2024-heraklion-data/exercises/tabular_split_apply_combine/split_apply_combine_solution.ipynb

Exercise: Compute summary statistics for the neural data¶

In [1]:

%matplotlib inline

import matplotlib.pyplot as plt
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 processed neural data¶

In [2]:

df = pd.read_csv('processed_QC_passed_2024-07-04_collected_v1.csv')

In [3]:

df.shape

Out[3]:

(659, 35)

In [4]:

df.head()

Out[4]:

	OP	filename	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	patcher
0	OP230420	23420003.abf	S1	1	23420S1c1	D1	TTX	0.0	no	10.416389	6.675643	39.025301	-74.285889	24	200.0	80.749512	-35.278320	336.181641	-60.791016	19.40	510.601767	0	753.380113	1.151009	NaN	NaN	NaN	NaN	NaN	8 mM	-61.828554	Bielefeld	temporal	13.0	Verji
1	OP230420	23420003.abf	S1	3	23420S1c3	D1	TTX	0.0	no	10.416389	7.867174	48.728367	-69.573975	26	300.0	78.448486	-32.043457	350.097656	-67.138672	17.30	393.397918	1	585.102837	1.006321	NaN	NaN	NaN	NaN	NaN	8 mM	-60.460298	Bielefeld	temporal	13.0	Verji
2	OP230420	23420003.abf	S1	6	23420S1c6	D1	TTX	0.0	no	10.416389	8.820134	35.971082	-54.956055	22	300.0	76.660156	-29.827881	270.629883	-52.246094	14.85	426.098774	3	173.915797	1.266335	NaN	NaN	NaN	NaN	NaN	8 mM	-59.615979	Bielefeld	temporal	13.0	Verji
3	OP230420	23420003.abf	S1	8	23420S1c8	D1	TTX	0.0	yes	10.416389	6.000400	31.599917	-70.550537	22	350.0	81.011963	-33.068848	309.448242	-61.401367	16.65	575.513924	5	786.927898	1.182830	NaN	NaN	NaN	NaN	NaN	8 mM	-60.956350	Bielefeld	temporal	13.0	Verji
4	OP230420	23420061.abf	S1_D2	8	23420S1c8	D2	TTX	19.0	yes	29.633333	8.271614	30.607259	-70.745850	1	1300.0	48.883057	-20.855713	100.952148	-27.465820	13.25	864.892430	29	565.938865	1.504127	NaN	NaN	NaN	NaN	NaN	8 mM	-61.283967	Bielefeld	temporal	13.0	Verji

1. Does capacitance change with age?¶

• Compute the capacitance by patient age, and plot it
• Does it change with age? (eyeballing is enough)

In [5]:

df.groupby('patient_age')['capacitance'].mean().plot(ls='', marker='.')

Out[5]:

<Axes: xlabel='patient_age'>

2. Spiking threshold after potassium incubation¶

1. Does the spiking threshold (TH) change between Day 1 and Day 2?
2. Does this result depend on the treatment?

In [6]:

df.groupby('day')['TH'].mean()

Out[6]:

day
D1   -37.276805
D2   -33.039838
Name: TH, dtype: float64

In [7]:

th_per_treatment_and_day = df.pivot_table(index='treatment', columns='day', values='TH', aggfunc='mean')

In [8]:

th_per_treatment_and_day

Out[8]:

day	D1	D2
treatment
Ctrl	-37.865372	-35.751658
TTX	-36.852170	-30.008780
high K	-36.926069	-33.162092
wash in high K	NaN	-35.791016

In [9]:

th_per_treatment_and_day = df.pivot_table(index='treatment', columns='day', values='TH', aggfunc=['mean', 'std'])
th_per_treatment_and_day

Out[9]:

	mean		std
day	D1	D2	D1	D2
treatment
Ctrl	-37.865372	-35.751658	4.260140	5.459102
TTX	-36.852170	-30.008780	5.406569	6.577182
high K	-36.926069	-33.162092	3.966294	4.479648
wash in high K	NaN	-35.791016	NaN	1.009906

In [ ]: