API¶

This page contains a comprehensive list of functionality within blaze. Docstrings should provide sufficient understanding for any individual function or class.

Interactive Use¶

_Data

Expressions¶

`Projection`(args, *kwargs)	Select a subset of fields from data.
`Selection`(args, *kwargs)	Filter elements of expression based on predicate
`Label`(args, *kwargs)	An expression with a name.
`ReLabel`(args, *kwargs)	Table with same content but with new labels
`Map`(args, *kwargs)	Map an arbitrary Python function across elements in a collection
`Apply`(args, *kwargs)	Apply an arbitrary Python function onto an expression
`Coerce`(args, *kwargs)	Coerce an expression to a different type.
`Coalesce`
`Cast`

`Sort`(args, *kwargs)	Table in sorted order
`Distinct`(args, *kwargs)	Remove duplicate elements from an expression
`Head`(args, *kwargs)	First n elements of collection
`Merge`(args, *kwargs)	Merge many fields together
`Join`(args, *kwargs)	Join two tables on common columns
`Concat`(args, *kwargs)	Stack tables on common columns
`IsIn`(args, *kwargs)	Check if an expression contains values from a set.

By(*args, **kwargs) Split-Apply-Combine Operator

Blaze Server¶

Server([data, formats, authorization]) Blaze Data Server

Client(url[, serial, loads, dumps, default, ...]) Client for Blaze Server

Additional Server Utilities¶

`expr_md5`
`to_tree`(expr[, names])	Represent Blaze expression with core data structures
`from_tree`(expr[, namespace])	Convert core data structures to Blaze expression

`data_spider`(path[, ignore, followlinks, ...])	Traverse a directory and call `odo.resource` on its contents.
`from_yaml`(path[, ignore, followlinks, hidden])	Construct a dictionary of resources from a YAML specification.

Definitions¶

blaze.interactive.Data(data, dshape=None, name=None, fields=None, schema=None, **kwargs)¶

Interactive data.

The Data object presents a familiar view onto a variety of forms of data. This user-level object provides an interactive experience to using Blaze’s abstract expressions.

Parameters:

data : object

Any type with discover and compute implementations

fields : list, optional

Field or column names, will be inferred from datasource if possible

dshape : str or DataShape, optional

DataShape describing input data

name : str, optional

A name for the data.

Examples

>>> t = Data([(1, 'Alice', 100),
...           (2, 'Bob', -200),
...           (3, 'Charlie', 300),
...           (4, 'Denis', 400),
...           (5, 'Edith', -500)],
...          fields=['id', 'name', 'balance'])
>>> t[t.balance < 0].name
    name
0    Bob
1  Edith

blaze.interactive.Table(*args, **kwargs)¶: Deprecated, see Data instead

blaze.server.spider.data_spider(path, ignore=(<class 'ValueError'>, <class 'NotImplementedError'>), followlinks=True, hidden=False, extra_kwargs=None)¶

Traverse a directory and call odo.resource on its contents.

Parameters:

path : str

Path to a directory of resources to load

ignore : tuple of Exception, optional

Ignore these exceptions when calling resource

followlinks : bool, optional

Follow symbolic links

hidden : bool, optional

Load hidden files

extra_kwargs: dict, optional

extra kwargs to forward on to odo.resource.

Returns:

dict

Possibly nested dictionary of containing basenames mapping to resources

blaze.server.spider.from_yaml(path, ignore=(<class 'ValueError'>, <class 'NotImplementedError'>), followlinks=True, hidden=False)¶

Construct a dictionary of resources from a YAML specification.

Parameters:

path : str

Path to a YAML specification of resources to load

ignore : tuple of Exception, optional

Ignore these exceptions when calling resource

followlinks : bool, optional

Follow symbolic links

hidden : bool, optional

Load hidden files

Returns:

dict

A dictionary mapping top level keys in a YAML file to resources.

See also

data_spider: Traverse a directory tree for resources

class blaze.server.server.Server(data=None, formats=None, authorization=None)¶

Blaze Data Server

Host local data through a web API

Parameters:

data : dict, optional

A dictionary mapping dataset name to any data format that blaze understands.

formats : iterable, optional

An iterable of supported serialization formats. By default, the server will support JSON. A serialization format is an object that supports: name, loads, and dumps.

authorization : callable, optional

A callable to be used to check the auth header from the client. This callable should accept a single argument that will either be None indicating that no header was passed, or an object containing a username and password attribute. By default, all requests are allowed.

