[k]array: labeled multi-dimensional arrays

Karray is a simple tool that intends to abstract the users from the complexity of working with labelled multi-dimensional arrays. Numpy is the tool’s core, with an extensive collection of high-level mathematical functions to operate on multi-dimensional arrays efficiently thanks to its well-optimized C code. With Karray, we put effort into generating lightweight objects expecting to reduce overheads and avoid large loops that cause bottlenecks and impact performance. Numpy is the only relevant dependency, while Polars, Pandas, sparse and Pyarrow are required to import, export and store the arrays. karray is developed by the research group Transformation of the Energy Economy at DIW Berlin (German Institute of Economic Research).

Links

• Documentation: https://diw-evu.gitlab.io/karray
• Source code: https://gitlab.com/diw-evu/karray
• PyPI releases: https://pypi.org/project/karray

Table of contents

• Quick installation

• Importing karray

• Usage Examples

  • Creating an Array
  • Accessing Array Elements
  • Array Operations
  • Saving and Loading Arrays
  • Interoperability with Other Libraries

Getting started

Quick installation

To install karray, you can use pip:

pip install karray

Importing karray

To start using karray, import the necessary classes and functions:

import karray as ka

# then you can use ka.Array, ka.Long, and ka.settings

The Array class represents a labeled multidimensional array, while the Long class represents a labeled one-dimensional array. The settings object allows you to configure various options for karray.

Usage Examples

Creating an Array

You can create an Array object in several ways:

1. From a Long object and coordinates:

import pandas as pd

index = {'dim1': ['a', 'b'],
         'dim2': [1, 2],
         'dim3': pd.to_datetime(['2020-01-01', '2020-01-02'], utc=True)}
value = [10., 20.]
long = ka.Long(index=index, value=value)

arr1 = ka.Array(data=long)
arr1

[k]array

Long object size	64 bytes
Data object type	dense
Data object size	64 bytes
Dimensions	['dim1', 'dim2', 'dim3']
Shape	[2, 2, 2]
Capacity	8
Rows	2

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]
dim3	2	datetime64[ns]	['2020-01-01T00:00:00.000000000' '2020-01-02T00:00:00.000000000']

Data

	dim1	dim2	dim3	value
0	a	1	2020-01-01T00:00:00.000000000	10.00
1	b	2	2020-01-02T00:00:00.000000000	20.00

2. From a tuple of index and value, and coordinates:

index2 = {'dim1': ['a', 'b'], 'dim2': [1, 2]}
value2 = [10, 20]
coords2 = {'dim1': ['a', 'b'], 'dim2': [1, 2]}

arr2 = ka.Array(data=(index2, value2), coords=coords2)
arr2

[k]array

Long object size	48 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	2

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	10
1	b	2	20

3. From a dense NumPy array and coordinates:

import numpy as np
dense = np.array([[10, 20], [30, 40]])
coords3 = {'dim1': ['a', 'b'], 'dim2': [1, 2]}

arr3 = ka.Array(data=dense, coords=coords3)
arr3

[k]array

Long object size	96 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	4

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	10.00
1	a	2	20.00
2	b	1	30.00
3	b	2	40.00

4. From a sparse array (using the sparse library) and coordinates:

import sparse as sp

sparse_arr = sp.COO(data=[10, 20], coords=[[0, 1], [0, 1]], shape=(2, 2))
coords4 = {'dim1': ['a', 'b'], 'dim2': [1, 2]}

arr4 = ka.Array(data=sparse_arr, coords=coords4)
arr4

[k]array

Long object size	48 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	2

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	10
1	b	2	20

Accessing Array Elements

You can access elements of an Array object using various methods:

1. Using the items() method to iterate over the array elements:

for item in arr3.items():
    print(item)

('dim1', array(['a', 'a', 'b', 'b'], dtype=object))
('dim2', array([1, 2, 1, 2]))
('value', array([10., 20., 30., 40.]))

