如何有条件地（列中的值）在 python 循环中的另一列中搜索子字符串

Question

I need to do a substring search in a string by condition in the second column. I have 2 dataframes:

df1 = {'Descr': ["VALVE, PRESSURE", "pump ttf", "Valve, electrical", "Geeku, electrical","VALVE, OVERBOARD, BUTTERFLY"],
        'N_Product': ["VALVE", "PUMP", "VALVE", "GEEKU","VALVE"],
        }
df2 = {'N_Product': ["VALVE", "VALVE","VALVE", "PUMP", "GEEKU"],
        'M_Product': ["PRESSURE", "qwerty","", "", "ELECTRICAL"],
        }
df1 = pd.DataFrame(df1)
df2 = pd.DataFrame(df2)

(Step 1) For the first row in df1, the N_Product column is VALVE.

(Step 2) We look for VALVE in the N_Product column of every row of df2 and find 3 matches with the following (N_Product, M_Product) pairs: row 0 has VALVE,PRESSURE; row 1 has VALVE,qwerty; row2 has VALVE,"".

(Step 3) Then you need to check whether any of these pairs (df2 (M_Product)) is contained in Df1 ['Descr'], if it is contained then you need to write N_Product + ":" + M_Product + ";". For Valve you only need to search for "Pressure", "Electrical" and "", others are not required, for N_Product ('GEEKU') - only 'Electrical' ', etc., depending on which pairs are in the df2 file

c = df2['M_Product'].astype(str).to_list()
def matcher(x):
    for i in c:
        if i.lower() in x.lower():
            return i
    else:
        return np.nan
df1['Res'] = df1['Descr'].apply(matcher)

but I don't know how to cycle through the values of only the corresponding M_Product for N_Product.

Desired result:

df1 = {'Descr': ["VALVE, PRESSURE", "pump ttf", "Valve, electrical", "Geeku, electrical","VALVE, OVERBOARD, BUTTERFLY"],
        'N_Product': ["VALVE", "PUMP", "VALVE", "GEEKU","VALVE"],
        },
'Result': ["VALVE: PRESSURE;", "PUMP", "VALVE;", "GEEKU: ELECTRICAL;","VALVE;"],
        }

I would be grateful for any help. If you have any options, please help

Answer 1

( UPDATED )

Based on the updated question, my understanding of what's being asked is this:

• Create a new Result column
• If the N_Product columns in df1 and df2 for a given row match, then append to the value from the N_Product column in df1 the first match in column M_Product of df2 of a string found in the given row's Descr column in df1 (with an intervening : character).
• Otherwise, put N_Product from df1 in the Result column.
• Also append a ; character to what is put in Result .

Here is a way to do that:

def foo(x):
    descr = x['Descr'].upper()
    match = None
    for mStr in df2['M_Product'].str.upper():
        if mStr in descr:
            match = mStr
            break
    if match is None:
        return x['N_Product'] + ';'
    else:
        return x['N_Product'] + ': ' + match + ';'
mask = df1['N_Product'] == df2['N_Product']
df1.loc[mask, 'Result'] = df1.apply(foo, axis = 1)
df1.loc[~mask, 'Result'] = df1['N_Product'] + ';'

Explanation:

• Create a boolean Series mask that is True for rows of df1 with N_Product matching the corresponding value in df2.
• For rows in df1 where mask is True, use apply to call foo which performs the logic of identifying the first value (if any) in the M_Product column of df2 that is found in a given row's Descr column and packaging it in a string of the form N_Product: M_Product; if found, otherwise just N_Product; .
• For rows in df1 where mask is False (namely: ~mask ), set the Result column to be N_Product; .

Input:

df1:
                         Descr N_Product
0              VALVE, PRESSURE     VALVE
1                     pump ttf      PUMP
2            Valve, electrical     VALVE
3            Geeku, electrical     GEEKU
4  VALVE, OVERBOARD, BUTTERFLY     VALVE

df2:
  N_Product   M_Product
0     VALVE    PRESSURE
1     VALVE  ELECTRICAL
2     VALVE
3      PUMP
4     GEEKU         MBA

Output:

                         Descr N_Product              Result
0              VALVE, PRESSURE     VALVE    VALVE: PRESSURE;
1                     pump ttf      PUMP               PUMP;
2            Valve, electrical     VALVE  VALVE: ELECTRICAL;
3            Geeku, electrical     GEEKU              GEEKU;
4  VALVE, OVERBOARD, BUTTERFLY     VALVE              VALVE;

UPDATE #2:

Here's a solution based on a relaxation of the matching criteria for N_Product :

• Create a new Result column
• For each row in df1, if the N_Product value is found in the N_Product column of df2, then append to this value the first match in column M_Product of df2 of a string found in the given row's Descr column in df1 (with an intervening : character).
• Otherwise, put N_Product from df1 in the Result column.
• Also append a ; character to what is put in Result .

def foo(x):
    descr = x['Descr'].upper()
    match = None
    if x['N_Product'].upper() in list(df2['N_Product']):
        for mStr in df2['M_Product'].str.upper():
            if mStr in descr:
                match = mStr
                break
    if match is None:
        return x['N_Product'] + ';'
    else:
        return x['N_Product'] + ': ' + match + ';'
df1['Result'] = df1.apply(foo, axis = 1)