在 groupby 聚合之后指定列顺序

Specifying column order following groupby aggregation(在 groupby 聚合之后指定列顺序)

问题描述

每次运行代码时，我的年龄、身高和体重列的顺序都会发生变化.我需要保持 agg 列的顺序不变，因为我最终会根据列位置引用此输出文件.如何确保每次都以相同的顺序输出年龄、身高和体重?

The ordering of my age, height and weight columns is changing with each run of the code. I need to keep the order of my agg columns static because I ultimately refer to this output file according to the column locations. What can I do to make sure age, height and weight are output in the same order every time?

d = pd.read_csv(input_file, na_values=[''])
df = pd.DataFrame(d)
df.index_col = ['name', 'address']

df_out = df.groupby(df.index_col).agg({'age':np.mean, 'height':np.sum, 'weight':np.sum})
df_out.to_csv(output_file, sep=',')

推荐答案

我觉得你可以使用subset:

I think you can use subset:

df_out = df.groupby(df.index_col)
           .agg({'age':np.mean, 'height':np.sum, 'weight':np.sum})[['age','height','weight']]

你也可以使用 pandas 函数:

Also you can use pandas functions:

df_out = df.groupby(df.index_col)
           .agg({'age':'mean', 'height':sum, 'weight':sum})[['age','height','weight']]

示例:

df = pd.DataFrame({'name':['q','q','a','a'],
                   'address':['a','a','s','s'],
                   'age':[7,8,9,10],
                   'height':[1,3,5,7],
                   'weight':[5,3,6,8]})

print (df)
  address  age  height name  weight
0       a    7       1    q       5
1       a    8       3    q       3
2       s    9       5    a       6
3       s   10       7    a       8
df.index_col = ['name', 'address']
df_out = df.groupby(df.index_col)
           .agg({'age':'mean', 'height':sum, 'weight':sum})[['age','height','weight']]

print (df_out)
              age  height  weight
name address                     
a    s        9.5      12      14
q    a        7.5       4       8

根据建议编辑 - 添加 reset_index，如果也需要索引值，这里 as_index=False 不起作用:

EDIT by suggestion - add reset_index, here as_index=False does not work if need index values too:

df_out = df.groupby(df.index_col)
           .agg({'age':'mean', 'height':sum, 'weight':sum})[['age','height','weight']]
           .reset_index()

print (df_out)
  name address  age  height  weight
0    a       s  9.5      12      14
1    q       a  7.5       4       8

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