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Simple toolset with Python type hints made to deserve some needs on some Python projects.

Project description

Python tools

Simple toolset with Python type hints made to deserve my needs on some Python projects.

Both Python anc C++ implementations are in plans:

  • Any Python implementation is cross-platform but achieves less average performance goals
  • C++ implementations are currently supported only for Linux and provided in this repository in two ways:
    • Prebuilt .so files, so in average case you can just "git clone" and use Cxx-implemented classes in Python project
    • Source code (try your luck and build it for Windows)

Have fun!

If you find this code useful, I will be glad if you use it in your GPL3-compatible licensed project.

"Why GPL-3. Author, are you too proud?"

Nope. It's just that I'm fighting for free software, and any possibility that someone else is using my code on a project that people, myself included, will have to pay for is unacceptable. My code is neither perfect nor revolutionary. But the world is crazy, you know

Any help and criticism is greatly appreciated.


Latest version is available on PyPi:

pip install easy-pytools


  • SQL shit (Oh God. PYTHON ONLY) see docs
    • Basic Table operations
      • Single table CREATE
        • Single PRIMARY KEY
        • Foreign Keys
        • Multiple-field PRIMARY KEY
      • Single table INSERT
      • Single table SELECT
      • Single table UPDATE
      • Single table DELETE
      • GROUP BY expressions
    • Basic Table compositions
      • Automatical INNER JOIN on foreign keys
      • Cartesian product
      • SELECT from table composition
      • Treat Composition as Table
    • Advanced Table operations
      • WHERE selection
      • Ordering
      • Aggregate functions
      • HAVING section with auto-detection inside WHERE
      • Table UNION
      • Altering table
      • Add constraints to table
    • Views
    • Other SQL shit
      • Convert JSON-like data mappings to DBase, Table etc
      • Convert DBase, Table to data mappings
  • Pseudo structures
    • Stack (C++)
    • Queue (C++)
    • Prioritized Queue
      • Python
      • C++
  • Basic data structures see docs
    • Plain
      • LinkedList
        • Python
        • C++
      • LinkedList with O(1) index access (C++)
    • Trees
      • Tree of arbitrary objects
        • Python
        • C++
      • Binary tree with post-insertion balance
        • Python
        • C++
      • Prefix tree for sequential search
        • Python
        • C++
      • Red-black tree
        • Python
        • C++
      • AVL tree
        • Python
        • C++
      • B+ tree
        • Python
        • C++
    • Graphs
      • Graph (Python)
      • Oriented graph (Python)
      • Network with flows (Python)
  • Class and object enhancements
    • DataTransferObject class decorator (Python) see docs


Python implementations require the following:

  • Python >= 3.10 < 4.0

C++ implementations require the following to be built:

  • Cmake >= 3.20
  • PythonLibs >= 3.10
  • Boost with Python package

SQL shit

Database, Table and Query wrapper for SQLite with syntax inspired by Numpy.


Also known as "Why am I going to use your shit instead of SQL shit?"

Just compare theese two equal operations:

SQL shit

        FROM (((Users INNER JOIN User_Car ON = User_Car.user_id) INNER JOIN Cars ON Cars.reg = User_Car.car_reg) INNER JOIN Vendors ON = Cars.vendor_id) WHERE ((( = "Russia") OR ( = "US")));

My sql shit

(users & user_car & cars & vendors)['name', 'model', 'country'] == ((),(),("Russia", "US"))
# or with equal method-powered syntax:
users.INNER(user_car).INNER(cars).INNER(vendors).SELECT(['name', 'model', 'country']).WHERE_EQ(((),(),("Russia", "US")))

Quick Start

1. Find or create your SQLite dababase file

from easy_pytools.sql import DBase

your_base = DBase("tests/sql_test.sql")

2. Get existing tables names


3. Create new table

create table schema

Let us imagine that there is already Vendors table with primary key 'id'

schema = {
    "id": "integer primary key autoincrement",
    "name": "varchar(20)",
    "vendor_id": "integer",
    "foreign key(vendor_id)":"references Vendors(id)"

create new table

printers = your_base.new_table("Printers", schema)

Now the 'printers' variable references to 'Table' object that provides all the SQL shit for the 'Printers' table of database.

