Pandas python .describe() 格式/输出
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Pandas python .describe() formatting/output
提问by Mike
I am trying to get the .describe()function to output in a reformatted way.
Here is the csv data (testProp.csv)
我试图让.describe()函数以重新格式化的方式输出。这是 csv 数据 ( testProp.csv)
'name','prop'
A,1
A,2
B, 4
A, 3
B, 5
B, 2
when I type in the following:
当我输入以下内容时：
from pandas import *
data = read_csv('testProp.csv')
temp = data.groupby('name')['prop'].describe()
temp.to_csv('out.csv')
the output is:
输出是：
name
A count 3.000000
mean 2.000000
std 1.000000
min 1.000000
25% 1.500000
50% 2.000000
75% 2.500000
max 3.000000
B count 3.000000
mean 3.666667
std 1.527525
min 2.000000
25% 3.000000
50% 4.000000
75% 4.500000
max 5.000000
dtype: float64
However, I want the data in the format below. I have tried transpose()and would like to maintain using the describe()and manipulate that instead of a .agg([np.mean(), np.max(), etc.... ):
但是，我想要以下格式的数据。我已经尝试transpose()并希望保持使用describe()并操纵它而不是a .agg([np.mean(), np.max(), etc.... )：
count mean std min 25% 50% 75% max
A 3 2 1 1 1.5 2 2.5 3
B 3 3.666666667 1.527525232 2 3 4 4.5 5
采纳答案by Anand S Kumar
One way to do this would be to first do .reset_index(), to reset the index for your tempDataFrame, and then use DataFrame.pivotas you want . Example -
执行此操作的一种方法是首先执行.reset_index()，重置tempDataFrame的索引，然后DataFrame.pivot根据需要使用。例子 -
In [24]: df = pd.read_csv(io.StringIO("""name,prop
....: A,1
....: A,2
....: B, 4
....: A, 3
....: B, 5
....: B, 2"""))
In [25]: temp = df.groupby('name')['prop'].describe().reset_index()
In [26]: newdf = temp.pivot(index='name',columns='level_1',values=0)
In [27]: newdf.columns.name = '' #This is needed so that the name of the columns is not `'level_1'` .
In [28]: newdf
Out[28]:
25% 50% 75% count max mean min std
name
A 1.5 2 2.5 3 3 2.000000 1 1.000000
B 3.0 4 4.5 3 5 3.666667 2 1.527525
Then you can save this newdfto csv.
然后您可以将其保存newdf到 csv。
回答by Vitalis
In pandas v0.22, you can use the unstack feature. Building on from @Kumar answer above, you can use the pandas stack/unstack feature and play around with it's variation.
在 pandas v0.22 中，您可以使用 unstack 功能。基于上面的@Kumar 回答，您可以使用 Pandas 堆栈/取消堆栈功能并尝试使用它的变体。
from io import StringIO
import pandas as pd
df = pd.read_csv(StringIO("""name,prop
A,1
A,2
B, 4
A, 3
B, 5
B, 2"""))
df.shape
df
temp = df.groupby(['name'])['prop'].describe()
temp
temp.stack() #unstack(),unstack(level=-1) level can be -1, 0
Check out the documentation pandas unstackfor more details
查看文档pandas unstack以获取更多详细信息
