Python 如何计算分类值(包括零出现)?
我想按月计算代码的数量。 这是我的示例数据帧Python 如何计算分类值(包括零出现)?,python,pandas,dataframe,Python,Pandas,Dataframe,我想按月计算代码的数量。 这是我的示例数据帧 id month code 0 sally 0 s_A 1 sally 0 s_B 2 sally 0 s_C 3 sally 0 s_D 4 sally 0 s_E 5 sally 0 s_A 6 sally 0 s_A 7 sally 0 s_B 8 sally
id month code
0 sally 0 s_A
1 sally 0 s_B
2 sally 0 s_C
3 sally 0 s_D
4 sally 0 s_E
5 sally 0 s_A
6 sally 0 s_A
7 sally 0 s_B
8 sally 0 s_C
9 sally 0 s_A
我使用count()转换到这个系列
但是,我想包括零发生率,像这样
id code month count
sally s_A 0 12
1 10
2 3
3 0
4 0
5 0
6 0
7 15
8 0
9 0
10 0
11 0
您可以与新建的Multindex
一起使用:
注意:旧的解决方案:
使用and重新索引
,但随后需要一些数据清理:
df = df.groupby(['id', 'code', 'month']).size() \
.to_frame('count') \
.unstack([0,1], fill_value=0) \
.reindex(range(13), fill_value=0) \
.stack([1,2], dropna=False) \
.fillna(0) \
.astype(int) \
.swaplevel(0,2) \
.sort_index()
print (df)
count
code id month
s_A sally 0 1
1 3
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
sally1 0 3
1 0
2 0
3 0
4 0
您没有显示
'm'
column@splinter我更新了。m是月。Mux是很好的解决方案。你救了我一天!
df = pd.DataFrame({
'month': [0, 0, 0, 0, 1, 1, 1, 2, 2, 7],
'code': ['s_A', 's_A', 's_A', 's_A', 's_A', 's_A', 's_A', 's_B', 's_B', 's_B'],
'id': ['sally1','sally1','sally1','sally','sally','sally','sally','sally','sally','sally']})
print (df)
code id month
0 s_A sally1 0
1 s_A sally1 0
2 s_A sally1 0
3 s_A sally 0
4 s_A sally 1
5 s_A sally 1
6 s_A sally 1
7 s_B sally 2
8 s_B sally 2
9 s_B sally 7
df = df.groupby(['id', 'code', 'month']).size()
n = ['id','code','month']
mux = pd.MultiIndex.from_product([df.index.levels[0],df.index.levels[1], range(13)], names=n)
df = df.reindex(mux, fill_value=0)
print (df)
id code month
sally s_A 0 1
1 3
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
s_B 0 0
1 0
2 2
3 0
...
...
df = df.groupby(['id', 'code', 'month']).size() \
.to_frame('count') \
.unstack([0,1], fill_value=0) \
.reindex(range(13), fill_value=0) \
.stack([1,2], dropna=False) \
.fillna(0) \
.astype(int) \
.swaplevel(0,2) \
.sort_index()
print (df)
count
code id month
s_A sally 0 1
1 3
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
sally1 0 3
1 0
2 0
3 0
4 0