0

df looks like this:

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

What I want:

  • When column description and keybenefits (14) contains the value 'cooltouch' column brand_cooltouch (1711) needs to be set to value 1 (int).
  • When column description and keybenefits (14) contains the value 'easylogic' column brand_easylogic (1712) needs to be set to value 1 (int).

Output that I want:

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

Any help is very much appreciated.

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3 Answers 3

Reset to default
1

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.
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2 Comments

@Isabella didn't understand your goal... If the goal is to replace None with 0, one might want to check this.
Alternatively, if that doesn't answer your question, and assuming it doesn't exist yet, I would recommend asking a new question. Most likely you will get the needed help from the community.
0

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)
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Comments

-1

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
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