import pandas as pd from sqlalchemy import create_engine # 创建 SQLite 数据库引擎 engine = create_engine("sqlite:///database.sqlite", encoding='gbk') # 查询所需数据 query = "SELECT * FROM table_name" # 从数据库中读取数据到 DataFrame df = pd.read_sql(query, engine) # 将编码方式不为 UTF-8 的列转换为指定的编码方式 df['pinyinName'] = df['pinyinName'].apply(lambda x: x.encode('gbk').decode('gbk')) # 显示 DataFrame 的前几行 print(df.head()) # 关闭连接 engine.dispose() 在df = pd.read_sql(query, engine)上出现(sqlite3.OperationalError) Could not decode to UTF-8 column 'pinyinName' with text '�pai' (Background on this error at: https://sqlalche.me/e/14/e3q8)

这个错误通常发生在读取数据库中的数据时，因为某些列的编码方式不是 UTF-8。在这种情况下，您可以尝试使用不同的编码方式来读取这些列，或者将这些列的编码方式转换为 UTF-8。根据错误信息，您可以尝试使用以下代码将编码方式不为 UTF-8 的列转换为指定的编码方式：

df['pinyinName'] = df['pinyinName'].apply(lambda x: x.encode('gbk').decode('utf-8', 'ignore'))

这里使用了 `ignore` 参数来忽略无法转换的字符。如果您不能确定这些列的实际编码方式，您可以尝试使用 `chardet` 等第三方库来检测列的编码方式。

相关问题

--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[2], line 6 4 engine = create_engine('sqlite:///data/chinook.db') 5 # 加载其中的 tracks 数据表的数据 ----> 6 tracks = pd.read_sql_table('tracks', engine) 7 # 查看前 5 个数据 8 tracks.head() File D:\anaconda3\envs\test02\lib\site-packages\pandas\io\sql.py:318, in read_sql_table(table_name, con, schema, index_col, coerce_float, parse_dates, columns, chunksize) 315 import sqlalchemy 316 from sqlalchemy.schema import MetaData --> 318 meta = MetaData(con, schema=schema) 319 try: 320 meta.reflect(only=[table_name], views=True) TypeError: __init__() got multiple values for argument 'schema'

printf("3. 修改学生资料\n");
printf("4. 显示学生资料\n");
printf("0.这个错误是因为在创建 `MetaData` 对象时，`schema` 参数被重复传递了多次。你 退出\n");
printf("-----------------\n");
printf("请输入选项：");
scanf("%d", &choice);

switch可以检查一下你的代码，确认在调用 `pd.read_sql_table` 函数时是否重复传递了 `schema (choice) {
case 1:
addStudent();
break;
case 2:
searchStudent();
break;
` 参数。如果是的话，可以考虑删除多余的参数值，或者使用正确的参数名。另外， case 3:
modifyStudent();
break;
case 4:
displayStudents();
break;
case 0也可以查看一下 `create_engine` 函数中是否正确地指定了数据库的连接信息。

import pandas as pd import pymysql # 连接到数据库 conn = pymysql.connect(host='localhost', user='user', password='password', database='database') # 获取所有表格的名称 cursor = conn.cursor() cursor.execute("SHOW TABLES") tables = cursor.fetchall() # 遍历所有表格 for table in tables: table_name = table[0] table_name_quoted = '' + table_name + '' # 检查是否存在名为'a'的列，如果不存在则添加'a'和'b'列 cursor.execute("SHOW COLUMNS FROM " + table_name_quoted + " LIKE 'a'") a_column = cursor.fetchone() if a_column is None: cursor.execute("ALTER TABLE " + table_name_quoted + " ADD COLUMN a DECIMAL(10,2)") cursor.execute("ALTER TABLE " + table_name_quoted + " ADD COLUMN b DECIMAL(10,2)") conn.commit() # 查询net_mf_amount列的数据 query = "SELECT trade_date, net_mf_amount FROM " + table_name_quoted + " ORDER BY trade_date DESC" df = pd.read_sql_query(query, conn) # 计算a和b列 a_column = [] b_column = [] for i in range(len(df)): if i == 0: a_column.append(None) b_column.append(None) else: if pd.notnull(df.iloc[i]['net_mf_amount']) and pd.notnull(df.iloc[i-1]['net_mf_amount']): if i-2 >= 0: if pd.notnull(df.iloc[i-2]['net_mf_amount']): a = df.iloc[i]['net_mf_amount'] - df.iloc[i-1]['net_mf_amount'] b = df.iloc[i]['net_mf_amount'] - df.iloc[i-2]['net_mf_amount'] a_column.append(a) b_column.append(b) else: j = i-3 while j >= 0: if pd.notnull(df.iloc[j]['net_mf_amount']): a = df.iloc[i]['net_mf_amount'] - df.iloc[i-1]['net_mf_amount'] b = df.iloc[i]['net_mf_amount'] - df.iloc[j]['net_mf_amount'] a_column.append(a) b_column.append(b) break j -= 1 else: a = df.iloc[i]['net_mf_amount'] - df.iloc[i-1]['net_mf_amount'] b = None a_column.append(a) b_column.append(b) else: a_column.append(None) b_column.append(None) # 将结果保存到数据库 for i in range(len(df)): cursor.execute("UPDATE " + table_name_quoted + " SET a=%s, b=%s WHERE trade_date=%s", (a_column[i], b_column[i], df.iloc[i]['trade_date'])) conn.commit() # 关闭连接 cursor.close() conn.close() 报错/Users/ljy/stock/daily_analyse.py:65: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy. df = pd.read_sql_query(query, conn)