Examples

>>> from pandas import DataFrame
>>> df = DataFrame([[1, 'Alice',   100],
...                 [2, 'Bob',    -200],
...                 [3, 'Alice',   300],
...                 [4, 'Dennis',  400],
...                 [5,  'Bob',   -500]],
...                columns=['id', 'name', 'amount'])

>>> server = Server({'accounts': df})
>>> server.run() 

run(port=6363, retry=False, **kwargs)¶

Run the server.

Parameters:

port : int, optional

The port to bind to.

retry : bool, optional

If the port is busy, should we retry with the next available port?

**kwargs

Forwarded to the underlying flask app’s run method.

Notes

This function blocks forever when successful.

blaze.server.server.to_tree(expr, names=None)¶

Represent Blaze expression with core data structures

Transform a Blaze expression into a form using only strings, dicts, lists and base types (int, float, datetime, ....) This form can be useful for serialization.

Parameters:

expr : Expr

A Blaze expression

See also

from_tree

Examples

>>> t = symbol('t', 'var * {x: int32, y: int32}')
>>> to_tree(t) 
{'op': 'Symbol',
 'args': ['t', 'var * { x : int32, y : int32 }', False]}

>>> to_tree(t.x.sum()) 
{'op': 'sum',
 'args': [
     {'op': 'Column',
     'args': [
         {
          'op': 'Symbol'
          'args': ['t', 'var * { x : int32, y : int32 }', False]
         }
         'x']
     }]
 }

Simplify expresion using explicit names dictionary. In the example below we replace the Symbol node with the string 't'.

>>> tree = to_tree(t.x, names={t: 't'})
>>> tree 
{'op': 'Column', 'args': ['t', 'x']}

>>> from_tree(tree, namespace={'t': t})
t.x

blaze.server.server.from_tree(expr, namespace=None)¶

Convert core data structures to Blaze expression

Core data structure representations created by to_tree are converted back into Blaze expressions.

Parameters:	expr : dict

See also

to_tree

Examples

>>> t = symbol('t', 'var * {x: int32, y: int32}')
>>> tree = to_tree(t)
>>> tree 
{'op': 'Symbol',
 'args': ['t', 'var * { x : int32, y : int32 }', False]}

>>> from_tree(tree)
t

>>> tree = to_tree(t.x.sum())
>>> tree 
{'op': 'sum',
 'args': [
     {'op': 'Field',
     'args': [
         {
          'op': 'Symbol'
          'args': ['t', 'var * { x : int32, y : int32 }', False]
         }
         'x']
     }]
 }

>>> from_tree(tree)
sum(t.x)

Simplify expresion using explicit names dictionary. In the example below we replace the Symbol node with the string 't'.

>>> tree = to_tree(t.x, names={t: 't'})
>>> tree 
{'op': 'Field', 'args': ['t', 'x']}

>>> from_tree(tree, namespace={'t': t})
t.x

class blaze.server.client.Client(url, serial=SerializationFormat(name='json', loads=<function <lambda> at 0x105776950>, dumps=functools.partial(<function dumps at 0x102fe5950>, default=<dispatched json_dumps>)), verify_ssl=True, auth=None, **kwargs)¶

Client for Blaze Server

Provides programmatic access to datasets living on Blaze Server

Parameters:

url : str

URL of a Blaze server

serial : SerializationFormat, optional

The serialization format object to use. Defaults to JSON. A serialization format is an object that supports: name, loads, and dumps.

verify_ssl : bool, optional

Verify the ssl certificate from the server. This is enabled by default.

auth : tuple, optional

The username and password to use when connecting to the server. If not provided, no auth header will be sent.

See also

blaze.server.server.Server

Examples

>>> # This example matches with the docstring of ``Server``
>>> from blaze import Data
>>> c = Client('localhost:6363')
>>> t = Data(c) 

dshape¶: The datashape of the client

class blaze.expr.collections.Sort(*args, **kwargs)¶

Table in sorted order

Examples

>>> from blaze import symbol
>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> accounts.sort('amount', ascending=False).schema
dshape("{name: string, amount: int32}")

Some backends support sorting by arbitrary rowwise tables, e.g.

>>> accounts.sort(-accounts.amount) 

class blaze.expr.collections.Distinct(*args, **kwargs)¶

Remove duplicate elements from an expression

Parameters:

on : tuple of Field

The subset of fields or names of fields to be distinct on.

