Question

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?

Answer 1

没有挖掘，这似乎对我来说是错误的。

我的工作
pd.DataFrame.apply pd.Series.replace的{{1}} 这样做的好处是您不需要改变任何类型。

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

或者

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的工作

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

Answer 2

此类行为的原因是每列的不同分类值集：

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')

因此，如果您要替换两个类别中的值，它将起作用：

In [226]: df.replace('d','a')
Out[226]:
  s1 s2
0  a  a
1  b  c
2  c  a

作为一种解决方案，您可能希望使用以下方法手动对列进行分类：

pd.Categorical(..., categories=[...])

其中类别将具有所有列的所有可能值...

How to replace values in multiple categoricals in a pandas DataFrame

2 个答案: