I am trying to find a good way to calculate mean values from values in a dataframe. It contains measured data from an experiment and is imported from an excel sheet. The columns contain the time passed by, electric current and the corresponding voltage.
The current is changed in steps and then held for some time (the current values vary a little bit, so they are not exactly the same for each step). Now I want to calculate the mean voltage for each current step. Since it takes some time after the voltage gets stable after a step, I also want to leave out the first few voltage values after a step.
Currently I am doing this with loops, but I was wondering wether there is a nicer way with the usage of the groupby function (or others maybe).
Just say if you need more details or clarification.
Example of data:
s [A] [V]
0 6.0 -0.001420 0.780122
1 12.0 -0.002484 0.783297
2 18.0 -0.001478 0.785870
3 24.0 -0.001256 0.793559
4 30.0 -0.001167 0.806086
5 36.0 -0.000982 0.815364
6 42.0 -0.003038 0.825018
7 48.0 -0.001174 0.831739
8 54.0 0.000478 0.838861
9 60.0 -0.001330 0.846086
10 66.0 -0.001456 0.851556
11 72.0 0.000764 0.855950
12 78.0 -0.000916 0.859778
13 84.0 -0.000916 0.859778
14 90.0 -0.001445 0.863569
15 96.0 -0.000287 0.864303
16 102.0 0.000056 0.865080
17 108.0 -0.001119 0.865642
18 114.0 -0.000843 0.866434
19 120.0 -0.000997 0.866809
20 126.0 -0.001243 0.866964
21 132.0 -0.002238 0.867180
22 138.0 -0.001015 0.867177
23 144.0 -0.000604 0.867505
24 150.0 0.000507 0.867571
25 156.0 -0.001569 0.867525
26 162.0 -0.001569 0.867525
27 168.0 -0.001131 0.866756
28 174.0 -0.001567 0.866884
29 180.0 -0.002645 0.867240
.. ... ... ...
242 1708.0 24.703866 0.288902
243 1714.0 26.469208 0.219226
244 1720.0 26.468838 0.250437
245 1726.0 26.468681 0.254972
246 1732.0 26.468173 0.271525
247 1738.0 26.468260 0.247282
248 1744.0 26.467666 0.296894
249 1750.0 26.468085 0.247300
250 1756.0 26.468085 0.247300
251 1762.0 26.467808 0.261096
252 1768.0 26.467958 0.259615
253 1774.0 26.467828 0.260871
254 1780.0 28.232325 0.185291
255 1786.0 28.231697 0.197642
256 1792.0 28.231170 0.172802
257 1798.0 28.231103 0.170685
258 1804.0 28.229453 0.184009
259 1810.0 28.230816 0.181833
260 1816.0 28.230913 0.188348
261 1822.0 28.230609 0.178440
262 1828.0 28.231144 0.168507
263 1834.0 28.231144 0.168507
264 1840.0 8.813723 0.641954
265 1846.0 8.814301 0.652373
266 1852.0 8.818517 0.651234
267 1858.0 8.820255 0.637536
268 1864.0 8.821443 0.628136
269 1870.0 8.823643 0.636616
270 1876.0 8.823297 0.635422
271 1882.0 8.823575 0.622253
Output:
s [A] [V]
0 303.000000 -0.000982 0.857416
1 636.000000 0.879220 0.792504
2 699.000000 1.759356 0.752446
3 759.000000 3.519479 0.707161
4 816.000000 5.278372 0.669020
5 876.000000 7.064800 0.637848
6 939.000000 8.828799 0.611196
7 999.000000 10.593054 0.584402
8 1115.333333 12.357359 0.556127
9 1352.000000 14.117167 0.528826
10 1382.000000 15.882287 0.498577
11 1439.000000 17.646748 0.468379
12 1502.000000 19.410817 0.437342
13 1562.666667 21.175572 0.402381
14 1621.000000 22.939826 0.365724
15 1681.000000 24.704600 0.317134
16 1744.000000 26.468235 0.256047
17 1807.000000 28.231037 0.179606
18 1861.000000 8.819844 0.638190
The current approach:
df = df[['s','[A]','[V]']]
#Looping over the rows to separate current points
b=df['[A]'].iloc[0]
start=0
list = []
for index, row in df.iterrows():
if not math.isclose(row['[A]'], b, abs_tol=1e-02):
b=row['[A]']
list.append(df.iloc[start:index])
start=index
list.append(df.iloc[start:])
#Deleting first few points after each current change
list_b = []
for l in list:
list_b.append(l.iloc[3:])
#Calculating mean values for each current point
list_c = []
for l in list_b:
list_c.append(l.mean())
result=pd.DataFrame(list_c)
