如何用列迭代填充熊猫数据框

Question

我正在尝试创建一个从另一个dataframe迭代计数statisitcs的pandas dataframe，它通过列(用Regex过滤(。如何创建结果数据框架？输入数据帧：

    In [4]: control.head()
    Out[4]:
  Patient Gender  Age  Left-Lateral-Ventricle_NVoxels  Left-Inf-Lat- 
Vent_NVoxels  ...  supramarginal_CurvInd_lh
0    P008      M   30                            9414                        
311  ...                       7.5
1    P013      F   35                            7668                         
85  ...                      10.4
2    P018      F   27                            7350                        
202  ...                       8.0
3    P033      F   55                            7548                        
372  ...                       9.2
4    P036      F   31                            8598                         
48  ...                       8.0
    [5 rows x 930 columns]

我写了一个代码来计算统计量，但坚持创建结果pandas dataframe

def select_volumes(group_c,group_k):
    Select_list = ["Amygdala", "Hippocampus", "Lateral-Ventricle", 
"Pallidum", "Putamen", "Thalamus"]
    Side = ["Left", "Right"]
    for s in Side:
        for struct in Select_list:
            volumes_c = group_c.filter(regex="^(?=.*"+s+")(?=.*"+struct+") 
   (?=.*Volume)")
            volumes_k = group_k.filter(regex="^(?=.*"+s+")(?=.*"+struct+") 
   (?=.*Volume)")
            k = cohens_d(volumes_c, volumes_k)
            meand = volumes_c.mean()
            result_df = pd.Dataframe(
{
     "Cohen's norm": some result
     "Mean Value": meand
}
)
            return k

函数select_volumes为我提供了结果：

Left-Amygdala_Volume_mm3   -0.29729
dtype: float64
Left-Hippocampus_Volume_mm3    0.33139
dtype: float64
Left-Lateral-Ventricle_Volume_mm3   -0.111853
dtype: float64
Left-Pallidum_Volume_mm3    0.28857
dtype: float64
Left-Putamen_Volume_mm3    0.696645
dtype: float64
Left-Thalamus-Proper_Volume_mm3    0.772492
dtype: float64
Right-Amygdala_Volume_mm3   -0.358333
dtype: float64
Right-Hippocampus_Volume_mm3    0.275668
dtype: float64
Right-Lateral-Ventricle_Volume_mm3   -0.092283
dtype: float64
Right-Pallidum_Volume_mm3    0.279258
dtype: float64
Right-Putamen_Volume_mm3    0.484879
dtype: float64
Right-Thalamus-Proper_Volume_mm3    0.809775
dtype: float64

我想要左amygdala_volume_mm3 ...是带有值-0.29729的行，带有列名的cohen d是每个select_list的列：例如，数据框应如何外观

Answer 1

我仍然无法真正理解如何和何处，但是您在函数中显示了您能够构建一个float64系列，其中包含Left-Amygdala_Volume_mm3为index和 -0.29729作为值。我认为同时，您具有相同索引值的meand值。

我将更准确地假设：

k = pd.Series([-0.29729], dtype=np.float64,index=['Left-Amygdala_Volume_mm3'])

因为它打印为：

print(k)
Left-Amygdala_Volume_mm3   -0.29729
dtype: float64

同时，我认为meand也是一个类似的系列。因此，我们将以meand.iloc[0]的价格访问其值(假设值为9174.1(

您应该组合它们以构建一排的内容：

row = k.reset_index().iloc[0].tolist() + [meand.iloc[0]]

在示例中，我们有 row： ['Left-Amygdala_Volume_mm3', -0.29729, 9174.1]

因此，您现在需要构建一大列表：

def select_volumes(group_c,group_k):
    Select_list = ["Amygdala", "Hippocampus", "Lateral-Ventricle", 
"Pallidum", "Putamen", "Thalamus"]
    Side = ["Left", "Right"]
    data = []
    for s in Side:
        for struct in Select_list:
            volumes_c = group_c.filter(regex="^(?=.*"+s+")(?=.*"+struct+") 
   (?=.*Volume)")
            volumes_k = group_k.filter(regex="^(?=.*"+s+")(?=.*"+struct+") 
   (?=.*Volume)")
            k = cohens_d(volumes_c, volumes_k)
            meand = volumes_c.mean()
            # build a row of result df
            data.append(k.reset_index().iloc[0].tolist() + [meand.iloc[0]])
    # after the loop combine the rows into a dataframe and return it:
    result = pd.DataFrame(data, columns=['index', "Cohen's d", 'Mean']).set_index('index')
    return result

Answer 2

我在函数内写入pd.dataframe：

k = cohens_d(volumes_c, volumes_k)
meand = volumes_c.mean()    
volumes_df.append([cohen.index[0],cohen.values[0], meand)
return volumes_df

和我称为pd.dataframe的功能：

finaldf=pd.DataFrame(select_volumes(control,patolog))
finaldf.columns=['Structure','Cohensd','Meand')