共有8个可选参数:sql,con,index_col,coerce_float,params,parse_date,columns,chunksize。 该函数基础功能为将SQL查询或数据库表读入DataFrame。此函数是read_sql_table和read_sql_query(向后兼容性)两个函数功能结合。它将根据提供的输入参数传入给特定功能。一个SQL查询将传入到read_sql_query查询,而数据库表名称...
df = pd.read_sql_query(query, self.connection,params=sql_parameter) 我的参数字典如下所示 sql_parameter = {'itemids':itemids_str} 其中itemids_str是类似于 282940499, 276686324, 2665846, 46875436, 530272885, 2590230, 557021480, 282937154, 46259344 SQL代码看起来像 SELECT xxx, yyy, zzz FROM tab...
后来发现，既然你传递了一个字符串到read_sql，你就可以只使用f-string。用MSSQL pyodbc尝试了同样的方...
Pandasread_sqlfunction allows you to pass parameters in different data types such as lists, tuples, and dictionaries. Let’s examine how to use these different data types with theparamsparameter: List: Ideal for positional parameters in your SQL query. Tuple: Similar to lists, but immutable, ...
SQL数据查询 1.基本语句 SQL代码可用小写也可用大写,但标点符号必须用英文输入。 2.演示 (1)查询学生库学生年龄大于等于20岁的。 (2)求学生库里学生入学成绩 第一个案例我加入两个字段一个是姓名一个是年龄,这样输出后就会显示姓名字段和年龄字段。 Avg函数用于求出字段平均值,AS后面的"入学成绩平均分"是创建...
params: An optional list or dictionary of parameters to pass into thesqlquery. parse_dates: An optional parameter to parse columns into datetime. columns: If you’re reading a table (not a query), this allows you to select which columns to load. ...
请参见http://initd.org/psycopg/docs/usage.html#query-parameters。因此，使用这种风格应该会奏效：...
parse_dates=None, params=None, chunksize=None):"""Read SQL query into a DataFrame. Parameters --- sql : string SQL query to be executed. index_col : string, optional, default: None Column name to use as index for the returned DataFrame object. coerce_float...
parse_dates=None, params=None, chunksize=None):"""Read SQL query into a DataFrame. Parameters --- sql : string SQL query to be executed. index_col : string, optional, default: None Column name to use as index for the returned DataFrame object. coerce_float...
In [55]: import io In [56]: data = io.StringIO("""a,b,c,d,e,f,g,h,i ...: 1,2.5,True,a,,, ...: 3,4.5,False,b,6,7.5,True,a, ...: """) ...: In [57]: df_pyarrow = pd.read_csv(data, dtype_backend="pyarrow") In [58]: df_pyarrow.dtypes Out[58]: a in...