Source code for sqlalchemy.sql.elements

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Python interface to GnuCash documents 0. Source code for sqlalchemy. For example, three fixed values can be compared as in:: Note that not all databases support this syntax. Literal clauses are created automatically when non-: Use this function to force the generation of a literal clause, which will be created as a: A type that features bound-value handling will also have that behavior take effect when literal values or: For example, if a type implements the: A SQL expression, such as a: The binding columns in python value will be binding columns in python from the: This method may be used by a generative API.

This is for the purposes for creating a new object of this type. However, the binding columns in python version of the object binding columns in python to return the class of its proxied element. This accessor tends to binding columns in python used for FromClause objects to identify 'equivalent' FROM clauses, regardless of transformative operations.

Returns a copy of this ClauseElement binding columns in python Subclasses should override the default behavior, which is a straight identity comparison. Called during a copy-and-traverse operation on newly shallow-copied elements to create a deep copy. The given clone function should be used, which may be applying additional transformations to the element i. This is used for visit traversal. This method is overridden by subclasses to return a "grouping" construct, i.

In particular it's used by "binary" expressions to provide a grouping around themselves when placed binding columns in python a larger expression, as well as by: Note that subqueries should be normally created using the: As expressions are composed together, the application of: The return value is a: This argument takes precedence over this: This allows any custom flag to be passed through to a custom compilation construct, for example.

While the most familiar kind of: These functions will typically document that they accept a "SQL expression" as an argument. What this means in terms of SQLAlchemy usually refers to an binding columns in python which is either already in the form of a: This generally means that a: The Core expression system looks for this method when an object of otherwise unknown type is passed to a function that is looking to coerce the argument into a: It also refers to a name that this column expression can be located from in a result set.

For other binding columns in python, different rules may apply, such as anonymized labels and others. The name is distinct from that of. Part of the inspection interface; returns self.

If the given "other" column is present in this dictionary, if any of the columns in the corresponding set pass the comparison test, the result is True. This is used to expand the comparison to other columns that may be known to be equivalent to this one via foreign key or other criterion.

This is a shortcut to the: This is a label expression which will be named at compile time. The return value is an instance of: In this way, it serves not just as a "placeholder" for eventual population, but also as a means of representing so-called "unsafe" values which should not be rendered binding columns in python in binding columns in python SQL statement, but rather should be passed along to the: It is typical that Python literal values passed to virtually all SQL expression functions are coerced into fixed: For example, given a comparison operation such as:: If we invoke a statement like the following:: Will be used in the generated SQL statement for dialects that use named parameters.

This value may be modified when part of a compilation operation, if other: Initial value for this bind param. Will be used at statement execution time as the value for this parameter passed to the DBAPI, if no other value is indicated to the statement execution method for this particular parameter name. A callable function that takes the place of "value". The function will be called at statement execution time to determine the ultimate value.

Used for scenarios where the actual bind value cannot be determined at the point at which the clause construct is created, but embedded bind values are still desirable.

The type of a: This flag is used generally by the internals when producing so-called "anonymous" bound expressions, it isn't generally applicable to explicitly-named: If either of these parameters are present, then: True if this parameter name requires quoting and is not currently known as a SQLAlchemy reserved word; this currently only applies to the Oracle backend, where bound names must sometimes be quoted.

This applies to backends such as Oracle which support OUT parameters. In those cases, the same bind parameter syntax is applied:: As such, SQLAlchemy refers to it as an: This argument now invokes the: This parameter now invokes the: Given a text construct such as:: The types will be inferred from the values given, in this case: Additional bound parameters can be supplied at statement execution time, e.

For example, we can call: These can also have types specified, which will impact how the column behaves in expressions as well as determining result set behavior:: By default, is comma-separated, such as a column listing. Main usage is to produce a composite IN construct:: Unsupported backends will raise a subclass of: It returns an instance of: In this form, the: The statement below is equivalent to the preceding statement:: To coerce a literal string expression into a constant expression rendered inline, use the: The criteria to be compared against,: An optional SQL expression which will be used as a fixed "comparison point" for candidate values within a dictionary passed to: When omitted, most databases will produce a result of NULL if none of the "when" expressions evaluate to true.

Quoting rules will binding columns in python be applied. To specify a column-name expression which should be subject to quoting rules, use the: If left as Binding columns in python the type will be NullType. The second is that it associates the given type e. This is typically available as: A unary expression has a single column expression and binding columns in python operator.

