Question

我有从Excel导入的4个数据集，其中包含2013,2014,2015和2016年学校的total_budget。所有数据集都有一个公共列，其中包含每个学校的ID代码（LAESTAB列）。

我想要一个新的数据集，其左边是公共列LAESTAB（4个数据集中的值相同），接下来是total2013，total2014，total2015和total2016（来自不同数据集）的列。

我还希望摆脱其余的数据，包括所有数据集中都没有的学校ID。

我将尝试在一个例子中进一步阐述它：

以下是其中一个Excel数据集的示例：

>>> print cuts2016.head()

    LA_codelocal_authority_name    UPIN  URN  LAESTAB  \
0      201       City of London  500000  0.0  2013614   
1      202               Camden  500005  0.0  2022095   
2      202               Camden  500007  0.0  2022219   
3      202               Camden  500012  0.0  2022502   
4      202               Camden  500014  0.0  2022603   

       School Name Academy?    Phase Provider Type  \
0  Sir John Cass's Foundation Primary School       No  Primary        School   
1                     Carlton Primary School       No  Primary        School   
2                       Fleet Primary School       No  Primary        School   
3                        Rhyl Primary School       No  Primary        School   
4                    Torriano Primary School       No  Primary        School   


   MFG protection (+ve) or capping/scaling (-ve)  total2016  \
0                                          35000    1659000   
1                                          68000    1956000   
2                                         -10000    1059000   
3                                          97000    2234000   
4                                              0    2284000

2005年的另一个Excel数据集：

print cuts2015.head()
   LA_code local_authority_name  UPIN     URN  LAESTAB  \
0      201       City of London   NaN  100000  2013614   
1      202               Camden   NaN  100008  2022019   
2      202               Camden   NaN  100009  2022036   
3      202               Camden   NaN  100010  2022065   
4      202               Camden   NaN  100011  2022078   

                                 school_name    Phase Provider Type  \
0  Sir John Cass's Foundation Primary School  Primary        School   
1                      Argyle Primary School  Primary        School   
2                    Beckford Primary School  Primary        School   
3                   Brecknock Primary School  Primary        School   
4                  Brookfield Primary School  Primary        School   

  Basic Entitlement Total Funding Deprivation Total Funding total_pre_MFG  \
0                       1,206,000                   215,000     1,644,000   
1                       1,333,000                   367,000     2,068,000   
2                       1,482,000                   359,000     2,221,000   
3                       1,234,000                   348,000     1,974,000   
4                       1,436,000                   256,000     2,028,000   

  MFG protection (+ve) or capping/scaling (-ve) total2015  \
0                                             0   1644000   
1                                        25,000   2093000   
2                                             0   2221000   
3                                        72,000   2046000   
4                                       -58,000   1970000

我需要的最终结果如下（应显示总计2014和总计2013）：

LAESTAB  total2016    total2015   etc...\
2013614  1956000      1644000      
2022019  1059000      2093000 
2022036  2234000      2221000 
2022065  2284000      1970000 
...

我尝试了'reduce'，如下所示，但它返回0行×66列。

dataframe_list = [cuts2013, cuts2014, cuts2015, cuts2016]
df_final = reduce(lambda left,right: pd.merge(left,right,on='LAESTAB'), dataframe_list)

Answer 1

使用LAESTAB列合并数据框SQL样式，然后根据需要从data_merged删除列。

import pandas as pd
data_merged = pd.merge(cuts2016,cuts2015,on = "LAESTAB")

有关合并的更多信息，请查看以下链接：

http://chrisalbon.com/python/pandas_join_merge_dataframe.html

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html

Answer 2

实现这一目标的一种方法是使用Mainul Islam指出的合并。在这里，您必须执行3次合并操作才能合并4个数据帧。否则，您可以连接所有4个数据帧并执行groupby操作。

dataframe_list = [cuts2013, cuts2014, cuts2015, cuts2016]
total = pd.concat(dataframe_list)
total = total.groupby('LAESTAB')['total2013', 'total2014', 'total2015','total2016'].sum().reset_index()

Pandas：根据公共列名

2 个答案: