pandas 在 groupby 聚合之后指定列顺序
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Specifying column order following groupby aggregation
提问by DJohnson1990
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?
我的年龄、身高和体重列的顺序随着代码的每次运行而变化。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。我该怎么做才能确保每次都以相同的顺序输出年龄、身高和体重？
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=',')
回答by jezrael
I think you can use subset:
我认为你可以使用子集：
df_out = df.groupby(df.index_col)
.agg({'age':np.mean, 'height':np.sum, 'weight':np.sum})[['age','height','weight']]
Also you can use pandasfunctions:
您也可以使用pandas函数：
df_out = df.groupby(df.index_col)
.agg({'age':'mean', 'height':sum, 'weight':sum})[['age','height','weight']]
Sample:
样本：
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
EDIT by suggestion - add reset_index, here as_index=Falsedoes not work if need index values too:
按建议编辑 - 添加reset_index，as_index=False如果也需要索引值，则此处不起作用：
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
回答by goodmami
If you care mostly about the order when written to a file and not while its still in a DataFrame object, you can set the columnsparameter of the to_csv()method:
如果您最关心写入文件时的顺序而不是它仍在 DataFrame 对象中时，您可以设置columns该to_csv()方法的参数：
>>> df = pd.DataFrame(
{'age': [28,63,28,45],
'height': [183,156,170,201],
'weight': [70.2, 62.5, 65.9, 81.0],
'name': ['Kim', 'Pat', 'Yuu', 'Sacha']},
columns=['name','age','weight', 'height'])
>>> df
name age weight height
0 Kim 28 70.2 183
1 Pat 63 62.5 156
2 Yuu 28 65.9 170
3 Sacha 45 81.0 201
>>> df_out = df.groupby(['age'], as_index=False).agg(
{'weight': sum, 'height': sum})
>>> df_out
age height weight
0 28 353 136.1
1 45 201 81.0
2 63 156 62.5
>>> df_out.to_csv('out.csv', sep=',', columns=['age','height','weight'])
out.csvthen looks like this:
out.csv然后看起来像这样：
,age,height,weight
0,28,353,136.10000000000002
1,45,201,81.0
2,63,156,62.5
