2024-heraklion-data/exercises/tabular_window_functions/.ipynb_checkpoints/window_functions_solution-checkpoint.ipynb

Exercise: For each patcher, compute the average number of days they waited between experiments¶

Here is how to proceed

1. Use a window function to compute the number of days that elapse between experiment (i.e., the distance between date), for each patcher. Add that as a new column, 'days from prev'
2. Compute the average 'days from prev' per patcher

With your new awesome vectorization skills, it should take two lines!

In [1]:

import pandas as pd

# Set some Pandas options: maximum number of rows/columns it's going to display
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 100)

Load the neural data¶

In [61]:

df = pd.read_csv('shuffled_QC_passed_2024-07-04_collected_v1.csv', parse_dates=['date'])

In [62]:

df.head()

Out[62]:

	OP	patcher	date	slice	cell_ch	cell_ID	day	treatment	hrs_incubation	repatch	hrs_after_OP	Rs	Rin	resting_potential	max_spikes	Rheobase	AP_heigth	TH	max_depol	max_repol	membra_time_constant_tau	capacitance	comments	rheo_ramp	AP_halfwidth	Rheobse_ramp	Unnamed: 27	rheos_ramp	comment		high K concentration	RMP_from_char	tissue_source	area	patient_age
0	OP211209	Verji	2024-03-13	S2	8	21d10S2c8	D1	Ctrl	0.0	no	13.298889	14.470281	166.878916	-67.962646	34	50.0	83.190918	-36.132812	302.124023	-72.631836	20.75	152.623120	NaN	NaN	0.966102	NaN	NaN	NaN	NaN	NaN	15 mM	-59.101382	Bielefeld	temporal	27.0
1	OP221024	Verji	2024-06-16	S3	4	22o24S3c4	D1	Ctrl	0.0	no	23.964167	11.521243	137.820797	-71.789551	41	50.0	93.322754	-42.968750	465.820312	-83.740234	14.85	124.324170	17	NaN	0.959995	NaN	NaN	NaN	NaN	NaN	8 mM	-62.265689	Bielefeld	temporal	42.0
2	OP230810	Verji	2024-05-14	S2	5	23810S2c5	D1	TTX	0.0	no	7.043056	10.120637	67.739416	-70.629883	47	100.0	91.973877	-37.817383	415.771484	-107.666016	13.00	228.654858	10	402.013400	0.760052	NaN	NaN	NaN	NaN	NaN	8 mM	-61.329228	Mitte	temporal	63.0
3	OP230209	Verji	2024-04-27	S2_D2	3	23209S2_D2c3	D2	high K	25.0	no	21.848333	7.745503	43.009610	-68.371582	31	500.0	67.163086	-29.284668	212.036133	-61.645508	11.05	215.784505	30	672.202407	0.958735	NaN	NaN	NaN	NaN	NaN	8 mM	-62.577472	Bielefeld	temporal	63.0
4	OP240321	Verji	2024-04-11	S2	4	24321S2c4	D1	Ctrl	0.0	no	11.530278	7.632941	32.884808	-52.453613	21	200.0	84.008789	-36.785889	403.442383	-71.899414	14.80	695.791105	8	NaN	1.063838	324.520817	NaN	NaN	NaN	NaN	8 mM	-63.149769	Bielefeld	temporal	31.0

In [64]:

df['days from prev'] = df['date'] - df.sort_values('date').groupby('patcher')['date'].shift()

In [68]:

df.sort_values(['patcher', 'date'])[['patcher', 'date', 'days from prev']].head()

Out[68]:

	patcher	date	days from prev
251	Rosie	2024-01-01	NaT
102	Rosie	2024-01-04	3 days
355	Rosie	2024-01-04	0 days
47	Rosie	2024-01-05	1 days
477	Rosie	2024-01-05	0 days

In [74]:

df.groupby('patcher')['days from prev'].mean()

Out[74]:

patcher
Rosie   1 days 05:45:15.789473684
Verji   0 days 10:53:42.269807280
Name: days from prev, dtype: timedelta64[ns]

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