行写入

txt_filename = './files/test_write.txt'
# 打开文件
file_obj = open(txt_filename, 'w', encoding='utf-8')
# 写入字符串列表
lines = ['这是第%i行\n' %n for n in range(100)]
file_obj.writelines(lines)
file_obj.close()

⑦with

txt_filename = './files/test_write.txt'
with open(txt_filename, 'r', encoding='utf-8') as f_obj:
    print(f_obj.read())
#不需要关闭，会自动异常处理

2、CSV（以纯文本存储表格，逗号为分隔符）
①pandas读CSV

import pandas as pd
filename = './files/gender_country.csv'
df = pd.read_csv(filename, encoding='utf-16')
print(type(df))
print(df.head())#head预览前五行

②pandas写入CSV

filename = './files/pandas_output.csv'
df.to_csv(filename, index=None, encoding='utf-8')

3、JSON {key1：val1，key2：val2}
①读取

import json
filename = './files/global_temperature.json'
with open(filename, 'r') as f_obj:
    json_data = json.load(f_obj)
# 返回值是dict类型
print(type(json_data))

②读取keys和values

#print(json_data['data'].keys())
print(json_data['data'].values())

③转换成CSV

year_str_lst = json_data['data'].keys()#读出来都是str
year_lst = [int(year_str) for year_str in year_str_lst]#如果有数字，转换成int
print(year_lst）

import pandas as pd
# 构建 dataframe
year_se = pd.Series(year_lst, name = 'year')#得到一维列数据
temp_se = pd.Series(temp_lst, name = 'temperature')
result_df = pd.concat([year_se, temp_se], axis = 1)#得到二维dataframe，axis为0是两列竖着排，1是并排放
print(result_df.head())
# 保存csv
result_df.to_csv('./files/json_to_csv.csv', index = None)#index=none没有行索引

④写成json

book_dict = [{'书名':'无声告白', '作者':'伍绮诗'}, {'书名':'我不是潘金莲', '作者':'刘震云'}, {'书名':'沉默的大多数 (王小波集)', '作者':'王小波'}]
filename = './files/json_output.json'
with open(filename, 'w', encoding='utf-8') as f_obj:
    f_obj.write(json.dumps(book_dict, ensure_ascii=False))

4、sqlite

import sqlite3
db_path = './files/test.sqlite'
conn = sqlite3.connect(db_path)
cur = conn.cursor()
conn.text_factory = str  # 处理中文

cur.execute("DROP TABLE IF EXISTS book")
cur.execute("CREATE TABLE book(id INT, name TEXT, price DOUBLE)")

#逐条插入数据
cur.execute("INSERT INTO book VALUES(1,'肖秀荣考研书系列:肖秀荣(2017)考研政治命题人终极预测4套卷',14.40)")
cur.execute("INSERT INTO book VALUES(2,'法医秦明作品集:幸存者+清道夫+尸语者+无声的证词+第十一根手指(套装共5册) (两种封面随机发货)',100.00)")
cur.execute("INSERT INTO book VALUES(3,'活着本来单纯:丰子恺散文漫画精品集(收藏本)',30.90)")
cur.execute("INSERT INTO book VALUES(4,'自在独行:贾平凹的独行世界',26.80)")
cur.execute("INSERT INTO book VALUES(5,'当你的才华还撑不起你的梦想时',23.00)")
cur.execute("INSERT INTO book VALUES(6,'巨人的陨落(套装共3册)',84.90)")
cur.execute("INSERT INTO book VALUES(7,'孤独深处(收录雨果奖获奖作品《北京折叠》)',21.90)")
cur.execute("INSERT INTO book VALUES(8,'世界知名企业员工指定培训教材:所谓情商高,就是会说话',22.00)")

#批量插入数据
books = (
    (9, '人间草木', 30.00),
    (10,'你的善良必须有点锋芒', 20.50),
    (11, '这么慢,那么美', 24.80),
    (12, '考拉小巫的英语学习日记:写给为梦想而奋斗的人(全新修订版)', 23.90)
)#元组里套元组
cur.executemany("INSERT INTO book VALUES(?, ?, ?)", books)

#提交
conn.commit()

#查找数据
cur.execute('SELECT * FROM book')
rows = cur.fetchall()
# 通过索引号访问
for row in rows:
    print('序号: {}, 书名: {}, 价格: {}'.format(row[0], row[1], row[2]))

#关闭
conn.close()