/
transforms.py
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/
transforms.py
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# Copyright (c) 2022-2024, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import typing
import pandas as pd
from nvtabular import ColumnSelector
import cudf
def json_flatten(col_selector: ColumnSelector,
df: typing.Union[pd.DataFrame, cudf.DataFrame]) -> typing.Union[pd.DataFrame, cudf.DataFrame]:
"""
Flattens JSON columns in the given DataFrame and concatenates them into a single DataFrame.
Parameters
----------
col_selector : ColumnSelector
An instance of ColumnSelector that contains the names of the columns to flatten.
df : Union[pd.DataFrame, cudf.DataFrame]
The input DataFrame that contains the JSON columns to flatten.
Returns
-------
Union[pd.DataFrame, cudf.DataFrame]
A new DataFrame with flattened JSON columns. If 'df' was a cudf.DataFrame,
the return type is cudf.DataFrame. Otherwise, it is pd.DataFrame.
"""
convert_to_cudf = False
if isinstance(df, cudf.DataFrame):
convert_to_cudf = True
# Normalize JSON columns and accumulate into a single dataframe
df_normalized = None
for col in col_selector.names:
pd_series = df[col] if not convert_to_cudf else df[col].to_pandas()
pd_series = pd_series.apply(lambda x: x if isinstance(x, dict) else json.loads(x))
pdf_norm = pd.json_normalize(pd_series)
pdf_norm.rename(columns=lambda x, col=col: col + "." + x, inplace=True)
pdf_norm.reset_index(drop=True, inplace=True)
if (df_normalized is None):
df_normalized = pdf_norm
else:
df_normalized = pd.concat([df_normalized, pdf_norm], axis=1)
# Convert back to cudf if necessary
if convert_to_cudf:
df_normalized = cudf.from_pandas(df_normalized)
return df_normalized