pandas 计算多列

Question

我有一个看起来像这样的数据框

Measure1 Measure2 Measure3 ...
0        1         3
1        3         2
3        0

我想计算要生成的列中值的出现次数：

Measure Count Percentage
0       2     0.25
1       2     0.25
2       1     0.125
3       3     0.373

有

outcome_measure_count = cdss_data.groupby(key_columns=['Measure1'],operations={'count': agg.COUNT()}).sort('count', ascending=True)

我只得到第一列（实际上使用 graphlab 包，但我更喜欢 pandas）

有人可以帮助我吗？

Answer 1

您可以通过使用 ravel 和 value_counts 展平 df 来生成计数，由此您可以构建最终的 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，但你应该明白这一点

Answer 2

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

pandas 计算多列

pandas count over multiple columns

python

pandas

graphlab