df looks like this:

What I want: When Column description and keybenefits (14) contains the value 'cooltouch' columm brand_cooltouch (1711) needs to be set to value 1 (int). When Column description and keybenefits (14) contains the value 'easylogic' columm brand_easylogic (1712) needs to be set to value 1 (int).

Output that I want:

Any help is very much appreciated.

CodePudding user response：

One can use pandas.Series.str.contains.

For the string cooltouch do the following

df['brand_cooltouch (1711)'] = df['description and keybenefits (14)'].str.contains('cooltouch', case=False).astype(int)

[Out]:

    description and keybenefits (14)  brand_cooltouch (1711)  brand_easylogic (1712)
0  Lorem Ipsum cooltouch Lorem Ipsum                       1                     None
1  Lorem Ipsum easylogic Lorem Ipsum                       0                     None
2            Lorem Ipsum Lorem Ipsum                       0                     None

For the string easylogic, do the following

df['brand_easylogic (1712)'] = df['description and keybenefits (14)'].str.contains('easylogic', case=False).astype(int)

[Out]:

    description and keybenefits (14)  brand_cooltouch (1711)  brand_easylogic (1712)
0  Lorem Ipsum cooltouch Lorem Ipsum                       1                     0
1  Lorem Ipsum easylogic Lorem Ipsum                       0                     1
2            Lorem Ipsum Lorem Ipsum                       0                     0

Notes:

• case=False is to make it case insensitive.

CodePudding user response：

you can use np.where. I'd suggest to fill all cells where the condition is not met with NaN or 0. Here is a solution using np.nan

df["brand_cooltouch (1711)“] = np.where(df["description and keybenefits (14)“].str.contains("cooltouch"), 1, np.nan)
df["brand_easylogic (1712)“] = np.where(df["description and keybenefits (14)“].str.contains("easylogic"), 1, np.nan)

CodePudding user response：

Use Series.str.contains -

df['brand_cooltouch (1711)'] = df['description and keybenefits (14)'].str.contains("cooltouch").astype(int)

Output

    description and keybenefits (14)  brand_cooltouch (1711)  brand_easylogic (1712)
0  Lorem Ipsum cooltouch Lorem Ipsum                       1                     NaN
1  Lorem Ipsum easylogic Lorem Ipsum                       0                     NaN
2            Lorem Ipsum Lorem Ipsum                       0                     NaN

If you do not wish the resulting column to be 1's and 0's - you could also do something like -

df.loc[df['description and keybenefits (14)'].str.contains("cooltouch"), ['brand_cooltouch (1711)']] = '1'
df.loc[~df['description and keybenefits (14)'].str.contains("cooltouch"), ['brand_cooltouch (1711)']] = ''

Output

    description and keybenefits (14) brand_cooltouch (1711)  brand_easylogic (1712)
0  Lorem Ipsum cooltouch Lorem Ipsum                      1                     NaN
1  Lorem Ipsum easylogic Lorem Ipsum                                            NaN
2            Lorem Ipsum Lorem Ipsum                                            NaN