Examples

>>> from blaze import symbol
>>> t = symbol('t', 'var * {name: string, amount: int, id: int}')
>>> e = distinct(t)

>>> data = [('Alice', 100, 1),
...         ('Bob', 200, 2),
...         ('Alice', 100, 1)]

>>> from blaze.compute.python import compute
>>> sorted(compute(e, data))
[('Alice', 100, 1), ('Bob', 200, 2)]

Use a subset by passing on:

>>> import pandas as pd
>>> e = distinct(t, 'name')
>>> data = pd.DataFrame([['Alice', 100, 1],
...                      ['Alice', 200, 2],
...                      ['Bob', 100, 1],
...                      ['Bob', 200, 2]],
...                     columns=['name', 'amount', 'id'])
>>> compute(e, data)
    name  amount  id
0  Alice     100   1
1    Bob     100   1

class blaze.expr.collections.Head(*args, **kwargs)¶

First n elements of collection

See also

blaze.expr.collections.Tail

Examples

>>> from blaze import symbol
>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> accounts.head(5).dshape
dshape("5 * {name: string, amount: int32}")

class blaze.expr.collections.Merge(*args, **kwargs)¶

Merge many fields together

Examples

>>> from blaze import symbol
>>> accounts = symbol('accounts', 'var * {name: string, x: int, y: real}')
>>> merge(accounts.name, z=accounts.x + accounts.y).fields
['name', 'z']

class blaze.expr.collections.IsIn(*args, **kwargs)¶

Check if an expression contains values from a set.

Return a boolean expression indicating whether another expression contains values that are members of a collection.

Parameters:

expr : Expr

Expression whose elements to check for membership in keys

keys : Sequence

Elements to test against. Blaze stores this as a frozenset.

Examples

Check if a vector contains any of 1, 2 or 3:

>>> from blaze import symbol
>>> t = symbol('t', '10 * int64')
>>> expr = t.isin([1, 2, 3])
>>> expr.dshape
dshape("10 * bool")

blaze.expr.collections.isin(expr, keys)¶

Check if an expression contains values from a set.

Return a boolean expression indicating whether another expression contains values that are members of a collection.

Parameters:

expr : Expr

Expression whose elements to check for membership in keys

keys : Sequence

Elements to test against. Blaze stores this as a frozenset.

Examples

Check if a vector contains any of 1, 2 or 3:

>>> from blaze import symbol
>>> t = symbol('t', '10 * int64')
>>> expr = t.isin([1, 2, 3])
>>> expr.dshape
dshape("10 * bool")

blaze.expr.collections.distinct(expr, *on)¶

Remove duplicate elements from an expression

Parameters:

on : tuple of Field

The subset of fields or names of fields to be distinct on.

Examples

>>> from blaze import symbol
>>> t = symbol('t', 'var * {name: string, amount: int, id: int}')
>>> e = distinct(t)

>>> data = [('Alice', 100, 1),
...         ('Bob', 200, 2),
...         ('Alice', 100, 1)]

>>> from blaze.compute.python import compute
>>> sorted(compute(e, data))
[('Alice', 100, 1), ('Bob', 200, 2)]

Use a subset by passing on:

>>> import pandas as pd
>>> e = distinct(t, 'name')
>>> data = pd.DataFrame([['Alice', 100, 1],
...                      ['Alice', 200, 2],
...                      ['Bob', 100, 1],
...                      ['Bob', 200, 2]],
...                     columns=['name', 'amount', 'id'])
>>> compute(e, data)
    name  amount  id
0  Alice     100   1
1    Bob     100   1

blaze.expr.collections.merge(*exprs, **kwargs)¶

Merge many fields together

Examples

>>> from blaze import symbol
>>> accounts = symbol('accounts', 'var * {name: string, x: int, y: real}')
>>> merge(accounts.name, z=accounts.x + accounts.y).fields
['name', 'z']

blaze.expr.collections.head(child, n=10)¶

First n elements of collection

See also

blaze.expr.collections.Tail

Examples

>>> from blaze import symbol
>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> accounts.head(5).dshape
dshape("5 * {name: string, amount: int32}")

blaze.expr.collections.sort(child, key=None, ascending=True)¶

Sort a collection

Parameters:

key : str, list of str, or Expr

Defines by what you want to sort.

A single column string: t.sort('amount')

A list of column strings: t.sort(['name', 'amount'])

An expression: t.sort(-t.amount)

ascending : bool, optional

Determines order of the sort

class blaze.expr.collections.Join(*args, **kwargs)¶

Join two tables on common columns

Parameters:

lhs, rhs : Expr

Expressions to join

on_left : str, optional

The fields from the left side to join on. If no on_right is passed, then these are the fields for both sides.

on_right : str, optional

The fields from the right side to join on.

how : {‘inner’, ‘outer’, ‘left’, ‘right’}

What type of join to perform.

suffixes: pair of str

The suffixes to be applied to the left and right sides in order to resolve duplicate field names.

