# 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--')