Pandas count True/False from last row since a switch
Question
I have a df like this:
timestamp open high low close volume previous_close high-low high-pc low-pc tr atr upperband lowerband in_uptrend
89 2022-08-01 03:50:00 65.07 65.29 65.07 65.21 1846.148 65.08 0.22 0.21 0.01 0.22 0.190000 65.341429 64.610000 False
90 2022-08-01 03:55:00 65.21 65.29 65.08 65.16 1498.980 65.21 0.21 0.08 0.13 0.21 0.185714 65.341429 64.627857 False
91 2022-08-01 04:00:00 65.16 65.27 65.14 65.26 1403.845 65.16 0.13 0.11 0.02 0.13 0.185714 65.341429 64.647857 False
92 2022-08-01 04:05:00 65.24 65.28 64.93 64.97 1301.370 65.26 0.35 0.02 0.33 0.35 0.220000 65.341429 64.445000 False
93 2022-08-01 04:10:00 64.96 65.09 64.96 65.05 975.097 64.97 0.13 0.12 0.01 0.13 0.220000 65.341429 64.365000 False
94 2022-08-01 04:15:00 65.04 65.05 64.90 64.98 820.264 65.05 0.15 0.00 0.15 0.15 0.224286 65.341429 64.302143 False
95 2022-08-01 04:20:00 65.00 65.32 64.95 65.29 797.967 64.98 0.37 0.34 0.03 0.37 0.222857 65.341429 64.466429 False
96 2022-08-01 04:25:00 65.30 65.33 65.19 65.25 720.771 65.29 0.14 0.04 0.10 0.14 0.211429 65.341429 64.625714 False
97 2022-08-01 04:30:00 65.26 65.44 65.24 65.41 1067.216 65.25 0.20 0.19 0.01 0.20 0.210000 65.970000 64.710000 True
98 2022-08-01 04:35:00 65.40 65.47 65.22 65.22 951.461 65.41 0.25 0.06 0.19 0.25 0.227143 66.026429 64.710000 True
if the last row of 'in_uptrend' is True I want to write in an other row the quantity since the switch from False to True, in my df it's 2 ( there is 2 True since the last False)
Same if the last row is False, I would like to the quantity of False after the switch from True to False.
Expected Result could be like this:
timestamp open high low close volume previous_close high-low high-pc low-pc tr atr upperband lowerband in_uptrend count_uptrend
89 2022-08-01 03:50:00 65.07 65.29 65.07 65.21 1846.148 65.08 0.22 0.21 0.01 0.22 0.190000 65.341429 64.610000 False
90 2022-08-01 03:55:00 65.21 65.29 65.08 65.16 1498.980 65.21 0.21 0.08 0.13 0.21 0.185714 65.341429 64.627857 False
91 2022-08-01 04:00:00 65.16 65.27 65.14 65.26 1403.845 65.16 0.13 0.11 0.02 0.13 0.185714 65.341429 64.647857 False
92 2022-08-01 04:05:00 65.24 65.28 64.93 64.97 1301.370 65.26 0.35 0.02 0.33 0.35 0.220000 65.341429 64.445000 False
93 2022-08-01 04:10:00 64.96 65.09 64.96 65.05 975.097 64.97 0.13 0.12 0.01 0.13 0.220000 65.341429 64.365000 False
94 2022-08-01 04:15:00 65.04 65.05 64.90 64.98 820.264 65.05 0.15 0.00 0.15 0.15 0.224286 65.341429 64.302143 False
95 2022-08-01 04:20:00 65.00 65.32 64.95 65.29 797.967 64.98 0.37 0.34 0.03 0.37 0.222857 65.341429 64.466429 False
96 2022-08-01 04:25:00 65.30 65.33 65.19 65.25 720.771 65.29 0.14 0.04 0.10 0.14 0.211429 65.341429 64.625714 False
97 2022-08-01 04:30:00 65.26 65.44 65.24 65.41 1067.216 65.25 0.20 0.19 0.01 0.20 0.210000 65.970000 64.710000 True
98 2022-08-01 04:35:00 65.40 65.47 65.22 65.22 951.461 65.41 0.25 0.06 0.19 0.25 0.227143 66.026429 64.710000 True 2
