考虑以下具有两个分类列的 DataFrame:
df = pd.DataFrame({
"state": pd.Categorical(["AK", "AL", "AK", "AL"]),
"gender": pd.Categorical(["M", "M", "M", "F"]),
"name": list("abcd"),
})
在df.groupby()
中,默认值为observed=False
。 description对于观察
(Pandas 0.25.0)是:
When using a Categorical grouper (as a single grouper, or as part of multiple groupers), the observed keyword controls whether to return a cartesian product of all possible groupers values (observed=False) or only those that are observed groupers (observed=True).
因此,这是我期望的结果:
>>> # Expected result
>>> df.groupby(["state", "gender"])["name"].count()
state gender
AK M 2
F 0
AL F 1
M 1
Name: name, dtype: int64
这是实际结果:
>>> df.groupby(["state", "gender"])["name"].count()
state gender
AK M 2
AL F 1
M 1
Name: name, dtype: int64
我是否误解了这里的描述?
这个解决方法似乎是一个巨大的痛苦,并且正是应该由observed=False
创建的。我是否缺少替代方案?
>>> idx = pd.MultiIndex.from_product(
... (
... df["state"].cat.categories,
... df["gender"].cat.categories,
... ),
... names=["state", "gender"]
... )
>>> df.groupby(["state", "gender"])["name"].count().reindex(idx).fillna(0.).astype(int)
state gender
AK F 0
M 2
AL F 1
M 1
Name: name, dtype: int64
最佳答案
看起来你放置["name"]
的地方把它扔掉了。我认为这有效:
df.groupby(["state", "gender"]).count().fillna(0)["name"]
state gender
AK F 0.0
M 2.0
AL F 1.0
M 1.0
Name: name, dtype: float64
以下是一些有用的变体:
In [16]: df.groupby(["state", "gender"], observed=False).count().fillna(0)["name"].astype(int)
Out[16]:
state gender
AK F 0
M 2
AL F 1
M 1
Name: name, dtype: int64
In [17]: df.groupby(["state", "gender"], observed=True).count()["name"]
Out[17]:
state gender
AK M 2
AL M 1
F 1
Name: name, dtype: int64
关于python - Pandas Groupby : 'observed' parameter with multiple categoricals,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/57385009/