使用groupby对多列进行加权平均,删除NaNs columnwis

2024-03-28 10:46:04 发布

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我现在的处境 Pandas Group Weighted Average of Multiple Columns但其中一列的某些值有时是NaN。你知道吗

也就是说,我正在做以下工作:

import pandas as pd
import numpy as np

df=pd.DataFrame({'category':['a','a','b','b'],
 'var1':np.random.randint(0,100,4),
 'var2':np.random.randint(0,100,4),
 'weights':np.random.randint(0,10,4)})
df.loc[1,'var1']=np.nan
df


      category  var1  var2  weights
0        a      74.0    99        9
1        a       NaN     8        4
2        b      13.0    86        2
3        b      49.0    38        7

def weighted(x, cols, w="weights"):
    # Following fails when NaNs might be present:
    #return pd.Series(np.average(x[cols], weights=x[w], axis=0), cols)
    return pd.Series([np.nan if x.dropna(subset=[c]).empty else np.average(x.dropna(subset=[c])[c], weights =x.dropna(subset=[c])[w] ) for c in cols], cols)

df.groupby('category').apply(weighted, ['var1', 'var2'])


          var1       var2
category                 
a         74.0  57.846154
b         23.0   8.000000

我想要个更好的方法,但是np.平均值不允许重量。np.平均值不允许选择控制NAN的治疗。你知道吗


Tags: importdfasnprandomnanpdcols
3条回答

没有比我的建议更清晰的答案了,我建议使用下面的函数并不是那么糟糕:

import pandas as pd
import numpy as np

def weighted_means_by_column_ignoring_NaNs(x, cols, w="weights"):
    """ This takes a DataFrame and averages each data column (cols),
        weighting observations by column w, but ignoring individual NaN
        observations within each column.
    """
    return pd.Series([np.nan if x.dropna(subset=[c]).empty else \
                      np.average(x.dropna(subset=[c])[c], 
                      weights =x.dropna(subset=[c])[w] )  \
                      for c in cols], cols)

用法示例如下

df=pd.DataFrame({'category':['a','a','b','b'],
 'var1':np.random.randint(0,100,4),
 'var2':np.random.randint(0,100,4),
 'weights':np.random.randint(0,10,4)})
df.loc[1,'var1']=np.nan
df


      category  var1  var2  weights
0        a      74.0    99        9
1        a       NaN     8        4
2        b      13.0    86        2
3        b      49.0    38        7

df.groupby('category').apply(weighted_means_by_column_ignoring_NaNs), 
        ['var1', 'var2'])


          var1       var2
category                 
a         74.0  57.846154
b         23.0   8.000000

把Nan值设为零,然后创建一个新列var * weight。然后可以使用groupby获得结果。你知道吗

在调用applyunstack之前,可以使用meltdropna预处理数据帧

wa=lambda x: np.average(x.value, weights=x.weights)
df_avg = (df.melt(['category', 'weights']).dropna().groupby(['category', 'variable'])
                                                   .apply(wa).unstack())

Out[40]:
variable  var1       var2
category
a         74.0  71.000000
b         41.0  48.666667

注意:所需的输出与示例不匹配。(a, 'var2')的值是(99 * 9 + 8 * 4) / (9 + 4) = 71

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