Sorting pandas dataframe by groups - pandas

Question

I would like to sort a dataframe by certain priority rules.
I've achieved this in the code below but I think this is a very hacky solution.
Is there a more proper Pandas way of doing this?
import pandas as pd
import numpy as np
df=pd.DataFrame({"Primary Metric":[80,100,90,100,80,100,80,90,90,100,90,90,80,90,90,80,80,80,90,90,100,80,80,100,80],
"Secondary Metric Flag":[0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0],
"Secondary Value":[15, 59, 70, 56, 73, 88, 83, 64, 12, 90, 64, 18, 100, 79, 7, 71, 83, 3, 26, 73, 44, 46, 99,24, 20],
"Final Metric":[222, 883, 830, 907, 589, 93, 479, 498, 636, 761, 851, 349, 25, 405, 132, 491, 253, 318, 183, 635, 419, 885, 305, 258, 924]})
Primary_List=list(np.unique(df['Primary Metric']))
Primary_List.sort(reverse=True)
df_sorted=pd.DataFrame()
for p in Primary_List:
lol=df[df["Primary Metric"]==p]
lol.sort_values(["Secondary Metric Flag"],ascending = False)
pt1=lol[lol["Secondary Metric Flag"]==1].sort_values(by=['Secondary Value', 'Final Metric'], ascending=[False, False])
pt0=lol[lol["Secondary Metric Flag"]==0].sort_values(["Final Metric"],ascending = False)
df_sorted=df_sorted.append(pt1)
df_sorted=df_sorted.append(pt0)
df_sorted
The priority rules are:
First sort by the 'Primary Metric', then by the 'Secondary Metric
Flag'.
If the 'Secondary Metric Flag' ==1, sort by 'Secondary Value' then
the 'Final Metric'
If ==0, go right for the 'Final Metric'.
Appreciate any feedback.

Answer 1

You do not need for loop and groupby here , just split them and sort_values
df1=df.loc[df['Secondary Metric Flag']==1].sort_values(by=['Primary Metric','Secondary Value', 'Final Metric'], ascending=[True,False, False])
df0=df.loc[df['Secondary Metric Flag']==0].sort_values(["Primary Metric","Final Metric"],ascending = [True,False])
df=pd.concat([df1,df0]).sort_values('Primary Metric')

Answer 2

sorted with loc
def k(t):
p, s, v, f = df.loc[t]
return (-p, -s, -s * v, -f)
df.loc[sorted(df.index, key=k)]
Primary Metric Secondary Metric Flag Secondary Value Final Metric
9 100 1 90 761
5 100 1 88 93
1 100 1 59 883
3 100 1 56 907
23 100 1 24 258
20 100 0 44 419
13 90 1 79 405
19 90 1 73 635
7 90 1 64 498
11 90 1 18 349
10 90 0 64 851
2 90 0 70 830
8 90 0 12 636
18 90 0 26 183
14 90 0 7 132
15 80 1 71 491
21 80 1 46 885
17 80 1 3 318
24 80 0 20 924
4 80 0 73 589
6 80 0 83 479
22 80 0 99 305
16 80 0 83 253
0 80 0 15 222
12 80 0 100 25
sorted with itertuples
def k(t):
_, p, s, v, f = t
return (-p, -s, -s * v, -f)
idx, *tups = zip(*sorted(df.itertuples(), key=k))
pd.DataFrame(dict(zip(df, tups)), idx)
lexsort
p = df['Primary Metric']
s = df['Secondary Metric Flag']
v = df['Secondary Value']
f = df['Final Metric']
a = np.lexsort([
-p, -s, -s * v, -f
][::-1])
df.iloc[a]
Construct New DataFrame
df.mul([-1, -1, 1, -1]).assign(
**{'Secondary Value': lambda d: d['Secondary Metric Flag'] * d['Secondary Value']}
).pipe(
lambda d: df.loc[d.sort_values([*d]).index]
)