pandas使用concat基於單個列添加和重命名多個列

Question

我有這個df：

  group owner  failed granted_pe  slots
0    g1    u1       0     single      1
1   g50   u92       0     shared      8
2   g50   u92       0     shared      1

可以使用以下代碼創建df ：

df = pd.DataFrame([['g1', 'u1', 0, 'single', 1],
                   ['g50', 'u92', '0', 'shared', '8'],
                   ['g50', 'u92', '0', 'shared', '1']], 
                  columns=['group', 'owner', 'failed','granted_pe', 'slots'])
df = (df.astype(dtype={'group':'str', 'owner':'str','failed':'int', 'granted_pe':'str', 'slots':'int'}))
print(df)

使用groupby，我創建了三個在“ slots”列上計算的列：

df_calculated = pd.concat([
    df.loc[:,['group', 'slots']].groupby(['group']).sum(),
    df.loc[:,['group', 'slots']].groupby(['group']).mean(),
    df.loc[:,['group', 'slots']].groupby(['group']).max()
    ], axis=1)
print(df_calculated)

       slots  slots  slots
group                     
g1         1    1.0      1
g50        9    4.5      8

問題1 ：適當命名新列
我可以在concat中添加參數以將這些列命名為“ slots_sum”，“ slots_avg”和“ slots_max”嗎？

問題2 ：將列添加到df
我希望將新列添加到df的“源”列（在本例中為“插槽”）的右側。 所需的輸出如下所示：

  group owner  failed granted_pe  slots  slots_sum  slots_avg  slots_max
0    g1    u1       0     single      1          1        1.0          1
1   g50   u92       0     shared      8          9        4.5          8
2   g50   u92       0     shared      1  

我的實際df是450萬行（23列）。 我將對其他專欄做類似的事情。

Answer 1

將agg與add_prefix使用，然后merge其merge回去

yourdf=df.merge(df.groupby('group')['slots'].agg(['sum','mean','max']).add_prefix('slots_').reset_index(),how='left')
Out[86]: 
  group owner  failed    ...     slots_sum  slots_mean  slots_max
0    g1    u1       0    ...             1         1.0          1
1   g50   u92       0    ...             9         4.5          8
2   g50   u92       0    ...             9         4.5          8

Answer 2

另一種方法是在pd.concat中使用keys參數，然后合並multiindex列標題

df = pd.DataFrame([['g1', 'u1', 0, 'single', 1],
                   ['g50', 'u92', '0', 'shared', '8'],
                   ['g50', 'u92', '0', 'shared', '1']], 
                  columns=['group', 'owner', 'failed','granted_pe', 'slots'])
df = (df.astype(dtype={'group':'str', 'owner':'str','failed':'int', 'granted_pe':'str', 'slots':'int'}))

df_calculated = pd.concat([
    df.loc[:,['group', 'slots']].groupby(['group']).sum(),
    df.loc[:,['group', 'slots']].groupby(['group']).mean(),
    df.loc[:,['group', 'slots']].groupby(['group']).max()
    ], axis=1, keys=['sum','mean','max'])
df_calculated.columns = [f'{j}_{i}' for i,j in df_calculated.columns]
print(df_calculated)

輸出：

       slots_sum  slots_mean  slots_max
group                                  
g1             1         1.0          1
g50            9         4.5          8