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MNT Use copy=False when creating DataFrames #26272

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Merged
merged 1 commit into from
Apr 25, 2023
Merged

MNT Use copy=False when creating DataFrames #26272

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6 changes: 4 additions & 2 deletions 6 sklearn/datasets/_arff_parser.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,7 +187,7 @@ def _io_to_generator(gzip_file):

# calculate chunksize
first_row = next(arff_container["data"])
first_df = pd.DataFrame([first_row], columns=columns_names)
first_df = pd.DataFrame([first_row], columns=columns_names, copy=False)

row_bytes = first_df.memory_usage(deep=True).sum()
chunksize = get_chunk_n_rows(row_bytes)
Expand All @@ -196,7 +196,9 @@ def _io_to_generator(gzip_file):
columns_to_keep = [col for col in columns_names if col in columns_to_select]
dfs = [first_df[columns_to_keep]]
for data in _chunk_generator(arff_container["data"], chunksize):
dfs.append(pd.DataFrame(data, columns=columns_names)[columns_to_keep])
dfs.append(
pd.DataFrame(data, columns=columns_names, copy=False)[columns_to_keep]
)
frame = pd.concat(dfs, ignore_index=True)
del dfs, first_df

Expand Down
2 changes: 1 addition & 1 deletion 2 sklearn/datasets/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@ def _convert_data_dataframe(
):
pd = check_pandas_support("{} with as_frame=True".format(caller_name))
if not sparse_data:
data_df = pd.DataFrame(data, columns=feature_names)
data_df = pd.DataFrame(data, columns=feature_names, copy=False)
else:
data_df = pd.DataFrame.sparse.from_spmatrix(data, columns=feature_names)

Expand Down
2 changes: 1 addition & 1 deletion 2 sklearn/utils/_set_output.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ def _wrap_in_pandas_container(
data_to_wrap.columns = columns
return data_to_wrap

return pd.DataFrame(data_to_wrap, index=index, columns=columns)
return pd.DataFrame(data_to_wrap, index=index, columns=columns, copy=False)


def _get_output_config(method, estimator=None):
Expand Down
2 changes: 1 addition & 1 deletion 2 sklearn/utils/_testing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -843,7 +843,7 @@ def _convert_container(container, constructor_name, columns_name=None, dtype=Non
return sp.sparse.csr_matrix(container, dtype=dtype)
elif constructor_name == "dataframe":
pd = pytest.importorskip("pandas")
return pd.DataFrame(container, columns=columns_name, dtype=dtype)
return pd.DataFrame(container, columns=columns_name, dtype=dtype, copy=False)
elif constructor_name == "series":
pd = pytest.importorskip("pandas")
return pd.Series(container, dtype=dtype)
Expand Down
22 changes: 11 additions & 11 deletions 22 sklearn/utils/estimator_checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -925,11 +925,11 @@ def check_sample_weights_pandas_series(name, estimator_orig):
[3, 4],
]
)
X = pd.DataFrame(_enforce_estimator_tags_X(estimator_orig, X))
X = pd.DataFrame(_enforce_estimator_tags_X(estimator_orig, X), copy=False)
y = pd.Series([1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2])
weights = pd.Series([1] * 12)
if _safe_tags(estimator, key="multioutput_only"):
y = pd.DataFrame(y)
y = pd.DataFrame(y, copy=False)
try:
estimator.fit(X, y, sample_weight=weights)
except ValueError:
Expand Down Expand Up @@ -3218,10 +3218,10 @@ def check_estimators_data_not_an_array(name, estimator_orig, X, y, obj_type):

y_ = np.asarray(y)
if y_.ndim == 1:
y_ = pd.Series(y_)
y_ = pd.Series(y_, copy=False)
else:
y_ = pd.DataFrame(y_)
X_ = pd.DataFrame(np.asarray(X))
y_ = pd.DataFrame(y_, copy=False)
X_ = pd.DataFrame(np.asarray(X), copy=False)

except ImportError:
raise SkipTest(
Expand Down Expand Up @@ -3897,7 +3897,7 @@ def check_dataframe_column_names_consistency(name, estimator_orig):
n_samples, n_features = X_orig.shape

names = np.array([f"col_{i}" for i in range(n_features)])
X = pd.DataFrame(X_orig, columns=names)
X = pd.DataFrame(X_orig, columns=names, copy=False)

if is_regressor(estimator):
y = rng.normal(size=n_samples)
Expand Down Expand Up @@ -3985,7 +3985,7 @@ def check_dataframe_column_names_consistency(name, estimator_orig):
early_stopping_enabled = any(value is True for value in params.values())

for invalid_name, additional_message in invalid_names:
X_bad = pd.DataFrame(X, columns=invalid_name)
X_bad = pd.DataFrame(X, columns=invalid_name, copy=False)

expected_msg = re.escape(
"The feature names should match those that were passed during fit.\n"
Expand Down Expand Up @@ -4094,7 +4094,7 @@ def check_transformer_get_feature_names_out_pandas(name, transformer_orig):
y_[::2, 1] *= 2

feature_names_in = [f"col{i}" for i in range(n_features)]
df = pd.DataFrame(X, columns=feature_names_in)
df = pd.DataFrame(X, columns=feature_names_in, copy=False)
X_transform = transformer.fit_transform(df, y=y_)

# error is raised when `input_features` do not match feature_names_in
Expand Down Expand Up @@ -4324,7 +4324,7 @@ def _check_generated_dataframe(name, case, outputs_default, outputs_pandas):
# We always rely on the output of `get_feature_names_out` of the
# transformer used to generate the dataframe as a ground-truth of the
# columns.
expected_dataframe = pd.DataFrame(X_trans, columns=feature_names_pandas)
expected_dataframe = pd.DataFrame(X_trans, columns=feature_names_pandas, copy=False)

try:
pd.testing.assert_frame_equal(df_trans, expected_dataframe)
Expand Down Expand Up @@ -4359,7 +4359,7 @@ def check_set_output_transform_pandas(name, transformer_orig):
set_random_state(transformer)

feature_names_in = [f"col{i}" for i in range(X.shape[1])]
df = pd.DataFrame(X, columns=feature_names_in)
df = pd.DataFrame(X, columns=feature_names_in, copy=False)

transformer_default = clone(transformer).set_output(transform="default")
outputs_default = _output_from_fit_transform(transformer_default, name, X, df, y)
Expand Down Expand Up @@ -4401,7 +4401,7 @@ def check_global_ouptut_transform_pandas(name, transformer_orig):
set_random_state(transformer)

feature_names_in = [f"col{i}" for i in range(X.shape[1])]
df = pd.DataFrame(X, columns=feature_names_in)
df = pd.DataFrame(X, columns=feature_names_in, copy=False)

transformer_default = clone(transformer).set_output(transform="default")
outputs_default = _output_from_fit_transform(transformer_default, name, X, df, y)
Expand Down
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