2. Using the to_pandas() method to convert the array to a pandas DataFrame:

df = arr1.to_pandas()
print(df)

  dim1  dim2       dim3  value
0    a     1 2020-01-01   10.0
1    b     2 2020-01-02   20.0

3. Using the to_polars() method to convert the array to a polars DataFrame:

df = arr1.to_polars()
print(df)

shape: (2, 4)
┌──────┬──────┬─────────────────────┬───────┐
│ dim1 ┆ dim2 ┆ dim3                ┆ value │
│ ---  ┆ ---  ┆ ---                 ┆ ---   │
│ str  ┆ i64  ┆ datetime[ns]        ┆ f64   │
╞══════╪══════╪═════════════════════╪═══════╡
│ a    ┆ 1    ┆ 2020-01-01 00:00:00 ┆ 10.0  │
│ b    ┆ 2    ┆ 2020-01-02 00:00:00 ┆ 20.0  │
└──────┴──────┴─────────────────────┴───────┘

Array Operations

karray provides various operations that can be performed on Array objects:

1. Arithmetic operations:

result = arr1 + arr2
result

[k]array

Long object size	128 bytes
Data object type	dense
Data object size	64 bytes
Dimensions	['dim1', 'dim2', 'dim3']
Shape	[2, 2, 2]
Capacity	8
Rows	4

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]
dim3	2	datetime64[ns]	['2020-01-01T00:00:00.000000000' '2020-01-02T00:00:00.000000000']

Data

	dim1	dim2	dim3	value
0	a	1	2020-01-01T00:00:00.000000000	20.00
1	a	1	2020-01-02T00:00:00.000000000	10.00
2	b	2	2020-01-01T00:00:00.000000000	20.00
3	b	2	2020-01-02T00:00:00.000000000	40.00

result = arr3 * 2
result

[k]array

Long object size	96 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	4

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	20.00
1	a	2	40.00
2	b	1	60.00
3	b	2	80.00

result = arr4 - 1
result

[k]array

Long object size	96 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	4

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	9.00
1	a	2	-1.00
2	b	1	-1.00
3	b	2	19.00

2. Comparison operations:

mask = arr2 > 10

mask = arr2 == 5

3. Logical operations:

result = arr2 & arr4

result = arr2 | arr4

result = ~arr2

4. Reduction operations:

result = arr1.reduce('dim1', aggfunc='sum')
result

[k]array

Long object size	48 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim2', 'dim3']
Shape	[2, 2]
Capacity	4
Rows	2

Coords

Dimension	Length	Type	Items
dim2	2	int64	[1 2]
dim3	2	datetime64[ns]	['2020-01-01T00:00:00.000000000' '2020-01-02T00:00:00.000000000']

Data

	dim2	dim3	value
0	1	2020-01-01T00:00:00.000000000	10.00
1	2	2020-01-02T00:00:00.000000000	20.00

result = arr1.reduce('dim2', aggfunc=np.mean)
result

[k]array

Long object size	48 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim3']
Shape	[2, 2]
Capacity	4
Rows	2

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim3	2	datetime64[ns]	['2020-01-01T00:00:00.000000000' '2020-01-02T00:00:00.000000000']

Data

	dim1	dim3	value
0	a	2020-01-01T00:00:00.000000000	5.00
1	b	2020-01-02T00:00:00.000000000	10.00

5. Shifting and rolling operations:

shifted = arr3.shift(dim1=1, dim2=-1, fill_value=0.)
shifted

[k]array

Long object size	24 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	1

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	b	1	20.00

rolled = arr3.roll(dim1=2)
rolled

[k]array

Long object size	96 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	4

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	10.00
1	a	2	20.00
2	b	1	30.00
3	b	2	40.00

6. Inserting new dimensions:

# One dimension with one element
result = arr2.insert(dim3='x')
result

[k]array

Long object size	64 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim3', 'dim1', 'dim2']
Shape	[1, 2, 2]
Capacity	4
Rows	2

Coords

Dimension	Length	Type	Items
dim3	1	object	['x']
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim3	dim1	dim2	value
0	x	a	1	10
1	x	b	2	20

# One dimension with several elements related to an existing dimension using a dict
result = arr2.insert(dim3={'dim1': {'a': -1, 'b': -2}})
result

[k]array

Long object size	64 bytes
Data object type	dense
Data object size	64 bytes
Dimensions	['dim3', 'dim1', 'dim2']
Shape	[2, 2, 2]
Capacity	8
Rows	2

Coords

Dimension	Length	Type	Items
dim3	2	int64	[-2 -1]
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim3	dim1	dim2	value
0	-1	a	1	10
1	-2	b	2	20

