# 在 groupby 聚合之后指定列顺序 [英] Specifying column order following groupby aggregation

### 问题描述

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_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

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']]
``````

Also you can use `pandas` functions:

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

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

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

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

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)