How to get the median of different intervals of dataframe based on label name? [duplicate] - python-3.x

Question

This question already has answers here:
How to groupby consecutive values in pandas DataFrame
(4 answers)
Closed 3 years ago.
So I have a DataFrame with two columns, one with label names (df['Labels']) and the other with int values (df['Volume']).
df = pd.DataFrame({'Labels':
['A','A','A','A','B','B','B','B','B','B','A','A','A','A','A','A','A','A','C','C','C','C','C'],
'Volume':[10,40,20,20,50,60,40,50,50,60,10,10,10,10,20,20,10,20,80,90,90,80,100]})
I would like to identify intervals where my labels change and then calculate the median on the column 'Volume' for each of these intervals. Later I should replace every value of column 'Volume' by the respective median of each interval.
In case of label A, I would like to have the median for both intervals.
Here is how my DataFrame should looks like:
df2 = pd.DataFrame({'Labels':['A','A','A','A','B','B','B','B','B','B','A','A','A','A','A','A','A','A','C','C','C','C','C'],
'Volume':[20,20,20,20,50,50,50,50,50,50,10,10,10,10,10,10,10,10,90,90,90,90,90]})

Answer 1

You want to groupby the blocks and transform median:
blocks = df['Labels'].ne(df['Labels'].shift()).cumsum()
df['group_median'] = df['Volume'].groupby(blocks).transform('median')

Answer 2

Use Series.cumsum + Series.shift() to create groups using groupby and then use transform
df['Volume']=df.groupby(df['Labels'].ne(df['Labels'].shift()).cumsum())['Volume'].transform('median')
print(df)
Labels Volume
0 A 20
1 A 20
2 A 20
3 A 20
4 B 50
5 B 50
6 B 50
7 B 50
8 B 50
9 B 50
10 A 10
11 A 10
12 A 10
13 A 10
14 A 10
15 A 10
16 A 10
17 A 10
18 C 90
19 C 90
20 C 90
21 C 90
22 C 90