Using If/else In Pandas Series To Create New Series Based On Conditions

I have a pandas df. Say I have a column 'activity' which can be 'fun' or 'work' and I want to convert it to an integer. What I do is: df['activity_id'] = 1*(df['activity']=='fun')

Solution 1:

You can create a dictionary with id as the key and the string as the value and then use the map series method to convert the integer to a string.

my_map = {1:'fun', 2:'work'}

df['activity']= df.activity_id.map(my_map)

Solution 2:

You could instead convert your activity column to categorical dtype:

df['activity'] = pd.Categorical(df['activity'])

Then you would automatically have access to integer labels for the values via df['activity'].cat.codes.

---

import pandas as pd

df = pd.DataFrame({'activity':['fun','work','fun']})
df['activity'] = pd.Categorical(df['activity'])

print(df['activity'].cat.codes)
0    0
1    1
2    0
dtype: int8

Meanwhile the string values can still be accessed as before while saving memory:

print(df)

still yields

  activity
0fun1     work
2fun

Solution 3:

You could also use a dictionary and list comprehension to recalculate values for an entire column. This makes it easy to define the reverse mapping as well:

>>> import pandas as pd
>>> forward_map = {'fun': 1, 'work': 2}
>>> reverse_map = {v: k for k, v in forward_map.iteritems()}
>>> df = pd.DataFrame(
    {'activity': ['work', 'work', 'fun', 'fun', 'work'],
     'detail': ['reports', 'coding', 'hiking', 'games', 'secret games']})
>>> df

  activity        detail
0     work       reports
1     work        coding
2fun        hiking3fun         games4     work  secret games

>>> df['activity'] = [forward_map[i] for i in df['activity']]
>>> df

   activity        detail
02       reports
12        coding
21        hiking
31         games
42  secret games

>>> df['activity'] = [reverse_map[i] for i in df['activity']]
>>> df

  activity        detail
0     work       reports
1     work        coding
2fun        hiking3fun         games4     work  secret games