Convert DataFrame of options with 5*5 grid of choice

Question

Following are the standard values for score and grades. Score column contains numbers within specified range.

Score	Grade1	Score2	Grade3	Grade4
<260	A1	<200	D1	ID1
260-275	A2	200-250	D2	ID2
275-280	A3	250-260	D3	ID3
280-285	A4	260-270	D4	ID4
>=285	A5	>=270	D5	ID5

Input :-

Score	Grade1	Score2	Grade3	Grade4
290	A1	265	D3	ID2

Output:-

Tiers	Grade	Grade1	Score2	Grade3	Grade4
1		A1
2					ID2
3				D3
4			265
5	290

Input :-

Score	Grade1	Score2	Grade3	Grade4
290	A1	265	D3	ID2

Answer 1

Assuming df and df_input your two DataFrames, you can use a combination of pandas and numpy:

# get df_input as Series
s = df_input.loc[0]

# find identical values
mask = df.eq(s)

# match ranges
def match_range(val, ref):
    # extract the upper value, for the last replace by np.inf
    s = pd.to_numeric(ref.str.extract('[<-](\d+)', expand=False)).fillna(np.inf)
    # define default output as False
    out = np.zeros(len(ref), dtype=bool)
    # find matching position
    out[np.searchsorted(s, val)] = True
    return out

# apply match_range on columns for which a direct match failed
m = ~mask.any()
mask.loc[:, m] = df.loc[:, m].apply(lambda x: match_range(s[x.name], x))

# generate output
out = (pd.DataFrame(np.where(mask, s, ''),
                    index=np.arange(len(df))+1,
                    columns=df.columns)
         .rename_axis('Tiers').reset_index()
      )

Output:

   Tiers Score Grade1 Score2 Grade3 Grade4
0      1           A1                     
1      2                               ID2
2      3                         D3       
3      4                 265              
4      5   290                            

Used inputs:

df = pd.DataFrame({'Score': ['<260', '260-275', '275-280', '280-285', '>=285'],
                   'Grade1': ['A1', 'A2', 'A3', 'A4', 'A5'],
                   'Score2': ['<200', '200-250', '250-260', '260-270', '>=270'],
                   'Grade3': ['D1', 'D2', 'D3', 'D4', 'D5'],
                   'Grade4': ['ID1', 'ID2', 'ID3', 'ID4', 'ID5']})

df_input = pd.DataFrame({'Score': [290], 'Grade1': ['A1'], 'Score2': [265], 'Grade3': ['D3'], 'Grade4': ['ID2']})