Table<Printers of tests/sql_test.sql>

4. Get existing table from base

vendors = your_base.table("Vendors")

Now the 'vendors' variable references to 'Table' object that provides all the SQL shit for the 'Vendors' table of database.

Table<Vendors of tests/sql_test.sql, Real>

5. Get information about table

    '': Field<0, id, INTEGER of Table<Printers of tests/sql_test.sql, Real>>,
    '': Field<1, name, varchar(20) of Table<Printers of tests/sql_test.sql, Real>>,
    'Printers.vendor_id': Field<2, vendor_id, INTEGER of Table<Printers of tests/sql_test.sql, Real>>

    'Printers.vendor_id': TableFK<Field<2, vendor_id, INTEGER of Table<Printers of tests/sql_test.sql, Real>> -> Field<0, id, INTEGER of Table<Vendors of tests/sql_test.sql, Real>>>

    Table<Vendors of tests/sql_test.sql, Real>

6. Add rows to table

printers << {"name":"Canon L100", "vendor_id": 1}
printers << {"name":"Canon L200", "vendor_id": 1}
# or use instant method call:
printers.INSERT({"name":"Canon L200", "vendor_id": 1})

Foreign Key constraints checked as long as Python sqlite3 checks them.

Also, if your table has no Primary Keys with Autoincrement, you can add rows as tuples:

table << (10, "Hello", 20)

In that case values order must match fields order of table.

7. Select from table

    (1, 'Canon L100', 1),
    (2, 'Canon L200', 1)

# or use instant method call:
    ('Canon L100',)
    ('Canon L200',)

7.1. Ordering

You can order selected rows by fields with following syntax:

printers[:] / ('name',)
# or with instant method call:

7.2. Grouping

You can group selected rows by fields with following syntaxL

printers[:] % ('country',)
# or with instant method call:

8. Table compositioning

Any table composition result is 'Table' object with some restrictions.

So you can, f.ex., select from table composition just as from real table.

Cartesian product

printers_vendors = printers * vendors
# or with instant method call:
printers_vendors = printers.AND(vendors)

Table<Printers_x_Vendors of tests/sql_test.sql, Composition>
[(1, 'Canon L100', 1, 1, 'Tayouta', 'Japan'),
 (1, 'Canon L100', 1, 2, 'Lada', 'Russia'),
 (1, 'Canon L100', 1, 3, 'KAMAZ', 'Russia'),
 (2, 'Canon L200', 1, 1, 'Tayouta', 'Japan'),
 (2, 'Canon L200', 1, 2, 'Lada', 'Russia'),
 (2, 'Canon L200', 1, 3, 'KAMAZ', 'Russia')]

JOIN tables

Joining tables may be done two ways:

  • automatical join on foreign key with cute syntax
  • join on any keys equalities with method


# Foreign key to composite tables will be automatically detected
# Otherwise exception will be thrown:
printers_vendors = printers & vendors
# or use instant method call:
printers_vendors = printers.INNER(vendors)
# can even manually specify keys to join table:
printers_vendors = printers.join(vendors, 'INNER', printers.f_vendor_id, vendors.f_id)

Table<Printers_INNER_Vendors of tests/sql_test.sql, Composition>
{'': Field<0, id, INTEGER of Table<Printers of tests/sql_test.sql, Real>>,
 '': Field<1, name, varchar(20) of Table<Printers of tests/sql_test.sql, Real>>,
 'Printers.vendor_id': Field<2, vendor_id, INTEGER of Table<Printers of tests/sql_test.sql, Real>>,
 '': Field<0, id, INTEGER of Table<Vendors of tests/sql_test.sql, Real>>,
 '': Field<1, name, varchar(20) of Table<Vendors of tests/sql_test.sql, Real>>,
 '': Field<2, country, varchar(20) of Table<Vendors of tests/sql_test.sql, Real>>}
    (1, 'Canon L100', 1, 1, 'Tayouta', 'Japan'),
    (2, 'Canon L200', 1, 1, 'Tayouta', 'Japan')


# Foreign key to composite tables will be automatically detected
# Otherwise exception will be thrown:
printers_vendors = vendors - printers
# or use instant method call:
printers_vendors = printers.LEFT(vendors)
# ...or you can manually specify keys to join table:
printers_vendors = vendors.join(printers, 'LEFT', printers.f_id, printers.f_vendor_id)

