Tukukkk

You have a list of names to match, and a Series of names to check against. Use a regular expression with str.extract here.

df.A.str.extract(r'({})'.format('|'.join(name)))

       0

0   Jake

1   Matt

2   Matt

3  Henry

4   Jake

Here is one method to achieve this:

first_name = ['Jake','Matt', 'Henry']



df = pd.DataFrame({'A': ['Jake Hill', 'Matt Dawn', 'Matt King', 'Henry White', 'Jake Hyde']})



df['B'] = df['A'].str.split().apply(lambda x: x[0] if x[0] in first_name else ' '.join(x))

and you get:

             A      B

0    Jake Hill   Jake

1    Matt Dawn   Matt

2    Matt King   Matt

3  Henry White  Henry

4    Jake Hyde   Jake

name = ['Jake','Matt', 'Henry']

df = pd.read_csv("file.csv")



#filling nan values in-case if it is there

df.fillna(0, inplace = True)

df["First Name"] = df.A.apply(lambda x: list(set(x.split(" ")) & set(name))[0]  if x != 0 else "Not Found")

Output:

             A First Name

0    Jake Hill       Jake

1    Matt Dawn       Matt

2    Matt King       Matt

3  Henry White      Henry

4    Hyde Jake       Jake

Try using:

A_final=A[0].str.split(' ',expand=True, n=1).str.get(0)
A_final[0]
, your problem is resolved.

In addition to earlier edit, Which i understood now you want to inplace replacement, Which can be done with list comprehension as follows with splitting the column A Fist and choose the First Index of of it and passing to lambda using apply method.

DataFrame Structure:

df

             A

0    Jake Hill

1    Matt Dawn

2    Matt King

3  Henry White

4    Jake Hyde

Your name Var..

$ name

['Jake', 'Matt', 'Henry']

Your Final desired Dataset:

Parameter n can be used to limit the number of splits in the output.

df['A'] = df['A'].str.split(n=1, expand=True)[0].apply(lambda x: x if x in name else ' '.join(x))



   print(df)

           A

    0   Jake

    1   Matt

    2   Matt

    3  Henry

    4   Jake

It should be simple if you not pressed to take names from a Var and end goal is to get the First name from the dataframe :

>>> df

             A

0    Jake Hill

1    Matt Dawn

2    Matt King

3  Henry White

4    Jake Hyde





>>> df['A'].str.split(n=1, expand=True)[0]

0     Jake

1     Matt

2     Matt

3    Henry

4     Jake

Name: 0, dtype: object

OR In case you want inplace replacement for column A ..

df['A'] = df['A'].str.split(n=1, expand=True)[0]

This method won't be fooled by a last name containing one of the first name strings, such as "Matten" or "Jakes", and will combine a first and last name if they are both found in the first names list, such as "Matt Henry" (shows "MattHenry" in the output dataframe).

# split the name strings into columns as new dataframe

df1 = df.A.str.split(' ', expand=True)

# Keep the first names in the new dataframe and fill the rest with

# empty strings, then sum the df1 column string values to make a new array

names_result = np.where(df1.isin(name), df1, '').sum(axis=1)

# find the array indexes where no first names were found

no_match_idx = np.where(names_result == '')[0]

# fill the no first name index locations with original dataframe values

names_result[no_match_idx] = df.A.values[no_match_idx]

# make a dataframe using the results

df_out = pd.DataFrame(names_result, columns=['A'])



# to find names with a first and last name that are both found in the

# first names list:

# df_out['dups'] = df1.isin(name).sum(axis=1) > 1