pluck-graphql 0.3.5

Transform GraphQL queries into Pandas data-frames.

Pluck 🚀 🍊

Pluck is a GraphQL client that transforms queries into Pandas data-frames.

Installation

Install Pluck from PyPi:

pip install pluck-graphql

Introduction

The easiest way to get started is to use pluck.execute to execute a query.

Let's read the first five SpaceX launches into a data-frame:

import pluck

SpaceX = "https://main--spacex-l4uc6p.apollographos.net/graphql"

query = """
{
  launches(limit: 5) {
    mission_name
    launch_date_local
    rocket {
      rocket_name
    }
  }
}
"""
frame, = pluck.execute(query, url=SpaceX)
frame

launches.mission_name	launches.launch_date_local	launches.rocket.rocket_name
FalconSat	2006-03-25T10:30:00+12:00	Falcon 1
DemoSat	2007-03-21T13:10:00+12:00	Falcon 1
Trailblazer	2008-08-03T15:34:00+12:00	Falcon 1
RatSat	2008-09-28T11:15:00+12:00	Falcon 1
RazakSat	2009-07-13T15:35:00+12:00	Falcon 1

Implicit Mode

The query above uses implicit mode. This is where the entire response is normalized into a single data-frame.

The return value from execute is an instance of pluck.Response. This object is iterable and enumerates the data-frames in the query. Because this query uses implicit mode, the iterator contains only a single data-frame (note that the trailing comma is still required).

@frame directive

But Pluck is more powerful than implicit mode because it provides a custom @frame directive.

The @frame directive specifies portions of the GraphQL response that we want to transform into data-frames. The directive is removed before the query is sent to the GraphQL server.

Using the same query, rather than use implicit mode, let's pluck the launches field from the response:

query = """
{
  launches(limit: 5) @frame {
    mission_name
    launch_date_local
    rocket {
      rocket_name
    }
  }
}
"""
launches, = pluck.execute(query, url=SpaceX)
launches

mission_name	launch_date_local	rocket.rocket_name
FalconSat	2006-03-25T10:30:00+12:00	Falcon 1
DemoSat	2007-03-21T13:10:00+12:00	Falcon 1
Trailblazer	2008-08-03T15:34:00+12:00	Falcon 1
RatSat	2008-09-28T11:15:00+12:00	Falcon 1
RazakSat	2009-07-13T15:35:00+12:00	Falcon 1

The column names are no longer prefixed with launches because it is now the root of the data-frame.

Multiple @frame directives

We can also pluck multiple data-frames from a single GraphQL query.

Let's query the first five SpaceX rockets as well:

query = """
{
  launches(limit: 5) @frame {
    mission_name
    launch_date_local
    rocket {
      rocket_name
    }
  }
  rockets(limit: 5) @frame {
    name
    type
    company
    height {
      meters
    }
    mass {
      kg
    }
  }
}
"""
launches, rockets = pluck.execute(query, url=SpaceX)

Now we have the original launches and a new rockets data-frame:

rockets

name	type	company	height.meters	mass.kg
Falcon 1	rocket	SpaceX	22.25	30146
Falcon 9	rocket	SpaceX	70	549054
Falcon Heavy	rocket	SpaceX	70	1420788
Starship	rocket	SpaceX	118	1335000

Lists

When a response includes a list, the data-frame is automatically expanded to include one row per item in the list. This is repeated for every subsequent list in the response.

For example, let's query the first five rockets and their payload_weights:

query = """
{
  rockets(limit: 5) @frame {
    id
    type
    name
    payload_weights {
      name
      kg
    }
  }
}
"""
rockets, = pluck.execute(query, url=SpaceX)
rockets

id	type	name	payload_weights.name	payload_weights.kg
5e9d0d95eda69955f709d1eb	rocket	Falcon 1	Low Earth Orbit	450
5e9d0d95eda69973a809d1ec	rocket	Falcon 9	Low Earth Orbit	22800
5e9d0d95eda69973a809d1ec	rocket	Falcon 9	Geosynchronous Transfer Orbit	8300
5e9d0d95eda69973a809d1ec	rocket	Falcon 9	Mars Orbit	4020
5e9d0d95eda69974db09d1ed	rocket	Falcon Heavy	Low Earth Orbit	63800
5e9d0d95eda69974db09d1ed	rocket	Falcon Heavy	Geosynchronous Transfer Orbit	26700
5e9d0d95eda69974db09d1ed	rocket	Falcon Heavy	Mars Orbit	16800
5e9d0d95eda69974db09d1ed	rocket	Falcon Heavy	Pluto Orbit	3500
5e9d0d96eda699382d09d1ee	rocket	Starship	Low Earth Orbit	150000
5e9d0d96eda699382d09d1ee	rocket	Starship	Mars Orbit	100000
5e9d0d96eda699382d09d1ee	rocket	Starship	Moon Orbit	100000

Rather than five rows, we have 11; each row contains a rocket and it's payload.

Nested @frame directives

Frames can also be nested and if a nested @frame is within a list, the rows are combined into a single data-frame.

