在熊猫系列中使用if / else根据条件创建新系列

Question

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") + 2*(df["activity"]=="work") 

This works, since I do not know how to put an if/else in there (and if you have 10 activities it can get complicated).

However, say I now have the opposite problem, and I want to convert from an id to a string, I cannot use this trick anymore because I cannot multiply a string with a Boolean. How do I do it? Is there a way to use if/else?

Answer 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)

Answer 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
0      fun
1     work
2      fun

Answer 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
2      fun        hiking
3      fun         games
4     work  secret games

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

   activity        detail
0         2       reports
1         2        coding
2         1        hiking
3         1         games
4         2  secret games

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

  activity        detail
0     work       reports
1     work        coding
2      fun        hiking
3      fun         games
4     work  secret games