Priority based categorization using pandas/python

Question

I\'ve invoice related data in the below Dataframe and lists of codes

df = pd.DataFrame({
    \'invoice\':[1,1,2,2,2,3,3,3,4,4,4,5,5,6,6,6,7],
    \'code\':[10         


        

Answer 1

You can try to use np.select

df['category'] = np.select([
    df.groupby('invoice')['qty'].transform('sum') >= 10,
    df['code'].isin(Milk).groupby(df.invoice).transform('any'),
    (df['qty']*df['code'].isin(Juice)).groupby(df.invoice).transform('sum') == 1,
    (df['qty']*df['code'].isin(Juice)).groupby(df.invoice).transform('sum') > 1,
    df['code'].isin(Hot).groupby(df.invoice).transform('any'),
    df['code'].isin(Dessert).groupby(df.invoice).transform('any')
],
    ['Mega','Healthy','OneJuice','ManyJuice','HotLovers','DessertLovers'],
    'Other'
)
print(df)

Output

    invoice  code  qty       category
0         1   101    2       OneJuice
1         1   104    1       OneJuice
2         2   105    1        Healthy
3         2   101    3        Healthy
4         2   106    2        Healthy
5         3   106    4           Mega
6         3   104    7           Mega
7         3   101    1           Mega
8         4   104    1      ManyJuice
9         4   105    1      ManyJuice
10        4   111    1      ManyJuice
11        5   109    4      HotLovers
12        5   111    2      HotLovers
13        6   110    1  DessertLovers
14        6   101    2  DessertLovers
15        6   114    2  DessertLovers
16        7   104    2      ManyJuice

---

Micro-Benchmark

pd.show_versions()

commit           : None
python           : 3.7.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.4.0-18362-Microsoft
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : C.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.3
numpy            : 1.17.4

Data was created with

def make_data(n):
     return pd.DataFrame({
    'invoice':np.arange(n)//3,
    'code':np.random.choice(np.arange(101,112),n),
    'qty':np.random.choice(np.arange(1,8), n, p=[10/25,10/25,1/25,1/25,1/25,1/25,1/25])
})

Results

perfplot.show(
    setup=make_data,
    kernels=[get_category, get_with_np_select],
    n_range=[2**k for k in range(8, 20)],
    logx=True,
    logy=True,
    equality_check=False,
    xlabel='len(df)')