Adding a column to pandas dataframe based on bool values in existing column

Question

I have a pandas dataframe which I would like to add a new column to. The new column values will be determined by an existing column in the dataframe which contains bools. The code below is my C++ logic applied in python but I would like a more 'pythonic' way to do this. 'isfixed' contains the bools and the new column will be 'color code'

for i in range(data_2015['isfixed'].count()):
     if data_2015['isfixed'][i] == True:
         data_2015['color code'][i] = 'Blue'
     else:
         data_2015['color code'][i] = 'Green'

Thanks in advance for the help!

I would say this approach is perfectly pythonic but not very pandonic! — juanpa.arrivillaga
– juanpa.arrivillaga, Commented Dec 9, 2016 at 20:31

user2285236 · Accepted Answer · 2016-12-09 20:28:02Z

2

You can use numpy.where:

import numpy as np
data_2015['color_code'] = np.where(data_2015['isfixed'], 'Blue', 'Green')

A demo:

df = pd.DataFrame({'isfixed': [True, False, True]})

df
Out: 
  isfixed
0    True
1   False
2    True


df['color_code'] = np.where(df['isfixed'], 'Blue', 'Green')

df
Out: 
  isfixed color_code
0    True       Blue
1   False      Green
2    True       Blue

answered Dec 9, 2016 at 20:28

user2285236

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4 Comments

ProficientInMath Over a year ago

Thanks @ayhan. I like this, just one line. The pythonic way rules!

user2285236 Over a year ago

You are welcome. Just a note: this can also be done by df['color_code'] = ['Blue' if row else 'Green' for row in df['isfixed']] but built-in numpy/pandas functions are generally much faster than iterating over a Series/array.

ProficientInMath Over a year ago

Right, I have been warned about iterating over series with respect to speed. My understanding is that it is fastest to apply functions over the entire series rather than iterate through each element, right?

user2285236 Over a year ago

Exactly. Here's a more detailed explanation since you mentioned C: (data types and ufuncs)

chad39 · Accepted Answer · 2016-12-09 20:58:59Z

0

For a pure Pandas solution:

df = pd.DataFrame({'isfixed': [True, False, True]})

df['color_code'] = ["Blue" if value==True else "Green" for value in df['isfixed']]

answered Dec 9, 2016 at 20:58

chad39

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