Dummy variables when not all categories are present
I have a set of dataframes where one of the columns contains a categorical variable. I'd like to convert it to several dummy variables, in which case I'd normally use get_dummies
.
What happens is that get_dummies
looks at the data available in each dataframe to find out how many categories there are, and thus create the appropriate number of dummy variables. However, in the problem I'm working right now, I actually know in advance what the possible categories are. But when looking at each dataframe individually, not all categories necessarily appear.
My question is: is there a way to pass to get_dummies
(or an equivalent function) the names of the categories, so that, for the categories that don't appear in a given dataframe, it'd just create a column of 0s?
Something that would make this:
categories = ['a', 'b', 'c']
cat
1 a
2 b
3 a
Become this:
cat_a cat_b cat_c
1 1 0 0
2 0 1 0
3 1 0 0
TL;DR:
pd.get_dummies(cat.astype(pd.CategoricalDtype(categories=categories)))
- Older pandas:
pd.get_dummies(cat.astype('category', categories=categories))
is there a way to pass to get_dummies (or an equivalent function) the names of the categories, so that, for the categories that don't appear in a given dataframe, it'd just create a column of 0s?
Yes, there is! Pandas has a special type of Series just for categorical data. One of the attributes of this series is the possible categories, which get_dummies
takes into account. Here's an example:
In [1]: import pandas as pd
In [2]: possible_categories = list('abc')
In [3]: cat = pd.Series(list('aba'))
In [4]: cat = cat.astype(pd.CategoricalDtype(categories=possible_categories))
In [5]: cat
Out[5]:
0 a
1 b
2 a
dtype: category
Categories (3, object): [a, b, c]
Then, get_dummies
will do exactly what you want!
In [6]: pd.get_dummies(cat)
Out[6]:
a b c
0 1 0 0
1 0 1 0
2 1 0 0
There are a bunch of other ways to create a categorical Series
or DataFrame
, this is just the one I find most convenient. You can read about all of them in the pandas documentation.
EDIT:
I haven't followed the exact versioning, but there was a bug in how pandas treats sparse matrices, at least until version 0.17.0. It was corrected by version 0.18.1 (released May 2016).
For version 0.17.0, if you try to do this with the sparse=True
option with a DataFrame
, the column of zeros for the missing dummy variable will be a column of NaN
, and it will be converted to dense.
It looks like pandas 0.21.0 added a CategoricalDType
, and creating categoricals which explicitly include the categories as in the original answer was deprecated, I'm not quite sure when.
Using transpose and reindex
import pandas as pd
cats = ['a', 'b', 'c']
df = pd.DataFrame({'cat': ['a', 'b', 'a']})
dummies = pd.get_dummies(df, prefix='', prefix_sep='')
dummies = dummies.T.reindex(cats).T.fillna(0)
print dummies
a b c
0 1.0 0.0 0.0
1 0.0 1.0 0.0
2 1.0 0.0 0.0
Try this:
In[1]: import pandas as pd
cats = ["a", "b", "c"]
In[2]: df = pd.DataFrame({"cat": ["a", "b", "a"]})
In[3]: pd.concat((pd.get_dummies(df.cat, columns=cats), pd.DataFrame(columns=cats))).fillna(0)
Out[3]:
a b c
0 1.0 0.0 0
1 0.0 1.0 0
2 1.0 0.0 0
I did ask this on the pandas github. Turns out it is really easy to get around it when you define the column as a Categorical
where you define all the possible categories.
df['col'] = pd.Categorical(df['col'], categories=['a', 'b', 'c', 'd'])
get_dummies()
will do the rest then as expected.
I don't think get_dummies
provides this out of the box, it only allows for creating an extra column
that highlights NaN
values.
To add the missing columns
yourself, you could use pd.concat
along axis=0
to vertically 'stack' the DataFrames
(the dummy columns plus a DataFrame
id
) and automatically create any missing columns, use fillna(0)
to replace missing values, and then use .groupby('id')
to separate the various DataFrame
again.