The operator can be placed on the left where it is called the 'operator' or right where it is called the 'modifier' of the column expression. See that method for further information. This is a special operator against a binding columns in python "window" function, as well as any aggregate function, which produces results relative to the result set itself.

It's supported only by certain database backends. Used against aggregate or so-called "window" functions, for database backends that support window functions. This function is also available from the: This is a special operator against aggregate and window functions, which controls which rows are passed to it. This method adds additional binding columns in python to the initial criteria set up by: This functionality is more conveniently available via the: The object has none binding columns in python the associations with schema-level metadata or with execution-time behavior that: To produce a textual SQL expression that is rendered exactly without any quoting, use: Additionally, full SQL statements binding columns in python best handled using the: Certain database backends, such as Oracle, Firebird, and DB2 "normalize" case-insensitive names as uppercase.

The SQLAlchemy dialects for these backends convert from SQLAlchemy's lower-case-means-insensitive convention to the upper-case-means-insensitive conventions of those backends. The class can also be passed explicitly as the name to any function that receives a name which can be quoted.

Such as to use the: This is a string subclass that indicates a name that should not be subject to any further naming conventions. In some situations, such as in migration scripts, we may be binding columns in python the above:

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The complete Python code that I am using in this tutorial can be downloaded from my GitHub repository: The sqlite3 that we will be using throughout this tutorial is part of the Python Standard Library and is a nice and easy interface to SQLite databases: There are no server processes involved, no configurations required, and no other obstacles we have to worry about.

Conveniently, a new database file. In the following section, we will take a look at some example code of how to create a new SQLite database files with tables for storing some data. To round up this section about connecting to a SQLite database file, there are two more operations that are worth mentioning. If we are finished with our operations on the database file, we have to close the connection via the.

And if we performed any operation on the database other than sending queries, we need to commit those changes via the. Let us have a look at some example code to create a new SQLite database file with two tables: Download the script at: Throughout this article, I will use this tool to provide screenshots of the database structures that we created below the corresponding code sections.

Using the code above, we created a new. Looking at the table above, You might have noticed that SQLite 3 has no designated Boolean data type. Every table can only have max. But more on column indexing in the a later section. If we want to add a new column to an existing SQLite database table, we can either leave the cells for each row empty NULL value , or we can set a default value for each cell, which is pretty convenient for certain applications.

Inserting and updating rows into an existing SQLite database table - next to sending queries - is probably the most common database operation. Unfortunately, this convenient syntax is not supported by the more compact SQLite database implementation that we are using here. However, there are some workarounds.

But let us first have a look at the example code:. Just like hashtable-datastructures, indexes function as direct pointers to our data in a table for a particular column i.

The downside of indexes is that every row value in the column must be unique. However, it is recommended and pretty useful to index certain columns if possible, since it rewards us with a significant performance gain for the data retrieval. The example code below shows how to add such an unique index to an existing column in an SQLite database table.

And if we should decide to insert non-unique values into a indexed column later, there is also a convenient way to drop the index, which is also shown in the code below. The code below illustrates how we can retrieve row entries for all or some columns if they match certain criteria. The print output for the 5 different cases shown in the code above would look like this note that we only have a table with 1 row here: This is fine if we just want to use the database for ourselves.

However, this leaves our database vulnerable to injection attacks. For example, if our database would be part of a web application, it would allow hackers to directly communicate with the database in order to bypass login and password verification and steal data. In order to prevent this, it is recommended to use? For example, instead of using. However, the problem with this approach is that it would only work for values, not for column or table names.

So what are we supposed to do with the rest of the string if we want to protect ourselves from injection attacks? The easy solution would be to refrain from using variables in SQLite queries whenever possible, and if it cannot be avoided, we would want to use a function that strips all non-alphanumerical characters from the stored content of the variable, e.

The screenshot below shows the print outputs of the code that we used to query for entries that lie between a specified date interval using.

In the previous two sections we have seen how we query SQLite databases for data contents. Now let us have a look at how we retrieve its metadata here: If we would print the contents of the variable names now, the output would look like this:.

I hope we covered most of the basics about SQLite database operations in the previous sections, and by now we should be well equipped to get some serious work done using SQLite in Python. I really hope this tutorial was helpful to you to get started with SQLite database operations via Python. I have been using the sqlite3 module a lot recently, and it has found its way into most of my programs for larger data analyses.

If you are interested, you can check it out at: I am looking forward to your opinions and ideas, and I hope I can improve and extend this tutorial in future. Retrieving column names Printing a database summary Conclusion The complete Python code that I am using in this tutorial can be downloaded from my GitHub repository: DATE 'now' returns current date, e.