Manipulating Dataframes with pandas (Python)

Question

Python 2.7.11 // Pandas 0.18.1

I have a made up dataset (csv) to practice with for an imaginary liquor store with 250 items. The columns cover 'Brewery', 'Label', 'Year', 'Store Price', 'MSRP', 'Supplier Price', and a few others. However for this question, the relevant pieces are the Brewery and the Store Price (current price that is queried at checkout).

         Brewery  Store Price
104  Glenfiddich       109.99
105  Glenfiddich        89.99
108  Glenfiddich       114.99
110  Glenfiddich        99.99
119  Glenfiddich       169.99

If I were running a sale on Glenfiddich, I could locate the Glenfiddich items with something like this:

df = pd.read_csv('liquorStore.csv')    
df.Brewery.str.contains('Glenfiddich')

I know how to find the Glenfiddich products but I do not know how to alter values of the row within the dataframe. For instance, I want to:

Find 'Glenfiddich' items
Adjust 'Store Price' to reflect a sale/new price (e.g. 10% off)

Note: I am just doing this to practice with pandas.

jezrael · Accepted Answer · 2017-02-23 07:17:38Z

2

You can use loc with boolean indexing for select and then multiple by 0.9:

df.loc[df.Brewery == 'Glenfiddich', 'Store Price'] *= .9

Sample:

print (df)
         Brewery  Store Price
104  Glenfiddich       109.99
105  Glenfiddich        89.99
120      Another       100.00

df.loc[df.Brewery == 'Glenfiddich', 'Store Price'] *= .9
print (df)
         Brewery  Store Price
104  Glenfiddich       98.991
105  Glenfiddich       80.991
120      Another      100.000

Another possible solution is use mask:

df['Store Price'] = df['Store Price'].mask(df.Brewery == 'Glenfiddich',
                                           df['Store Price'] * .9)
print (df)
         Brewery  Store Price
104  Glenfiddich       98.991
105  Glenfiddich       80.991
120      Another      100.000

edited Feb 23, 2017 at 7:17

answered Feb 23, 2017 at 7:15

jezrael

868k103 gold badges1.4k silver badges1.3k bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

jezrael Over a year ago

Yes, It is another solution, but if check here

The rough rule is any time you see back-to-back square brackets, ][, you're in asking for trouble. Replace that with a .loc[..., ...] and you'll be set.

Collectives™ on Stack Overflow

Manipulating Dataframes with pandas (Python)

1 Answer 1

1 Comment

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

1 Comment

Your Answer

Sign up or log in

Post as a guest

Related