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python - 引发 InvalidIndexError(key) 奇怪

问题描述

我正在尝试这个

    import dask.dataframe as dd
    import pandas as pd
    
    salary_df = pd.DataFrame({"Salary":[10000, 50000, 25000, 30000, 7000, 100000]})
    salary_category = pd.DataFrame({"Hi":[5000, 20000, 25000, 30000, 90000, 120000],
                            "Low":[0,  5001, 20001, 25001, 30001, 90001],
                            "category":["Very Poor", "Poor", "Medium", "Rich", "Super Rich", "Ultra Rich" ]
                            })
    sal_ddf = dd.from_pandas(salary_df, npartitions=10)
    salary_category.index = pd.IntervalIndex.from_arrays(salary_category['Low'],salary_category['Hi'],closed='both')
    sal_ddf['Category'] = sal_ddf['Salary'].map_partitions(lambda x : salary_category.iloc[salary_category.index.get_loc(x)]['category'], meta=('Category', 'str'))
    
    print(salary_category)
    print(sal_ddf.head())

我对 Salary_category 的输出是

                         Hi    Low    category
    [0, 5000]          5000      0   Very Poor
    [5001, 20000]     20000   5001        Poor
    [20001, 25000]    25000  20001      Medium
    [25001, 30000]    30000  25001        Rich
    [30001, 90000]    90000  30001  Super Rich
    [90001, 120000]  120000  90001  Ultra Rich

不是 10000 会属于“差”的类别吗？但我仍然收到这样的索引错误

        sal_ddf['Category'] = sal_ddf['Salary'].map_partitions(lambda x : salary_category.iloc[salary_category.index.get_loc(x)]['category'], meta=('Category', 'str'))
      File "C:\Python\Python310\lib\site-packages\pandas\core\indexes\interval.py", line 613, in get_loc
        raise InvalidIndexError(key)
    pandas.errors.InvalidIndexError: 0    10000

为什么关键错误？

标签： pythonpandasdask

解决方案

Using.map_partitions假设传递了一个完整的数据帧，而上面的代码将一个 dask 系列传递给它。这会导致问题。一种快速的纠正方法是定义一个自定义函数并将其应用于.map_partitions：

sal_ddf = dd.from_pandas(salary_df, npartitions=10)
salary_category.index = pd.IntervalIndex.from_arrays(salary_category['Low'],salary_category['Hi'],closed='both')


def get_salary(df):
    df = df.copy()
    df['category'] = df['Salary'].apply(lambda x: salary_category.iloc[salary_category.index.get_loc(x)]['category'])
    return df

sal_ddf = sal_ddf.map_partitions(get_salary)

print(salary_category)
print(sal_ddf.compute())

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