Table<Printers_LEFT_Vendors of tests/sql_test.sql, Composition>
{'': Field<0, id, INTEGER of Table<Printers of tests/sql_test.sql, Real>>,
 '': Field<1, name, varchar(20) of Table<Printers of tests/sql_test.sql, Real>>,
 'Printers.vendor_id': Field<2, vendor_id, INTEGER of Table<Printers of tests/sql_test.sql, Real>>,
 '': Field<0, id, INTEGER of Table<Vendors of tests/sql_test.sql, Real>>,
 '': Field<1, name, varchar(20) of Table<Vendors of tests/sql_test.sql, Real>>,
 '': Field<2, country, varchar(20) of Table<Vendors of tests/sql_test.sql, Real>>}
    (1, 'Canon L100', 1, 1, 'Tayouta', 'Japan'),
    (2, 'Canon L200', 1, 1, 'Tayouta', 'Japan'),
    (None, None, None, 2, 'Lada', 'Russia'),
    (None, None, None, 3, 'KAMAZ', 'Russia')


Is supported by my lib, but not supported by Python sqlite3. When Python's sqlite3 achieves this support, my lib will work out-of-the-box with that.

printers_vendors = printers ^ vendors
printers_vendors = printers.FULL(vendors)

9. Field access

You can access fields from tables. It is useful, f.ex, if you want to use join method, which requires TableField object as ref, not string field name.

Any field within table can be accessed as TableField reference with that syntax:



Field<1, name, varchar(20) of Table<Printers of tests/sql_test.sql, Real>>

If your field name is, f.ex, "f_count", access it this way:


10. Selection coditions

There are two ways to combine and specify selection conditions:

  • AND combination of conditions is a list of conditions;
  • OR combinations is a tuple of conditions;

To limit rows with field value, you have to specify conditions for all fields mentioned in selection.

Empty tuple means no limitation ob field.

There are several condition types:

  • Greater -- ">" operator (or WHERE_GT method)
  • Equals -- "==" operator (or WHERE_EQ method)
  • Lesser -- "<" operator (or WHERE_LT metho)

To limit selection, use one of theese operators with selection on left and limitation on right:

# Selection with 'country' from vendors without limitations:
# Selection with 'country' and value limitations
vendors['country'] == ("Russia",)
# or even such:

# Select country, id from vendors where country = "Russia" or id = 2:
vendors['country', 'id'] == ("Russia", 2)
vendors['country', 'id'].WHERE_EQ(("Russia", 2))

# Select country, id from vendors where country = "Russia" and id = 2:
vendors['country', 'id'] == ['Russia', 2]
vendors['country', 'id'].WHERE_EQ(["Russia", 2])

# Select id, country from vendors where country = "Russia" or country = "Japan":
vendors['id', 'country'] == [(), ("Russia","Japan"),]
vendors['id', 'country'].WHERE_EQ([(), ("Russia","Japan"),])

# Select id, country, name from vendors where (country = "Russia" or country = "Japan") or (id = 10)
vendors['id', 'country'] == (10, ("Russia","Japan"),)
vendors['id', 'country'].WHERE_EQ((10, ("Russia","Japan"),))

# Select id, country, name from vendors where (country = "Russia" or country = "Japan") and (id = 10 or id = 20)
vendors['id', 'country'] == [(10, 20), ("Russia","Japan"),]
vendors['id', 'country'].WHERE_EQ([(10, 20), ("Russia","Japan"),])

You can also combine different comparisons with AND operator:

# select id, country from vandors where (id = 10 or country = "Japan") AND (id < 20):
(vendors['id'] == (10, "Japan")) < (20,)
# these two are equal to each other and to the previous one
vendors['id'].WHERE_EQ((10, "Japan")).WHERE_LT((20,))
vendors['id'].WHERE_EQ((10, "Japan")).AND.WHERE_LT((20,))

To combine different comparisons with OR, use special property:

# select id, country from vandors where (id = 10 or country = "Japan") OR (id < 20):
(vendors['id'] == (10, "Japan")).OR < (20,)
vendors['id'].WHERE_EQ((10, "JAPAN")).OR.WHERE_LT((20,))

Note, that every selection is a SelectQuery object. To select values from database, call selection:

        FROM Vendors ;

[(1, 'Tayouta', 'Japan'), (2, 'Lada', 'Russia'), (3, 'KAMAZ', 'Russia')]

q = vendors['id', 'name'] > (1,)
# or use a method:
q = vendors['id', 'name'].WHERE_GT((1,))

        FROM Vendors WHERE (( > 1));
[(2, 'Lada'), (3, 'KAMAZ')]

11. Update values in table

As for now, UPDATE operation is very similar to SELECT: you have to select rows to update and specify fields to update in selected rows.

