Skip to content
Ladybug
Blog

Python API

Ladybug provides a Python package that you can install via PyPI. A full list of the available functions and classes can be found in the Python API documentation, linked below.

Python API source documentation

Sync and Async APIs

Ladybug provides both a synchronous and an asynchronous Python API. The synchronous API is the default and is more convenient to use.

Download the CSV files used in the examples below:

Terminal window
mkdir ./data/
curl -L -o ./data/city.csv https://raw.githubusercontent.com/lbugdb/lbug/refs/heads/master/dataset/demo-db/csv/city.csv
curl -L -o ./data/user.csv https://raw.githubusercontent.com/lbugdb/lbug/refs/heads/master/dataset/demo-db/csv/user.csv
curl -L -o ./data/follows.csv https://raw.githubusercontent.com/lbugdb/lbug/refs/heads/master/dataset/demo-db/csv/follows.csv
curl -L -o ./data/lives-in.csv https://raw.githubusercontent.com/lbugdb/lbug/refs/heads/master/dataset/demo-db/csv/lives-in.csv

The synchronous API is the default and is a common way to work with Ladybug in Python.

import lbug


def main() -> None:
    # Create an empty on-disk database and connect to it
    db = lbug.Database("example.lbug")
    conn = lbug.Connection(db)


    create_tables(conn)
    copy_data(conn)
    query(conn)


def create_tables(conn: lbug.Connection) -> None:
    conn.execute("CREATE NODE TABLE User(name STRING PRIMARY KEY, age INT64)")
    conn.execute("CREATE NODE TABLE City(name STRING PRIMARY KEY, population INT64)")
    conn.execute("CREATE REL TABLE Follows(FROM User TO User, since INT64)")
    conn.execute("CREATE REL TABLE LivesIn(FROM User TO City)")


def copy_data(conn: lbug.Connection) -> None:
    conn.execute('COPY User FROM "./data/user.csv"')
    conn.execute('COPY City FROM "./data/city.csv"')
    conn.execute('COPY Follows FROM "./data/follows.csv"')
    conn.execute('COPY LivesIn FROM "./data/lives-in.csv"')


def query(conn: lbug.Connection) -> None:
    results = conn.execute("""
        MATCH (a:User)-[f:Follows]->(b:User)
        RETURN a.name, b.name, f.since;
    """)
    for row in results:
        print(row)


if __name__ == "__main__":
    main()

Output utilities

The Python API provides some utilities to process query results.

# Get all rows as a list of tuples
print(response.get_all())


# Change the output format to a dictionary, where the keys are the column names
print(response.rows_as_dict().get_all())


# Get the first 2 rows as a list of tuples
print(response.get_n(2))


# Get the next row as a tuple
if response.has_next():
    print(response.get_next())


# Get all rows as a list of tuples
print(list(response))

Run multiple queries in one execution

By default, executing a single query in the Python API will return a QueryResult object. However, if you submit multiple Cypher queries separated by semicolons in a single execution, the API will return a list of QueryResult objects.

You can loop through each result of a QueryResult object and get its contents.

responses = conn.execute("""
    RETURN 42;
    RETURN 'Alice', 'Bob', 'Charlie';
    RETURN [true, false], 3.14;
""")
for response in responses:
    for row in response:
        print(row)
[42]
['Alice', 'Bob', 'Charlie']
[[True, False], 3.14]

DataFrames and Arrow Tables

In Python, Ladybug supports the use of Pandas and Polars DataFrames, as well as PyArrow Tables. This allows you to leverage the data manipulation capabilities of these libraries in your graph workflows.

Output query results

You can output the results of a Cypher query to a Pandas DataFrame, Polars DataFrame, or PyArrow Table. The following examples show how to output query results to each of these data structures.

You can output the results of a Cypher query to a Pandas DataFrame using the get_as_df() method:

import lbug
import pandas as pd


db = lbug.Database(":memory:")
conn = lbug.Connection(db)


conn.execute("CREATE NODE TABLE Person(name STRING PRIMARY KEY, age INT64)")
conn.execute("CREATE (a:Person {name: 'Adam', age: 30})")
conn.execute("CREATE (a:Person {name: 'Karissa', age: 40})")
conn.execute("CREATE (a:Person {name: 'Zhang', age: 50})")


result = conn.execute("MATCH (p:Person) RETURN p.*")
print(result.get_as_df())
    p.name  p.age
0     Adam     30
1  Karissa     40
2    Zhang     50

Return specific columns by name and optionally alias them as follows:

result = conn.execute("MATCH (p:Person) RETURN p.name AS name")
print(result.get_as_df())
      name
0     Adam
1  Karissa
2    Zhang

LOAD FROM

You can scan a Pandas DataFrame, Polars DataFrame, or PyArrow Table in Ladybug using the LOAD FROM clause. Scanning a DataFrame or Table does not copy the data into Ladybug, it only reads the data.

import lbug
import pandas as pd


db = lbug.Database(":memory:")
conn = lbug.Connection(db)


df = pd.DataFrame({
    "name": ["Adam", "Karissa", "Zhang"],
    "age": [30, 40, 50]
})


result = conn.execute("LOAD FROM df RETURN *")
print(result.get_as_df())
      name  age
0     Adam   30
1  Karissa   40
2    Zhang   50

COPY FROM

Copy from a Pandas DataFrame into a Ladybug table using the COPY FROM command:

import lbug
import pandas as pd


db = lbug.Database(":memory:")
conn = lbug.Connection(db)


conn.execute("CREATE NODE TABLE Person(name STRING PRIMARY KEY, age INT64)")


df = pd.DataFrame({
    "name": ["Adam", "Karissa", "Zhang"],
    "age": [30, 40, 50]
})


conn.execute("COPY Person FROM df")


result = conn.execute("MATCH (p:Person) RETURN p.*")
print(result.get_as_df())
    p.name  p.age
0     Adam     30
1  Karissa     40
2    Zhang     50

Type notation

This section summarizes the type notation used in Ladybug’s Python API. Below is a table from Python types to a Ladybug LogicalTypeID, which will be used to infer types via Python type annotations.

