Pandas Dataframe 迭代并根据条件选择行 - 需求变化

Question

I have sorted data frame as mentioned below(Input DataFrame) and I need to iterate the rows,select & retrive the rows into output data frame based on below conditions.

• Condition 1: For a given R1,R2,W - if we have two records with TYPE 'A' and 'B' a) If (amoun1& amount2) of TYPE 'A' is > (amoun1& amount2 )of TYPE 'B' we need to bring the TYPE 'A' record into the output b) If (amoun1& amount2) of TYPE 'B' is > (amoun1& amount2 )of TYPE 'A' we need to bring the TYPE 'B' record into the output c) If (amoun1& amount2) of TYPE 'A' is = (amoun1& amount2 )of TYPE 'B' we need to bring the TYPE 'A' record into the output

• Condition 2: For a given R1,R2,W - if we have only record with TYPE 'A', we need to bring the TYPE 'A' record into the output • Condition 3: For a given R1,R2,W - if we have only record with TYPE 'B', we need to bring the TYPE 'B' record into the output Input Dataframe

    R1  R2  W   TYPE    amount1 amount2
0   123 12  1   A   111 222
1   123 12  1   B   111 222
2   123 12  2   A   222 222
3   123 12  2   B   333 333
4   123 12  3   A   444 444
5   123 12  3   B   333 333
6   123 34  1   A   111 222
7   123 34  2   A   333 444
8   123 34  2   B   333 444
9   123 34  3   B   444 555
10  123 34  4   A   555 666
11  123 34  4   B   666 777

Output dataframe

    R1  R2  W   TYPE    amount1 amount1
0   123 12  1   A   111 222
3   123 12  2   B   333 333
4   123 12  3   A   444 444
6   123 34  1   A   111 222
7   123 34  2   A   333 444
9   123 34  3   B   444 555
11  123 34  4   B   666 777

Answer 1

Selection based on your criteria's

def my_selection(idf):
  # If 'A' and 'B' in 'TYPE' then give me the row with 'A'
  if idf['TYPE'].unique().shape[0] == 2:
    return idf[idf['TYPE'] == 'A']
  else:
    return idf

df2 = df.groupby(['R1', 'R2', 'W'], as_index=False).apply(lambda idf: my_selection(idf))
df2.index = df2.index.droplevels(-1)

#     R1  R2  W TYPE  amount1  amount2
# 0  123  12  1    A      111      222
# 1  123  12  2    A      333      444
# 2  123  12  3    A      555      666
# 3  123  34  1    A      111      222
# 4  123  34  2    A      222      333
# 5  123  34  3    B      444      555
# 6  123  34  4    A      555      666

Answer 2

All you have to do is groupby R1,R2,W and operate on Type column as follows:

data.groupby(['R1','R2','W']).apply(lambda x: 'A' if 'A' in x['Type'].values else 'B').reset_index() 

You can merge this output with original DataFrame on the obtained columns from the above output to get corresponding 'amount1', 'amount2' values

Answer 3

This is what I would do:

categories =  ['B','A'] #create a list of categories in ascending order of precedence
d={i:e for e,i in enumerate(categories)} #create a dictionary:{'A': 0, 'B': 1}
s=df['TYPE'].map(d) #map to df['TYPE'] and create a helper series

then assign this series to the dataframe and groupby+transform max and check if it is equal to the helper series and return where both value matches:

out = df[s.eq(df.assign(TYPE=s).groupby(['R1','R2','W'])['TYPE'].transform('max'))]
print(out)

---

     R1  R2  W TYPE  amount1  amount2
0   123  12  1    A      111      222
2   123  12  2    A      333      444
4   123  12  3    A      555      666
6   123  34  1    A      111      222
7   123  34  2    A      222      333
9   123  34  3    B      444      555
10  123  34  4    A      555      666