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'}] # UpdatedSupported 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=";")
