velf/ladybug.py

Handle CSV files using table models and queries.

ladybug.py

Handle CSV files using table models and queries.

Table models

ladybug.py handles CSV files through table models. You need to define a table model for your files by subclassing Table.

An example

from ladybug.model import Table, Field, field

class ExampleTable(Table):
    name = Field()
    salary = Field(format=int)
    department = Field()
    private_office_number = Field(format=int, default=None)

    @field
    def name_length(name):
        return len(name)

Table managers

Table managers are objects that contain actual tables, built from a table model. There are two ways to create a table manager:

# Open a .csv file
ExampleTable.open("example.csv")

# Create an empty manager
ExampleTable.create()

Working with table managers

.filter

Returns a subset that matches a given query

table.filter(department="Management")

.column

Returns a column of a table

print table.column("name")
print sum(table.column("salary"))

Result:

['Bob', 'Rob', 'Joe', 'Maria', 'Carlos']
16900

.order_by & .reverse

.order_by and .reverse are used for changing the order of rows in a table.

table.order_by("salary")
table.order_by("salary").reverse

.insert

Inserts a row into a table manager

table.insert(name="Carlos", salary=6000, department="Management")

.group_by

Returns a dictionary of grouped elements.

table.group_by("department", key="name")

Result:

{'Management': ['Carlos'], 'IT': ['Bob', 'Joe'], 'Sales': ['Rob', 'Maria']}

It's also possible to pass the rows to a given function:

table.group_by("department", key="name", function=tuple)
table.group_by("department", function=sum, key="salary")

Result:

{'IT': ('Bob', 'Joe'), 'Sales': ('Rob', 'Maria')}
{'IT': 3500, 'Sales': 5500}

.update

Updates existing rows in a (filtered) manager. This is an in-place mutation.

# Set salary for entire office
table.update(salary=2000)

# Set salary for whole IT department
table.filter(department="IT").update(salary=3000)

# Set salary for Joe alone
table.filter(department="IT", name="Joe").update(salary=2700)

.save

Save manager into a .csv file.

table.save("example_output.csv")

.copy

Using .copy it is possible to have a copy of a (filtered) managers. This means that you can change rows in the copy while the original remains intact.

Updating a copy

cc_table = table.copy
print list(table.filter(name="Maria").rows)
print list(cc_table.filter(name="Maria").rows)
cc_table.filter(name="Maria").update(department="IT")
print list(table.filter(name="Maria").rows)
print list(cc_table.filter(name="Maria").rows)

Output:

[{'salary': 2500, 'department': 'Sales', 'name': 'Maria'}]
[{'salary': 2500, 'department': 'Sales', 'name': 'Maria'}]
[{'salary': 2500, 'department': 'Sales', 'name': 'Maria'}]  # Intact
[{'salary': 2500, 'department': 'IT', 'name': 'Maria'}]  # Updated

Supported dialects

ladybug.py uses DictReader and DictWriter. It's possible to use keyword arguments of those, so all dialects accepted by DictReader and DictWriter can be handled.

The following example shows how to convert a CSV file to another dialect using ladybug.py

ExampleTable.open("example.csv", delimiter=",").save("example_output.csv", delimiter=";")