pandas使用concat基于单列添加和重命名多个列

Question

我有这个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

可以使用以下代码创建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我创建了在“slots”列上计算的三列：

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

问题1：适当地命名新列 我可以在concat中添加一个参数来命名这些列“slots_sum”，“slots_avg”和“slots_max”吗？

问题2：向df添加列 我更愿意将新列添加到“源”列右侧的df（在本例中为“slots”）。期望的输出看起来像这样：

  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  

我的实际df是4.5密耳行，23列。我想为其他专栏做类似的事情。

Answer 1

使用agg与add_prefix然后merge它回来

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

另一种方法是在pd.concat中使用keys参数，然后合并多索引列标题

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)

输出：

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