So, update syntax is very similar to insert syntax, just for SelectQuery instead of Table:

# Update values with tuple. You must specify all values in same order as they are in selection:
(vendors['name'] == ("Tayouta",)) << ("Tamoyo",)
(vendors['name'].WHERE_EQ(("Tayouta",))) << ("Tamoyo",) # etc..

# Wrong assignment type, throws exception:
vendors['id'] == (10,)) << ("Tamoyo",) # Exception
# Wrong assignment shape, throws exception:
vendors['id', 'name'] == ((10, 2),)) << ("Tamoyo",) # Exception
# Gaps are NOT SUPPORTED due to a logical deviation of operation:
vendors['id', 'name'] == ((10, 2),)) << ((), "Tamoyo",) # Excption

# Update values with dict. You can specify any field assignment:
(vendors['id'] == ((2, 3),)) << {"name": "Tamoyo"}
(vendors['id', 'name'] == ((2, 3),)) << {"name": "Tamoyo"}

12. Delete rows from table

As for now, DELETE operation is very similar to SELECT: you have to select rows to delete and negotate SelectQuery object or use DELETE method to get DeleteQueryObject.

Note, that delete query does not support such things as joining, grouping etc. Only WHERE considions are supported.

Also, you can only delete rows from real tabled, not from views of table compositions.

# Delete all vendors with name "Tamoyo"
-(vendors['name'] == ("Tamoyo",))
# or
(vendoes['name'] == ("Tamoyo",)).DELETE()

# Only real tables supported (exception will be thrown):
((vendors & printers)['name'] == ("Tamoyo",)).DELETE()

All expressions and operations of SelectQuery except of WHERE will be ignored when creating DELETE query.

13. Aggregate functions

Aggregate functions are available in separate submodule:

from easy_pytools.sql.aggregate import *

Use aggregates instead of regular field names in selection query:

(printers & vendors)[COUNT('')]

# or you can use group by expressions as well:
(printers & vendors)[COUNT(''), 'country'] % ('country',)
(printers & vendors)[COUNT(''), 'country'].GROUPBY(('country',))

13.1. Aggregate function selection conditions

Just set conditions like to regular fields, selection object would automatically detect calculated fields and put them to HAVING section of query:

((printers & vendors)[COUNT(''), 'country'] % ('country',)) == [2, ("Japan", "Russia")]
(printers & vendors)[COUNT(''), 'country'].GROUPBY(('country',)).WHERE_EQ([2, ("Japan", "Russia")])
        FROM (Printers INNER JOIN Vendors ON = Printers.vendor_id) WHERE ((( = "Japan") OR ( = "Russia"))) GROUP BY HAVING ((COUNT( = 2));

14. Drop table

# or

Advanced stuff

Convert your data mapping to a DBase or Table

You can automatically create Table or even a DBase from existing JSON-like data with parsing submodule.

Available conversions:

  • Convert Python type to SQL type with python_to_sql_type function
  • Convert data map to Table schema with map_to_schema function
  • Convert Sequence of data maps to Table schema with seq_to_schema function
  • Convert Sequence of data maps to Table with seq_to_table function
  • Convert Map of Sequences of data maps to DBase with map_to_base function

let me show you some examples:

from easy_pytools.sql.parsing import python_to_sql_type, map_to_schema, seq_to_schema, seq_to_table, map_to_base

Type parsing:

python_to_sql_type(12.4), python_to_sql_type("Hello")

Map to table schema:

data = {'name': "Vasya", 'age': 12.5}
{'name': 'TEXT', 'age': 'REAL'}

Sequence to schema:

data = [
    {'name': "Vasya", 'age': 12.5},
    {'name': "Markus", 'age': 12.5, 'sex':"M"},
    {'name': "Jane", 'age': 12.5, 'email':"v@om"}
 'name': 'TEXT',
 'age': 'REAL',
 'sex': 'TEXT',
 'email': 'TEXT'}
data = [
    {'pass':10298, 'name': "Vasya", 'age': 12.5},
    {'pass':2839, 'name': "Markus", 'age': 12.5, 'sex':"M"},
    {'pass':819279, 'name': "Jane", 'age': 12.5, 'email':"v@om"}
seq_to_schema(data, primary_field='pass')
 'name': 'TEXT',
 'age': 'REAL',
 'sex': 'TEXT',
 'email': 'TEXT'}

