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Co-authored-by: Rachel Pan <r.pan@mail.utoronto.ca> Co-authored-by: Rachel Pan <panrrachel@gmail.com> Co-authored-by: Janani Gurram <68124448+JG-ctrl@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
96 lines
3.4 KiB
Python
96 lines
3.4 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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from __future__ import annotations
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import numpy as np
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from pandas import DataFrame, Series, to_numeric
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# pylint: disable=too-many-arguments
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def histogram(
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df: DataFrame,
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column: str,
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groupby: list[str] | None,
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bins: int = 5,
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cumulative: bool = False,
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normalize: bool = False,
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) -> DataFrame:
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"""
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Generate a histogram DataFrame from a given DataFrame.
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Parameters:
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df (DataFrame): The input DataFrame.
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column (str): The column of the DataFrame to calculate the histogram on.
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groupby (list[str]): The columns to group by. If empty, no grouping is performed.
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bins (int): The number of bins to use for the histogram. Default is 5.
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cumulative (bool): Whether to calculate a cumulative histogram. Default is False.
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normalize (bool): Whether to normalize the histogram. Default is False.
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Returns:
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DataFrame: A DataFrame where each row corresponds to a group (or the entire DataFrame if no grouping is performed),
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and each column corresponds to a histogram bin. The values are the counts in each bin.
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""" # noqa: E501
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if groupby is None:
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groupby = []
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# drop empty values from the target column
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df = df.dropna(subset=[column])
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if df.empty:
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return df
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# convert to numeric, coercing errors to NaN
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df[column] = to_numeric(df[column], errors="coerce")
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# check if the column contains non-numeric values
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if df[column].isna().any():
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raise ValueError(f"Column '{column}' contains non-numeric values")
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# calculate the histogram bin edges
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bin_edges = np.histogram_bin_edges(df[column], bins=bins)
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# convert the bin edges to strings
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bin_edges_str = [
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f"{bin_edges[i]} - {bin_edges[i + 1]}" for i in range(len(bin_edges) - 1)
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]
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def hist_values(series: Series) -> np.ndarray:
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# we might have NaN values as the result of grouping so we need to drop them
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result = np.histogram(series.dropna(), bins=bin_edges)[0]
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return result if not cumulative else np.cumsum(result)
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if len(groupby) == 0:
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# without grouping
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hist_dict = dict(zip(bin_edges_str, hist_values(df[column]), strict=False))
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histogram_df = DataFrame(hist_dict, index=[0])
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else:
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# with grouping
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histogram_df = (
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df.groupby(groupby)[column]
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.apply(lambda x: Series(hist_values(x)))
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.unstack(fill_value=0)
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)
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histogram_df.columns = bin_edges_str
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if normalize:
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histogram_df = histogram_df / histogram_df.values.sum()
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# reorder the columns to have the groupby columns first
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histogram_df = histogram_df.reset_index().loc[:, groupby + bin_edges_str]
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return histogram_df
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