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lmnsv03
Asked:2024-02-21 05:11:55 +0000 UTC2024-02-21 05:11:55 +0000 UTC

从 pandas 库调用 .mean() 方法时出错

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import pandas as pd
dict = {'Company': ['COMP1', 'COMP1', 'COMP2', 'COMP2', 'COMP3', 'COMP3'],
        'Months': ['MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG'],
        'Salary': [2000, 1500, 3000, 5000, 2500, 3500]}
df = pd.DataFrame(dict)
print(df)
df.groupby('Company').mean()

我正在使用3.12版本，我认为该版本有错误，然后我切换到3.10，但问题仍然存在。请各位程序员帮忙！！！

File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\generic.py", line 12370, in _stat_function
    return self._reduce(   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\series.py", line 6437, in _reduce
    return op(delegate, skipna=skipna, **kwds)   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\nanops.py", line 147, in f
    result = alt(values, axis=axis, skipna=skipna, **kwds)   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\nanops.py", line 404, in new_func
    result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\nanops.py", line 720, in nanmean
    the_sum = _ensure_numeric(the_sum)   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\nanops.py", line 1701, in _ensure_numeric
    raise TypeError(f"Could not convert string '{x}' to numeric") TypeError: Could not convert string 'MARAPR' to numeric

The above exception was the direct cause of the following exception:

Traceback (most recent call last):   File "D:\pyprojects\pythonProject1\pandas&numpy.py", line 245, in <module>
    df.groupby('Company').mean()   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\groupby\groupby.py", line 2452, in mean
    result = self._cython_agg_general(   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\groupby\groupby.py", line 1998, in _cython_agg_general
    new_mgr = data.grouped_reduce(array_func)   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\internals\managers.py", line 1470, in grouped_reduce
    applied = sb.apply(func)   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\internals\blocks.py", line 393, in apply
    result = func(self.values, **kwargs)   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\groupby\groupby.py", line 1995, in array_func
    result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)   File "C:\Users\respu\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\groupby\groupby.py", line 1946, in _agg_py_fallback
    raise type(err)(msg) from err TypeError: agg function failed [how->mean,dtype->object]
python

1 个回答

  1. Best Answer

    错误在于，分组后仍然有一列不Months包含数字，而是包含字符串- 如何计算它们的平均值？

    解决方案 - 有两种可能性：

    1. Salary限制为这样的列：

      df.groupby('Company')['Salary'].mean()

      或缩写为：

      df.groupby('Company').Salary.mean()

      这些命令输出该系列：

      Company
COMP1    1750.0
COMP2    4000.0
COMP3    3000.0
Name: Salary, dtype: float64

      当您想要输出datatime时，请使用方括号对：

      df.groupby('Company')[['Salary']].mean()

               Salary
Company        
COMP1    1750.0
COMP2    4000.0
COMP3    3000.0

    1. 通过使用参数限制所有数字列（在您的情况下 - 只有一个这样的列 - 产生相同的结果）numeric_only=True：

      df.groupby('Company').mean(numeric_only=True)

      正如我所说，您将获得相同的数据框

               Salary
Company        
COMP1    1750.0
COMP2    4000.0
COMP3    3000.0
    • 2

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