Pandas 将所有对象列转换为类别
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Pandas cast all object columns to category
提问by Georg Heiler
I want to have ha elegant function to cast all object columns in a pandas data frame to categories
我想拥有优雅的功能来将 Pandas 数据框中的所有对象列转换为类别
df[x] = df[x].astype("category")performs the type cast
df.select_dtypes(include=['object'])would sub-select all categories columns. However this results in a loss of the other columns / a manual merge is required. Is there a solution which "just works in place" or does not require a manual cast?
df[x] = df[x].astype("category")执行类型转换
df.select_dtypes(include=['object'])将子选择所有类别列。但是，这会导致其他列丢失/需要手动合并。是否有“就地工作”或不需要手动演员的解决方案？
edit
编辑
I am looking for something similar as http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.convert_objects.htmlfor a conversion to categorical data
我正在寻找类似于http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.convert_objects.html 的东西来转换为分类数据
回答by piRSquared
use applyand pd.Series.astypewith dtype='category'
使用apply并pd.Series.astype与dtype='category'
Consider the pd.DataFramedf
考虑 pd.DataFramedf
df = pd.DataFrame(dict(
A=[1, 2, 3, 4],
B=list('abcd'),
C=[2, 3, 4, 5],
D=list('defg')
))
df
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
A 4 non-null int64
B 4 non-null object
C 4 non-null int64
D 4 non-null object
dtypes: int64(2), object(2)
memory usage: 200.0+ bytes
Lets use select_dtypesto include all 'object'types to convert and recombine with a select_dtypesto exclude them.
让我们使用select_dtypes来包含所有'object'类型以进行转换和重新组合select_dtypes以排除它们。
df = pd.concat([
df.select_dtypes([], ['object']),
df.select_dtypes(['object']).apply(pd.Series.astype, dtype='category')
], axis=1).reindex_axis(df.columns, axis=1)
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
A 4 non-null int64
B 4 non-null category
C 4 non-null int64
D 4 non-null category
dtypes: category(2), int64(2)
memory usage: 208.0 bytes
回答by KG in Chicago
I think that this is a more elegant way:
我认为这是一种更优雅的方式：
df = pd.DataFrame(dict(
A=[1, 2, 3, 4],
B=list('abcd'),
C=[2, 3, 4, 5],
D=list('defg')
))
df.info()
df.loc[:, df.dtypes == 'object'] =\
df.select_dtypes(['object'])\
.apply(lambda x: x.astype('category'))
df.info()
回答by a Data Head
Wish I could add this as a comment, but can't.
希望我可以将其添加为评论，但不能。
The accepted answer doesn't work for pandas version 0.25 and higher. Use .reindexinstead of reindex_axis. See here for more information:
https://github.com/scikit-hep/root_pandas/issues/82
接受的答案不适用于 0.25 版及更高版本的Pandas。使用.reindex代替reindex_axis。有关更多信息，请参见此处：https:
//github.com/scikit-hep/root_pandas/issues/82
回答by Anton Golubev
Often the order of categories has meaning, for example t-short sizes 'S', 'M', 'L' 'XL' are ordered categories (in SPSS - ordinals). If you are interested in creating ordered categories from strings you can use this code:
通常类别的顺序是有意义的，例如 t-short 尺寸“S”、“M”、“L”、“XL”是有序的类别（在 SPSS 中 - 序数）。如果您有兴趣从字符串创建有序类别，您可以使用以下代码：
df = pd.concat([
df.select_dtypes([], ['object']),
df.select_dtypes(['object']).apply(pd.Categorical, ordered=True)
], axis=1).reindex(df.columns, axis=1)
In the resulting DataFrame categorical columns can be sorted by values the same way as you used to sort strings.
在生成的 DataFrame 中，分类列可以按照与用于对字符串进行排序相同的方式按值进行排序。
