csvms 0.2.14

Comma-separated values management system

Comma-Separated Values Management System

Python module to manage CSV data like a DBMS application with educational purposes

Installation

pip install csvms

Usage

This is an simple example of use:

from csvms.table import Table
# Create table
A = Table(
    name="A",
    columns={"c1":int,"c2":str,"c3":float},
    data=[(1,"Hello",0.1),(2,"World",1.0),])
# Insert one row
A.append(3,"Some",0.8)
# Create a new table 'B' with extended 'A' result of (100 * c3) called c4, and then 
# select rows where c4 are greater then 50
B = A.Π({'mul':[{'literal':100},'c3']},"c4").σ({"gt":['c4',50]})
# Project columns c1, c2 and c4 and print table
print(B.π(['c1','c2','c4']))

the expected result will be like this

 TABLE: default.(((AΠ)π)σ)
 +---+-----+-----+
 |c1 |c2   |c4   |
 +---+-----+-----+
0|  2|World|100.0|
1|  3| Some| 80.0|
 +---+-----+-----+

Table

The Table object represents a CSV data file.

You can create a sample table like

from csvms.table import Table
tbl = Table(
    name="sample",
    columns={
        "c1":int,
        "c2":str,
        "c3":float
    },
    data=[
        (1,"Hello",0.1),
        (2,"World",1.0),
    ]
)

Without spefify a database on name this table will be created under a default directory ($CSVMS_DEFAULT_DB). The columns is a dictionary composed by the name and type using python primitive data types and the data need to be a list of tuples

Using print you can they see the object as a table representation

>>> print(tbl)
   TABLE: default.sample
   +---+-----+---+
   |c1 |c2   |c3 |
   +---+-----+---+
  0|  1|Hello|0.1|
  1|  2|World|1.0|
   +---+-----+---+

the save function will write all data in a CSV format based on the table location property

tbl.save()

cat data/default/sample.csv
1;Hello;0.1
2;World;1.0

For more informatios use help(Table)

Data access

It's possible to access a row by your index value, like a simple python tuple

>>> tbl[1]
{'c1': 2, 'c2': 'World', 'c3': 1.0}

The row will be return as an dictionary, so, with the column name (after the index) you can access the value associated

>>> tbl[1]["c2"]
'World'

It's also possible iterate into all rows using a for loop

>>> for row in tbl:
...     print(row)
... 
(1, 'Hello', 0.1)
(2, 'World', 1.0)

Data manipulation

You can add a new row using the append function

>>> tbl.append(3, "Some", 0)
>>> print(tbl)
   TABLE: default.sample
   +---+-----+---+
   |c1 |c2   |c3 |
   +---+-----+---+
  0|  1|Hello|0.1|
  1|  2|World|1.0|
  2|  3| Some|0.0|
   +---+-----+---+

Update a specific row by your index

>>> tbl[0] = (4, "Value", 3.3)
>>> print(tbl)
   TABLE: default.sample
   +---+-----+---+
   |c1 |c2   |c3 |
   +---+-----+---+
  0|  4|Value|3.3|
  1|  2|World|1.0|
  2|  3| Some|0.0|
   +---+-----+---+

And also remove a row by the index

>>> del tbl[1]
>>> print(tbl)
   TABLE: default.sample
   +---+-----+---+
   |c1 |c2   |c3 |
   +---+-----+---+
  0|  4|Value|3.3|
  1|  3| Some|0.0|
   +---+-----+---+

Relational algebra

The main purpose of the relational algebra is to define operators that transform one or more input relations to an output relation. Given that these operators accept relations as input and produce relations as output, they can be combined and used to express potentially complex queries that transform potentially many input relations (whose data are stored in the database) into a single output relation (the query results).

This are the current operations supported:

Simbolo	Oprador	Operação	Sintaxe
∪	+	Union	A + B
∩	%	Intersection	A % B
-	-	Difference	A – B
×	*	Product	A * B
π	π	Project	A.π(<attribute list>)
σ	σ	Select	A.σ([<logic functions>])
ρ	ρ	Rename	A.ρ(name)
Π	Π	Extend	A.Π(<arithmetic functions>)
⋈	ᐅᐊ	Join	A.ᐅᐊ( B, <logic functions> )

Database

This object represents a physical location on the file system with a set of tables

from csvms.schema import Database
db = Database("dbname")

This will create a new directory, if not exists, inside $CSVMS_FILE_DIR path In most cases will not be necessary explicitly create this object because the Database is implicit created based on the Table name using the notation database.table_name

For more informatios use help(Database)

Catalog

When you instantiate an object the Catalog objet will save the table definitions for future queries and save in json format on root directory.

from csvms.catalog import Catalog
cat = Catalog('file/system/location/path')

In the path used to initialize the catalog contains the $CSVMS CATALOG json file with all table definitions

{
    "default.sample": {
        "name": "default.sample",
        "columns": {
            "c1": "integer",
            "c2": "text",
            "c3": "float"
        }
    }
}

Important: You don't need to explicit create the catalog. That will be automatic created when you initiate any table

For more informatios use help(Catalog)