[Python] Interpolate & extrapolate

[Python] Interpolate & extrapolate

from scipy.optimize import curve_fit

# interpolate

df_int[field] = df[field].interpolate(method='piecewise_polynomial')

'''
'linear’, ‘time’, ‘index’, ‘values’, ‘nearest’, ‘zero’,
‘slinear’, ‘quadratic’, ‘cubic’, ‘barycentric’, ‘krogh’, ‘polynomial’, ‘spline’ ‘piecewise_polynomial’, ‘pchip’}
'''

# extrapolate

df_ext = df_int.copy()

# Function to curve fit to the data
def func(x, a, b, c, d):
    return a * (x ** 3) + b * (x ** 2) + c * x + d

# Initial parameter guess, just to kick off the optimization
guess = (0.5, 0.5, 0.5, 0.5)

# Create copy of data to remove NaNs for curve fitting
fit_df = df_ext.dropna()

# Place to store function parameters for each column
col_params = {}

# Curve fit each column
for col in fit_df.columns:
    # Get x & y
    x = fit_df.index.astype(float).values
    y = fit_df[col].values
    # Curve fit column and get curve parameters
    params = curve_fit(func, x, y, guess)
    # Store optimized parameters
    col_params[col] = params[0]

# Extrapolate each column
for col in df_ext.columns:
    # Get the index values for NaNs in the column
    x = df_ext[pd.isnull(df_ext[col])].index.astype(float).values
    # Extrapolate those points with the fitted function
    df_ext[col][x] = func(x, *col_params[col])

# Display result
print('Extrapolated data:')
printdf_extdf)
print('Data was extrapolated with these column functions:')
for col in col_params:
    print('f_{}(x) = {:0.3e} x^3 + {:0.3e} x^2 + {:0.4f} x + {:0.4f}'.format(col, *col_params[col]))