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83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import io
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from typing import Any
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import pandas as pd
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from superset.utils.core import GenericDataType
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def quote_formulas(df: pd.DataFrame) -> pd.DataFrame:
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"""
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Make sure to quote any formulas for security reasons.
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"""
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formula_prefixes = {"=", "+", "-", "@"}
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for col in df.select_dtypes(include="object").columns:
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df[col] = df[col].apply(
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lambda x: (
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f"'{x}"
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if isinstance(x, str) and len(x) and x[0] in formula_prefixes
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else x
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)
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)
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return df
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def df_to_excel(df: pd.DataFrame, **kwargs: Any) -> Any:
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output = io.BytesIO()
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# make sure formulas are quoted, to prevent malicious injections
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df = quote_formulas(df)
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# pylint: disable=abstract-class-instantiated
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with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
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df.to_excel(writer, **kwargs)
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return output.getvalue()
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def apply_column_types(
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df: pd.DataFrame, column_types: list[GenericDataType]
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) -> pd.DataFrame:
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"""
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Applies the column types to the dataframe to prepare for an excel export
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:param df: The dataframe to apply the column types to
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:param column_types: The types of the columns
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:return: The dataframe with the column types applied
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"""
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for column, column_type in zip(df.columns, column_types, strict=False):
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if column_type == GenericDataType.NUMERIC:
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try:
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df[column] = pd.to_numeric(df[column])
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# if the number is too large, convert it to a string
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# Excel does not support numbers larger than 10^15
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df[column] = df[column].apply(
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lambda x: str(x)
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if isinstance(x, (int, float)) and abs(x) > 10**15
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else x
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)
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except ValueError:
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df[column] = df[column].astype(str)
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elif pd.api.types.is_datetime64tz_dtype(df[column]):
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# timezones are not supported
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df[column] = df[column].astype(str)
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return df
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