See also

blaze.expr.collections.Merge

Examples

>>> from blaze import symbol
>>> names = symbol('names', 'var * {name: string, id: int}')
>>> amounts = symbol('amounts', 'var * {amount: int, id: int}')

Join tables based on shared column name

>>> joined = join(names, amounts, 'id')

Join based on different column names

>>> amounts = symbol('amounts', 'var * {amount: int, acctNumber: int}')
>>> joined = join(names, amounts, 'id', 'acctNumber')

blaze.expr.collections.join(lhs, rhs, on_left=None, on_right=None, how='inner', suffixes=('_left', '_right'))¶

Join two tables on common columns

Parameters:

lhs, rhs : Expr

Expressions to join

on_left : str, optional

The fields from the left side to join on. If no on_right is passed, then these are the fields for both sides.

on_right : str, optional

The fields from the right side to join on.

how : {‘inner’, ‘outer’, ‘left’, ‘right’}

What type of join to perform.

suffixes: pair of str

The suffixes to be applied to the left and right sides in order to resolve duplicate field names.

See also

blaze.expr.collections.Merge

Examples

>>> from blaze import symbol
>>> names = symbol('names', 'var * {name: string, id: int}')
>>> amounts = symbol('amounts', 'var * {amount: int, id: int}')

Join tables based on shared column name

>>> joined = join(names, amounts, 'id')

Join based on different column names

>>> amounts = symbol('amounts', 'var * {amount: int, acctNumber: int}')
>>> joined = join(names, amounts, 'id', 'acctNumber')

blaze.expr.collections.transform(t, replace=True, **kwargs)¶

Add named columns to table

>>> from blaze import symbol
>>> t = symbol('t', 'var * {x: int, y: int}')
>>> transform(t, z=t.x + t.y).fields
['x', 'y', 'z']

class blaze.expr.collections.Concat(*args, **kwargs)¶

Stack tables on common columns

Parameters:

lhs, rhs : Expr

Collections to concatenate

axis : int, optional

The axis to concatenate on.

See also

blaze.expr.collections.Merge

Examples

>>> from blaze import symbol

Vertically stack tables:

>>> names = symbol('names', '5 * {name: string, id: int32}')
>>> more_names = symbol('more_names', '7 * {name: string, id: int32}')
>>> stacked = concat(names, more_names)
>>> stacked.dshape
dshape("12 * {name: string, id: int32}")

Vertically stack matrices:

>>> mat_a = symbol('a', '3 * 5 * int32')
>>> mat_b = symbol('b', '3 * 5 * int32')
>>> vstacked = concat(mat_a, mat_b, axis=0)
>>> vstacked.dshape
dshape("6 * 5 * int32")

Horizontally stack matrices:

>>> hstacked = concat(mat_a, mat_b, axis=1)
>>> hstacked.dshape
dshape("3 * 10 * int32")

blaze.expr.collections.concat(lhs, rhs, axis=0)¶

Stack tables on common columns

Parameters:

lhs, rhs : Expr

Collections to concatenate

axis : int, optional

The axis to concatenate on.

See also

blaze.expr.collections.Merge

Examples

>>> from blaze import symbol

Vertically stack tables:

>>> names = symbol('names', '5 * {name: string, id: int32}')
>>> more_names = symbol('more_names', '7 * {name: string, id: int32}')
>>> stacked = concat(names, more_names)
>>> stacked.dshape
dshape("12 * {name: string, id: int32}")

Vertically stack matrices:

>>> mat_a = symbol('a', '3 * 5 * int32')
>>> mat_b = symbol('b', '3 * 5 * int32')
>>> vstacked = concat(mat_a, mat_b, axis=0)
>>> vstacked.dshape
dshape("6 * 5 * int32")

Horizontally stack matrices:

>>> hstacked = concat(mat_a, mat_b, axis=1)
>>> hstacked.dshape
dshape("3 * 10 * int32")

class blaze.expr.collections.Tail(*args, **kwargs)¶

Last n elements of collection

See also

blaze.expr.collections.Head

Examples

>>> from blaze import symbol
>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> accounts.tail(5).dshape
dshape("5 * {name: string, amount: int32}")

blaze.expr.collections.tail(child, n=10)¶

Last n elements of collection

See also

blaze.expr.collections.Head

Examples

>>> from blaze import symbol
>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> accounts.tail(5).dshape
dshape("5 * {name: string, amount: int32}")

class blaze.expr.collections.Shift(*args, **kwargs)¶

Shift a column backward or forward by N elements

Parameters:

expr : Expr

The expression to shift. This expression’s dshape should be columnar

n : int

The number of elements to shift by. If n < 0 then shift backward, if n == 0 do nothing, else shift forward.