Python typeLadybug LogicalTypeID
boolBOOL
intINT64
floatDOUBLE
strSTRING
datetimeTIMESTAMP
dateDATE
timedeltaINTERVAL
uuidUUID
listLIST
dictMAP

UDF

Ladybug’s Python API also supports the registration of User Defined Functions (UDFs), allowing you to define custom functions in Python and use them in your Cypher queries. This can allow you to quickly extend Ladybug with new functions you need in your Python applications.

An example of using the UDF API is shown below. We will define a Python UDF that calculates the difference between two numbers, and then apply it in a Cypher query.

Register the UDF

import lbug


db = lbug.Database(":memory:")
conn = lbug.Connection(db)


# define your function
def difference(a, b):
    return a - b


# define the expected type of your parameters
parameters = [lbug.Type.INT64, lbug.Type.INT64]


# define expected type of the returned value
return_type = lbug.Type.INT64


# register the UDF
conn.create_function("difference", difference, parameters, return_type)

Note that in the example above, we explicitly declared the expected types of the parameters and the return value in Ladybug, prior to registering the UDF.

Alternatively, you can simply use Python type annotations to denote the type signature of the parameters and return value.

def difference(a : int, b : int) -> int:
    return abs(a - b)


conn.create_function("difference", difference)

Additional parameters

The UDF API’s create_function provides the following additional parameters:

  • name: str : The name of the function to be invoked in cypher.
  • udf: Callable[[...], Any] : The function to be executed.
  • params_type: Optional[list[Type | str]] : A list whose elements can either be lbug.Type or str. lbug.Type can be used to denote nonnested parameter types, while str can be used to denote both nested and nonnested parameter types. Details on how to denote types are in the type notation section.
  • return_type: Optional[Type | str] : Either a lbug.Type enum or str. Details on how to denote types are in the type notation section.
  • default_null_handling: Optional[bool] : True by default. When true, if any one of the inputs is null, function execution is skipped and the output is resolved to null.
  • catch_exceptions: Optional[bool] : False by default. When true, if the UDF raises an exception, the output is resolved to null. Otherwise the Exception is rethrown.

Apply the UDF

Once the UDF is registered, you can apply it in a Cypher query. First, let’s create some data.

conn.execute("CREATE NODE TABLE IF NOT EXISTS Item (id INT64 PRIMARY KEY, a INT64, b INT64, c INT64)")


conn.execute("CREATE (i:Item {id: 1}) SET i.a = 134, i.b = 123")
conn.execute("CREATE (i:Item {id: 2}) SET i.a = 44, i.b = 29")
conn.execute("CREATE (i:Item {id: 3}) SET i.a = 32, i.b = 68")

We’re now ready to apply the UDF in a Cypher query:

result = conn.execute("MATCH (i:Item) RETURN i.a AS a, i.b AS b, difference (i.a, i.b) AS difference")
print(result.get_as_df())

The output should be:

     a    b  difference
0  134  123          11
1   44   29          15
2   32   68         -36

Remove UDF

In case you want to remove the UDF, you can call the remove_function method on the connection object.

conn.remove_function("difference")

Nested and complex types

When working with UDFs, you can also specify nested or complex types, though in this case, there are some differences from the examples shown above. With these additional types, a string representation should be given for the parameters which are then manually casted to the respective Ladybug type.

Some examples of where this is relevant are listed below:

  • A list of INT64 would be "INT64[]"
  • A map from a STRING to a DOUBLE would be "MAP(STRING, DOUBLE)"
  • A Decimal value with 7 significant figures and 2 decimals would be "DECIMAL(7, 2)"

Note that it’s also valid to define child types through Python’s type annotations, e.g. list[int], or dict(str, float) for simple types.

Below, we show an example to calculate the discounted price of an item using a Python UDF.

def calculate_discounted_price(price: float, has_discount: bool) -> float:
    # Assume 10% discount on all items for simplicity
    return float(price) * 0.9 if has_discount else price


parameters = ['DECIMAL(7, 2)', lbug.Type.BOOL]


return_type = 'DECIMAL(7, 2)'


conn.create_function(
    "current_price",
    calculate_discounted_price,
    parameters,
    return_type
)


result = conn.execute(
    """
    RETURN
        current_price(100, true) AS discount,
        current_price(100, false) AS no_discount;
    """
)
print(result.get_as_df())
  discount no_discount
0    90.00      100.00

The second parameter is a built-in native type in Ladybug, i.e., lbug.Type.BOOL. For the first parameter, we need to specify a string, i.e. DECIMAL(7,2) that is then parsed and used by Ladybug to map to the internal decimal representation.