这段代码的报错和之前的一样，是因为使用的连接对象 conn 不是 SQLAlchemy 连接对象或者 SQLite3 的 DBAPI2 连接对象。你可以将该连接对象转换为 SQLAlchemy 连接对象或者使用数据库字符串 URI 来连接数据库。将连接数据库的代码修改如下：

import pandas as pd
from sqlalchemy import create_engine

# 连接到数据库
engine = create_engine('mysql+pymysql://user:password@localhost/database')

# 获取所有表格的名称
with engine.connect() as conn, conn.begin():
    tables = conn.execute("SHOW TABLES").fetchall()

# 遍历所有表格
for table in tables:
    table_name = table[0]
    table_name_quoted = '`' + table_name + '`'

    # 检查是否存在名为'a'的列，如果不存在则添加'a'和'b'列
    with engine.connect() as conn, conn.begin():
        a_column = conn.execute("SHOW COLUMNS FROM " + table_name_quoted + " LIKE 'a'").fetchone()
        if a_column is None:
            conn.execute("ALTER TABLE " + table_name_quoted + " ADD COLUMN a DECIMAL(10,2)")
            conn.execute("ALTER TABLE " + table_name_quoted + " ADD COLUMN b DECIMAL(10,2)")

    # 查询net_mf_amount列的数据
    query = "SELECT trade_date, net_mf_amount FROM " + table_name_quoted + " ORDER BY trade_date DESC"
    df = pd.read_sql_query(query, engine)

    # 计算a和b列
    a_column = []
    b_column = []
    for i in range(len(df)):
        if i == 0:
            a_column.append(None)
            b_column.append(None)
        else:
            if pd.notnull(df.iloc[i]['net_mf_amount']) and pd.notnull(df.iloc[i-1]['net_mf_amount']):
                if i-2 >= 0:
                    if pd.notnull(df.iloc[i-2]['net_mf_amount']):
                        a = df.iloc[i]['net_mf_amount'] - df.iloc[i-1]['net_mf_amount']
                        b = df.iloc[i]['net_mf_amount'] - df.iloc[i-2]['net_mf_amount']
                        a_column.append(a)
                        b_column.append(b)
                    else:
                        j = i-3
                        while j >= 0:
                            if pd.notnull(df.iloc[j]['net_mf_amount']):
                                a = df.iloc[i]['net_mf_amount'] - df.iloc[i-1]['net_mf_amount']
                                b = df.iloc[i]['net_mf_amount'] - df.iloc[j]['net_mf_amount']
                                a_column.append(a)
                                b_column.append(b)
                                break
                            j -= 1
                else:
                    a = df.iloc[i]['net_mf_amount'] - df.iloc[i-1]['net_mf_amount']
                    b = None
                    a_column.append(a)
                    b_column.append(b)
            else:
                a_column.append(None)
                b_column.append(None)

    # 将结果保存到数据库
    with engine.connect() as conn, conn.begin():
        for i in range(len(df)):
            conn.execute("UPDATE " + table_name_quoted + " SET a=%s, b=%s WHERE trade_date=%s", (a_column[i], b_column[i], df.iloc[i]['trade_date']))

# 关闭连接
engine.dispose()

注意：上述代码中的 user, password 和 database 分别对应的是你自己的用户名、密码和数据库名，需要进行修改。