# One dimension with several elements related to an existing dimension using two lists
result = arr2.insert(dim3={'dim1': [['a', 'b'], [-1, -2]]})
result

[k]array

Long object size	64 bytes
Data object type	dense
Data object size	64 bytes
Dimensions	['dim3', 'dim1', 'dim2']
Shape	[2, 2, 2]
Capacity	8
Rows	2

Coords

Dimension	Length	Type	Items
dim3	2	int64	[-1 -2]
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim3	dim1	dim2	value
0	-1	a	1	10
1	-2	b	2	20

7. Drop a dimension:

result = arr1.drop('dim3')
result

[k]array

Long object size	48 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	2

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	10.00
1	b	2	20.00

!Note

Dropping a dimension will work only if the resulting array still has unique coordinates. If dropping a dimension leads to an array with duplicate coordinates, as a results of the removed dimension, karray will raise an error.

# Assertion error due to duplicate coords
try:
    arr3.drop('dim2')
except AssertionError as e:
    print(e)

Index items per row must be unique. By removing ['dim2'] leads the existence of repeated indexes 
e.g.:
  ('dim1',) value
0 ('a',) 10.0
1 ('a',) 20.0
Intead, you can use obj.reduce('dim2')
With an aggfunc: sum() by default

8. Expanding a dimension (Broadcasting)

result = arr3.expand(dim3=['x', 'y', 'z'])
result

[k]array

Long object size	384 bytes
Data object type	dense
Data object size	96 bytes
Dimensions	['dim1', 'dim2', 'dim3']
Shape	[2, 2, 3]
Capacity	12
Rows	12

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]
dim3	3	object	['x' 'y' 'z']

Data

	dim1	dim2	dim3	value
0	a	1	x	10.00
1	a	1	y	10.00
2	a	1	z	10.00
3	a	2	x	20.00
4	a	2	y	20.00
5	a	2	z	20.00
6	b	1	x	30.00
7	b	1	y	30.00
8	b	1	z	30.00
9	b	2	x	40.00
10	b	2	y	40.00
11	b	2	z	40.00

9. ufunc operations

arr3.ufunc(dim='dim2', func=np.prod, keepdims=True)

[k]array

Long object size	96 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	4

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	200.00
1	a	2	200.00
2	b	1	1200.00
3	b	2	1200.00

!Note

The dim argument is passed to ufunc as axis argument in numpy and keepdims argument is passed with the same name. You can add more arguments depending on the ufunc.

Saving and Loading Arrays

karray supports saving and loading arrays using the Feather format:

1. Saving an array to a Feather file:

arr1.to_feather('array.feather')

2. Loading an array from a Feather file:

loaded_arr1 = ka.from_feather('array.feather')
loaded_arr1

[k]array

Long object size	64 bytes
Data object type	dense
Data object size	64 bytes
Dimensions	['dim1', 'dim2', 'dim3']
Shape	[2, 2, 2]
Capacity	8
Rows	2

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]
dim3	2	int64	[1577836800000000000 1577923200000000000]

Data

	dim1	dim2	dim3	value
0	a	1	2020-01-01T00:00:00.000000000	10.00
1	b	2	2020-01-02T00:00:00.000000000	20.00

Interoperability with Other Libraries

karray provides interoperability with other popular data manipulation libraries:

1. Converting an array to a pandas DataFrame and then back to an array:

df = arr2.to_pandas()
new_arr = ka.from_pandas(df, coords=coords2)
new_arr

[k]array

Long object size	48 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	2

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	10
1	b	2	20

2. Converting an array to a polars DataFrame and then back to an array:

df = arr2.to_polars()
new_arr = ka.from_polars(df, coords=coords2)
new_arr

[k]array

Long object size	48 bytes
Data object type	dense
Data object size	32 bytes
Dimensions	['dim1', 'dim2']
Shape	[2, 2]
Capacity	4
Rows	2

Coords

Dimension	Length	Type	Items
dim1	2	object	['a' 'b']
dim2	2	int64	[1 2]

Data

	dim1	dim2	value
0	a	1	10
1	b	2	20

There are many more features and functionalities. Please refer to the source code section for more details.

!Note

karray is a work in progress. The API is subject to change in the future. We are looking for feedback, suggestions, and we appreciate your contributions.

© 2024 Carlos Gaete-Morales