Data to Table:

Let us imagine that 'db' is a instance of existing DBase

data = [
    {'pass':10298, 'name': "Vasya", 'age': 12.5},
    {'pass':2839, 'name': "Markus", 'age': 12.5, 'sex':"M"},
    {'pass':819279, 'name': "Jane", 'age': 12.5, 'email':"v@om"}

table = seq_to_table(db, 'Users', data)

Table<Users of, Real>

{'': Field<0, id, INTEGER of Table<Users of, Real>>,
 'Users.pass': Field<1, pass, INTEGER of Table<Users of, Real>>,
 '': Field<2, name, TEXT of Table<Users of, Real>>,
 'Users.age': Field<3, age, REAL of Table<Users of, Real>>,
 '': Field<4, sex, TEXT of Table<Users of, Real>>,
 '': Field<5, email, TEXT of Table<Users of, Real>>}

Data to Dbase:

data = {"Users": [{"name": "Vasya", "age": 30, "email": "vasyan@com"},
                  {"name": "Petya", 'age': 42},],
        "Cars": [{'model': 'Lada v1'},
                 {'model': 'KAMAZ'},
                 {'model': 'Logan x590'}],

db = map_to_base("mybase.sql", data)


{'Cars', 'Users', 'sqlite_sequence'}

You can also specifu foreign keys with 'foreign_fields' parameter. (See type hints and docstrings).

Note, that Master tables must go first in data mapping, and slave tables must go after them.

Convert Table of DBase to data mapping

Get schema of table

from easy_pytools.sql.parsing import table_to_schema

    'name': 'varchar(20) NOT NULL ',
    'vendor_id': 'INTEGER NOT NULL ',
    'foreign key(vendor_id)': 'references Vendors(id)'

Extract data from Table

from easy_pytools.sql.parsing import table_to_list

    {'id': 1, 'name': 'Canon L100', 'vendor_id': 1},
    {'id': 2, 'name': 'Canon L200', 'vendor_id': 1}

Extract data from DBase

from easy_pytools.sql.parsing import base_to_dict

  'sqlite_sequence': [
    {'name': 'Vendors','seq': 4},
    {'name': 'Users','seq': 4},
    {'name': 'Printers','seq': 2}
  'Printers': [
    {'id': 1,'name': 'Canon L100','vendor_id': 1},
    {'id': 2,'name': 'Canon L200','vendor_id': 1}
  'Cars': [
    {'reg': 10001,'vendor_id': 1,'model': 'Rangerover'},
    {'reg': 10003,'vendor_id': 1,'model': 'Rangerover'},
    {'reg': 10004,'vendor_id': 1,'model': 'Rangerover'},
    {'reg': 10005,'vendor_id': 1,'model': 'Rangerover'},
    {'reg': 10011,'vendor_id': 1,'model': 'Raf4'},
    {'reg': 10012,'vendor_id': 1,'model': 'Raf4'},
    {'reg': 10013,'vendor_id': 1,'model': 'Raf4'},
    {'reg': 10021,'vendor_id': 1,'model': 'Cruiser'},
    {'reg': 10022,'vendor_id': 1,'model': 'Cruiser'},
    {'reg': 20011,'vendor_id': 2,'model': 'Priora'},
    {'reg': 20012,'vendor_id': 2,'model': 'Priora'},
    {'reg': 20021,'vendor_id': 2,'model': 'Vesta'},
    {'reg': 20022,'vendor_id': 2,'model': 'Vesta'},
    {'reg': 20023,'vendor_id': 2,'model': 'Vesta'},
    {'reg': 20031,'vendor_id': 2,'model': 'Kalina'},
    {'reg': 20032,'vendor_id': 2,'model': 'Kalina'},
    {'reg': 20033,'vendor_id': 2,'model': 'Kalina'},
    {'reg': 20034,'vendor_id': 2,'model': 'Kalina'},
    {'reg': 20035,'vendor_id': 2,'model': 'Kalina'}
  'User_Car': [
    {'user_id': 1,'car_reg': 10001},
    {'user_id': 1,'car_reg': 10003},
    {'user_id': 1,'car_reg': 10012},
    {'user_id': 1,'car_reg': 10013},
    {'user_id': 1,'car_reg': 10021},
    {'user_id': 2,'car_reg': 10021},
    {'user_id': 2,'car_reg': 20011},
    {'user_id': 2,'car_reg': 20021},
    {'user_id': 3,'car_reg': 20022},
    {'user_id': 3,'car_reg': 20034},
    {'user_id': 3,'car_reg': 10005},
    {'user_id': 4,'car_reg': 10011},
    {'user_id': 4,'car_reg': 19911}
  'Users': [
    {'id': 1,'name': 'Vasily','email': '','card': 101010},
    {'id': 2,'name': 'Dmitry','email': '','card': 10123798},
    {'id': 3,'name': 'Michael','email': '','card': 9827798},
    {'id': 4,'name': 'John','email': '','card': 623898}
  'Vendors': [
    {'id': 1,'name': 'Tamoyo','country': 'Japan'},
    {'id': 2,'name': 'Lada','country': 'Russia'},
    {'id': 3,'name': 'KAMAZ','country': 'Russia'},
    {'id': 4,'name': 'Haviko','country': 'Japan'}

