Sep-21-2019, 11:24 AM

I would like to fit an exponential function based on data points of more than one trend. Is this possible?

The code below creates a fit for every column in the table but if I want to group these columns according to a certain criteria so say the points in column A, B and C needs to learn the same exponential fit, i am not very sure how to do incorporate that requirement here. Thanks.

The code below creates a fit for every column in the table but if I want to group these columns according to a certain criteria so say the points in column A, B and C needs to learn the same exponential fit, i am not very sure how to do incorporate that requirement here. Thanks.

# Initial parameter guess, just to kick off the optimization init = (0.1, 0.1) # Place to store function parameters for each column col_params = {} def func(x, a, b): return a*np.exp(-b*x) # Curve fit each column for col in df.columns: # Get x & y x = df.index.astype(float).values y = df[col].values # Curve fit column and get curve parameters params = curve_fit(func, x, y, init) # Store optimized parameters col_params[col] = params[0] # Extrapolate each column for col in df.columns: # Get the index values for NaNsY in the column x = df[pd.isnull(df[col])].index.astype(float).values y = df[col].values # Extrapolate those points with the fitted function df[col][x] = func(x, *col_params[col]) plt.plot(x1,y,func(x, *col_params[col]),'g--')