dplyr 通过多个函数汇总聚合的 pandas 类似功能是什么

相当于

df %>% groupby(col1) %>% summarize(col2_agg=max(col2), col3_agg=min(col3))

是

df.groupby('col1').agg({'col2': 'max', 'col3': 'min'})

它回来了

      col2  col3
col1            
1        5    -5
2        9    -9

The returning object 是 a pandas.DataFrame with an index called col1 and columns named col2 and col3. By default, when you group your data pandas sets the grouping column(s) as index for efficient access and modification. However, if you don't want that, there are two alternatives to set col1 as a column.

• 通过as_index=False分:

  df.groupby('col1', as_index=False).agg({'col2': 'max', 'col3': 'min'})
  

• 拨打reset_index:

  df.groupby('col1').agg({'col2': 'max', 'col3': 'min'}).reset_index()
  

两者都会屈服

col1  col2  col3           
   1     5    -5
   2     9    -9

您还可以将多个函数传递给groupby.agg.

agg_df = df.groupby('col1').agg({'col2': ['max', 'min', 'std'], 
                                 'col3': ['size', 'std', 'mean', 'max']})

Th是 also returns a DataFrame but now it has a MultiIndex for columns.

     col2               col3                   
      max min       std size       std mean max
col1                                           
1       5   1  1.581139    5  1.581139   -3  -1
2       9   0  3.535534    5  3.535534   -6   0

MultiIndex 是 very handy for selection and grouping. Here are some examples:

agg_df['col2']  # select the second column
      max  min       std
col1                    
1       5    1  1.581139
2       9    0  3.535534

agg_df[('col2', 'max')]  # select the maximum of the second column
Out: 
col1
1    5
2    9
Name: (col2, max), dtype: int64

agg_df.xs('max', ax是=1, level=1)  # select the maximum of all columns
Out: 
      col2  col3
col1            
1        5    -1
2        9     0

早些时候(version 0.20.0年之前)，可以使用字典来重命名agg调用中的列.例如

df.groupby('col1')['col2'].agg({'max_col2': 'max'})

将第二列的最大值返回为max_col2:

      max_col2
col1          
1            5
2            9

然而，人们反对使用重命名方法:

df.groupby('col1')['col2'].agg(['max']).rename(columns={'max': 'col2_max'})

      col2_max
col1          
1            5
2            9

对于上面定义的agg_df这样的数据帧，它可能会变得冗长.在这种情况下，您可以使用重命名功能展平这些级别:

agg_df.columns = ['_'.join(col) for col in agg_df.columns]

      col2_max  col2_min  col2_std  col3_size  col3_std  col3_mean  col3_max
col1                                                                        
1            5         1  1.581139          5  1.581139         -3        -1
2            9         0  3.535534          5  3.535534         -6         0

对于像groupby().summarize(newcolumn=max(col2 * col3))这样的操作，仍然可以使用agg，方法是首先添加一个带有assign的新列.

df.assign(new_col=df.eval('col2 * col3')).groupby('col1').agg('max') 

      col2  col3  new_col
col1                     
1        5    -1       -1
2        9     0        0

Th是 returns maximum for old and new columns but as always you can slice that.

df.assign(new_col=df.eval('col2 * col3')).groupby('col1')['new_col'].agg('max')

col1
1   -1
2    0
Name: new_col, dtype: int64

With groupby.apply th是 would be shorter:

df.groupby('col1').apply(lambda x: (x.col2 * x.col3).max())

col1
1   -1
2    0
dtype: int64

However, groupby.apply treats th是 as a custom function so it 是 not vectorized. Up to now, the functions we passed to agg ('min', 'max', 'min', 'size' etc.) are vectorized and these are aliases for those optimized functions. You can replace df.groupby('col1').agg('min') with df.groupby('col1').agg(min), df.groupby('col1').agg(np.min) or df.groupby('col1').min() and they will all execute the same function. You will not see the same efficiency when you use custom functions.

最后，从版本0.20开始，agg可以直接用于数据帧，而无需先分组.参见示例here.