Other things

You can always make any SQL request to DBase with query method.

There also some DBase methods, that can make querying more simple:

  • insert(target_table, target_fields, values)
  • select(source_table, fields)
  • drop(target_table)

DataTransferObject class enhancement


DTO pattern is great, but sometimes a bit annoying to implement.

In this part of easy_tools I try to implement some useful autimatizations for building DTO classes.


  • Class fields with metadata and constraints.
  • "Data contracts" -- descriptional dictionary with metadata, useful for building auto-generated UI
  • Type checking

Quick Start

import transfer_object decorator and create meta-class with default values:

from easy_pytools.dto import transfer_object

class Person:
    name = "Noname"
    sex = "M"
    age = 0

The value you set is the default value. It is required to extract field type. Do not set 'None' - this is NoneType.

Lets check some information about our class:

p1 = Person()
    {'field': 'name', 'name': 'name', 'type': <class 'str'>, 'default': 'Noname', 'value': 'Noname', 'allowed': ()},
    {'field': 'sex', 'name': 'sex', 'type': <class 'str'>, 'default': 'M', 'value': 'M', 'allowed': ()},
    {'field': 'age', 'name': 'age', 'type': <class 'int'>, 'default': 0, 'value': 0, 'allowed': ()}]

^^ This is a contract. Let us explore contract item:

    'field': 'name',  # Name of the class instance field
    'name': 'name',  # Associated 'friendly' name for the field
    'type': <class 'str'>,  # Type of data for the field
    'default': 'Noname',  # Default value
    'value': 'Noname',  # Current value
    'allowed': ()  # Tuple of allowed value variants

You should know smth about fields before we go forward:

  • type is hard-constrained: you would not be able to set value of other type, but you can set value of child-type
  • allowed tuple is the hardest constraint: you can set only values, that are in this tuple. If this tuple is empty, any value of specified type could be set
  • field is a real property name as it is in your instance
  • name is an associated name, useful for, fex, UI or just understanding about what is stored in this instance field

Lets try to violate type constraint: = "Vasyan"  # Works good
p1.age = None  # Exception will be thrown:
TypeError: <class 'NoneType'> is not a vaild type for age in <class '__main__.Person'> contract. Use <class 'int'>

How to set values

You can set value two ways:

p1.age = 20
p1.set_value('age', 20)

Also if you exactly know order of public fields you can use write_tuple method.

You'll have to pass values for all transfer object fields:

# Throws an exception:
name_sex = ("Vasya", "M")

# Lets do it right way:
name_sex_age = ("Vasya", "M", 19)

Playing with field metadata

Field associated names

You can associate any name for the field, just give not only default value on class definition, but associative name as well:

class Person:
    name = "Person's name", "Noname"
    sex = "Person's Sex", "M"
    age = "Person's Age", 0

p1 = Person()
    {'field': 'name', 'name': "Person's name", 'type': <class 'str'>, 'default': 'Noname', 'value': 'Noname', 'allowed': ()},
    {'field': 'sex', 'name': "Person's Sex", 'type': <class 'str'>, 'default': 'M', 'value': 'M', 'allowed': ()},
    {'field': 'age', 'name': "Person's Age", 'type': <class 'int'>, 'default': 0, 'value': 0, 'allowed': ()}

As you can see, the first given value -- is field associated name, and the second is default value.

