如何在python中获取行基础行中每个值的百分比

Question

I have the below data:

id  hours       class
1   67.91       V
1   65.56       V
1   51.14       V
1   41.51       V
1   33.55       V
1   26.45       G
1   26.09       V
1   25.77       G
1   25.50       P
1   25.13       G
1   24.49       P
1   21.88       B
1   18.57       V
1   17.90       B
...

18  92.2        B
18  81.06       V
18  70.48       V
18  67.10       B
18  62.92       B
18  62.88       V
18  54.36       B
18  52.77       V
18  44.55       V
18  40.61       P
18  40.51       P
18  40.06       V
18  37.67       V
18  33.78       B

I essentially need to get the data in pivot format and calculate the total hours within each class as a percentage of the total hours for each household in the data:

Expected Output:

id  B       G       P       V       Total
1   8.44%   16.41%  10.60%  64.55%  100.00%
18  39.74%  0.0%    10.39%  49.87%  100.00%

Can someone please help me with this? This has to be done id/row wise. The data is in a pandas data-frame.

Answer 1

I believe you need groupby + sum + unstack or pivot_table for pivoting:

df = df.groupby(['id','class'])['hours'].sum().unstack(fill_value=0)

---

df = df.pivot_table(index='id', columns='class', values='hours', aggfunc='sum', fill_value=0)

And then divide by div sum per rows, multiple by 100 , round and last add new column Total by assign with check if get 100 , thanks Paul H for idea:

df = df.div(df.sum(1), 0).mul(100).round(2).assign(Total=lambda df: df.sum(axis=1))
print (df)
class      B      G      P      V  Total
id                                      
1       8.44  16.41  10.60  64.55  100.0
18     39.74   0.00  10.39  49.87  100.0

And for percentage convert to string s and add % :

df1 = df.astype(str) + '%'
print (df1)
class       B       G       P       V   Total
id                                           
1       8.44%  16.41%   10.6%  64.55%  100.0%
18     39.74%    0.0%  10.39%  49.87%  100.0%

Timings :

np.random.seed(123)
N = 100000
L = list('BGPV')

df = pd.DataFrame({'class': np.random.choice(L, N),
                   'hours':np.random.rand(N),
                   'id':np.random.randint(20000, size=N)})
print (df)


def dark1(df):
    ndf = df.groupby('id').apply(lambda x : x.groupby('class')['hours'].sum()/x['hours'].sum())\
                          .reset_index().pivot(columns='class',index='id')*100
    return ndf.assign(Total=ndf.sum(1)).fillna(0)

def dark2(df):
    one =  df.groupby('id')['hours'].sum()
    two = df.pivot_table(index='id',values='hours',columns='class',aggfunc=sum)

    ndf = pd.DataFrame(two.values / one.values[:,None]*100,columns=two.columns)
    return ndf.assign(Total=ndf.sum(1)).fillna(0)

def jez1(df):
    df = df.groupby(['id','class'])['hours'].sum().unstack(fill_value=0)
    return df.div(df.sum(1), 0).mul(100).assign(Total=lambda df: df.sum(axis=1))

def jez2(df):
    df = df.pivot_table(index='id', columns='class', values='hours', aggfunc='sum', fill_value=0)
    return df.div(df.sum(1), 0).mul(100).assign(Total=lambda df: df.sum(axis=1))

print (dark1(df))
print (dark2(df))
print (jez1(df))
print (jez2(df))

---

In [39]: %timeit (dark1(df))
1 loop, best of 3: 15.4 s per loop

In [40]: %timeit (dark2(df))
10 loops, best of 3: 52.7 ms per loop

In [41]: %timeit (jez1(df))
10 loops, best of 3: 38.8 ms per loop

In [42]: %timeit (jez2(df))
10 loops, best of 3: 44.9 ms per loop

Caveat

The results do not address performance given the number of groups, which will affect timings for some of these solutions.

Answer 2

Another way is to use nested groupby ie

ndf = df.groupby('id').apply(lambda x : x.groupby('class')['hours'].sum()/x['hours'].sum())\
                      .reset_index().pivot(columns='class',index='id')*100
ndf = ndf.assign(Total=ndf.sum(1)).fillna(0)

           hours                                  Total
class          B         G          P          V       
id                                                     
1       8.437798  16.40683  10.603457  64.551914  100.0
18     39.741341         0  10.387349  49.871311  100.0

Or :

one =  df.groupby('id')['hours'].sum()
two = df.pivot_table(index='id',values='hours',columns='class',aggfunc=sum)

ndf = pd.DataFrame(two.values / one.values[:,None]*100,columns=two.columns)
ndf = ndf.assign(Total=ndf.sum(1)).fillna(0)

class          B         G          P          V  Total
0       8.437798  16.40683  10.603457  64.551914  100.0
1      39.741341   0.00000  10.387349  49.871311  100.0