Question

我有一个包含多列的Pandas DataFrame。

arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(3, 8), index=['A', 'B', 'C'], columns=index)
print(df)

first        bar                 baz                 foo                 qux  \
second       one       two       one       two       one       two       one   
A      -0.093829 -0.159939 -0.386961 -0.367417  0.625646  1.286186  0.429855   
B       0.440266  0.345161  1.798363 -1.265215  0.204303 -1.492993 -1.714360   
C       0.689076 -1.211060 -0.265888  0.769467 -0.706941  0.086907 -0.892892 

first             
second       two  
A      -1.006210  
B      -0.275578  
C      -0.563757

我想计算每列的平均值和标准偏差，按上一列分组。一旦我计算了平均值和标准偏差，我想将下一级中的列加倍，将与统计操作相关的信息（平均值或标准偏差）添加到列名称为＆＃34;列名称＆＃34; +＆＃34; _＆＃34; +＆＃34; std / mean＆＃34;。

group_cols = df.groupby(df.columns.get_level_values('first'), axis=1)
list_stat_dfs = []
for key, group in group_cols:
    group_descr = group.describe().loc[['mean', 'std'], :]  # Get mean and std from single site
    group_descr.loc[:, (key, 'stats')] = group_descr.index
    group_descr.loc[:, (key, 'first')] = key
    group_descr.columns = group_descr.columns.droplevel(0)  # Remove upper level column (site_name)
    group_descr = group_descr.pivot(columns='stats', index='first')  # Rows to columns
    col_prod = list(product(group_descr.columns.levels[0], group_descr.columns.levels[1]))
    cols = ['_'.join((col[0], col[1])) for col in col_prod]
    group_descr.columns = pd.MultiIndex.from_product(([key], cols))  # From multiple columns to single column
    group_descr.reset_index(inplace=True)
    list_stat_dfs.append(group_descr)

group_descr = pd.concat(list_stat_dfs, axis=1)
print(group_descr)

first       bar                              first       baz            \
         one_mean   one_std  two_mean  two_std        one_mean   one_std   
0   bar  0.507185  1.799053 -0.249692  1.41507   baz -0.147664  0.595927  

                     first       foo                               first  \
   two_mean   two_std        one_mean   one_std  two_mean   two_std         
0  0.160018  1.405113   foo -0.433644  1.245972  0.254995  0.846983   qux 

        qux                                
   one_mean   one_std  two_mean   two_std  
0  0.667629  0.315417 -0.757989  0.683273

正如您所看到的，我已经能够使用for循环和一些代码来管理它。有人可以用更优化的方式做同样的事情。我很确定使用Pandas，可以用几行代码完成同样的事情。

Answer 1

我认为您需要获得mean的{{1}}和std，然后concat，然后unstack重新塑造：

df

计算具有多列的pandas数据框中的聚合值

1 个答案: