Count appearances of a value until it changes to another value

Question

I have the following DataFrame:

df = pd.DataFrame([10, 10, 23, 23, 9, 9, 9, 10, 10, 10, 10, 12], columns=['values'])

I want to calculate the frequency of each value, but not an overall count - the count of each value until it changes to another value.

I tried:

df['values'].value_counts()

but it gives me

10    6
9     3
23    2
12    1

The desired output is

10:2 
23:2
 9:3
10:4
12:1

How can I do this?

Answer 1

Use:

df = df.groupby(df['values'].ne(df['values'].shift()).cumsum())['values'].value_counts()

Or:

df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size()

---

print (df)
values  values
1       10        2
2       23        2
3       9         3
4       10        4
5       12        1
Name: values, dtype: int64

Last for remove first level:

df = df.reset_index(level=0, drop=True)
print (df)
values
10    2
23    2
9     3
10    4
12    1
dtype: int64

Explanation:

Compare original column by shifted with not equal ne and then add cumsum for helper Series:

print (pd.concat([df['values'], a, b, c], 
                 keys=('orig','shifted', 'not_equal', 'cumsum'), axis=1))
    orig  shifted  not_equal  cumsum
0     10      NaN       True       1
1     10     10.0      False       1
2     23     10.0       True       2
3     23     23.0      False       2
4      9     23.0       True       3
5      9      9.0      False       3
6      9      9.0      False       3
7     10      9.0       True       4
8     10     10.0      False       4
9     10     10.0      False       4
10    10     10.0      False       4
11    12     10.0       True       5