Python 熊猫在多列上计数
我有一个像这样的数据框Python 熊猫在多列上计数,python,pandas,graphlab,Python,Pandas,Graphlab,我有一个像这样的数据框 Measure1 Measure2 Measure3 ... 0 1 3 1 3 2 3 0 我想计算列上出现的值,以生成: Measure Count Percentage 0 2 0.25 1 2 0.25 2 1 0.125 3 3 0.373 与 我只得到第一列(实际上使用graphlab包,
Measure1 Measure2 Measure3 ...
0 1 3
1 3 2
3 0
我想计算列上出现的值,以生成:
Measure Count Percentage
0 2 0.25
1 2 0.25
2 1 0.125
3 3 0.373
与
我只得到第一列(实际上使用graphlab包,但我更喜欢pandas)
有人能帮我吗 您可以通过使用
ravel
和value\u counts
展平df来生成计数,由此您可以构建最终df:
In [230]:
import io
import pandas as pd
t="""Measure1 Measure2 Measure3
0 1 3
1 3 2
3 0 0"""
df = pd.read_csv(io.StringIO(t), sep='\s+')
df
Out[230]:
Measure1 Measure2 Measure3
0 0 1 3
1 1 3 2
2 3 0 0
In [240]:
count = pd.Series(df.squeeze().values.ravel()).value_counts()
pd.DataFrame({'Measure': count.index, 'Count':count.values, 'Percentage':(count/count.sum()).values})
Out[240]:
Count Measure Percentage
0 3 3 0.333333
1 3 0 0.333333
2 2 1 0.222222
3 1 2 0.111111
我插入了一个
0
,只是为了使df形状正确,但是你应该得到点,当这个部分是更大df的一部分时?所以我需要指定列?当使用:count=pd.Series(cdss_数据['measure1','measure2'].squage().values.ravel()).value_counts()时,我得到一个错误(cdss_数据是我的df),你需要双下标count=pd.Series(cdss_数据['measure1','measure2'].squage().values.ravel()).value_counts()
太棒了!有没有一种方法可以强制列的顺序和行的顺序?你可以使用奇特的索引,例如:desired\u col\u list=[a,b,c,d]
df=df.ix[:,desired\u col\u list]
In [68]: df=DataFrame({'m1':[0,1,3], 'm2':[1,3,0], 'm3':[3,2, np.nan]})
In [69]: df
Out[69]:
m1 m2 m3
0 0 1 3.0
1 1 3 2.0
2 3 0 NaN
In [70]: df=df.apply(Series.value_counts).sum(1).to_frame(name='Count')
In [71]: df
Out[71]:
Count
0.0 2.0
1.0 2.0
2.0 1.0
3.0 3.0
In [72]: df.index.name='Measure'
In [73]: df
Out[73]:
Count
Measure
0.0 2.0
1.0 2.0
2.0 1.0
3.0 3.0
In [74]: df['Percentage']=df.Count.div(df.Count.sum())
In [75]: df
Out[75]:
Count Percentage
Measure
0.0 2.0 0.250
1.0 2.0 0.250
2.0 1.0 0.125
3.0 3.0 0.375
In [68]: df=DataFrame({'m1':[0,1,3], 'm2':[1,3,0], 'm3':[3,2, np.nan]})
In [69]: df
Out[69]:
m1 m2 m3
0 0 1 3.0
1 1 3 2.0
2 3 0 NaN
In [70]: df=df.apply(Series.value_counts).sum(1).to_frame(name='Count')
In [71]: df
Out[71]:
Count
0.0 2.0
1.0 2.0
2.0 1.0
3.0 3.0
In [72]: df.index.name='Measure'
In [73]: df
Out[73]:
Count
Measure
0.0 2.0
1.0 2.0
2.0 1.0
3.0 3.0
In [74]: df['Percentage']=df.Count.div(df.Count.sum())
In [75]: df
Out[75]:
Count Percentage
Measure
0.0 2.0 0.250
1.0 2.0 0.250
2.0 1.0 0.125
3.0 3.0 0.375