Python 按绝对值对系列进行排序

我有一个熊猫系列，如下所示：

admission_age_inyears                                           [3.86703690989]
emergencydepartmentlengthofstayminutes                          [3.84708584711]
current_los_from_admissions                                     [3.83956976064]
total_time_in_progressive_inpatient                             [3.63955027973]
total_time_spent_inpatient                                      [2.59339330312]
nbr_of_hosp_last_90_days                                        [2.44570139977]
total_time_spent_in_er                                          [2.37914969651]
prior_admittype_emergency                                       [2.18467109815]
nbr_inpatient_visits                                            [2.09615621507]
curr_rx_gen_atorvastatin_calcium                                [2.08752966479]
substanceusehistory       
curr_rx_gen_tranexamic_acid                                    [-1.87352757522]
total_current_thera_ana                                        [-1.89007660143]
total_surgery_duration                                         [-2.50947580961]
avg_surgery_duration                                           [-2.58916844719]
curr_rx_gen_celecoxib                                           [-2.6594131822]
curr_rx_gen_propofol_iv                                        [-3.04498593439]
curr_rx_gen_fentanyl_citrate                                   [-3.14017330213]
curr_rx_gen_ketorolac_tromethamine                             [-3.14424766125]
curr_rx_gen_acetaminophen                                      [-3.47925239986]
tot_est_median_income_dollars                                  [-3.59465137553]
curr_rx_gen_midazolam_hcl                                      [-3.73260903286]

我想按降序对这个系列进行排序，这样最高的绝对值特性应该位于顶部。因此，在这里，所有权重为3+的特征，不管它是负的还是正的，都将出现在顶部，然后是所有的2，依此类推。还希望相应的实际值与名称一起出现

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请建议对值进行排序，您可以执行以下操作

In [95]:
order = S.map(lambda x : x[0]).abs().sort_values(ascending = False)
order
Out[95]:
admission_age_inyears                     3.867037
emergencydepartmentlengthofstayminutes    3.847086
current_los_from_admissions               3.839570
curr_rx_gen_midazolam_hcl                 3.732609
total_time_in_progressive_inpatient       3.639550
tot_est_median_income_dollars             3.594651
curr_rx_gen_acetaminophen                 3.479252
curr_rx_gen_ketorolac_tromethamine        3.144248
curr_rx_gen_fentanyl_citrate              3.140173
curr_rx_gen_propofol_iv                   3.044986
curr_rx_gen_celecoxib                     2.659413
total_time_spent_inpatient                2.593393
avg_surgery_duration                      2.589168
total_surgery_duration                    2.509476
nbr_of_hosp_last_90_days                  2.445701
total_time_spent_in_er                    2.379150
prior_admittype_emergency                 2.184671
nbr_inpatient_visits                      2.096156
curr_rx_gen_atorvastatin_calcium          2.087530
total_current_thera_ana                   1.890077
curr_rx_gen_tranexamic_acid               1.873528
substanceusehistory                            NaN
dtype: float64

如果要对值进行排序并保留与原始序列中相同的数据，可以执行以下操作

In [96]:
order.index
S[order.index]
Out[96]:
admission_age_inyears                      [3.86703690989]
emergencydepartmentlengthofstayminutes     [3.84708584711]
current_los_from_admissions                [3.83956976064]
curr_rx_gen_midazolam_hcl                 [-3.73260903286]
total_time_in_progressive_inpatient        [3.63955027973]
tot_est_median_income_dollars             [-3.59465137553]
curr_rx_gen_acetaminophen                 [-3.47925239986]
curr_rx_gen_ketorolac_tromethamine        [-3.14424766125]
curr_rx_gen_fentanyl_citrate              [-3.14017330213]
curr_rx_gen_propofol_iv                   [-3.04498593439]
curr_rx_gen_celecoxib                      [-2.6594131822]
total_time_spent_inpatient                 [2.59339330312]
avg_surgery_duration                      [-2.58916844719]
total_surgery_duration                    [-2.50947580961]
nbr_of_hosp_last_90_days                   [2.44570139977]
total_time_spent_in_er                     [2.37914969651]
prior_admittype_emergency                  [2.18467109815]
nbr_inpatient_visits                       [2.09615621507]
curr_rx_gen_atorvastatin_calcium           [2.08752966479]
total_current_thera_ana                   [-1.89007660143]
curr_rx_gen_tranexamic_acid               [-1.87352757522]
substanceusehistory                                  [nan]
dtype: object

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你能发布原始输入数据和代码来重现你的df吗，不清楚您的值是float还是带有单个float元素的列表，还是string。这是一个以系列索引作为功能名称、以系列列作为值列表的系列。它是一个带有单个float元素的列表，对应于每个索引功能请参见此处-另一种方式

df.iloc[df.col_name.abs（）.argsort（）]

：）