Converting values into %

Question

I have a dataframe below, with sales of meat, vegetable and bread from each store. I would like to convert the values into %, for example, the value of Store N will become 74%, 7% and 19%. In other words, 74% is the % of sales of meat in terms of total sales of store N. What is the simplest way of doing it?

import pandas as pd

df=pd.DataFrame({'Store':['N','S','E','W']
                    ,'Meat':[200,250,100,400]
                    ,'Veg':[20,100,30,80]
                    ,'Bread':[50,230,150,100]})
df=df[['Store','Meat','Veg','Bread']]    

Answer 1

You can also use pandas.apply with a lambda function:

df.ix[:, 1:]=df.ix[:,1:].apply(lambda x: x*100/x.sum(), axis=1)

Which gives you:

  Store       Meat        Veg      Bread
0     N  74.074074   7.407407  18.518519
1     S  43.103448  17.241379  39.655172
2     E  35.714286  10.714286  53.571429
3     W  68.965517  13.793103  17.241379

Answer 2

You can just calculate the percentages manually:

df['MeatPerc'] = df['Meat']/df['Meat'].sum()

Answer 3

A pure pandas solution without using a cycle would be:

df.ix[:, 1:] = (df.ix[:, 1:].T / df.ix[:, 1:].sum(1)).T
print(df)

Result:

  Store      Meat       Veg     Bread
0     N  0.740741  0.074074  0.185185
1     S  0.431034  0.172414  0.396552
2     E  0.357143  0.107143  0.535714
3     W  0.689655  0.137931  0.172414

Answer 4

You can first set_index with column Store, then divide by div of sum and last reset_index:

df.set_index('Store', inplace=True)
df = df.div(df.sum(1), axis=0)
print (df.reset_index())
  Store      Meat       Veg     Bread
0     N  0.740741  0.074074  0.185185
1     S  0.431034  0.172414  0.396552
2     E  0.357143  0.107143  0.535714
3     W  0.689655  0.137931  0.172414

Another solution with selecting by ix or iloc:

df.ix[:,'Meat':] = df.ix[:,'Meat':].div(df.ix[:,'Meat':].sum(1), axis=0)
print (df)
  Store      Meat       Veg     Bread
0     N  0.740741  0.074074  0.185185
1     S  0.431034  0.172414  0.396552
2     E  0.357143  0.107143  0.535714
3     W  0.689655  0.137931  0.172414

---

df.iloc[:,1:] = df.iloc[:,1:].div(df.iloc[:,1:].sum(1), axis=0)
print (df)
  Store      Meat       Veg     Bread
0     N  0.740741  0.074074  0.185185
1     S  0.431034  0.172414  0.396552
2     E  0.357143  0.107143  0.535714
3     W  0.689655  0.137931  0.172414

Timings:

In [187]: %timeit (jez1(df))
100 loops, best of 3: 4.07 ms per loop

In [188]: %timeit (jez2(df1))
100 loops, best of 3: 5.61 ms per loop

In [189]: %timeit (jez3(df2))
100 loops, best of 3: 5.44 ms per loop

In [190]: %timeit (ric(df3))
100 loops, best of 3: 6.18 ms per loop

In [191]: %timeit (ogi(df4))
1 loop, best of 3: 2.2 s per loop

Code for timings:

#random dataframe
np.random.seed(100)

#10 data columns + first Store col, 10k rows
df = pd.DataFrame(np.random.randint(10, size=(10000,10)), columns=list('ABCDEFGHIJ'))
df.index = 'a' + df.index.astype(str)
df = df.reset_index().rename(columns={'index':'Store'})
print (df)
df1, df2, df3, df4 = df.copy(), df.copy(), df.copy(), df.copy()

def jez1(df):
    df = df.set_index('Store')
    df = 100 * df.div(df.sum(1), axis=0)
    return (df.reset_index())


def jez2(df):
    df.ix[:,'A':] = df.ix[:,'A':].div(df.ix[:,'A':].sum(1), axis=0)
    return df
def jez3(df):    
    df.iloc[:,1:] = df.iloc[:,1:].div(df.iloc[:,1:].sum(1), axis=0)
    return df

def ric(df):    
    df.ix[:, 1:] = (df.ix[:, 1:].T / df.ix[:, 1:].sum(1)).T
    return df

def ogi(df):    
    df.ix[:, 1:]=df.ix[:,1:].apply(lambda x: x/x.sum(), axis=1)
    return df    

print (jez1(df))
print (jez2(df1)) 
print (jez3(df2)) 
print (ric(df3))
print (ogi(df4))