Supported Python data structures
This page is a support matrix for various input types.
Markers
T: Supported.
F: Not supported.
NE: Invalid type for the use case. For instance,
pandas.Seriescan not be multi-target label.NPA: Support with the help of numpy array.
AT: Support with the help of arrow table.
CPA: Support with the help of cupy array.
SciCSR: Support with the help of scipy sparse CSR
scipy.sparse.csr_matrix. The conversion to scipy CSR may or may not be possible. Raise a type error if conversion fails.FF: We can look forward to having its support in recent future if requested.
empty: To be filled in.
Table Header
X means predictor matrix.
Meta info: label, weight, etc.
Multi Label: 2-dim label for multi-target.
Others: Anything else that we don’t list here explicitly including formats like lil, dia, bsr. XGBoost will try to convert it into scipy csr.
Support Matrix
Name |
DMatrix X |
QuantileDMatrix X |
Sklearn X |
Meta Info |
Inplace prediction |
Multi Label |
|---|---|---|---|---|---|---|
numpy.ndarray |
T |
T |
T |
T |
T |
T |
scipy.sparse.csr |
T |
T |
T |
NE |
T |
F |
scipy.sparse.csc |
T |
F |
T |
NE |
F |
F |
scipy.sparse.coo |
SciCSR |
F |
SciCSR |
NE |
F |
F |
uri |
T |
F |
F |
F |
NE |
F |
list |
NPA |
NPA |
NPA |
NPA |
NPA |
T |
tuple |
NPA |
NPA |
NPA |
NPA |
NPA |
T |
pandas.DataFrame |
NPA |
NPA |
NPA |
NPA |
NPA |
NPA |
pandas.Series |
NPA |
NPA |
NPA |
NPA |
NPA |
NE |
cudf.DataFrame |
T |
T |
T |
T |
T |
T |
cudf.Series |
T |
T |
T |
T |
FF |
NE |
cupy.ndarray |
T |
T |
T |
T |
T |
T |
torch.Tensor |
T |
T |
T |
T |
T |
T |
dlpack |
CPA |
CPA |
CPA |
FF |
FF |
|
modin.DataFrame |
NPA |
FF |
NPA |
NPA |
FF |
|
modin.Series |
NPA |
FF |
NPA |
NPA |
FF |
|
pyarrow.Table |
T |
T |
T |
T |
T |
T |
polars.DataFrame |
AT |
AT |
AT |
AT |
AT |
AT |
polars.LazyFrame (WARN) |
AT |
AT |
AT |
AT |
AT |
AT |
polars.Series |
AT |
AT |
AT |
AT |
AT |
NE |
__array__ |
NPA |
F |
NPA |
NPA |
H |
|
Others |
SciCSR |
F |
F |
F |
The polars LazyFrame.collect supports many configurations, ranging from the choice of
query engine to type coercion. XGBoost simply uses the default parameter. Please run
collect to obtain the DataFrame before passing it into XGBoost for finer control
over the behaviour.