How to use a for loop to create new columns in a Pandas dataframe

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Say I have this dataframe

import pandas as pd
df = pd.DataFrame({'Verbatim': ['Pants', 'Shirts', 'Shirts', 'Pants', 'Shoes', 'Shoes', 'Shoes', 'Shoes', 'Dresses, Shoes, Dresses', 'Pants', 'Pants', 'Shirts', 'Dresses Pants Shoes', 'Shoes Pants', 'Pants', 'Pants', 'Dresses', 'Pants', 'Pants', 'Dresses']})

Through various steps, I determine that all of my unique words in the above are

unique_words = ('Pants', 'Shirts', 'Shoes', 'Dresses')

I now want to add columns to my Data Frame that denote the presence of each Unique word in the "verbatim" column. I am creating dummies from verbatim text. So, if a respondent noted "Dresses" in their response, they would get a 1 in the Dresses column.

How do I use a loop/apply statement to automate this? I would like to do something like this

for word in unique_words:
    df['word'] = 0
    df.loc[df['Verbatim'].str.contains("word"), 'word'] = 1

Essentially, I want to know how to use the iterator ('word') to create a column in a dataframe named after the same as that iterator. How do I reference the iterator in the loop? This code works manually, but I can't figure out how to loop it.

Thanks!

pandas apply dataframe python loops
1 Answers

You can use the apply function:

for word in unique_words:
    df[word] = df.apply(lambda x: word in x["Verbatim"], axis=1)

print(df.head()

The output is:

  Verbatim  Pants  Shirts  Shoes  Dresses
0    Pants   True   False  False    False
1   Shirts  False    True  False    False
2   Shirts  False    True  False    False
3    Pants   True   False  False    False
4    Shoes  False   False   True    False

If you don't like the type you can put 0s and 1s like this:

for word in unique_words:
    df[word] = df.apply(lambda x: 1 if word in x["Verbatim"] else 0, axis=1)

print(df.head())

Resulting in:

  Verbatim  Pants  Shirts  Shoes  Dresses
0    Pants      1       0      0        0
1   Shirts      0       1      0        0
2   Shirts      0       1      0        0
3    Pants      1       0      0        0
4    Shoes      0       0      1        0
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