fit each group and extract coefficients

[Progressive]

I have a data frame df as follows:

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Board   Time    CO  H2 0   B000653BE   05.11.2018 13:28    89720   20320 1   B000653BE   05.11.2018 13:32    112760  35070 2   B000653BE   05.11.2018 13:36    130783  47063 (...) Board   Time    CO  H2 B000653BF   08.11.2018 14:04    261254  217003 B000653BF   08.11.2018 14:08    261395  216402 B000653C2   05.11.2018 13:28    95564   49094 B000653C2   05.11.2018 13:32    90978   73274 B000653C2   05.11.2018 13:36    87743   93204 (...)

And I want to fit each group of Board via

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def func_exp(x, a, b, c):         #c = 0         return a * np.exp(b * x) + c           def exponential_regression (x_data, y_data):     popt, pcov = curve_fit(func_exp,                            x_data,                            y_data,                                     p0 = (1000.1, 0.01, 1000000),                                   maxfev=5000                            )     print(popt)       return func_exp(x_data, *popt)

but I can't figure out how to proceed. I thought about

1	df.groupby('Board').apply(exponential_regression(df.index, df["H2"]))

but I only get different errors (when I try to adjust the syntax somehow..)
I'm totally used to R, there I know how to proceed but I don't know how to do the same in python. However, I need to fit each group in regards to H2 and CO and extract the corresponding regression coefficients.

Can somebody please help me?

[scidam]

exponential_regression function should return a list (an array) of coefficients. But you return y-value estimations.

I slightly restructure the code:

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from scipy.optimize import curve_fit import pandas as pd   c = pd.np.random.choice(range(1, 5), 1000) df = pd.DataFrame({'Board': c, 'x':pd.np.linspace(2, 10, 1000), 'y': 2+c*pd.np.exp(pd.np.linspace(2, 10, 1000))})

df is a sample data frame, it contains 'board' variable. This variable takes random values 1, ... , 4. This is just sample data.

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def func_exp(x, a, b, c):         #c = 0         return a * pd.np.exp(b * x) + c            def exponential_regression (x_data, y_data):     popt, pcov = curve_fit(func_exp,                            x_data,                            y_data,                                     p0 = (1,1,1),                                   maxfev=5000                            )     return popt

1 2	res = df.groupby('Board').apply(lambda x: exponential_regression(x['x'], x['y'])) res

Output:
Board
1                       [1.0, 1.0, 2.0000000000021814]
2    [1.9999999999999998, 0.9999999999999998, 1.999...
3    [3.000000000000001, 0.9999999999999997, 2.0000...
4    [3.9999999999999956, 1.0000000000000002, 2.000...
dtype: object

So, the first value consequently takes 1, 1.9999, ... ,3.99999. These are coefficients a for our groups of data. Everything
works as expected.