blaze.expr.collections.shift(expr, n)¶

Shift a column backward or forward by N elements

Parameters:

expr : Expr

The expression to shift. This expression’s dshape should be columnar

n : int

The number of elements to shift by. If n < 0 then shift backward, if n == 0 do nothing, else shift forward.

class blaze.expr.expressions.Apply(*args, **kwargs)¶

Apply an arbitrary Python function onto an expression

See also

blaze.expr.expressions.Map

Examples

>>> t = symbol('t', 'var * {name: string, amount: int}')
>>> h = t.apply(hash, dshape='int64')  # Hash value of resultant dataset

You must provide the datashape of the result with the dshape= keyword. For datashape examples see http://datashape.pydata.org/grammar.html#some-simple-examples

If using a chunking backend and your operation may be safely split and concatenated then add the splittable=True keyword argument

>>> t.apply(f, dshape='...', splittable=True) 

class blaze.expr.expressions.Coerce(*args, **kwargs)¶

Coerce an expression to a different type.

Examples

>>> t = symbol('t', '100 * float64')
>>> t.coerce(to='int64')
t.coerce(to='int64')
>>> t.coerce('float32')
t.coerce(to='float32')
>>> t.coerce('int8').dshape
dshape("100 * int8")

class blaze.expr.expressions.ElemWise(*args, **kwargs)¶

Elementwise operation.

The shape of this expression matches the shape of the child.

class blaze.expr.expressions.Expr(*args, **kwargs)¶

Symbolic expression of a computation

All Blaze expressions (Join, By, Sort, ...) descend from this class. It contains shared logic and syntax. It in turn inherits from Node which holds all tree traversal logic

map(func, schema=None, name=None)¶

Map an arbitrary Python function across elements in a collection

See also

blaze.expr.expresions.Apply

Examples

>>> from datetime import datetime

>>> t = symbol('t', 'var * {price: real, time: int64}')  # times as integers
>>> datetimes = t.time.map(datetime.utcfromtimestamp)

Optionally provide extra schema information

>>> datetimes = t.time.map(datetime.utcfromtimestamp,
...                           schema='{time: datetime}')

class blaze.expr.expressions.Field(*args, **kwargs)¶

A single field from an expression.

Get a single field from an expression with record-type schema. We store the name of the field in the _name attribute.

Examples

>>> points = symbol('points', '5 * 3 * {x: int32, y: int32}')
>>> points.x.dshape
dshape("5 * 3 * int32")

For fields that aren’t valid Python identifiers, use [] syntax:

>>> points = symbol('points', '5 * 3 * {"space station": float64}')
>>> points['space station'].dshape
dshape("5 * 3 * float64")

class blaze.expr.expressions.Label(*args, **kwargs)¶

An expression with a name.

See also

blaze.expr.expressions.ReLabel

Examples

>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> expr = accounts.amount * 100
>>> expr._name
'amount'
>>> expr.label('new_amount')._name
'new_amount'

class blaze.expr.expressions.Map(*args, **kwargs)¶

Map an arbitrary Python function across elements in a collection

See also

blaze.expr.expresions.Apply

Examples

>>> from datetime import datetime

>>> t = symbol('t', 'var * {price: real, time: int64}')  # times as integers
>>> datetimes = t.time.map(datetime.utcfromtimestamp)

Optionally provide extra schema information

>>> datetimes = t.time.map(datetime.utcfromtimestamp,
...                           schema='{time: datetime}')

class blaze.expr.expressions.Projection(*args, **kwargs)¶

Select a subset of fields from data.

