# Crouching Alchemist Hidden Panda (CALCHIPAN)

## What is it?

A SQLAlchemy dialect which will consider a series of Pandas DataFrame objects as relational database tables, allowing them to be queried and manipulated in all the usual SQLAlchemy ways.

## Why is it?

Me: Naive Pandas idea #1: map SQLAlchemy declarative classes to pandas dataframes, use ORM or core queries to query it. Pointless?

Wes McKinney: @chriswithers13 Michael Bayer integration layers = good

From the man himself, and a Pycon sprint project was born!

## Example

from sqlalchemy import create_engine, MetaData, Table, Column, \
Integer, select, String

engine = create_engine("pandas://")

employee = Table('emp', m,
Column('emp_id', Integer, primary_key=True),
Column('name', String)
)
m.create_all(engine)
engine.execute(employee.insert().values(emp_id=1, name='ed'))

result = engine.execute(
select([employee]).where(employee.c.name == 'ed')
)

print(result.fetchall())


Note we didn't need to import Pandas at all above; we created and populated the table entirely using SQLAlchemy means, and Pandas remained hidden as a storage engine - hence the name!

Of course, you can and in most cases probably should start with regular Pandas dataframes, and send them off into an engine to be queried. Using this approach, the Table objects can be built explicitly or more easily just reflected as below; obviously primary/foreign key constraints, useful for when using the ORM, would still have to be established manually:

import pandas as pd

dept_df = pd.DataFrame([{"dep_id": 1, "name": "Engineering"},
{"dep_id": 2, "name": "Accounting"}])

emp_df = pd.DataFrame([
{"emp_id": 1, "name": "ed", "fullname": "Ed Jones", "dep_id": 1},
{"emp_id": 2, "name": "wendy", "fullname": "Wendy Wharton", "dep_id": 1}
])

engine = create_engine("pandas://",
namespace={"dept": dept_df, "emp": emp_df})

result = engine.execute(
select([employee.c.name, department.c.name]).
select_from(
employee.join(department,
employee.c.dep_id == department.c.dep_id))
)
print(result.fetchall())


Since we're totally in Python now, you can make any kind of Python function into a SQL function, most handily the various numpy functions:

from calchipan import aggregate_fn
@aggregate_fn(package='numpy')
def stddev(values):
return values.std()


Above, stddev is now a SQLAlchemy aggregate function:

result = engine.execute(select([stddev(mytable.c.data)]))


And it is also available from the func namespace (note we also put it into a sub-"package" called numpy:

from sqlalchemy import func
result = engine.execute(select([func.numpy.stddev(mytable.c.data)]))


## Autoincrement

The Table object can be configured to deliver the index of the data frame as the "primary key" of the table, which will also work as an "autoincrement" field when rows are inserted. This feature requires SQLAlchemy 0.8.1 (or latest tip) to work:

my_table = Table('my_dataframe', metadata,
Column('id', Integer, primary_key=True),
Column('data', Integer),
pandas_index_pk=True
)

result = engine.execute(my_table.insert().values(data=5))
pk = result.inserted_primary_key()


## Great, so Pandas is totally SQL-capable now right?

Well let's just slow down there. The library here is actually working quite nicely, and yes, you can do a pretty decent range of SQL operations here, noting the caveats that this is super duper alpha stuff I just started a week ago. Some SQL operations that we normally take for granted will perform pretty badly (guide is below), so at the moment it's not entirely clear how much speed will be an issue. There's a good number of tests for all the SQL functionality that's been implemented, though these are all rudimentary "does it work at all" tests dealing with at most only three or four rows and two tables. Additional functional tests with real world ORM examples have shown very good results, illustrating queries with fairly complex geometries (lots of subqueries, aliases, and joins) working very well with no errors. The performance of some operations, particularly data mutation operations, are fairly slow, but Pandas is not oriented towards manipulation of DataFrames in a CRUD-style way in any case. For straight up SELECTs that stay close to primary Pandas use cases, results should be pretty decent.

## Can I just type "select * from table" and it will work?

No, we're dealing here strictly with SQLAlchemy expression constructs as the source of the SQL parse tree. So while the ORM works just fine, there's no facility here to actually receive a SQL string itself. However, the (more) ambitious (than me) programmer should be able to take a product like sqlparse and use that product's parse tree to deliver the same command objects that the compiler does here, the calchipan.compiler (SQLAlchemy compiler) and calchipan.resolver (command objects understood by the Pandas DBAPI) are entirely separate, and the resolver has minimal dependencies on SQLAlchemy.

Here's the pandasql package, which does basically the same thing that sqldf does for R, which is copies data out to SQLite databases as needed and lets you run SQL against that. So if you want straight up SQL queries delivered perfectly, use that. You just have to wait while it copies all your dataframes out to the database for each table (which might not be a problem at all). pandasql also doesn't provide easy hooks for usage with packages like SQLAlchemy, though the whole thing is only 50 lines so hacking its approach might be worth it.

