Standalone Processor Examples

Field Processor Standalone use

Processor function definitions

def add1(x):
    return x + 1

def cast_to_int(x):
    return int(x)

def replace_big_huge(x):
    return x.replace("Big", "Huge")

Field processors and sample rows

from csvio.processors import FieldProcessor
from json import dumps

row1 = {
    "Supplier": "Big Apples",
    "Fruit": "Apple",
    "Origin": "Spain",
    "Quantity": "1"
}

row2 = {
    "Supplier": "Big Melons",
    "Fruit": "Melons",
    "Origin": "Italy",
    "Quantity": "2"
}

row3 = {
    "Supplier": "Long Mangoes",
    "Fruit": "Mango",
    "Origin": "India",
    "Quantity": "3"
}

rows = [row1, row2, row3]

proc1 = FieldProcessor('increment_qty')
proc1.add_processor("Quantity", cast_to_int)
proc1.add_processor("Quantity", add1)

proc2 = FieldProcessor('replace')
proc2.add_processor("Supplier", replace_big_huge)

Using implicit processor object

If a processor object or handle is not passed to the process_row method, the processor functions associated with the processor object whose process_row method we are calling are used implicitly.

print("Using implicit processor object:")
pretty_print("Before:", row1)
pretty_print("After:", proc1.process_row(row1)) # Using implicit processor object

Output

Using implicit processor object:
Before:
{
    "Supplier": "Big Apples",
    "Fruit": "Apple",
    "Origin": "Spain",
    "Quantity": "1"
}

After:
{
    "Supplier": "Big Apples",
    "Fruit": "Apple",
    "Origin": "Spain",
    "Quantity": 2
}

Using processor handle

Any processor object can be used to apply the processors from another object be using the handle reference as shown below. We are using the handle 'replace' associated with the proc2 object, however we are using the proc1 object to apply the processor.

print("Using processor handle:")
pretty_print("Before:", rows)
pretty_print("After:", proc1.process_rows(rows, 'replace')) # Using processor handle

Output

Using processor handle:
Before:
[
    {
        "Supplier": "Big Apples",
        "Fruit": "Apple",
        "Origin": "Spain",
        "Quantity": "1"
    },
    {
        "Supplier": "Big Melons",
        "Fruit": "Melons",
        "Origin": "Italy",
        "Quantity": "2"
    },
    {
        "Supplier": "Long Mangoes",
        "Fruit": "Mango",
        "Origin": "India",
        "Quantity": "3"
    }
]

After:
[
    {
        "Supplier": "Huge Apples",
        "Fruit": "Apple",
        "Origin": "Spain",
        "Quantity": "1"
    },
    {
        "Supplier": "Huge Melons",
        "Fruit": "Melons",
        "Origin": "Italy",
        "Quantity": "2"
    },
    {
        "Supplier": "Long Mangoes",
        "Fruit": "Mango",
        "Origin": "India",
        "Quantity": "3"
    }
]

Using explicit processor object

Similarly we can also pass any other processor object instead of a handle.

print("Using explicit processor object:")
pretty_print("Before:", rows)
pretty_print("After:", proc1.process_rows(rows, proc2)) # Using explicit processor object

Output

Using explicit processor object:
Before:
[
    {
        "Supplier": "Big Apples",
        "Fruit": "Apple",
        "Origin": "Spain",
        "Quantity": "1"
    },
    {
        "Supplier": "Big Melons",
        "Fruit": "Melons",
        "Origin": "Italy",
        "Quantity": "2"
    },
    {
        "Supplier": "Long Mangoes",
        "Fruit": "Mango",
        "Origin": "India",
        "Quantity": "3"
    }
]

After:
[
    {
        "Supplier": "Huge Apples",
        "Fruit": "Apple",
        "Origin": "Spain",
        "Quantity": "1"
    },
    {
        "Supplier": "Huge Melons",
        "Fruit": "Melons",
        "Origin": "Italy",
        "Quantity": "2"
    },
    {
        "Supplier": "Long Mangoes",
        "Fruit": "Mango",
        "Origin": "India",
        "Quantity": "3"
    }
]

Row Processor Standalone use

Processor function definitions

def update_row(row):

    row["Supplier"] = f"{row['Supplier']} ({row['Origin']})"

    row["Quantity"] = int(row["Quantity"])

    if row["Quantity"] > 2:
        row["Quantity"] += 1

    return row

Row processor and sample rows

from csvio.processors import RowProcessor
from json import dumps

row1 = {
    "Supplier": "Big Apples",
    "Fruit": "Apple",
    "Origin": "Spain",
    "Quantity": "1"
}

row2 = {
    "Supplier": "Big Melons",
    "Fruit": "Melons",
    "Origin": "Italy",
    "Quantity": "2"
}

row3 = {
    "Supplier": "Long Mangoes",
    "Fruit": "Mango",
    "Origin": "India",
    "Quantity": "3"
}

rows = [row1, row2, row3]

rowproc = RowProcessor("rp1")

rowproc.add_processor(update_row)

processed_rows = rowproc.process_rows(rows)

print("Before:")
print(dumps(rows, indent=4))
print()

print("After:")
print(dumps(processed_rows, indent=4))

Output

Before:
[
    {
        "Supplier": "Big Apples",
        "Fruit": "Apple",
        "Origin": "Spain",
        "Quantity": "1"
    },
    {
        "Supplier": "Big Melons",
        "Fruit": "Melons",
        "Origin": "Italy",
        "Quantity": "2"
    },
    {
        "Supplier": "Long Mangoes",
        "Fruit": "Mango",
        "Origin": "India",
        "Quantity": "3"
    }
]

After:
[
    {
        "Supplier": "Big Apples (Spain)",
        "Fruit": "Apple",
        "Origin": "Spain",
        "Quantity": 1
    },
    {
        "Supplier": "Big Melons (Italy)",
        "Fruit": "Melons",
        "Origin": "Italy",
        "Quantity": 2
    },
    {
        "Supplier": "Long Mangoes (India)",
        "Fruit": "Mango",
        "Origin": "India",
        "Quantity": 4
    }
]