See also

blaze.expr.expressions.Field

Examples

>>> accounts = symbol('accounts',
...                   'var * {name: string, amount: int, id: int}')
>>> accounts[['name', 'amount']].schema
dshape("{name: string, amount: int32}")
>>> accounts[['name', 'amount']]
accounts[['name', 'amount']]

class blaze.expr.expressions.ReLabel(*args, **kwargs)¶

Table with same content but with new labels

See also

blaze.expr.expressions.Label

Notes

When names are not valid Python names, such as integers or string with spaces, you must pass a dictionary to relabel. For example

>>> s = symbol('s', 'var * {"0": int64}')
>>> s.relabel({'0': 'foo'})
s.relabel({'0': 'foo'})
>>> t = symbol('t', 'var * {"whoo hoo": ?float32}')
>>> t.relabel({"whoo hoo": 'foo'})
t.relabel({'whoo hoo': 'foo'})

Examples

>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> accounts.schema
dshape("{name: string, amount: int32}")
>>> accounts.relabel(amount='balance').schema
dshape("{name: string, balance: int32}")
>>> accounts.relabel(not_a_column='definitely_not_a_column')
Traceback (most recent call last):
    ...
ValueError: Cannot relabel non-existent child fields: {'not_a_column'}
>>> s = symbol('s', 'var * {"0": int64}')
>>> s.relabel({'0': 'foo'})
s.relabel({'0': 'foo'})
>>> s.relabel(0='foo') 
Traceback (most recent call last):
    ...
SyntaxError: keyword can't be an expression

class blaze.expr.expressions.Selection(*args, **kwargs)¶

Filter elements of expression based on predicate

Examples

>>> accounts = symbol('accounts',
...                   'var * {name: string, amount: int, id: int}')
>>> deadbeats = accounts[accounts.amount < 0]

class blaze.expr.expressions.SimpleSelection(*args, **kwargs)¶: Internal selection class that does not treat the predicate as an input.

class blaze.expr.expressions.Slice(*args, **kwargs)¶

Elements start until stop. On many backends, a step parameter is also allowed.

Examples

>>> from blaze import symbol
>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> accounts[2:7].dshape
dshape("5 * {name: string, amount: int32}")
>>> accounts[2:7:2].dshape
dshape("3 * {name: string, amount: int32}")

class blaze.expr.expressions.Symbol(name, dshape, token=0)¶

Symbolic data. The leaf of a Blaze expression

Examples

>>> points = symbol('points', '5 * 3 * {x: int, y: int}')
>>> points
points
>>> points.dshape
dshape("5 * 3 * {x: int32, y: int32}")

blaze.expr.expressions.apply(expr, func, dshape, splittable=False)¶

Apply an arbitrary Python function onto an expression

See also

blaze.expr.expressions.Map

Examples

>>> t = symbol('t', 'var * {name: string, amount: int}')
>>> h = t.apply(hash, dshape='int64')  # Hash value of resultant dataset

You must provide the datashape of the result with the dshape= keyword. For datashape examples see http://datashape.pydata.org/grammar.html#some-simple-examples

If using a chunking backend and your operation may be safely split and concatenated then add the splittable=True keyword argument

>>> t.apply(f, dshape='...', splittable=True) 

blaze.expr.expressions.coerce(expr, to)¶

Coerce an expression to a different type.

Examples

>>> t = symbol('t', '100 * float64')
>>> t.coerce(to='int64')
t.coerce(to='int64')
>>> t.coerce('float32')
t.coerce(to='float32')
>>> t.coerce('int8').dshape
dshape("100 * int8")

blaze.expr.expressions.label(expr, lab)¶

An expression with a name.

See also

blaze.expr.expressions.ReLabel

Examples

>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> expr = accounts.amount * 100
>>> expr._name
'amount'
>>> expr.label('new_amount')._name
'new_amount'

blaze.expr.expressions.label(expr, lab)

An expression with a name.

See also

blaze.expr.expressions.ReLabel

Examples

>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> expr = accounts.amount * 100
>>> expr._name
'amount'
>>> expr.label('new_amount')._name
'new_amount'

blaze.expr.expressions.ndim(expr)¶

Number of dimensions of expression

>>> symbol('s', '3 * var * int32').ndim
2

blaze.expr.expressions.projection(expr, names)¶

Select a subset of fields from data.

See also

blaze.expr.expressions.Field

Examples

>>> accounts = symbol('accounts',
...                   'var * {name: string, amount: int, id: int}')
>>> accounts[['name', 'amount']].schema
dshape("{name: string, amount: int32}")
>>> accounts[['name', 'amount']]
accounts[['name', 'amount']]

blaze.expr.expressions.relabel(child, labels=None, **kwargs)¶

Table with same content but with new labels

See also

blaze.expr.expressions.Label

Notes

When names are not valid Python names, such as integers or string with spaces, you must pass a dictionary to relabel. For example

>>> s = symbol('s', 'var * {"0": int64}')
>>> s.relabel({'0': 'foo'})
s.relabel({'0': 'foo'})
>>> t = symbol('t', 'var * {"whoo hoo": ?float32}')
>>> t.relabel({"whoo hoo": 'foo'})
t.relabel({'whoo hoo': 'foo'})

