I want to replace certain values in a dataframe containing multiple categoricals.
df = pd.DataFrame({'s1': ['a', 'b', 'c'], 's2': ['a', 'c', 'd']}, dtype='category')
If I apply .replace
on a single column, the result is as expected:
>>> df.s1.replace('a', 1)
0 1
1 b
2 c
Name: s1, dtype: object
If I apply the same operation to the whole dataframe, an error is shown (short version):
>>> df.replace('a', 1)
ValueError: Cannot setitem on a Categorical with a new category, set the categories first
During handling of the above exception, another exception occurred:
ValueError: Wrong number of dimensions
If the dataframe contains integers as categories, the following happens:
df = pd.DataFrame({'s1': [1, 2, 3], 's2': [1, 3, 4]}, dtype='category')
>>> df.replace(1, 3)
s1 s2
0 3 3
1 2 3
2 3 4
But,
>>> df.replace(1, 2)
ValueError: Wrong number of dimensions
What am I missing?
Without digging, that seems to be buggy to me.
My Work Around
pd.DataFrame.apply
with pd.Series.replace
This has the advantage that you don't need to mess with changing any types.
df = pd.DataFrame({'s1': [1, 2, 3], 's2': [1, 3, 4]}, dtype='category')
df.apply(pd.Series.replace, to_replace=1, value=2)
s1 s2
0 2 2
1 2 3
2 3 4
Or
df = pd.DataFrame({'s1': ['a', 'b', 'c'], 's2': ['a', 'c', 'd']}, dtype='category')
df.apply(pd.Series.replace, to_replace='a', value=1)
s1 s2
0 1 1
1 b c
2 c d
@cᴏʟᴅsᴘᴇᴇᴅ's Work Around
df = pd.DataFrame({'s1': ['a', 'b', 'c'], 's2': ['a', 'c', 'd']}, dtype='category')
df.applymap(str).replace('a', 1)
s1 s2
0 1 1
1 b c
2 c d
The reason for such behavior is different set of categorical values for each column:
In [224]: df.s1.cat.categories
Out[224]: Index(['a', 'b', 'c'], dtype='object')
In [225]: df.s2.cat.categories
Out[225]: Index(['a', 'c', 'd'], dtype='object')
so if you will replace to a value that is in both categories it'll work:
In [226]: df.replace('d','a')
Out[226]:
s1 s2
0 a a
1 b c
2 c a
As a solution you might want to make your columns categorical manually, using:
pd.Categorical(..., categories=[...])
where categories would have all possible values for all columns...
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