修改 Python Pandas 的输出描述
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Modify output from Python Pandas describe
提问by KHibma
Is there a way to omit some of the output from the pandas describe? This command gives me exactly what I want with a table output (count and mean of executeTime's by a simpleDate)
有没有办法省略熊猫描述的一些输出？这个命令给了我我想要的表格输出（通过 simpleDate 计算 executeTime 的计数和平均值）
df.groupby('simpleDate').executeTime.describe().unstack(1)
However that's all I want, count and mean. I want to drop std, min, max, etc... So far I've only read how to modify column size.
然而，这就是我想要的，数数和意思。我想删除 std、min、max 等......到目前为止，我只阅读了如何修改列大小。
I'm guessing the answer is going to be to re-write the line, not using describe, but I haven't had any luck grouping by simpleDate andgetting the count with a mean on executeTime.
我猜答案将是重新编写该行，而不是使用描述，但是我没有通过 simpleDate 进行分组并在 executeTime 上获得平均值的计数。
I can do count by date:
我可以按日期计算：
df.groupby(['simpleDate']).size()
or executeTime by date:
或按日期执行时间：
df.groupby(['simpleDate']).mean()['executeTime'].reset_index()
But can't figure out the syntax to combine them.
但无法弄清楚将它们组合起来的语法。
My desired output:
我想要的输出：
count mean
09-10-2013 8 20.523
09-11-2013 4 21.112
09-12-2013 3 18.531
... .. ...
采纳答案by Jeff
Describe returns a series, so you can just select out what you want
描述返回一个系列，所以你可以选择你想要的
In [6]: s = Series(np.random.rand(10))
In [7]: s
Out[7]:
0 0.302041
1 0.353838
2 0.421416
3 0.174497
4 0.600932
5 0.871461
6 0.116874
7 0.233738
8 0.859147
9 0.145515
dtype: float64
In [8]: s.describe()
Out[8]:
count 10.000000
mean 0.407946
std 0.280562
min 0.116874
25% 0.189307
50% 0.327940
75% 0.556053
max 0.871461
dtype: float64
In [9]: s.describe()[['count','mean']]
Out[9]:
count 10.000000
mean 0.407946
dtype: float64
回答by Rafa
.describe()attribute generates a dataframe where count,std,max... are values of the index, so according to the documentationyou should use, for example:
.describe()属性生成一个数据帧，其中 count,std,max... 是索引的值，因此根据您应该使用的文档，例如：
df.describe().loc[['count','max']]
回答by st19297
The solution @Jeff provided just works for series.
@Jeff 提供的解决方案仅适用于系列。
@Rafa is on the point: df.describe().info()reveals that the resulting dataframe has Index: 8 entries, count to max
@Rafa 说到点子上了：df.describe().info()揭示结果数据帧有Index: 8 entries, count to max
df.describe().loc[['count','max']]does work, but df.groupby('simpleDate').describe().loc[['count','max']], which is what the OP asked, does not work.
df.describe().loc[['count','max']]确实有效，但是df.groupby('simpleDate').describe().loc[['count','max']]，这是 OP 所要求的，不起作用。
I think a solution may be this:
我认为一个解决方案可能是这样的：
df = pd.DataFrame({'Y': ['A', 'B', 'B', 'A', 'B'],
'Z': [10, 5, 6, 11, 12],
})
grouping the df by Y:
将 df 分组为Y：
df_grouped=df.groupby(by='Y')
In [207]df_grouped.agg([np.mean, len])
Out[207]:
Z
mean len
Y
A 10.500 2
B 7.667 3
回答by Geoff Counihan
Sticking with describe, you can unstack the indexes and then slice normally too
坚持使用describe，您可以拆开索引，然后也可以正常切片
df.describe().unstack()[['count','max']]
df.describe().unstack()[['count','max']]