Allowed values constraints

Lets specify allowed values for SEX field:

class Person:
    name = "Person's name", "Noname"
    sex = "Person's Sex", "M", ("M", "F")  # Here you are
    age = "Person's Age", 0

p1 = Person()
    {'field': 'name', 'name': "Person's name", 'type': <class 'str'>, 'default': 'Noname', 'value': 'Noname', 'allowed': ()},
    {'field': 'sex', 'name': "Person's Sex", 'type': <class 'str'>, 'default': 'M', 'value': 'M', 'allowed': ('M', 'F')},
    {'field': 'age', 'name': "Person's Age", 'type': <class 'int'>, 'default': 0, 'value': 0, 'allowed': ()}

Now let us try to set different values to sex field: = "M"  # Good = "F"  # Good = 10  # TypeError = "Other"  # Type if OK, but 'Other' is not allowed value, so TypeError:
ValueError: 'Other' in not allowed for sex in <class '__main__.Person'> contract. Allowed: ('M', 'F')

Manual metadata setup

You can set metadata manually by including protected attributes:

class Person:
    name = "Noname"
    _name_contract_name = "Person's name"
    _name_contract_type = str
    sex = "M"
    _sex_contract_name = "Person's sex"
    _sex_contract_type = str
    _sex_contract_allowed = ("M", "F", "Other")
    age = 0
    _age_contract_name = "Person's age"
    _age_contract_type = float

As you can see, I am a bit noughty this time: default value for age is Integer, but contract type set to Float.

Let us see, what happens when I try to set Age value:

p1 = Person()
p1.age = 10  # Exception:
TypeError: <class 'int'> is not a vaild type for age in <class '__main__.Person'> contract. Use <class 'float'>

This is right way:

p1 = Person()
p1.age = 10.0  # Good


Quick start

Submodule structure:


  • trees
    • BinaryTree (C++, Linux)
    • PyBinaryTree (Python)
    • PrefixTree (C++, Linux)
    • PyPrefixTree (Python)
  • primitives
    • Queue (C++, Linux)
    • Stack (C++, Linux)
  • graphs
    • PyGraph (Python)
    • PyOrientedGraph (Python)


BinaryTree and PyBinaryTree are similar in behavour.

from easy_pytools.structures.trees import BinaryTree

tree = BinaryTree()

Add value to tree

All values have to be comparable with each other with standad Python lt, gt, eq magic methods.

tree.add(20)   # Now all added values have to be comparable with int
tree.add(15)   # Good
tree.add(7)    # Good
tree.add(17.5) # Good
tree.add("h")  # Exception

Using fulfilled tree

# Useful properties with "talking" names:
tree.depth  # Depth of the tree. I try to keep it near to log2

# Check containment
15 in tree

Deletion is not currently supported

Prefix Tree

Also known as "Bor".

PrefixTree and PyPrefixTree are similar in behavour.

from easy_pytools.structures.trees import PrefixTree

tree = PrefixTree()

Enroll sequence into tree

Any iterable and ordered sequence can be enrolled into the tree:

tree.add([10, 20, 30])
tree.add([10, 30, 40])

Using fulfilled tree

# Count how much times sequence was enrolled:
tree.count("Hello")      # Returns 2
tree.count("Helluva")    # Returns 1
tree.count([10, 20, 30]) # Returns 1
tree.count("Hell")       # Returns 0
tree.count([10])         # Returns 0

# Check if tree contains sequence:
tree.contains("Hello")   # True
tree.contains("Hell")    # False

Deletion and "in" operators are not supported.