or like this would be better:
timestamp open high low close volume previous_close high-low high-pc low-pc tr atr upperband lowerband in_uptrend count_uptrend
89 2022-08-01 03:50:00 65.07 65.29 65.07 65.21 1846.148 65.08 0.22 0.21 0.01 0.22 0.190000 65.341429 64.610000 False 1
90 2022-08-01 03:55:00 65.21 65.29 65.08 65.16 1498.980 65.21 0.21 0.08 0.13 0.21 0.185714 65.341429 64.627857 False 2
91 2022-08-01 04:00:00 65.16 65.27 65.14 65.26 1403.845 65.16 0.13 0.11 0.02 0.13 0.185714 65.341429 64.647857 False 3
92 2022-08-01 04:05:00 65.24 65.28 64.93 64.97 1301.370 65.26 0.35 0.02 0.33 0.35 0.220000 65.341429 64.445000 False 4
93 2022-08-01 04:10:00 64.96 65.09 64.96 65.05 975.097 64.97 0.13 0.12 0.01 0.13 0.220000 65.341429 64.365000 False 5
94 2022-08-01 04:15:00 65.04 65.05 64.90 64.98 820.264 65.05 0.15 0.00 0.15 0.15 0.224286 65.341429 64.302143 False 6
95 2022-08-01 04:20:00 65.00 65.32 64.95 65.29 797.967 64.98 0.37 0.34 0.03 0.37 0.222857 65.341429 64.466429 False 7
96 2022-08-01 04:25:00 65.30 65.33 65.19 65.25 720.771 65.29 0.14 0.04 0.10 0.14 0.211429 65.341429 64.625714 False 8
97 2022-08-01 04:30:00 65.26 65.44 65.24 65.41 1067.216 65.25 0.20 0.19 0.01 0.20 0.210000 65.970000 64.710000 True 1
98 2022-08-01 04:35:00 65.40 65.47 65.22 65.22 951.461 65.41 0.25 0.06 0.19 0.25 0.227143 66.026429 64.710000 True 2
1 answers
solution1
1 ACCPTED 2022-08-01 05:01:27
IIUC, use cumcount :
df['count_uptrend'] = (df.groupby(df['in_uptrend']
.ne(df['in_uptrend'].shift())
.cumsum()
)
.cumcount().add(1)
)
Output:
timestamp open high low close volume previous_close high-low high-pc low-pc tr atr upperband lowerband in_uptrend count_uptrend
89 2022-08-01 03:50:00 65.07 65.29 65.07 65.21 1846.148 65.08 0.22 0.21 0.01 0.22 0.190000 65.341429 64.610000 False 1
90 2022-08-01 03:55:00 65.21 65.29 65.08 65.16 1498.980 65.21 0.21 0.08 0.13 0.21 0.185714 65.341429 64.627857 False 2
91 2022-08-01 04:00:00 65.16 65.27 65.14 65.26 1403.845 65.16 0.13 0.11 0.02 0.13 0.185714 65.341429 64.647857 False 3
92 2022-08-01 04:05:00 65.24 65.28 64.93 64.97 1301.370 65.26 0.35 0.02 0.33 0.35 0.220000 65.341429 64.445000 False 4
93 2022-08-01 04:10:00 64.96 65.09 64.96 65.05 975.097 64.97 0.13 0.12 0.01 0.13 0.220000 65.341429 64.365000 False 5
94 2022-08-01 04:15:00 65.04 65.05 64.90 64.98 820.264 65.05 0.15 0.00 0.15 0.15 0.224286 65.341429 64.302143 False 6
95 2022-08-01 04:20:00 65.00 65.32 64.95 65.29 797.967 64.98 0.37 0.34 0.03 0.37 0.222857 65.341429 64.466429 False 7
96 2022-08-01 04:25:00 65.30 65.33 65.19 65.25 720.771 65.29 0.14 0.04 0.10 0.14 0.211429 65.341429 64.625714 False 8
97 2022-08-01 04:30:00 65.26 65.44 65.24 65.41 1067.216 65.25 0.20 0.19 0.01 0.20 0.210000 65.970000 64.710000 True 1
98 2022-08-01 04:35:00 65.40 65.47 65.22 65.22 951.461 65.41 0.25 0.06 0.19 0.25 0.227143 66.026429 64.710000 True 2
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