如何基于另一个数据框的多索引值应用数据框的值？

Question

我有以下内容：

数据框1（多索引数据框）：

                             |      Assay_A       |
---------------------------------------------------
Index_A | Index_B | Index_C  | mean | std | count |
---------------------------------------------------
   128     12345      AAA      123    2     4

数据框2：

    Index | Col_A | Col_B | Col_C | mean
    -------------------------------------
      1      128    12345   AAA     456

其中Col_X = a，b，c的Index_X。

我整个上午都在尝试以下操作：

我如何在数据框2中选择正确的均值（必须在Col ABC上进行匹配），以便可以对其进行数学运算。 例如，我要取数据帧1的平均值，然后将其除以正确选择的数据帧2的平均值。

理想情况下，我想将操作结果存储在新列中。 因此，最终输出应如下所示：

                             |            Assay_A          |
------------------------------------------------------------
Index_A | Index_B | Index_C  | mean | std | count | result |
------------------------------------------------------------
   128     12345      AAA      123    2     4       0.26

也许有一种更简单的方法可以做到这一点，我也会接受任何此类建议。

Answer 1

我建议您做的是1）将Dataframe 2的列重命名为Dataframe 1的索引列的相应名称，2）重置Dataframe 1的索引，并3）根据现在匹配的列名称合并两个表。 之后，您可以计算所需的任何数据。 数据帧2列上的MultiIndex增加了一些额外的开销。

明确：

import pandas as pd

# re-create table1
row_index = pd.MultiIndex.from_tuples([(128, 12345, 'AAA')])
row_index.names=['Index_A', 'Index_B', 'Index_C']
table1 = pd.DataFrame(data={'mean': 123, 'std': 2, 'count': 4}, index=row_index)
table1.columns = pd.MultiIndex.from_tuples(zip(['Assay A'] * 3, table1.columns))
print "*** table 1:"
print table1
print ""

# re-create table2
table2 = pd.DataFrame([{'Col_A': 128, 'Col_B': 12345, 'Col_C': 'AAA', 'mean': 456}], index=[1])
table2.index.name = 'Index'
print "*** table 2:"
print table2
print ""

# re-name columns of table2 to match names of respective index columns in table1
table2 = table2.rename(columns={'Col_A': 'Index_A', 'Col_B': 'Index_B', 'Col_C': 'Index_C'})

# Drop 'Assay A' index level on columns of table1;
# without doing that, the following reset_index() will produce a column multi-index
# for Index_A/B/C, so column names will not match the simple column index of table2_renamed.
# If you need to keep the 'Assay A' level here, you will need to also construct a column
# multi-index for table2_renamed (with empty values for the second level).
table1.columns = table1.columns.levels[1]

# Move index columns of table1 back to regular columns
table1 = table1.reset_index()

# Merge the two tables on the now common column names. 'mean' appears in both tables,
# give the column from table2 a suffix '_2'.
joint = pd.merge(table1.reset_index(), table2, on=['Index_A', 'Index_B', 'Index_C'], suffixes={'', '_2'})
print "*** joint, before re-setting index:"
print joint
print ""

# Restore index of the joint table
joint = joint.set_index(['Index_A', 'Index_B', 'Index_C'])

# Compute the 'result'
joint['result'] = joint['mean'] / joint['mean_2']

# drop unused columns
joint = joint.drop(['index', 'mean_2'], axis=1)

# restore column index level
joint.columns = pd.MultiIndex.from_tuples(zip(['Assay A'] * 4, joint.columns))

print "*** final result:"
print joint
print ""

脚本输出为：

*** table 1:
                        Assay A         
                          count mean std
Index_A Index_B Index_C                 
128     12345   AAA           4  123   2

*** table 2:
       Col_A  Col_B Col_C  mean
Index                          
1        128  12345   AAA   456

*** joint, before re-setting index:
   index  Index_A  Index_B Index_C  count  mean  std  mean_2
0      0      128    12345     AAA      4   123    2     456

*** final result:
                        Assay A                   
                          count mean std    result
Index_A Index_B Index_C                           
128     12345   AAA           4  123   2  0.269737

希望有帮助！