Python：Pandas - 基于列值分离数据框
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Python: Pandas - Separate a Dataframe based on a column value
提问by user1452759
Suppose I have a dataframe as shown below:
假设我有一个如下所示的数据框：
in:
mydata = [{'subid' : 'B14-111', 'age': 75, 'fdg':1.78},
{'subid' : 'B14-112', 'age': 22, 'fdg':1.56},]
df = pd.DataFrame(mydata)
out:
age fdg subid
0 75 1.78 B14-111
1 22 1.56 B14-112
I want to separe the dataframe to two different dataframes based on the "age" column, as shown below:
我想根据“年龄”列将数据框分成两个不同的数据框，如下所示：
out:
df1:
age fdg subid
0 75 1.78 B14-111
df2:
age fdg subid
1 22 1.56 B14-112
How can I achieve this?
我怎样才能做到这一点？
回答by EdChum
We can do this directly using boolean condition as the filter:
我们可以直接使用布尔条件作为过滤器来做到这一点：
In [5]:
df1 = df[df.age == 75]
df2 = df[df.age == 22]
print(df1)
print(df2)
age fdg subid
0 75 1.78 B14-111
age fdg subid
1 22 1.56 B14-112
but if you have more age values perhaps you want to group them:
但是如果你有更多的年龄值，也许你想把它们分组：
In [13]:
# group by the age column
gp = df.groupby('age')
# we can get the unique age values as a dict where the values are the key values
print(gp.groups)
# we can get a specific value passing the key value for the name
gp.get_group(name=75)
{75: [0], 22: [1]}
Out[13]:
age fdg subid
0 75 1.78 B14-111
We can also get the unique values and again use this to filter the df:
我们还可以获取唯一值并再次使用它来过滤 df：
In [15]:
ages = df.age.unique()
for age in ages:
print(df[df.age == age])
age fdg subid
0 75 1.78 B14-111
age fdg subid
1 22 1.56 B14-112
