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

Answer 1

This is far from the most time/memory efficient method that in this thread but here's an iterative approach that is pretty straightforward. Please feel encouraged to suggest improvements on this method.

import pandas as pd

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

dict_count = {}
for v in df['values'].unique():
    dict_count[v] = 0

curr_val = df.iloc[0]['values']
count = 1
for i in range(1, len(df)):
    if df.iloc[i]['values'] == curr_val:
        count += 1
    else:
        if count > dict_count[curr_val]:
            dict_count[curr_val] = count
        curr_val = df.iloc[i]['values']
        count = 1
if count > dict_count[curr_val]:
    dict_count[curr_val] = count

df_count = pd.DataFrame(dict_count, index=[0])
print(df_count)