Examples

>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> accounts.schema
dshape("{name: string, amount: int32}")
>>> accounts.relabel(amount='balance').schema
dshape("{name: string, balance: int32}")
>>> accounts.relabel(not_a_column='definitely_not_a_column')
Traceback (most recent call last):
    ...
ValueError: Cannot relabel non-existent child fields: {'not_a_column'}
>>> s = symbol('s', 'var * {"0": int64}')
>>> s.relabel({'0': 'foo'})
s.relabel({'0': 'foo'})
>>> s.relabel(0='foo') 
Traceback (most recent call last):
    ...
SyntaxError: keyword can't be an expression

blaze.expr.expressions.selection(table, predicate)¶

Filter elements of expression based on predicate

Examples

>>> accounts = symbol('accounts',
...                   'var * {name: string, amount: int, id: int}')
>>> deadbeats = accounts[accounts.amount < 0]

blaze.expr.expressions.symbol(*args, **kwargs)¶

Symbolic data. The leaf of a Blaze expression

Examples

>>> points = symbol('points', '5 * 3 * {x: int, y: int}')
>>> points
points
>>> points.dshape
dshape("5 * 3 * {x: int32, y: int32}")

class blaze.expr.reductions.Reduction(_child, axis=None, keepdims=False)¶

A column-wise reduction

Blaze supports the same class of reductions as NumPy and Pandas.

sum, min, max, any, all, mean, var, std, count, nunique

Examples

>>> from blaze import symbol
>>> t = symbol('t', 'var * {name: string, amount: int, id: int}')
>>> e = t['amount'].sum()

>>> data = [['Alice', 100, 1],
...         ['Bob', 200, 2],
...         ['Alice', 50, 3]]

>>> from blaze.compute.python import compute
>>> compute(e, data)
350

class blaze.expr.reductions.Summary(_child, names, values, axis=None, keepdims=False)¶

A collection of named reductions

Examples

>>> from blaze import symbol
>>> t = symbol('t', 'var * {name: string, amount: int, id: int}')
>>> expr = summary(number=t.id.nunique(), sum=t.amount.sum())

>>> data = [['Alice', 100, 1],
...         ['Bob', 200, 2],
...         ['Alice', 50, 1]]

>>> from blaze import compute
>>> compute(expr, data)
(2, 350)

class blaze.expr.reductions.count(_child, axis=None, keepdims=False)¶: The number of non-null elements

class blaze.expr.reductions.nelements(_child, axis=None, keepdims=False)¶

Compute the number of elements in a collection, including missing values.

See also

blaze.expr.reductions.count: compute the number of non-null elements

Examples

>>> from blaze import symbol
>>> t = symbol('t', 'var * {name: string, amount: float64}')
>>> t[t.amount < 1].nelements()
nelements(t[t.amount < 1])

class blaze.expr.reductions.std(child, unbiased=False, *args, **kwargs)¶

Standard Deviation

Parameters:

child : Expr

An expression

unbiased : bool, optional

Compute the square root of an unbiased estimate of the population variance if this is True.

Warning

This does not return an unbiased estimate of the population standard deviation.

See also

var

blaze.expr.reductions.summary(keepdims=False, axis=None, **kwargs)¶

A collection of named reductions

Examples

>>> from blaze import symbol
>>> t = symbol('t', 'var * {name: string, amount: int, id: int}')
>>> expr = summary(number=t.id.nunique(), sum=t.amount.sum())

>>> data = [['Alice', 100, 1],
...         ['Bob', 200, 2],
...         ['Alice', 50, 1]]

>>> from blaze import compute
>>> compute(expr, data)
(2, 350)

class blaze.expr.reductions.var(child, unbiased=False, *args, **kwargs)¶

Variance

Parameters:

child : Expr

An expression

unbiased : bool, optional

Compute an unbiased estimate of the population variance if this is True. In NumPy and pandas, this parameter is called ddof (delta degrees of freedom) and is equal to 1 for unbiased and 0 for biased.

blaze.expr.reductions.vnorm(expr, ord=None, axis=None, keepdims=False)¶

Vector norm

See np.linalg.norm

class blaze.expr.arrays.Transpose(*args, **kwargs)¶

Transpose dimensions in an N-Dimensional array

Examples

>>> x = symbol('x', '10 * 20 * int32')
>>> x.T
transpose(x)
>>> x.T.shape
(20, 10)