Stack, Queue

These are pseudo-structures as they do not affect the way data is organized in memory.

from easy_pytools.structures.primitives import Stack, Queue

s = Stack()
q = Queue()

s.push(20)         # 20, top element still remains in the stack         # 20
s.size        # 2
s.is_empty()  # Returns False
s.pop()       # Returns 20
s.pop()       # Returns None
s.pop()       # Stack is empty, returns None
s.is_empty()  # Returns True

q.first       # None
q.last        # 20
q.size        # 3
q.is_epmty()  # Returns False      # Returns None      # Returns 10      # Returns 20
q.is_empty()  # Returns True
q.size        # 0      # Queue is empty, returns None

Graph, OrientedGraph

The difference between them is that when adding an edge in a regular graph, the order of the vertices does not matter, but in an oriented graph it does. Accordingly, the check and search for the path is carried out on the basis of this.

from easy_pytools.structures.graphs import PyGraph, PyOrientedGraph

g = PyGraph()
og = PyOrientedGraph()

Add vertex:


Add Edge:

# You can set edge between existing vertexes:
g.add_edge(0, 1)
og.add_edge(0, 1)

# Or vertexes can be added automatically:
g.add_edge(10, 20)
g.add_edge(10, 30)
g.add_edge(20, 30)
g.add_edge(30, 40)
og.add_edge(10, 20)
og.add_edge(10, 30)
og.add_edge(20, 30)
og.add_edge(30, 40)

Vertexes checks:

1 in g       # True
1 in og      # True
30 in g      # False
30 in og     # False

Edges checks:

# You can check if vertexes has edge:
g.are_adjacent(1, 0)    # True
g.are_adjacent(0, 1)    # True
og.are_adjecent(1, 0)   # True
og.are_adjecent(0, 1)   # False. Orientations is 1 -> 0, so they are not adjecent!

# You can see all vertexes that can be directly reached from some:
g.adjacents(10)         # {20,}
g.adjacents(20)         # {10,} 
og.adjecents(10)        # {20,}
og.adjecents(20)        # set() -- empty set

# You can travel through vertexes from start point:
list(      # [10,20,30,30,40,40]
list(      # [30,40,10,20,20]
list(     # [10,20,30,30,40,40]
list(     # [30,40]

# And you can travel through vertexes by levels:
[(20, {10, 30}),
 (10, {20, 30}),
 (30, {10, 20, 40}),
 (30, {10, 20, 40}),
 (40, {30}),
 (40, {30})]
[(20, {30}),
 (30, {40}),
 (40, set())]

Find minimal path from one to other:

g.minimal_path_deikstra(10, 40)   # {'path': [10, 30, 40], 'weight': 2}
g.minimal_path_deikstra(40, 10)   # {'path': [40, 30, 10], 'weight': 2}

og.minimal_path_deikstra(10, 40)   # {'path': [10, 30, 40], 'weight': 2}
og.minimal_path_deikstra(40, 10)   # {'path': [], 'weight': inf}

Weights (Yep, edges can have weights)

g.add_edge(10, 1, 40)  # weight of edge is 40
og.add_edge(10, 1, 40) # weight of edge is 40

And minimal path is calculated not by adges count, but by edges weight. Default weight is 1. 'inf' means no edge, or infinite edge weight (similar).

g.edge_weight(10, 30)   # 1
g.edge_weight(30, 10)   # 1
g.edge_weight(10, 40)   # inf

og.edge_weight(10, 30)  # 1
og.edge_weight(30, 10)  # inf
og.edge_weight(10, 40)  # inf


1.0.3 -> 1.0.4

Backward compatible


  • easy_pytools.sql
    • Table
      • DELETE query implemented as production of SELECT query

1.0.2 -> 1.0.3:

Backward compatible


  • easy_pytools.dto
    • transfer_object
      • new write_tuple injected method

1.0.1 -> 1.0.2:

Backward compatible


  • easy_pytools.sql
    • Table
      • Constraints more informative
    • DBase
      • connection and cursor close on del

1.0 -> 1.0.1:

Backward compatible


  • easy_pytools.sql.parsing
    • Affected entities:
      • map_to_schema
      • seq_to_schema
      • seq_to_table
      • map_to_base
    • What is it:
      • Now you can specify foreign fields in 'foreign_fields' parameter

                   Version 3, 29 June 2007

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        How to Apply These Terms to Your New Programs

If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.

To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found.

<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year>  <name of author>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <>.

Also add information on how to contact you by electronic and paper mail.

If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:

<program>  Copyright (C) <year>  <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.

The hypothetical commands show w' and show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box".

You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see

The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read

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