For example, we can pluck the top five rockets and engines:

query = """
{
  rockets(limit: 5) @frame {
    id
    type
    name
    engines @frame {
      number
      type
      version
    }
  }
}
"""
rockets, engines = pluck.execute(query, url=SpaceX)

Now we have the rockets:

rockets

id	type	name	engines.number	engines.type	engines.version
5e9d0d95eda69955f709d1eb	rocket	Falcon 1	1	merlin	1C
5e9d0d95eda69973a809d1ec	rocket	Falcon 9	9	merlin	1D+
5e9d0d95eda69974db09d1ed	rocket	Falcon Heavy	27	merlin	1D+
5e9d0d96eda699382d09d1ee	rocket	Starship	37	raptor

And we also have the engines data-frame that has been combined from every item in rockets:

engines

number	type	version
1	merlin	1C
9	merlin	1D+
27	merlin	1D+
37	raptor

Aliases

Column names can be modified using normal GraphQL aliases.

For example, let's tidy-up the field names in the launches data-frame:

query = """
{
  launches(limit: 5) @frame {
    mission: mission_name
    launch_date: launch_date_local
    rocket {
      name: rocket_name
    }
  }
}
"""
launches, = pluck.execute(query, url=SpaceX)
launches

mission	launch_date	rocket.name
FalconSat	2006-03-25T10:30:00+12:00	Falcon 1
DemoSat	2007-03-21T13:10:00+12:00	Falcon 1
Trailblazer	2008-08-03T15:34:00+12:00	Falcon 1
RatSat	2008-09-28T11:15:00+12:00	Falcon 1
RazakSat	2009-07-13T15:35:00+12:00	Falcon 1

Column names

Columns are named according to the JSON path of the element in the response.

However, we can use a different naming strategy by specifying column_names to execute.

For example, let's use short for the column names:

query = """
{
  launches: launches(limit: 5) @frame {
    name: mission_name
    launch_date: launch_date_local
    rocket {
      rocket: rocket_name
    }
  }
}
"""
launches, = pluck.execute(query, column_names="short", url=SpaceX)
launches

name	launch_date	rocket
FalconSat	2006-03-25T10:30:00+12:00	Falcon 1
DemoSat	2007-03-21T13:10:00+12:00	Falcon 1
Trailblazer	2008-08-03T15:34:00+12:00	Falcon 1
RatSat	2008-09-28T11:15:00+12:00	Falcon 1
RazakSat	2009-07-13T15:35:00+12:00	Falcon 1

If the short column name results in a conflict (two or more columns with the same name), the conflict is resolved by prefixing the name with the name of it's parent.

The naming strategy can also be changed per data-frame by specifying a dict[str, str] where the key is name of the data-frame.

Leaf fields

The @frame directive can also be used on leaf fields.

For example, we can extract only the name of the mission from past launches:

query = """
{
  launchesPast(limit: 5) {
    mission: mission_name @frame
  }
}
"""
launches, = pluck.execute(query, url=SpaceX)
launches

mission
FalconSat
DemoSat
Trailblazer
RatSat
RazakSat

Responses

Most of the time, Pluck is used to transform the GraphQL query directly into one or more data-frames. However, it is also possible to retreive the the raw GraphQL response (as well as the data-frames) by not immeadiately iterating over the return value.

The return value is a pluck.Response object and contains the data and errors from the raw GraphQL response and map of Dict[str, DataFrame] containing each data-frame in the query. The name of the frame corresponds to the field on which the @frame directive is placed or default when using implicit mode.

query = """
{
  launches(limit: 5) @frame {
    id
    mission_name
    rocket {
      rocket_name
    }
  }
  landpads(limit: 5) @frame {
    id
    full_name
    wikipedia
  }
}
"""
response = pluck.execute(query, url=SpaceX)

# print(response.data.keys())
# print(response.errors)
# print(response.frames.keys())

launches, landpads = response
launches
print()
landpads

id	mission_name	rocket.rocket_name
5eb87cd9ffd86e000604b32a	FalconSat	Falcon 1
5eb87cdaffd86e000604b32b	DemoSat	Falcon 1
5eb87cdbffd86e000604b32c	Trailblazer	Falcon 1
5eb87cdbffd86e000604b32d	RatSat	Falcon 1
5eb87cdcffd86e000604b32e	RazakSat	Falcon 1

id	full_name	wikipedia
5e9e3032383ecb267a34e7c7	Landing Zone 1	https://en.wikipedia.org/wiki/Landing_Zones_1_and_2
5e9e3032383ecb90a834e7c8	Landing Zone 2	https://en.wikipedia.org/wiki/Landing_Zones_1_and_2
5e9e3032383ecb554034e7c9	Landing Zone 4	https://en.wikipedia.org/wiki/Vandenberg_AFB_Space_Launch_Complex_4#LZ-4_landing_history
5e9e3032383ecb6bb234e7ca	Of Course I Still Love You	https://en.wikipedia.org/wiki/Autonomous_spaceport_drone_ship
5e9e3032383ecb761634e7cb	Just Read The Instructions V1	https://en.wikipedia.org/wiki/Autonomous_spaceport_drone_ship

pluck.create

Pluck also provides a create factory function which returns a customized execute function which closes over the url and other configuration.

execute = pluck.create(url=SpaceX)

query = """
{
  launches(limit: 5) @frame {
    id
    mission_name
    rocket {
      rocket_name
    }
  }
}
"""
launches, = execute(query)
launches

id	mission_name	rocket.rocket_name
5eb87cd9ffd86e000604b32a	FalconSat	Falcon 1
5eb87cdaffd86e000604b32b	DemoSat	Falcon 1
5eb87cdbffd86e000604b32c	Trailblazer	Falcon 1
5eb87cdbffd86e000604b32d	RatSat	Falcon 1
5eb87cdcffd86e000604b32e	RazakSat	Falcon 1