Specify axis ordering with axes keyword argument

>>> x = symbol('x', '10 * 20 * 30 * int32')
>>> x.transpose([2, 0, 1])
transpose(x, axes=[2, 0, 1])
>>> x.transpose([2, 0, 1]).shape
(30, 10, 20)

class blaze.expr.arrays.TensorDot(*args, **kwargs)¶

Dot Product: Contract and sum dimensions of two arrays

>>> x = symbol('x', '20 * 20 * int32')
>>> y = symbol('y', '20 * 30 * int32')

>>> x.dot(y)
tensordot(x, y)

>>> tensordot(x, y, axes=[0, 0])
tensordot(x, y, axes=[0, 0])

blaze.expr.arrays.dot(lhs, rhs)¶

Dot Product: Contract and sum dimensions of two arrays

>>> x = symbol('x', '20 * 20 * int32')
>>> y = symbol('y', '20 * 30 * int32')

>>> x.dot(y)
tensordot(x, y)

>>> tensordot(x, y, axes=[0, 0])
tensordot(x, y, axes=[0, 0])

blaze.expr.arrays.transpose(expr, axes=None)¶

Transpose dimensions in an N-Dimensional array

Examples

>>> x = symbol('x', '10 * 20 * int32')
>>> x.T
transpose(x)
>>> x.T.shape
(20, 10)

Specify axis ordering with axes keyword argument

>>> x = symbol('x', '10 * 20 * 30 * int32')
>>> x.transpose([2, 0, 1])
transpose(x, axes=[2, 0, 1])
>>> x.transpose([2, 0, 1]).shape
(30, 10, 20)

blaze.expr.arrays.tensordot(lhs, rhs, axes=None)¶

Dot Product: Contract and sum dimensions of two arrays

>>> x = symbol('x', '20 * 20 * int32')
>>> y = symbol('y', '20 * 30 * int32')

>>> x.dot(y)
tensordot(x, y)

>>> tensordot(x, y, axes=[0, 0])
tensordot(x, y, axes=[0, 0])

class blaze.expr.arithmetic.Arithmetic(lhs, rhs)¶: Super class for arithmetic operators like add or mul

class blaze.expr.math.notnull(child)¶

Return whether an expression is not null

Examples

>>> from blaze import symbol, compute
>>> s = symbol('s', 'var * int64')
>>> expr = notnull(s)
>>> expr.dshape
dshape("var * bool")
>>> list(compute(expr, [1, 2, None, 3]))
[True, True, False, True]

class blaze.expr.math.UnaryMath(child)¶: Mathematical unary operator with real valued dshape like sin, or exp

class blaze.expr.broadcast.Broadcast(*args, **kwargs)¶

Fuse scalar expressions over collections

Given elementwise operations on collections, e.g.

>>> from blaze import sin
>>> a = symbol('a', '100 * int')
>>> t = symbol('t', '100 * {x: int, y: int}')

>>> expr = sin(a) + t.y**2

It may be best to represent this as a scalar expression mapped over a collection

>>> sa = symbol('a', 'int')
>>> st = symbol('t', '{x: int, y: int}')

>>> sexpr = sin(sa) + st.y**2

>>> expr = Broadcast((a, t), (sa, st), sexpr)

This provides opportunities for optimized computation.

In practice, expressions are often collected into Broadcast expressions automatically. This class is mainly intented for internal use.

blaze.expr.broadcast.scalar_symbols(exprs)¶

Gives a sequence of scalar symbols to mirror these expressions

Examples

>>> x = symbol('x', '5 * 3 * int32')
>>> y = symbol('y', '5 * 3 * int32')

>>> xx, yy = scalar_symbols([x, y])

>>> xx._name, xx.dshape
('x', dshape("int32"))
>>> yy._name, yy.dshape
('y', dshape("int32"))

class blaze.expr.datetime.DateTime(*args, **kwargs)¶: Superclass for datetime accessors

class blaze.expr.split_apply_combine.By(*args, **kwargs)¶

Split-Apply-Combine Operator

Examples

>>> from blaze import symbol
>>> t = symbol('t', 'var * {name: string, amount: int, id: int}')
>>> e = by(t['name'], total=t['amount'].sum())

>>> data = [['Alice', 100, 1],
...         ['Bob', 200, 2],
...         ['Alice', 50, 3]]

>>> from blaze.compute.python import compute
>>> sorted(compute(e, data))
[('Alice', 150), ('Bob', 200)]

blaze.expr.split_apply_combine.count_values(expr, sort=True)¶

Count occurrences of elements in this column

Sort by counts by default Add sort=False keyword to avoid this behavior.