CODEWITHSUNDEEP
python-3.xpandas
jceg316
jceg316

Reputation: 489

How do I search for a string in a pandas column and append to the row based on that string?

I have a pandas dataframe and I want to search through strings in column A, if there's a match I want to append 1 to a new column, if there is no match I want to append a 0.

My df currently looks like:

Column A          | Column B | Column C
company one       |   314    |   0.9
company one toast |   190    |   0.3
www.companyone    |   380    |   0.87
companyone home   |   850    |   0.1
toaster supplies  |   1100   |   0.5
toast rack        |   200    |   0.7
...

I'm trying to write a function which will read through column A, and if there's a match with either company one or companyone, then append 1 on the end of the row. If there is no match, then append 0. The output I'm looking for is:

    Column A          | Column B | Column C | Branded
    company one       |   314    |   0.9    |   1
    company one toast |   190    |   0.3    |   1
    www.companyone    |   380    |   0.87   |   1
    companyone home   |   850    |   0.1    |   1
    toaster supplies  |   1100   |   0.5    |   0
    toast rack        |   200    |   0.7    |   0
    ...

I've tried this function:

def branded(table):

    if 'company.*?one' in table[table['Column A']]:
        table['Branded'] = 1
    else:
        table['Branded'] = 0

    return table.head()

However I get a KeyError. I'm not sure what I'm missing though.

Upvotes: 0

Views: 799

Answers (2)

Ankur Sinha
Ankur Sinha

Reputation: 6649

The solution posted by zipa is better in my opinion. However, thought of sharing this which is a tweak version in case the strings to be looked for are entirely of different pattern. You can add the words to the list and then perform something similar:

import pandas as pd

df = pd.DataFrame({'column':['company one','companyone', 'company two']})
search = ['company one', 'companyone']
string_search = '|'.join(search)
df['flag'] = df['column'].str.contains(string_search)
df['flag'] = df['flag'].map({True: 1, False: 0})

Upvotes: 0

zipa
zipa

Reputation: 27869

You can do it like this:

df['Branded'] = df['Column A'].str.contains('company.*?one')*1

Upvotes: 1

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