## Will CALCHIPAN at least return the right results to me?

As noted before, initial testing looks very good. But note that this is half of a relational database implementation written in Python; if you look at sqlite's changelog you can see they are still fixing "I got the wrong answer" types of bugs after nine years of development, which is 46800% the number of weeks versus Calchipans one week of development time as of March 25, 2013. So as a rule of thumb I'd say Calchipan is way too new to be trusted with anything at all. Feel free to use the bugtracker here to report on early usage experiences and issues, the latter should absolutely be expected.

## Performance Notes

The SQL operations are all implemented with an emphasis on relying upon Pandas in the simplest and most idiomatic way possible for any query given. Two common SQL operations, implicit joins and correlated subqueries, work fully, but are not optimized at all - an implicit join (that is, selecting from more than one table without using join()) relies internally on producing a cartesian product, which you aren't going to like for large (or even a few thousand rows) datasets. Correlated subqueries involve running the subquery individually on every row, so these will also make the speed-hungry user sad (but the "holy crap correlated subqueries are possible with Pandas?" user should be really happy!). A join using join() or outerjoin() will internally make use of Pandas' merge() function directly for simple criteria, so if you stay within the lines, you should get pretty good Pandas-like performance, but if you try non-simple criteria like joinining on "x > y", you'll be back in cartesian land.

The libary also does a little bit of restatement of dataframes internally which has a modest performance hit, which is more significant if one is using the "index as primary key" feature, which involves making copies of the DataFrame's index into a column.

## What's Implemented

• select()
• WHERE criterion
• column expressions, functions
• implicit joins (where multiple tables are specified without using JOIN)
• explicit joins (i.e. using join()), on simple criteria (fast) and custom criteria (slower)
• explicit outerjoins (using outerjoin()), on simple criteria (sort of fast) and custom criteria (slower)
• subqueries in the FROM clause
• subqueries in the columns and WHERE clause which can be correlated; note that column/where queries are not very performant however as they invoke explicitly for every row in the parent result
• ORDER BY
• GROUP BY
• aggregate functions, including custom user-defined aggregate functions
• HAVING, including comparison of aggregate function values
• LIMIT, using select().limit()
• OFFSET, using select().offset()
• UNION ALL, using union_all()
• A few SQL functions are implemented so far, including count(), max(), min(), and now()
• Table reflection
• Only gets the names of columns, and at best only the "String", "Integer", "Float" types based on a dataframe. There's no primary key, foreign key constraints, defaults, indexes or anything like that. Primary and FK constraints would need to be specified to the Table() explicitly if one is using the ORM and wishes these constructs to be present.
• CRUD operations - Note that Pandas is not optimized for modifications of dataframes, and dataframes should normally be populated ahead of time using normal Pandas APIs, unless SQL-specific or ORM-specific functionality is needed in order to produce initial schemas and/or populate data. CRUD operations here work correctly but are not by any means fast, nor is there any notion of thread safety or anything like that. ORM models can be fully persisted to dataframes using this functionality.
• insert()
• Plain inserts
• multi-valued inserts, i.e. table.insert().values([{"a": 1, "b": 2}, {"a": 3, "b": 4}])
• Note that inserts here must create a new dataframe for each statement invoked! Generally, dataframes should be populated using Pandas standard methods; INSERT here is only a utility
• cursor.lastrowid - if the table is set up to use the Pandas "index" as the primary key, this value will function. The library is less efficient when used in this mode, however, as it needs to copy the index column every time the table is accessed. SQLAlchemy returns this value as result.inserted_primary_key().
• update()
• Expression updates, i.e. set the value of a column to an expression possibly deriving from other columns in the row
• Correlated subquery updates, i.e. set the value of a column to the result of a correlated subquery
• Full WHERE criterion including correlated subqueries
• cursor.rowcount, number of rows matched.
• delete()
• Plain deletes
• Full WHERE criterion including correlated subqueries
• cursor.rowcount, number of rows matched
• ORM
• The SQLAlchemy ORM builds entirely on top of the Core SQL constructs above, so it works fully.

## What's Egregiously Missing

• Other set ops besides UNION ALL - UNION, EXCEPT, INTERSECTION, etc., these should be easy to implement
• RETURNING for insert, update, delete, also should be straightforward to implement
• Lots of obvious functions are missing, only a few are present so far
• Coercion/testing of Python date and time values. Pandas seems to use an internal Timestamp format, so SQLAlchemy types that coerce to/from Python datetime() objects and such need to be added.
• EXISTS, needs to be evaluated
• CASE statements (should be very easy)
• anything fancy, window functions, CTEs, etc.
• ANY KIND OF INPUT SANITIZING - I've no idea if Pandas and/or numpy have any kind of remote code execution vulnerabilities, but if they do, they are here as well. This library has no security features of any kind, please do not send untrusted data into it.

Thanks, and have a nice day!